*2.2. Image Calibration*

The metallographic images were distributed to experts to determine the sorbite region and sorbite content in each image using the grid truncation method. The sorbite area (dark zone) was then labeled using LabelMe software version 5.1.1. Figure 1 shows an example of the sorbite labeling in a single metallographic image. For this study, 2500 valid metallographic images and their labeling files were collected, with an image size of 2048 × 1536.

**Figure 1.** Example of sorbite content labeling. (**a**) Original image; (**b**) Labeling image.


*2.1. Graph Representation Learning*

A growing number of applications use non-Euclidean methods to generate data, which are then represented as graphs with complex relationships and inter-object dependencies. Past feature representation and extraction algorithms face substantial difficulties in handling the complexity of graph data. Over the past decade, many studies on extending traditional feature extraction approaches for graph data have emerged. Among them, GRL has evolved considerably and can be roughly divided into three generations, including traditional graph embedding, modern graph embedding, and deep learning on graphs. The first generation of methods are classic dimension reduction techniques, such as IsoMap [28] and LLE [29]. The second generation of feature extraction methods on graphs are modern graph embedding methods, such as DeepWalk [30] and LINE [31]. GNNs can be broadly regarded as the third (and latest) generation of GRL after the traditional and modern graph embedding, and they are reported to achieve the most promising performance in a wide range of computational tasks on graphs [32].

A growing body of research has shown that GNNs are extremely effective for both traditional GRL tasks (e.g., recommender systems and social network analysis) and new research areas (e.g., healthcare, physics, and combinatorial optimization) [32]. A typical GNN consists of graph filters and/or graph pooling layers. The former take the node features and graph structure as inputs and output new node features. The latter take the graph as an input and output a coarsened graph with a few nodes.GNNs can be broadly classified into spatial and spectral approaches based on their graph filters. The former explicitly leverages the graph structure, for example, spatially close neighbors, whereas the

latter analyzes the graph using a graph Fourier transform and an inverse graph Fourier transform [33].

Classical spatial-based GNNs include [9–11,34–37]. Ref. [9] is a very early GNN that uses the local transition function as a graph filter. A GraphSAGE filter [10] uses different aggregators (mean/LSTM/pooling) to aggregate information regarding the one-hop neighbors of the nodes. In addition, a GAT-filter [11] relies on a self-attention mechanism to distinguish the importance of neighboring nodes during the aggregation process. An ECC-filter [34] was also proposed to handle graphs with different types of edges. Similarly, a GGNN-filter [36] was designed for graphs with different types of directed edges. By contrast, a Mo-filter [35] is based on a Gaussian kernel. Finally, an MPNN [37] is a more general framework, with the GraphSAGEfilter and GAT-filter mentioned above being special cases. In general, spatial-based GNNs are more generalized and flexible.

Spectral-based graph filters use the graph spectral theory in the design of the filtering operations within the spectral domain. Early studies [33] dealt with the eigen decomposition of the Laplacian matrix and the matrix multiplication between dense matrices; thus, they are computationally expensive. To overcome this problem, a Poly-filter [38] based on a K-order truncated polynomial was proposed. To solve the problem of a Poly-filter in which the basis of the polynomial is not orthogonal, a Cheby-filter [38] based on the Chebyshev polynomial was introduced. A GCN-filter [12] is a simplified version of a Cheby-filter. The latter involves a K-hop neighborhood of a node during the filtering process, whereas in the former, K = 1. A GCN-filter can also be regarded as a spatial-based filter. Currently, GCNs are among the most widely used types of GNN. In our model, we used a GCN as the key component.

Feature extraction methods such as graph embedding and graph kernel techniques are strongly related to the study on GNNs. Compared to GNNs, the former only focus on representing network nodes as low-dimensional vector representations without targeting subsequent tasks, such as graph node classification and link prediction. Many graph embedding techniques are linear and not in an end-to-end manner, such as random walk [39] and matrix factorization [40]. Graph kernel techniques employ a kernel function to obtain the vector representations of graphs. They are also an important type of approaches to solve the graph classification problem. However, compared to GNNs, they are not learnable and far less efficient.

GNNs are designed for different graph-based tasks, such as node classification, link prediction, graph classification, and community detection. In particular, our task is semisupervised, which means that we need to learn the representation of all nodes from a few labeled nodes and the remaining unlabeled nodes. GNNs are a rapidly growing field at the present time. For a more comprehensive and detailed introduction to this field, we refer the reader to [32].


