**Tan Yigitcanlar**

Tan Yigitcanlar is an eminent Australian researcher with international recognition and influence in the field of urban studies and planning. He is a Professor of Urban Studies and Planning at the School of Architecture and Built Environment, Queensland University of Technology, Brisbane, Australia. Along with this post, he holds an Honorary Professor role at the School of Technology, Federal University of Santa Catarina, Florianopolis, Brazil, and the Founding Director position of the Australia–Brazil Smart City Research and Practice Network. His research is clustered around the following three interdisciplinary themes: smart technologies, communities, cities, and urbanism; sustainable and resilient cities, communities, and urban ecosystems; and knowledge-based development of cities and innovation districts. His research findings are disseminated in over 250 articles published in high-impact journals, and 19 key reference books published by esteemed international publishing houses. Amongst the urban and regional planning scholars, 2020 Science-wide Author Databases of Standardised Citation Indicators ranked him as #1 most highly cited researcher in Australia, and #7 worldwide. For this achievement, he was recognised as an "Australian Research Superstar" in the Social Sciences Category at The Australian's 2020 Research Special Report.


**1. Introduction**

Clean technologies are defined as "all the techniques, processes, and products that are of importance in preventing or reducing the burden on the environment" [1]. They compete directly with the older and more traditional technologies employed in existing infrastructures, which are often less expensive (due to scale economies), more stable, and more widespread in the market. These rivalry aspects are detrimental to the market uptake of clean technologies and must therefore be mitigated by policies that incentivize the shift to a more sustainable society. Moreover, many older infrastructures are associated with significant sunk costs that cannot be recovered by incentives alone. For this reason, infrastructural restoration for clean technologies requires public support [2,3]. Furthermore, as clean technologies lack long-term research support, they are frequently considered risky and unattractive to private investors. Finally, public investment in sustainable innovations is a strategic policy decision that might be undermined by short-sighted political actions [4].

Against this background, the main goal of the present study was to map policies and assess their role in fostering or hindering the emergence of the clean energy niche and the deployment of clean energy technologies in the Boston area. (Note: The Boston area comprises the city of Boston, the city of Cambridge, the city of Somerville, neighboring cities and surrounding suburbs—all of which are home to numerous universities, research centers and firms. The Boston area is neither a statistical nor an administrative unit; rather, it is a delimitation of eastern Massachusetts (US) that is commonly used in scientific papers and thus suitable for the present study. In accordance with Berry et al. [5] and Owen-Smith and Powell [6], we define the Boston area as a functional economic area with a certain gravitational and commuting influence on its surrounding areas. With this geographical focus in mind, we attempt here to illustrate the historical evolution of the development of the area's clean energy niche.)We selected the Boston area because it is considered a leading region in research

and innovation relating to clean energy technologies (classified as second in the US) [7]. Nonetheless, this area generates only 10.5% of its net electricity from renewable energy resources; this is less than the US average (15.9%) [7], showing a mismatch (or incomplete transition) between technological development and deeper societal changes.

In our study, we investigated the transition process to clean energy technologies in the Boston area using the lens of strategic niche management (SNM). This perspective links the emergence and empowerment of technological niches to interactive learning processes and institutional changes [8,9] (Note: Bearing in mind the regional dimension of the case study, the Boston area clean energy transition could be equally understood through the framework of regional innovation systems (RIS), as this area possesses "significant supralocal governance capacity and cohesiveness," which differentiates it from the national context and that of other regions [10] (p. 480). RIS "opened up the way to exploring the extent to which innovation processes at regional level could be defined as systemic" [10] (p. 489) and, in this sense, is associated with both innovation research and regional science. However, RIS falls short in capturing the broader perspective of transition from an incumbent system to a more sustainable one by means of socio-technological change [11,12]. In fact, innovation systems provide only a narrow definition of socio-economic factors, disregarding the fulfillment of societal functions as a driver of innovation processes [11,13]). In socio-technical transitions, the emergence of a technological niche is not conceived as a technology push process, but one that is triggered by the interactions between technology, user practices, societal needs, and regulatory structures [14]. In the successful evolution of a niche, SNM involves three internal mechanisms [9,14,15]:

The destabilization of incumbent socio-technical systems and associated institutional structures is generated by the emergence of innovative and sustainable socio-technical configurations that receive increasing political support [16]. Furthermore, the process of creating legitimacy plays a pivotal role in boosting the maturity of socio-technical niches [17,18] by expanding the network of supportive actors.

Socio-technical transitions are not simple and linear, but complex and long-term transformations of socio-technical systems influenced by numerous dimensions at different levels of development [19], guided by sustainability goals and policies [16]. In these uncertain transitional environments, policy plays an important role in providing direct infrastructural support and building economic and regulatory framing conditions for the development and diffusion of sustainability innovations [17]. The transition to sustainable socio-technical systems can only be accomplished with the support of innovation policy (see, e.g., [20–22]). However, policy interventions maintain a conflicting position: on the one hand, they are crucial for building favorable conditions for niche maturity by boosting niche internal mechanisms; on the other hand, they are affected by path-dependent institutions and incumbent lobbies.

According to Markard et al. [16] policy affects socio-technical systems and their sustainability transitions in different ways. First, policy contributes to the development of innovations and technologies by means of knowledge generation and diffusion. From a deployment and diffusion perspective, policy plays a crucial role in market formation, regulation and the up-scaling of emergent socio-technical systems. Second, policy can contribute to destabilizing established socio-technical systems by removing subsidies and/or increasing taxes on traditional technologies.

In a study examining the role of policy in supporting energy democracy in the US, Burke and Stephens [23] identified four policy categories pertaining to socio-technical transitions. The first category, *regulatory context*, lays the ground for the implementation of further ad hoc policies boosting the diffusion of renewable energies. This category includes renewable energy standards, green public procurement and community benefit agreements, among other regulatory instruments. An additional policy category concerns *financial inclusion measures*, or financial instruments and monetary incentives that promote energy system changes. The most important instrument in this category is the feed-in tariff (FIT), which guarantees a long-term minimum fixed purchasing price for renewable energy. Other instruments may relate to green subsidies, on-bill financing and repayment programs, public bonds, carbon tax-and-invest programs and cooperative financing. A third policy category pertains to *economic institutions*, in the form of new socio-economic institutions and economic opportunities for communities. This category focuses on communities' and/or public actors' ownership of renewable energy systems. Policy instruments in this category include renewable energy cooperatives and the re-municipalization of public assets, such as water, sewage and electricity systems. Finally, the fourth policy category consists of *new energy system institutions*, which support and facilitate institutional change in the energy system. This category includes policy instruments concerning microgrids and democratized grid management, energy regions and sustainable energy utilities.

The necessity of and commitment to a transition to sustainable innovation has dominated the discourses of global and local actors; this is particularly true in the US, where industrial production still depends to a very large extent on non-renewable energy resources. Socio-technical transitions entail political negotiation [24,25] between stakeholders with conflicting positions and opinions; such negotiation depends on the framing and definition of the institutionalizing process of innovation [26]. Indeed, the sustainability transition is shaped by social values and political discourse, and further nuanced by stakeholders' differing perceptions of sustainability issues, goals and policies. Accordingly, in the present study, we used argumentative discourse analysis (ADA) to examine the discourse of key actors concerning the role of policy in the development and deployment of clean energy technologies in the Boston area.

The research investigated the way in which constellations of actors legitimized or delegitimized innovations within transition episodes in terms of multi-dimensional discursive interactions. The aim was not to provide a deterministic view of the transition dynamics, but to explain the differing perspectives of the conflicting actors and to link narratives at the micro level to discourse at the macro level, which influences collective knowledge and discursive events. To this end, our investigation: scrutinized dominant discourses relating to the clean energy transition in the Boston area; framed innovations within discrete narratives (with a particular focus on narratives about the role of policy interventions); and assessed any enduring narratives that could obstruct the sustainable transition to clean energy technologies in the Boston area.

#### **2. Materials and Methods**

Moving within this framework, we investigated the transition process to clean technologies in the Boston area by applying argumentative discourse analysis (ADA), as proposed by Hajer [26] and subsequently developed by Hajer and Versteeg [27]. This analysis of discourse and narratives has been applied by many scholars to describe a problem, identify solutions and mediate between actors' positions in the transition process [28–35].

ADA is a valuable methodology for critically examining the environmental discourse embedded in the analysis of energy policies. For instance, by examining the discourses of key actors, ADA exposes contradictory narratives and conflicts formed around particular opinions. The main component of ADA is the storyline, which is a narrative sustained by a socio-political coalition that plays a crucial role in "clustering of knowledge, positioning of actors, and ultimately, in the creation of coalitions amongst the actors of a given domain" [26]. Storylines within an environmental discourse are characterized by specific emblems or "issues that dominate the perception of the ecological dilemma in a specified period" [26]. Since storylines emerge between and within political boundaries and do not conform to specific political and institutional settings, they are very helpful in investigating the influence of policy on niche maturation, by revealing the hegemonic ways in which environmental conflicts are argued.

Hajer [26] defines 10 tasks of ADA, which are generally summarized in three main steps. The first step consists of making a preliminary assessment of the context and its development by analyzing written documents and official communications. This provides an overview of the developmental process, which is later enriched with further information or reframed by the interviews conducted in the second step [26]. In the present study, to explore the context of clean energy technologies in the Boston area, we reviewed reports, industry roadmaps, empirical studies and analyses [36–44], as well as official websites [45–55]. This enabled us to identify concepts and ideas that structured the discourse.

The second step of ADA consists of interviewing key players in order to collect more information on specific events. In our case, the relevant event involved the sustainability transition towards a clean energy sector. As a result of the preliminary assessment, we identified five key actors involved in clean energy technologies in the Boston area. Each actor was asked to provide at least two names of other relevant actors. After examining the suggested actors' profiles, we identified seven additional actors. In this way, we built a final list of 12 key actors, with whom we conducted formal interviews using a qualitative, semi-structured questionnaire (see Appendix A). The interviews were conducted between June and July 2016. Figure 1 presents a graphic representation of the types of actors interviewed in this step, differentiated between core and peripheral actors. Table 1 provides the full list of the interviewed actors.

**Figure 1.** Core and peripheral actors. *Source:* Authors, based on Rosenbloom et al. [28].

#### **Table 1.** List of key actors' organizations.

*Source:* Authors.

As shown in both Figure 1 and Table 1, four of the selected actors were classified as part of the "core" of the clean energy niche, given their role in developing and diffusing the innovative technology. This group was composed of actors from three technology transfer offices (Harvard University, MIT, Northeastern University) and one clean technology business accelerator (Greentown Labs). The second column of Table 1 indicates three peripheral actors at the "fringe" of the niche [28] (p. 1279); these actors participated in initiatives promoting the development and deployment of clean technologies, but were not directly involved in either the niche or relevant policy (New England Water Innovation Network, Venture Café Foundation, Massachusetts Technology Transfer Centre). For instance, some of these actors engaged in technology transfer and the promotion of new technologies and start-ups in support of other niches. The third and final group of interviewees, who are not reflected in Figure 1 because they operate outside of the niche, was composed of five actors at the policy level (Massachusetts Clean Energy Centre, Environmental Protection Agency (EPA), Massachusetts Technology Collaborative: Innovation Institute, Executive Office of Energy and Environmental Affairs: Office of Technical Assistance and Technology (OTA); Boston Redevelopment Authority); these actors are grouped in the third column of Table 1. Within each organization, the selection of interviewees was made on the basis of interviewees' strategic role (e.g., manager of grant programs, innovation projects, industry support, clean technology, etc.).

To complete the ADA, a third and final step was undertaken, involving the analysis of particular events or incidents that emerged from the reviews and interviews. The aim of this step was to validate the reliability of the storylines, given the controversial opinions and experiences they contained. To this end, we analyzed the interviews and documents, querying all relevant discourse elements and events.

By following these three steps, we identified a dominant storyline concerning the flourishing dynamics of the clean energy niche. We also identified two struggling storylines: one legitimizing the crucial role and commitment of public intervention in the development of the clean energy niche, and another delegitimizing the engagement of public bodies, showing a lack of niche power to break through the incumbent market. In Section 3, we present the identified storylines along with illustrative quotes from the interviews.

#### **3. Results**

Building on the theoretical and empirical framework depicted in the previous sections, we will now illustrate the identified discourse surrounding clean energy technologies in the Boston area. The storylines that emerged in the research highlighted two specific trajectories:

We placed the main storyline within the first trajectory. Within the second discourse trajectory, we placed two other storylines: one legitimizing the role of public intervention and another delegitimizing its commitment. In the following subsections, we discuss these three storylines in some detail.

#### *3.1. Dominant Storyline*

Overall, most actors expressed the need to shift to a cleaner energy system, and thereby commit to more sustainable production. As emerged from the discourse analysis, the adoption of the Green Community Act in 2008 was considered the breaking point with traditional energy production. This event was found to significantly influence the development of this dominant storyline, regarding the current development of clean technologies.

*STORYLINE 1*: Clean energy technologies are central to a thick network that exchanges knowledge and engages for a cleaner common future.

In this storyline, we identified all three mechanisms characterizing the development of the clean energy niche in the Boston area (Table 2). According to SNM, one of the main mechanisms for the development of an innovative niche is building a common vision through shared expectations. On the one hand, universities use the commercialization of research and patents to encourage social use of their inventions; on the other hand, the state adopts environmentally friendly laws and incentives for more energy efficient and cleaner production (e.g., the Commonwealth's 2016–2018 Three-Year Energy Efficiency Plan, the Affordable Access to Clean and Efficient Energy Initiative, etc.) [46]. The annual Industry Report of the Massachusetts Clean Energy Centre (MassCEC) [36] mentions specific goals: "In August 2008, Massachusetts required all economic sectors to reach a 25% reduction in GHG emissions by 2020 and an 80% reduction by 2050 under the Global Warming Solutions Act," becoming "one of the first States in the Nation to move forward with a comprehensive regulatory program to address climate change." Moreover, the private sector "continues the trend of becoming more 'pure-play', meaning that all of their activities are clean energy related" [55].

The achievement of this goal at all levels is driven by knowledge creation and sharing, which consists of local learning processes and investment in human capital and a specialized labor force. The interviewed actors focused particularly on research and innovation, for two reasons: first, the development of clean energy technologies requires intense research to generates radical innovation; and second, as mentioned above, the Boston area is characterized by a high number of academic research institutions conducting theoretical and applied clean energy research. More specifically, since the US government adopted the Bayh-Dole Act in 1980, universities have become key actors of innovation by commercializing their research to firms operating in the market. MIT's Technology Licensing Office files approximately 200 patents each year, but only a quarter of these relate to clean technology. Both MIT and Northeastern University license half of their filed patents to existing companies, and 20–25% of these result in spin-offs. These institutions have engaged in an intense transfer of mature technology, amounting to 147 patents awarded to 36 companies working with pre-commercial products, 229 patents awarded to firms focusing on energy efficiency and 25 patents awarded to firms working exclusively with energy goods and services [36]. Grentown Labs, a clean technology accelerator, has filed 32 innovative patents and supported nine university-born companies. On the other hand, according to Venture Café, there remains a dire need for technology transfer: "Clean energy in the Boston area is composed by small companies; there are no large companies yet. Therefore, technology transfer is really important because all the research and innovation comes from universities. Small companies don't have budget for research."

Indeed, knowledge sharing and technology transfer in the Boston area represent a regional competitive advantage that is disseminated through networked actors. Collaboration and networking are quoted as incentives, either for the location of clean energy companies in Boston or the success of clean energy development. Indeed, one-third of the patents filed at MIT are developed in collaboration with other universities or companies at which their students are likely to find employment. Also, according to the respondent at Greentown Labs, their success depends on their dense network, composed of 102 host start-ups and mature and specialized companies, with expertise in topics ranging from IP to tax filings, fundraising and clean energy technology.

#### **Table 2.** Overview of the dominant storyline.


*Article*

## **Impact of Users' Behavior and Real Weather Conditions on the Energy Consumption of Tenement Houses in Wroclaw, Poland: Energy Performance Gap Simulation Based on a Model Calibrated by Field Measurements**

**Małgorzata Szulgowska-Zgrzywa 1,\*, Ewelina Stefanowicz 1, Krzysztof Piechurski 1, Agnieszka Chmielewska 1 and Marek Kowalczyk 2**

Received: 6 November 2020; Accepted: 14 December 2020; Published: 18 December 2020

**Abstract:** This paper presents the results of measuring the final energy consumption for heating and domestic hot water (DHW) preparation and indoor conditions in 15 apartments located in pre-war tenement houses. The measurements were compared to the computed energy consumption. The calculations ware made based on the model calibrated by field measurements. The discrepancies between measurements and calculations were assessed using the energy performance gap (EPG). Calculations were made separately for energy for heating and for DHW preparation. Additionally, the results of EPG calculations for different levels of analysis are presented aiming at assessing the impact of weather, temperature in the surrounding zones and users' behavior. Users' behaviors influencing the size of the EPG were divided into typical (energy saving or excessive energy consumption) and forced (energy poverty, response to the apartment's surroundings, technical limitations. The connection between the heating sources and the heating habits has been clearly observed in the research. The former (typical) behaviors were the origin of the energy gap in the apartments heated with natural gas and district heating. The latter (forced) were the origin of the gap in the apartments heated with mostly electricity and solid fuel (with one exception: one apartment that utilized the district heating).

**Keywords:** energy performance gap; user behavior; energy poverty; pre-war tenement building


**2. Methods**

## *2.1. Study Location*

Wuhan, with a population of 10.6 million and an area of 8594 km2, is the largest city in central China and the capital city of Hubei Province. Wuhan has 13 administrative districts. As of 2014, Wuhan's gross domestic product (GDP) per capita reached 15.6 thousand U.S. dollars, making it one of the fastest growing cities in China [27]. However, its urbanization and industrialization have exposed local residents to a high level of air pollution. For example, the annual average concentration of PM2.5 in Wuhan was 106.5 μg/m3 as of 2017, which was significantly higher than that of other cities with similar economic development levels [27]. Figure 1 shows the geographical location of Wuhan, provincially-designated air sampling stations, and population by administrative districts.

**Figure 1.** Map of Wuhan. The red pentagram represents monitoring stations. Population density by district is represented by gradient color.

#### *2.2. Air Pollution and Meteorological Data*

The monitoring data used in the present study were collected by the Hubei Provincial Environmental Quality Supervision and Administration Bureau (http://www.hbemc.com.cn/). All air pollutants were measured based on a 24-h sampling schedule, except for ozone, which was collected using an 8-h schedule. In the current study, rather than calculating daily average concentrations for each air pollutant across multiple monitors as many studies usually do, we used kriging, a spatial interpolation method for exposure assessment, to estimate daily levels of ambient air pollutants including PM2.5, PM10, SO2, NO2, carbon monoxide (CO), and tropospheric ozone (O3) in Wuhan city. Although our interest was the Wuhan metropolitan area, we did include monitoring data from all sampling sites in Hubei Province to increase the statistical stability of our analysis. This led to a total of 51 monitoring sites included in the analysis, with 10 sites in Wuhan and an additional 41 sites across Hubei Province. We used ordinary kriging to determine the concentration of each air pollutant on each day for the Wuhan metropolitan area throughout the entire study period from 2015 to 2016. Specifically, kriging values generated from concentrations of air pollutants collected at the monitoring sites were first exported to a raster. The centroid of a given county or city was then used as the point data (represented by the geographical coordinates) to extract the interpolation results corresponding to the output raster. The analysis was repeated for each air pollutant on each day throughout the study period to obtain the complete exposure data set. It must be emphasized that kriging was used for predicting exposures, not TB incidence. The analysis was achieved using ArcMap 10.3 (ArcGIS. Version 10.3. Environmental Systems Research Institute, Redlands, CA, USA).

Daily meteorological data including temperature and relative humidity were collected at Wuhan Tianhe International Airport and obtained from https://rp5.ru/Weather\_in\_the\_world.

#### *2.3. TB Data*

Daily TB incidence data were obtained from the Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) for the 2015–2016 period. Hubei CDC collects information on a total of 39 infectious diseases that are legally regulated in China. Information on infectious diseases is collected at multiple levels, including health care institutions, individual practitioners, and rural doctors. Upon the collection of infectious disease information, designated epidemic reporters will diagnose infectious disease patients or suspected patients according to the latest diagnostic criteria for infectious diseases (Chinese Center for Disease Control and Prevention, 2008). In short, a chest X-ray is a common method for detecting tuberculosis, but the following tests are needed to confirm the diagnosis. First, check the patient's sputum for *Mycobacterium tuberculosis* to confirm the diagnosis. Second, use a lung X-ray examination to diagnose the location, scope, and nature of the disease. Third, conduct a tuberculin test. A positive test indicates infection. County-level CDCs will review the information collected to ensure the quality of the data and then file it online in the Infectious Disease Reporting Information Management System. We extracted data from this online reporting system and used the International Classification of Disease, 10th revision (WHO, 2007), to identify TB cases (coded as A15.0–A15.3; A16.0–A16.2).

Ethics approval and consent to participate: The study was approved by the Ethics Committee of Wuhan University School of Medicine.

#### *2.4. Statistical Analysis*

We used ageneralized additive model (GAM) to relax the assumption of linearity between predictors and response variables. GAM is the most widely used method in air pollution epidemiology because it allows for nonparametric adjustment of nonlinear confounding e ffects of seasonality, trends, and weather variables. Variables such as temperature and humidity can be controlled in the model by specifying a spline, a function to smooth the parameters for predicting the optimizing results [28]. We used GAMs to examine the associations between daily incidence of TB and daily concentrations

of air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Wuhan during the 2015–2016 period. We also examined the potential seasonal variation by stratifying the data set during warm (May–October) and cold (November–April) seasons. Typically, time-series data are "overly dispersed", which means the variance in a data set exceeds the mean. To account for overdispersion, the daily TB counts were assigned by quasi-Poisson distribution, a common way to deal with over-dispersed data. We used natural cubic spline functions to adjust for the time-varying variables. Specifically, the daily average temperature and relative humidity were adjusted with 3 degrees of freedom (df). The long-term and seasonal e ffects were adjusted with 8 df per year of data. Also included in the model is day of week (DOW), which is a set of indicator variables shown on each day of the week. The selection of covariates and df were based on a previously published study [25]. To explore the lag structures between incident TB and each air pollutant, a single-day lag model was examined from lag 0 through lag 14. The "lag" is defined as the time between the reporting of a TB case and days of exposure prior to this reporting. To report the results, we calculated the relative risk (RR) of TB associated with a 10 μg/m3 increase in each pollutant concentration, with corresponding 95% confidence intervals (CIs).

To test the robustness of the results, we performed two sensitivity analyses. In the first sensitivity analysis, we used di fferent df values for long-term e ffects (e.g., 4, 6, 10, and 12 df per year). In the second sensitivity analysis, we adopted di fferent temperature and humidity values in our model (e.g., values at t-1 and the cumulative e ffect calculated by three-day moving averages) to test whether the results were di fferent from those calculated using daily averages in the current model.

We used SAS (version 9.3; SAS Institute Inc., Cary, NC, USA) to combine TB data, meteorological data, and air pollution data, as well as generate descriptive statistics. Our time-series analysis used the R (version 3.0.2; http://R-project.org) statistical package "mgcv" to establish the model and calculate the results.

## **3. Results**

Overall, there were 12,648 incident cases of TB included in our analysis during this two-year study. The cumulative incidence rate of TB was 1.3% in Wuhan during the 2015–2016 period. Table 1 shows the descriptive statistics for TB incident cases, air pollutants, and meteorological data. On average, there were 17 incident cases of TB per day. The average daily number of TB was the highest during spring season (March, April, and May), whereas the lowest was observed in winter months (December, January, and February). Air pollutant data were close to completeness, with only 9 days missing. Daily average concentrations of air pollutants were all higher during the cold season than in the warm season. The respective daily mean and standard deviation of PM2.5 concentration were 58.94 μg/m3 and 35.41 μg/m3, lower than those of PM10 (89.34 ± 42.16 μg/m3). The highest level of PM2.5 was observed in January (113.4 μg/m3) while the lowest was observed in July (29.7 μg/m3). For gaseous pollutants SO2, NO2, CO and O3, the daily mean concentrations were 15.32, 38.61, 1.03 and 119.23 μg/m3, respectively.

We fit a separate model to the relative risk (RR) of TB incident cases for each air pollutant at lag 0 through 14 and present the results in Figure 2. The single-day lag models show that the e ffect of air pollutants on TB was greatest at lag 7. Among them, PM2.5, PM10, and O3 were statistically significantly (*p* < 0.05) associated with TB. We therefore report our time-series analysis results based on exposure at lag 7 because of this lag structure. The e ffect of air pollutants on TB at lag 7 is presented in Table 2. For a 10 μg/m3 increase in PM2.5, PM10, and O3, the TB risk increased by 17.03% (95% CI: 6.39, 28.74), 11.08% (95% CI: 6.39, 28.74), and 16.15% (95% CI: 1.88, 32.42), respectively. We also fit a multi-pollutant model including all pollutants simultaneously to assess whether associations found in single-pollutant models were confounded by the presence of co-pollutants. After controlling for co-pollutants, the association of PM2.5 with TB was still robust and remained statistically significant. The e ffect estimates were slightly attenuated for all remaining pollutants in the multi-pollutant model.

a Min = minimum, Max = Maximum; b Warm = May–October; Cold = November–April; c TB = Tuberculosis; d PM2.5 = particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 μm, PM10 = PM with an aerodynamic diameter less than or equal to 10 μm, SO2 = sulfur dioxide, NO2 = nitrogen dioxide, CO = carbon monoxide, O3 = ground-level ozone.

**Figure 2.** Relative risks (and 95% CIs) of TB incidence associated with an increase of 10 μg/m3 in air pollutant concentrations at lag 0–14 days in Wuhan, China, during the 2015–2016 period.

The season-specific effects of each air pollutant on TB are presented in Table 2 and further illustrated in Figure 3. We did not observe an obvious trend in seasonality between the warm season (May–October) and the cold season (November–April). The associations between PM2.5 and TB were statistically significant (*p* < 0.05) for both warm and cold seasons. The effect of NO2 on TB during the cold season was also statistically significant (*p* < 0.05). It is noteworthy that dividing the data into warm and cold seasons reduced the sample size and led to an expanded confidence interval.

**Table 2.** Increased risk of TB and corresponding 95% CIs associated with 10 μg/m3 increase in air pollutants a on the 7th lag day for single- and multi-pollutant models.

a PM2.5 = particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 μm, PM10 = PM with an aerodynamic diameter less than or equal to 10 μm, SO2 = sulfur dioxide, NO2 = nitrogen dioxide, CO = carbon monoxide, O3 = ground-level ozone; b The effect of each pollutant was adjusted for all the remaining pollutants; c Warm season = May–October; Cold season = November–April; d Statistically significant associations are shown in bold.

**Figure 3.** Relative risks (and 95% CIs) of TB incidence associated with an increase of 10 μg/m3 in air pollutant concentrations by season (all-year, warm season [May–October] and cold season [May–October]) at lag 7 in Wuhan, China, during the 2015–2016 period.

## **4. Discussion**

In this study, we conducted a time-series analysis to estimate short-term effects of air pollution on TB incidence in Wuhan city during the 2015–2016 period. We found that the effect of air pollutants on TB was greatest at lag 7. PM2.5 and PM10, and O3 were statistically significantly associated with the increased risk of TB at the 7th day's exposure. We fitted a multi-pollutant model to adjust for the possible confounding from the presence of co-pollutants. Our results showed that the effect of PM2.5 on TB remained robust after the adjustment while the effects for the rest of the pollutants were reduced or remained almost unchanged (O3). The seasonal analysis showed that the impact of air pollution on TB was not that different between the warm season and the cold season.

One noteworthy finding of this research is that the effect of air pollution on TB was greatest at lag 7. The surprising consistency observed in lag 7 may be due to the ubiquitous collinearity among the pollutants, that is, the effect of any pollutant on TB actually reflects the effect of another or a combination of several pollutants on TB. Our results are contrary to many previous studies in which the effect was larger in early lags (typically at lag 0–3). This is likely due to the incubation period of the TB bacterium *Mycobacterium tuberculosis*. Our finding agrees with a previously published article in which significant associations between respiratory infection and PM10/PM2.5 were observed at lag 9–10 days and 7–8 days, respectively [29]. Chen et al. (2017) also reported a lagged effect of PM2.5 on influenza and attributed this to the incubation of the influenza virus [30]. Considering that the incubation period of the TB bacterium varies from weeks to decades [18], it is possible that exposure to ambient air pollution shortened the incubation period and accelerated the progress of the disease. Many previous studies have provided a theoretical basis for this hypothesis. Animal studies and cellular-level studies suggest that biomass smoke impairs alveolar macrophage function [31–33]. PM2.5 contains heavy metals that may damage macrophages, resulting in weakened phagocytosis [33]. It has been suggested that air pollutants PM2.5, CO, NO2, and SO2 can modify the regulation of tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) [9,33–35], which functions to induce macrophage activation [33,34]. Consequently, the damaged system may lead to an increased incidence of TB [6]. Additionally, studies have found that bacteria and viruses can attach to particles in the air and be inhaled into the respiratory system [36–38]. This makes the mechanism even more complex as one cannot judge if infection occurred before or during exposure to air pollution.

We observed a statistically significant association between PM2.5 and PM10 and TB in our analysis. We noted that another study in Wuhan also examined the relationship between short-term exposure to air pollution and the risk of TB [15]. However, our research differs from theirs in three ways. First, the two studies used different statistical models. We used GAM, and they used distributed lag non-linear models (DLNMs). Second, exposure to air pollution was measured differently. We used ordinary kriging to estimate the exposure, whereas they used data collected from air monitoring stations directly. Third, our study conducted seasonal analysis, and they did not. Possibly due to these differences, the results of the two studies are also different. They observed the effects of NO2, SO2, CO, and PM2.5 on the risk of TB, while we only observed the effects of PM on the risk of TB. In addition, their study had more years of data and was, therefore, easier to detect significant statistical associations, which could explain the differences in results between the two articles. Because no consensus has been reached on which combination of co-pollutants should be included in the multi-pollutant model [39], we included all air pollutants in the model. After the adjustment, the effect of PM2.5 on TB remained robust, while the effects of other pollutants were attenuated. However, the attenuated effects of other pollutants in the multi-contaminant model may be due to the collinearity between the pollutants, which does not necessarily mean that they have no effect on TB. This finding is consistent with previous epidemiologic studies on TB and ambient PM2.5 [12,13]. Nevertheless, the interpretation should be regarded with caution, as the presence of collinearity cannot be ignored to influence the association. Our results showed certain degrees of positive correlations between pairs of air pollutants (*r*: 0.59 to 0.80). Therefore, a single-pollutant model might be more reasonable for these types of data. We also observed positive but insignificant associations between gaseous air pollutants and TB. As mentioned

earlier, CO, NO2, and SO2 all have roles in impairing the normal function of alveolar macrophages and result in an increased number of TB cases. Additionally, NO2 and SO2 can form ammonium sulfate and ammonium nitrate by naturally occurring chemical processes and can form part of PM and then further threaten human health [39].

Weather, especially temperature and humidity (or dew point temperature), are factors that can confound the relationship between health e ffects and air pollution at both the long- and short-term timescales. Temperature and humidity can be correlated with both air pollutants and health effects such as infectious diseases. We used GAM, a type of semiparametric model, to control for temperature and humidity. The semiparametric model combines the advantages of both parametric and nonparametric models, allowing for explicit parameter items that include exposures of interest and smooth nonparametric items such as temperature and humidity. The adjustment was achieved by using the smooth functions of temperature and humidity in the model. The smoothness of a model fit is controlled by the number of knots used, which was determined by the number of degrees of freedom assigned to each function. It has been reported that the potential confounding e ffect of temperature on short-term time scales may be less pronounced than long-term time scales, and the association between air pollution and health e ffects is relatively insensitive to the choice of the temperature model [40].

This article showed that TB incidence was highest in the spring. Despite that the underlying mechanism remains unknown, several factors may explain this phenomenon. First, people will reduce outdoor activities during the cold season. Consequently, the overcrowded, humid, and low airflow indoor environment makes TB easier to spread [41]. Meanwhile, more indoor activities reduce ultraviolet light exposure that can kill microorganisms [41]. Second, the decrease in human serum vitamin D in winter will increase the infection and re-activation of TB [42]. In addition, delays in seeking health care after the onset and diagnosis may also result in delayed reporting from the winter to the following spring [41]. This also led to misclassification of the actual exposure date. Our seasonal analysis suggested that the e ffects of air pollution on TB during the cold and warm seasons are not significantly di fferent, which may mean that the increase in TB during spring is related to the indoor environment and is less associated with outdoor air pollution.

We examined the seasonal variation by splitting the all-year data into warm (May–October) and cold (November–April) seasons. There was no clear trend that the e ffect of air pollutants on TB was influenced by season. The e ffect estimates of SO2 and NO2 during the warm season were slightly higher than during the cold season. Certain factors may explain this phenomenon. First, during the warm season, people tend to go outdoors more often than during the cold season, so they are exposed to more ambient air pollutants as compared to the opposite. Second, more outdoor activities increase the possibility of TB infection, as elevated levels of airborne *Mycobacterium tuberculosis* may be present in the air. We also observed that the e ffect estimates of PM2.5, PM10, CO, and O3 during the warm season were slightly lower than during the cold season. This was consistent with findings of another study that the e ffect of ambient PM2.5 on TB was most significant during winter [13]. The study explained that staying indoors during high ambient air pollution episodes would lead to an increase in human contact and thus the increased risk of TB transmission. The same hypothesis was given elsewhere as well [43]. Therefore, the real mechanism is not yet clear and warrants more related research.

A feature of this study is the use of kriging to estimate the concentration of air pollution. Kriging has been broadly used to estimate ambient air pollution levels in epidemiological studies [22,24,25,44,45]. However, only a small part of the literature used kriging to study TB. Many such studies used kriging for the detection of TB clusters for animals and humans [46–48].

To the best of our knowledge, no studies have used the exposures estimated by kriging to conduct time-series research on TB. We attempted to use kriging to overcome a long-standing limitation existing in the epidemiology of air pollution, that is, to average the pollution level across the study area as the proxy for the population exposure level. By using kriging, we hope to reduce the misclassification raised by the simple averaging method. Nevertheless, it must be pointed out that kriging itself also has limitations. The accuracy of prediction using kriging largely depends on the number of monitoring stations. Due to a low ratio of monitoring stations to total land area, prediction error is quite possible. Furthermore, kriging also assumes isotropy, that is, it assumes uniformity in all directions. This obviously ignores the impact of the real environment (such as the built environment) on exposure [49]. A previous study compared the values estimated by kriging with the values estimated by the Assessment System for Population Exposure Nationwide (ASPEN) model and found that the former was generally smaller than the latter [21]. As estimates from the ASPEN model yielded a good agreement with monitoring data in multiple studies [21], it is considered more reliable than kriged values. In China, we do not have an air pollution prediction model similar to the ASPEN for comparison; thus, the accuracy of the prediction results is still uncertain.

#### *Limitations and Implications*

Our study has several limitations. First, our ecological study design cannot capture exposure levels at an individual level, resulting in exposure misclassifications. Second, two years of data assume a small sample size and may reduce the statistical power for the detection of significance. Compared to studies with many years of data, this led to overly wide confidence intervals. Previous studies have reported that many air pollutants are statistically significantly associated with TB. However, we only found that there was a statistically significant association of PM2.5, PM10, and O3 with TB. Third, our O3 data were collected 8 h per day, while other pollutants were collected based on a 24-h sampling schedule. This will cause bias in our results. Fourth, due to the lack of data, some factors that can lead to confounding or e ffect modification, such as gender, age, socioeconomic status, and so on, have not been included in the analysis, which may result in bias in results. Fifth, because TB patients may not be able to seek health care immediately after they become ill, and it takes time from the diagnosis to the reporting of a case, the results of this article are a ffected by this unavoidable measurement error. Lastly, the existence of collinearity among air pollutants may create bias and a ffect the magnitude of the estimated associations. Considering the above limitations, caution is needed in interpreting the results of the article.


**4. Glandular trichome**

The term "glandular hair" refers to a wide variety of glands. They differ according to the chemical composition of the substances they secrete, accumulate, or absorb; according to their mode of production, structure, and location; and according to their functions. All of these differences serve a certain level, which overlaps the classification.

In many of the Lamiaceae, two main types of glandular trichomes are encountered, the capitate and the peltate. They differ according to the shape of their secretory head and according to the morphology as well as the nature of the substances to be secreted. The head of the capitate glands consists of 1–4 more or less rounded secretory cells, generally oriented horizontally; a stem, one to several long cells; and a basal cell. The head cell can sometimes be very large, as in some species of *Salvia* [5]. The peltate hairs' head consists of 4–18 more flattened cells on a horizontal plane, a stem cell, and a basal cell. Thus, intermediate shapes can be encountered.


**3. Results**

#### *3.1. Parameter Sensitivity Analysis*

The sensitivity analysis to determine optimum values of three morphological operations used to delineate markers indicated that overall accuracy was maximized for all models, with and without shadow removed, when the MKS was three (Figure 7b, Table 3). Overall accuracy decreased for all models when opening iteration was increased beyond one, except for the two ExGR models, in which case an opening iteration of two maximized the overall accuracy (Figure 7a, Table 3). Higher overall accuracy was achieved for ExGR\_s, ExGR\_ns, COM\_s, and GRB\_s when a dilation iteration of one

was used (Figure 7c, Table 3). For the remaining models, a dilation iteration of three increased the overall accuracy. Optimum values for the DTC were inconclusive (Figure 7d). The results indicate that, depending on the index image used for watershed segmentation, DTC can be selected accordingly to maximize accuracy (Figure 7d, Table 3). Across all models, overall accuracy ranged from 11.5 ± 0.01% for CIVE\_s to 93.4 ± 0.5% for GRB\_ns, the user's and producer's accuracies varied from 0% to 99.4 ± 0.01%.

**Figure 7.** Sensitivity analysis results for optimum values of (**a**) number of iterations for opening, (**b**) morphological kernel size, (**c**) number of iterations for dilation, and (**d**) distance transform coefficient for several vegetation indices (Table 2). ns = no shadow, s = with shadow. Error bars indicate 95% confidence intervals of the mean computed from the standard error.

**Table 3.** Optimum parameter values for marker detection using vegetation indices. For description purpose, subscript "\_ns" was added to the names of index images when shadows were removed after segmentation and "\_s" was added when shadows were not removed, for example, shadow removed ExG index image were named ExG\_ns and those with shadow present were named ExG\_s. Index names as in Table 1.

**Table 3.** *Cont.*

#### *3.2. Tree-Cover Area Estimation and Tree Detection Analysis*

Further analysis only considered models that had an overall accuracy of tree detection greater than 90% and that fell inside the confidence interval of the reference area estimate. The reference area was estimated from the reference dataset consisting of 2401 random point samples. The number of tree and no-tree samples was 650 and 1751, respectively, thus, on the basis of the sampling design to provide a 2% precision with a 95% confidence level, the percent tree cover was 27.1 ± 2%. Six models with shadow (COM\_s, ExG\_s, ExGR\_s, GRB\_s, TGI\_s, and VDVI\_s) and seven models after shadow removal (COM\_ns, ExG\_ns, ExGR\_ns, GRB\_ns, R-G\_ns, TGI\_ns, and VDVI\_ns) met both criteria (Table 4). Confusion matrix derived adjusted accuracy estimates for the selected shadow and shadow removed models are shown in Table 4. The overall accuracy for those 13 models ranged from 90.5 ± 0.6% to 93.4 ± 0.5% for VDVI\_s and GRB\_ns, respectively. User's accuracy was highest for GRB\_ns (90.1 ± 1.2%) and lowest for VDVI\_s (82.6 ± 1.5%), and producer's accuracy was highest for ExG\_s (87.4 ± 1.2%) and lowest for VDVI\_ns (81.1 ± 1.3%).

**Table 4.** Metrics derived from confusion matrix of segmented images by shadow removed and with shadow vegetation index models. Accuracies in percent ± standard errors. Index names as in Table 1.

We found that GRB\_ns model had highest overall accuracy of 93.4 ± 0.5%, closely followed by ExG\_s (93.1 ± 0.5%). The GRB\_ns also had the highest user's accuracy of 90.1 ± 1.2% followed by ExG\_ns (89.9 ± 1.2%). Higher user's accuracy of trees implies that trees were detected with lower commission error. Although the user's accuracy of ExG\_s was 87.9 ± 1.3%, this model had the highest

producer's accuracy of 87.4 ± 1.2%. Higher producer's accuracy indicates better performance of the models in detection of actual trees with the lowest omission error. The VDVI models, VDVI\_s and VDVI\_ns, had the lowest user's and producer's accuracy respectively (Table 4). The commission error in the GRB model decreased after shadow removal but the omission error increased slightly. When GRB\_ns was used, it attained the lowest commission error among all the index images (user's accuracy = 90.1 ± 1.2%), but had a higher omission error (producer's accuracy = 85.2 ± 1.3%) compared to ExG\_s, TGI\_ns, COM\_s, and GRB\_s. In contrast, ExG\_s and TGI\_ns had the lowest omission error (producer's accuracy = 87.4 ± 1.2% and 87.2 ± 1.2%, respectively). This indicated that the watershed segmentation using these two indices were able to detect trees with higher accuracy than other indices, but TGI\_ns had higher commission error (user's accuracy = 85.9 ± 1.4%) than ExG\_s (user's accuracy = 87.9 ± 1.3%).

We found that on an average overall accuracy and user's accuracy increased by 0.5% (standard deviation (SD) = 0.7%) and 2.9% (SD = 4%), respectively, when shadows were removed (Table 5). However, average producer's accuracy and the proportion of the area covered by trees decreased by 1.4% (SD = 3.2%) and 1.1% (SD = 2.1), respectively (Table 5). Although on an average accuracy increased or decreased only slightly, it must be noted that the overall accuracy and user's accuracy increased for six out of seven indices when shadows were removed, whereas producer's accuracy increased for only one index model (Table 5). The highest increase in user's accuracy of ~10% was observed when shadows were removed from the R-G derived segmented image followed by VDVI (~4%), although user's accuracy declined by 3% when TGI was used. The highest decrease in producer's accuracy after shadow removal was observed for the R-G index (6.7%), although producer's accuracy increased after shadow removal when TGI was used (4%). The estimated proportional area decreased in six index images when shadows were removed. The highest decrease in the proportion of tree-cover area was ~5% when the R-G index image was used (Table 5).

**Table 5.** Difference in proportional area, user's accuracy, producer's accuracy, and overall accuracy between shadow removed and with shadow vegetation index models. SD = Standard Deviation.

#### *3.3. Object-Based Overlap Analysis*

Two models (one with shadow (ExG\_s) and the other without shadow (GRB\_ns) that had the highest point-based overall accuracy were selected for overlap accuracy assessment. Using an object-based approach, 50 randomly sampled polygons for each of the two maps covered polygon size distributions including the 5th up to the 97th percentile for ExG\_s, and from the smallest polygon up to the 99th percentiles for GRB\_ns. The highest overlap accuracy between predicted and reference data was achieved by ExG\_s (~95%) when compared to GRB\_ns (88%) (Table 6). Although the GRB\_ns model had the highest overall accuracy (93.4%) based on the point-based accuracy assessment, the ExG\_s model performed better in delineation of actual crowns by as much as 7%. The omission area was very low when ExG\_s model was used (~5%) compared to GRB\_ns model (~12%), but the

commission error was much higher with ExG\_s model (21.4%). This is in line with the point-based accuracy assessment, where the GRB\_ns model had higher user's accuracy and lower producer's accuracy compared to ExG\_s.

**Table 6.** Percent (%) overlap, omission, and commission areas of predicted trees with reference trees by vegetation index models.

The mean patch sizes of tree clumps predicted by GRB\_ns and ExG\_s di ffered substantially. Patches predicted by GRB\_ns were much larger than those predicted by ExG\_s because ExG\_s separated clumped trees better than GRB\_ns. The mean size of patches delineated by GRB\_ns was about 3.86 m2 compared to roughly 1.16 m2 by ExG\_s. The data suggest that commission errors from GRB\_ns- and ExG\_s-predicted patches were similar, though GRB\_ns had a higher mean commission error (0.58 m2) compared to ExG\_s (~0.25 m2) (Figure 8). However, there was a significant di fference in omission error between the two, in which GRB\_ns had a higher mean omission error of 0.67 m2 compared to 0.06 m2 of ExG\_s model (Figure 8). The total cover estimated by GRB\_ns model was 0.49 hectare compared to ExG\_s model which was estimated as 0.53 hectare.

**Figure 8.** Comparison of omission and commission errors of patches predicted by GRB\_ns and EXG\_s models. Error bars are the 95% confidence intervals of the mean computed from the standard error.

Comparing the number of reference tree crowns that were fully within each of the predicted tree patches from the two models (GRB\_ns and ExG\_s), we found that ExG\_s detected more trees as individuals compared to GRB\_ns. The largest tree patch predicted by GRB\_ns had eleven reference trees compared to only three in ExG\_s. (Table 7). Individual predicted trees that coincided with one tree from the reference data were more common for ExG\_s, whereas more tree clumps were delineated by GRB\_ns (Table 7).

**Table 7.** Count of predicted tree crowns in patches versus number of reference tree crowns. 0 = tree not detected, 1 = individual isolated tree detected, >1 = number of tree crowns present in detected tree patch.


*3.2. Enhancing the Mechanical Performance of Bioceramics Composites*

CaP-based scaffolds generally exhibit poor mechanical properties compared to teeth and bone, especially due to their intrinsic brittleness, limiting their load-bearing bone applications [82,112]. Brittle materials are more likely to fail under tension or shearing rather than compression, essentially due to the crack propagation in preexisting flaws, such as micro-cracks or macro-pores [70,113].

Common approaches to improve mechanical performance and reduce the brittleness of ceramic materials are classified as intrinsic or extrinsic modifications (Figure 3).

**Figure 3.** Mechanical reinforcement strategies for CaP-based biomaterials in a load-bearing application.

The intrinsic approach involves changes in the inherent properties of the scaffold, such as the composition, porosity and microstructure, whereas extrinsic modifications involve the use of reinforcing fibers or, in the case of CaP-based cements and pastes, the use of a cross-linker agent or hydrogel for the optimization of the dual setting system [72].

#### 3.2.1. Intrinsic Material Modifications

The mechanical strength of scaffolds closely depends on their microstructure. Several factors, such as composition, crystal size and porosity, greatly affect the microstructure of scaffolds and its final strength [102]. One of the major factors affecting the mechanical performance is porosity, as the strength exponentially decreases with increasing voids [56,114,115].

A possible strategy to increase the mechanical strength is the reduction of intergranular voids by favoring the packing of the particles or using multimodal particle size distributions, leading to a decrease in the microporosity extent, especially in the struts [116].

The pore size distribution influences the degradation performance of the scaffold, and, therefore, the biodegradation kinetics can be modulated by varying the pore architecture [91]. Triangular, rectangular and elliptic pores were reported to support angiogenesis and faster cell migration due to their greater curvature [55]. Nevertheless, the increase in scaffold porosity is inversely related to mechanical strength; this is a key problem, difficult to solve and strongly limiting to load-bearing applications. In this respect, an exponential decrease in the compressive strength with increasing porosity was observed [115].

$$
\sigma = \left[ (\text{E}\_0 \text{R}) / (\text{\textdegree\text{c}}) \right]^{0.5} \exp(-\text{KP}) \tag{2}
$$

where E0 is the Young's modulus at zero porosity; *c* is the average pore size; R is the fracture surface energy; K is an empirical constant, which can be extracted from the slope of a semi-logarithmic plot of the strength–porosity curve; and P is the porosity extent (in volume) [117,118].

Higher compressive moduli are associated with smaller pore sizes, porosity gradients and oriented pores [114,115]. The capability to modulate the porosity extent and distribution is helpful in limiting the concentration of mechanical stresses toward damage-tolerant structures; that is, micro-fractures occur until the scaffold's failure.


**2. Radiometric Sensors for Extractive Pyrometallurgy of Copper**

Within copper smelters, the reality is that even the 3.0 industrial revolution (information technology (IT) and automation) still has room for continued development. This current condition limits the ability to integrate Industry 4.0 developments for process optimization. Indeed, the degree of analytical instrumentation usage for the monitoring and control of the smelter processes is limited. As a result, the information available for operational decision-making at many plants is mainly based on static mass and energy balances. In such cases, the operational dynamics continues to depend largely on the experience of ground operators.

Only a handful of advanced measurement instruments have gained industry acceptance for operational monitoring and control in copper pyrometallurgical reactors. The aforementioned Semtech OPC system, which has been on the market for more than 25 years, monitors pyrometallurgical variables by analyzing the emission spectrum of flames emitted by gases during the conversion of copper sulfide mattes within Peirce-Smith (PS) converters. Additionally, the Noranda Pyrometer, configured solely for bath furnaces using blowing tuyères, has been on the market since the 1980s without any change to the original concept and with only some upgrades to the unit's robustness (with regard to assembly, material selection, etc.). By measuring radiation through an analog array, it monitors the temperature of the molten bath by applying Planck's law for two fixed wavelengths. Other instruments include level measurement in a feed bin and furnace melts and equipment vibration monitoring.

One option for the advancement of process monitoring and control systems in copper smelters is to modernize the use of radiometric measurements, since this approach was previously validated with both the OPC system and the Noranda Pyrometer. A series of proposed concepts and results from initial industrial trials are discussed here.


**6.1 Sampling and storage of microbial material**

Microbial materials utilized for the present study have been collected from two wastewater treatment plants, notably:

Acerra wastewater undergoes a traditional activated sludge treatment process, following preliminary removal of gross materials and primary settlement. Activated sludge produced is split between 98% recirculated fraction and 2% fed to thickening and stabilization processes. Colonies populating the sludge flocs have been included in **Table 7**. Samples associated sludge parameters are B.O.D.5 = 20 mg/l and active mass concentration = 2600 mg/l. Solofra plant implements a biological/physical– chemical process over a C.O.D of 7000-8000 mg/l. After a preliminary phase of precipitation with limestone, polyelectrolyte and ferric chloride/aluminum sulphate, wastewater is supplied to a biological plant, with a final activated carbon adsorption stage for the removal or coloring and residual organic matter. Activated sludges of Solofra plant are composed predominantly of proteobacteria, Bacteroidetes, firmicutes with a presence of actinobacteria, planctomycetes and chloroflexi [36].

#### **Table 7.** *Microorganisms of activated sludges.*

Inventory of bacterial matters consisted of 1.5 ml 300 Eppendorf, of which 100 of sludge, 100 of untreated water and 100 of physically-chemically treated water. Also, No. 105 50 ml Falcon tubes, of which 35 of sludge, 100 of untreated water and 100 of physically-chemically treated water. Biodegradative inherent capacity of Acerra sludges in a suspended lab scale bioreactor about a specific substrate such as ethyl acetate had preliminarily been tested by the author [37].


**3. Alteration of miRNA biogenesis in response to DNA damage and repair**

Because miRNAs are actively involved in regulation of genes that are related to DNA damage and repair, it was not surprising to find that miRNA biogenesis changes in response to DNA damage and repair. Several studies demonstrated that both miRNA transcription and maturation process are altered in response to DNA damage and repair.

Recent studies show that transcription of miRNA can be directly affected by DNA damage. The *P53* gene plays a critical role in this regulation. For example, miR-34a can be up-regulated by the *P53* gene in response to DNA damage (Chang et al., 2007; Corney et al., 2007; He et al., 2007; Raver-Shapira et al., 2007; Welch et al., 2007). Up-regulation of miR-34a results in apoptosis, cell-cycle arrest, and DNA repair. miR-34a is a direct transcriptional target of P53 because the promoter region of miR-34a contains a canonical P53 binding site. When DNA damage activates the *P53* gene, P53 protein binds to the promoter of miR-34a and up-regulates miRNA expression. In *Caenorhabditis elegans*, miR-34a expression was enhanced by irradiation in a P53 independent manner, and knocking down of the *Cep1* gene (homolog of the *P53* gene) had no effect on the miR-34a response to irradiation (Kato et al., 2009). Up-regulation of miR-34a in response to genotoxin exposure is also observed in different biological systems (Chen et al., 2011; Li et al., 2010;

original sequence using the sister chromatid as a template. NHEJ is a relatively simple way for DNA double-strand repair and it just rejoins two broken ends without correcting any

A microRNA gene can be located in an intron of another gene, in either the sense or antisense orientation. miRNA can be coordinately expressed with its host gene, or it can have its own promoter independent of its host gene (Ozsolak et al., 2008). The biogenesis of miRNA is a complex process as shown in Figure 1. miRNA is first transcribed as a long primary miRNA (pri-miRNA) by RNA polymerase II in the nucleus (Lee et al., 2004). PrimiRNA is structurally similar to mRNA, but contains a stable stem-loop structure (Cai et al., 2004). Recognition of the hairpin and selection of a cleavage site are mediated by DGCR8. Nuclear RNase III (Drosha) then cleaves the pri-miRNA to release the hairpin-shaped precursor miRNA (pre-miRNAs). The pre-miRNA is exported from the nucleus to the cytoplasm by Exportin 5 (Exp5). In the cytoplasm, the pre-miRNA is subsequently cut by cytoplasmic RNase III (Dicer) in complex with Argonaute2 (Ago2) and TRBP, a doublestranded RNA-binding protein. This process cleaves the pre-miRNA hairpins to remove its hairpin loop, resulting in a miRNA duplex with the appropriate length (Gregory et al., 2005; Han et al., 2004; Lee et al., 2003). Normally, one strand of the duplex is then degraded. The mature miRNA are incorporated into an RNA-induced silencing complex (RISC) (Gregory, et al., 2005; Grishok et al., 2001; Hutvagner et al., 2001; Ketting et al., 2001; Maniataki and Mourelatos, 2005). RISC recognizes target mRNAs through full or partial base-pairing interactions between the miRNA and the to "3'-untranslated region (UTR) of the target mRNA. Depending on pairing interactions between miRNAs and their targets, miRNAs suppress their target gene expression by either mRNA cleavage or translational repression. If an mRNA target match perfectly or near-perfectly to the miRNA, the mRNA will be degraded;

otherwise, the mRNA will be translationally suppressed (Meister and Tuschl, 2004).

and maturation process are altered in response to DNA damage and repair.

**3. Alteration of miRNA biogenesis in response to DNA damage and repair**

Because miRNAs are actively involved in regulation of genes that are related to DNA damage and repair, it was not surprising to find that miRNA biogenesis changes in response to DNA damage and repair. Several studies demonstrated that both miRNA transcription

Recent studies show that transcription of miRNA can be directly affected by DNA damage. The *P53* gene plays a critical role in this regulation. For example, miR-34a can be up-regulated by the *P53* gene in response to DNA damage (Chang et al., 2007; Corney et al., 2007; He et al., 2007; Raver-Shapira et al., 2007; Welch et al., 2007). Up-regulation of miR-34a results in apoptosis, cell-cycle arrest, and DNA repair. miR-34a is a direct transcriptional target of P53 because the promoter region of miR-34a contains a canonical P53 binding site. When DNA damage activates the *P53* gene, P53 protein binds to the promoter of miR-34a and up-regulates miRNA expression. In *Caenorhabditis elegans*, miR-34a expression was enhanced by irradiation in a P53 independent manner, and knocking down of the *Cep1* gene (homolog of the *P53* gene) had no effect on the miR-34a response to irradiation (Kato et al., 2009). Up-regulation of miR-34a in response to genotoxin exposure is also observed in different biological systems (Chen et al., 2011; Li et al., 2010;

deletions or rearrangements of DNA.

**2. Biogenesis of miRNA**

Li et al., 2011; Zenz et al., 2009). miR-34c, another member of miR-34 family, is transcriptionally up-regulated by P53 following DNA damage (Cannell et al., 2010). In addition to miR-34a, P53 can also regulate the expression of miR-192, miR-194, and miR-215. These miRNAs are considered tumor suppressor miRNAs (Braun et al., 2008; Georges et al., 2008).

miRNA biogenesis is globally induced upon DNA damage in an ATM (ataxia telangiectasia mutated) dependent manner (Zhang et al., 2011). The ATM gene encodes a DNA damageinducible kinase. ATM controls cell grow rate by interacting with other proteins, for example BRCA1, following DNA damage. In response to strand breaks or other type of DNA damage, the ATM protein coordinates DNA repair by activating other proteins. Because of its central role in cell division and DNA repair, the ATM protein is important in carcinogenesis. More than one-fourth of miRNAs were significantly upregulated after DNA damage, while loss of ATM activity abolished their induction. Their results show that DNA damage activates the ATM kinase that directly binds to and phosphorylates KH-type splicing regulatory protein (KSRP), leading to enhanced interaction between KSRP and primiRNAs and increased KSRP activity in miRNA processing. The increased activity, in turn, results in more pre-miRNAs from pri-miRNAs, so that more miRNA products are produced to respond to the DNA damage.

Other studies show a different mechanism by which DNA damage signaling is linked to the miRNA maturation processes. Several miRNAs with growth suppressive function, including miR-16-1, miR-143 and miR-145, were regulated at the post transcriptional level through a P53-mediated miRNA maturation process in response to DNA damage (Suzuki et al., 2009; Toledo and Bardot, 2009). The P53 tumor suppressor protein binds to Drosha to facilitate the processing of pri-miRNAs to pre-miRNAs. Mutation in the DNA-binding domain of P53 decreases processing of pri-miRNAs by Drosha, and reduces the expression of the related miRNAs. In silico analyses, all three component of the P53 tumor suppressor, P53, P63, and P73, can regulate the major components of miRNA processing, such as Drosha-DGCR8, Dicer-TRBP2, and Agronaute proteins. Thus, when DNA damage activates the P53 gene, the activated P53 gene can modulate miRNA expression by affecting the miRNA biogenesis processes.

miR-24 regulates the DNA damage response by down-regulation of H2AX, the initial sensor protein for the DNA damage response. miR-100, miR-101 and miR-421 suppress ATM, the chief transducer of the DNA damage response, by targeting the 3'-UTR of ATM. miR-16 can up-regulate ATM activity by suppressing levels of Wip1. DNA repair pathways are regulated by a number of miRNAs involved in different types of DNA damage correction. the NER protein RAD23B was down-regulated by miR-373. MMR protein MSH2 and MSH6 were down regulated by miR-21 and MLH1/MSH2 were suppressed by miR-155. The HRR protein BRCA1 was down-regulated by miR-182 and RAD52 was suppressed by miR-210 and miR-373. The NHEJ protein DNA-PKcs was suppressed by miR-101 (Yan, Ng. 2010).


*6.1. Conduit Composition*

Regardless of their composition, empty conduits longer than 1 cm in length, generally induce poor, if any, axon regeneration. However, conduit composition is important. Silicon conduits induce axon regeneration [149,150] but can have the complication of rigidity, which can result in significant chronic nerve compression and irritation at the implantation site [151]. In this case, they may have to be removed, which endangers any recovered neurological function. Therefore, it is advisable to use a fully degradable matrix that does not negatively affect axon regeneration [152].

Axon regeneration is extensive through conduits composed of fibrin [145,153], hydrogel tubes [154], alginate/chitosan polyelectrolyte [155–157], poly epsilon-caprolactone [158,159], polyglycolic acid [160], poly(lactic-co-glycolic acid) or silk-based [161–164]. Conduits composed of decellularized human umbilical artery [165] and muscles [166] are also effective in promoting axon regeneration across nerve gaps.

Veins are promising as conduits [167,168]. Clinically, empty vein conduits appear to have a 3-cm-long limit for their ability to induce axon regeneration [169]. A meta-analysis of published papers determined that for nerve gaps up to 4 cm in length, vein conduits did not induce any significant improvement in sensory recovery outcome compared to conduits of other materials [170]. However, the axon regeneration-promoting e fficacy of vein conduits is enhanced when they are filled with PRP [171–173], muscle [174], pre-degenerated muscle [175,176], muscle seeded with neural-transdi fferentiated human mesenchymal stem cells [177], and minced peripheral nerves [178]. One study in sheep showed that using the median epineural sheath as a conduit can restore median nerve function across 6-cm-long nerve gaps [179].


*3.2. Changes in Tissue Histology of Starch–Surimi Gels with Non-Setting or Setting Effect* 3.2.1. Light Micrograph

The morphology of potato starch differed appreciably between direct heated gels and two-step heated gels (Figure 3). It was observed that a nearly spherical morphology of starch was shown in SCG, whereas a relatively irregular state was observed in CG. Starch showed non-obstructive expansion in CG with 3% or 6% starch content. The striation structures inside of starch granules were intact with an increase in starch content, showing that the swelling degree of starch decreased in CG. In comparison, it was observed that potato starch granules in SCG were almost circular, especially in the low starch-containing matrices. In addition, large granule starch could not realize valuable swelling in two-step heating. However, regardless of non-setting or setting samples, small starch granules showed extended states, which was more conducive for applying starch in the surimi matrix.

**Figure 3.** Light microscopy of cooking gels and setting-cooking gels at four starch contents (3%, 6%, 9%, and 12%). indicates large granule starch; indicates small granule starch. Caption: see Figure 1.

3.2.2. Scanning Electron Micrograph

Overall microstructures of starch–surimi gels were observed at 300× to reflect the morphology of starch and surimi matrix after cooking (Figure 4). The gelatinization temperature of potato starch ranged from 56 to 66 ◦C, and the amorphous region extended after reaching this temperature range [30]. For CG, as the temperature rose above 45–50 ◦C, the suwari was partially disrupted, resulting in modori formation and unhindered starch swelling [31]. Moreover, the loose gel network might be a key factor of the irregular swelling of starch. In contrast with non-setting gels, SCG showed more substantial resistance to starch swelling that avoided water loss in the gel network structure. It might contribute to the setting step, which resulted in the formation of a stable surimi gel network in SCG. Thus, it could be determined that the setting effect promoted cross-linking in surimi, hindered water absorption, and broke hydrogen bonds in the striations of starch granules.

**Figure 4.** Microstructure micrographs (magnification ×300, ×3000) of starch–surimi matrix subjected with different heating processes: 0, 3, 6, 9, and 12 represented 0%, 3%, 6%, 9%, and 12% starch content, respectively. Caption: See Figure 1.

Viewed at large magnifications, non-starch containing CG exhibited a coarse matrix with large cavities, as shown in Figure 4. However, the addition of the starch made cavities disappear, resulting in more compact surimi. According to the results from Kong et al. [16], these shrunk surimi networks were considered to be responsible for the "packing effect." Meanwhile, for SCG, no significant changes were observed, and smooth structures were shown throughout the addition of starch.


*4.5. Luciferase Reporter Assay*

LNCaP and C4-2B cells were transiently transfected with pGL3-Basic, pGL3-KLF5, or pGL3-PSA, or pGL3-MMTV and pRL-TK (Renilla luciferase, Promega, Madison, WI) as an internal control. After 48 h of transfection and enzalutamide (Beyotime Biotechnology, Shanghai, China, catalog number: SC0074) treatment, cells were lysed in 5 × lysis buffer (Promega) for 30 min and luciferase activity was measured using a luminometer (Tristar LB941, Berthold Technologies, BadWild, Germany). Firefly luciferase activity was normalized to Renilla luciferase activities in each reaction. Experiments were performed in triplicate.

#### *4.6. Colony Formation Assay*

One thousand C4-2B cells/well or 2000 LNCaP cells/well were seeded in 6-well plates and cultured in the presence of RPMI-1640 medium containing 10% FBS with DMSO or 10 μM enzalutamide (Beyotime Biotechnology,). The plates were incubated for 2 (C4-2B) or 3 (LNCaP) weeks, after which cultures were fixed with 4% paraformaldehyde for 30 min and stained by 0.05% crystal violet (BBI life sciences, Shanghai, China) for 1 h at room temperature and then photographed. In a single experiment, assays were conducted in triplicate and then as three independent experiments.


**2.7 Women and leadership**

The workplace can often become an unsettling place for women without the presence of mentors. Women and minority groups often feel a sense of incivility in the workplace when in a position of leadership [21]. Women are the recipients of insolent behaviors more often than men but experience an increase in rude and disrespectful behavior when placed in a supervisor position. When in leadership positions, women often face derogatory remarks, interruptions, doubts of ability, and a disregard for opinions from male colleagues [22].

In addition to experienced behaviors, women are often faced with perceived behaviors from colleagues when placed in leadership positions. Because women do not often hold supervisory positions, they are expected to be more socially adept, communal, and welcoming to colleagues [23]. Women in leadership roles are often stereotyped with possessing higher intensity in emotions while being more emotionally expressive. These stereotypes create a stigma that women in leadership positions lack the ability to control emotions and that they will lead with emotions and feelings more than their male counterparts, resulting in a double standard for women and men in the workplace [24]. Men often are greeted with acceptance and respect in instances

when higher emotions such as anger are displayed. However, women are often seen as unreasonable or too sensitive in cases where anger is detected. It is for this reason that many women seek female mentors in higher positions as a means of influence [25]. Women in higher-ranking positions are often more likely to assist lower-ranking colleagues with promotions once they reach a position of influence [24].

This chapter illuminates the evolution of the role of women as mentors and protégés in higher education. These considerations are especially important during periods of isolation that occurred during the pandemic. In higher education, the lack of women mentors has hindered the advancement of other women.


*3.4. Interaction Analysis*

To explore the possible interactions between HpzA and HSA, all the docked conformations for the ligand output file were separated. The interaction analysis of HpzA with HSA was carried out through the Discovery Studio Visualizer (2021) [43] and PyMOL (2021) [44] tools. First, the binding pose of HpzA with HSA was selected based on the specific interaction, and then the HSA–HpzA protein–ligand complex was generated for further studies in MD simulation.


4.2.1. Individual approaches

dysfunction. Severe symptoms are likely to result in patient stigmatization of self and loved ones, inadequate clinical care and rehabilitation, and the stigma of shame and family burden. Many family members hide their relationships or consider the illness to be a source of stigma when a relative suffers from PDD. Those in contact with dual-diagnosis persons may also experience distress, tension, and conflict within these relationships. Interpersonal conflicts are often associ-

Rates of treatment noncompliance may decrease, reducing motivation for change and making engagement more difficult. Persons may drop out of long-term programs, retard progress, and

Professional nurses have observed that PDDs have a low tolerance for stressors and a narrow repertoire of coping skills, some of them unhelpful even in the short term. These PDDs frequently develop idiosyncratic avoidance methods to manage positive symptoms such as delusions and hallucinations. These methods may become habitual and generalized. Research

Antipsychotics are a standard treatment for PDD, effectively managing symptoms. Case studies demonstrate that the antidepressant olanzapine may reduce psychotic symptoms induced by drug addiction. Dopamine antagonists have also demonstrated effectiveness in decreasing drug addiction. Research has shown that psychotic symptoms are associated with changes in brain chemistry. Antipsychotic medications restore the brain's natural chemical balance, reducing or eliminating psychotic symptoms. Medications may require weeks to work. Conventional antipsychotics are dopamine antagonists and target one of five subtypes of dopamine receptors in the brain. Dopamine 2 (D2) receptor antagonism in the mesolimbic tract improves hallucinations and delusions, but the conventional antipsychotic blockade of all D2 receptors causes other problems. Antagonizing D2 receptors in the mesocortical dopamine pathway worsens negative symptoms including avolition, anhedonia, alogia, and affective flattening. Atypical antipsychotics antagonize serotonin 5HT2A receptors as well as D2 antagonism seen with conventional antipsychotics. Serotonin affects dopamine differently in each of the four pathways. In the nigrostriatal pathway, serotonin antagonism increases dopamine release, resulting in fewer reports of movement disorders. Serotonin antagonism in the tuberoinfundibular pathway eliminates serotonin's ability to increase prolactin levels, mitigating the effect of two blockades in this pathway. In the mesocortical pathway, where serotonin 2A receptors predominate, antagonizing serotonin increases dopamine. This is thought to be responsible for improved cognition, affection, and motivation seen with antipsychotics. Weak serotonin 2A antagonism in the

destabilize illnesses, contributing to psychosocial instability [16, 19].

ated with dual diagnoses [3, 15, 17, 18].

52 Drug Addiction

3.4. Impact upon treatment adherence

4. Treatment modalities of PDD

4.1. Pharmacotherapy

has suggested the following treatment modalities:


Contents

Preface

When I was invited to edit and put together this book, as part of the Women in Science Competition in 2018 proposed by IntechOpen, I was very excited to get the opportunity to put together a collection of works in the field of communication that would resonate with the idea of pursuing a fundamental thread of how communication has evolved on life on Earth, from the very building blocks of life such as DNA or cells, to supra-human organisms such as institutions and computer systems.

The idea of finding universal, fundamental patterns of communication arose while I was working on a computational model of information exchange that could find its applications in molecular biology as well as in artificial intelligence. While researching this idea, I came across a wide variety of research from most fields of study, such as biology, anthropology, history, marketing, economics, information sciences, psychology, social sciences, to name only a few. Particularly in biology, psychology, and marketing or business, topics such as animal communication or communication with children or effective communication in business seemed very large, broad, and predominant, but without much reference between each other or across disciplines. This gap in the current literature and the siloed research within fields spurred the idea of trying to find what is common and fundamental in com-

A first step towards opening the dialogue and research on common threads of communication in various and different systems was done by the publication of the book "Emergence of Communication in Socio-Biological Networks" at Springer in 2018. But this has only scratched the surface and, given the wide diversity of the fields and researchers involved, the next logical step was to gather into a collection the works of worldwide researchers that are intimately familiar with specific aspects

This book is highly curated, as the chapters published here are not the typical research on communication specific to each field, but research on communication as a complex system that can be read and understood across disciplines and that can address a wider audience than within one's specialty. This effort shows again how big the gap is in the cross-disciplinary approach of communication, how open and wide the field is and how important it is to keep on pursuing in this search for

As this is another important step in the discovery of fundamental communication patterns, I am confident that it is also only another open door to an even wider and broader body of research and therefore I see this book as an invitation for the current and future scientists and scholars to look at this phenomenon through the

I would like to express my deepest gratitude to the authors that put in a great deal of effort into their papers and for submitting their work to this edition, to the

lenses of complex systems, multidisciplinary and universality.

munication between these seemingly different subfields.

of communication.

knowledge.