*Article* **Effective Extraction of the Al Element from Secondary Aluminum Dross Using a Combined Dry Pressing and Alkaline Roasting Process**

**Han Lv 1,2, Mingzhuang Xie 1,2, Zegang Wu 1,2, Lili Li 1,2, Runjie Yang 1,2, Jinshan Han 1,2, Fengqin Liu 1,2,\* and Hongliang Zhao 1,2,\***

**Abstract:** Secondary aluminum dross (SAD) is a hazardous solid waste discharged from aluminum electrolysis and processing and the secondary aluminum industries, which causes severe environmental pollution and public health disasters. The stable presence of the α-Al2O3 and MgAl2O4 phases in SAD makes it difficult for it to be efficiently utilized. A combined dry pressing and alkaline roasting process was proposed for extracting the valuable Al element from SAD. Two alkaline additives (NaOH and Na2CO3) were selected as a sodium source for extracting the aluminum source from SAD in order to perform the thermodynamic analysis and roasting experiments. The phase transition behavior and the leaching performance tests were conducted using X-ray diffraction, scanning electron microscopy, X-ray fluorescence, leaching kinetics and thermal analysis. The recovery of Al and Na reached the values of 90.79% and 92.03%, respectively, under the optimal conditions (roasting temperature of 1150 ◦C, Na2CO3/Al2O3 molar ratio of 1.3, roasting time of 1 h, leaching temperature of 90 ◦C, L/S ratio of 10 mL·g−1 and leaching time of 30 min). Meanwhile, the removal efficiency of N and Cl reached 98.93% and 97.14%, respectively. The leaching kinetics indicated that the dissolution of NaAlO2 clinkers was a first-order reaction and controlled by layer diffusion process. The green detoxification and effective extraction of the Al element from SAD were simultaneously achieved without any pretreatments.

**Keywords:** aluminum dross; recycling; thermodynamic analysis; kinetics


**4.1 Predisposing factors to venous thromboembolism in bariatric surgery**

Morbid (BMI>50) and truncal obesity are identified as major predisposing factors for VTE. Sedentary lifestyle, increased abdominal pressure and excessive weight resting on the inferior vena cava drainage attribute to the increased risk. Additional risk factors include advanced age, history of previous VTE, immobilization, venous insufficiency and stasis, smoking, estrogen- containing oral contraceptives and hormone replacement therapy, hypercoaguable state, hypoventilation syndrome and anastomotic leakage. According to current literature, obesity interferes in intrinsic and extrinsic coagulation pathways, as well as in the anticoagulant mechanism, leading to a hypercoagulating state. Plasma concentration of fibrogen, von Willebrand, t-PA, PAI-1 and factor VII are significantly elevated in obese patients, while platelet aggregation is promoted due to leptin. There is evidence that treatment of morbid obesity can reverse partially some of the above abnormalities, as weight loss is associated with significant reduction in fibrogen, t-PA, PAI-1 and improvement of deficiency of antithrombin III.

Perioperative factors contributing to VTE include extend of surgical trauma, operative duration, length of postoperative immobilization and the use of general versus regional anesthesia. The risk of developing VTE depends on the type of major abdominal surgical procedure. Mukherjee et al. reported lower incidence of VTE among bariatric surgery patients (0.35%), while VTE rates were higher in patients undergoing nephrectomy, hepatectomy, colorectal resection, splenectomy, gastrectomy, pancreatectomy and esophagectomy. This lower rate may reflect strict adherence of bariatric surgeons to VTE prophylaxis guidelines relative to other surgical specialties.

More specifically, in laparoscopic bariatric surgery, reverse Trendelenburg position and pneumoperitoneum are associated with venous stasis of lower extremity and impaired venous return due to the compression of iliac veins and inferior vena cava. Furthermore several studies show the development of a hypercoagulable state during laparoscopy. Conversely, the risk of VTE during laparoscopy could be compensated by lower degree of surgical injury, early mobilization and reduced postoperative acute-phase response. Podnos et al. in a review of 3464 cases of GBP demonstrated that although the difference was not statistically significant, the incidence of PE was lower in laparoscopic group rather than the open group. In absence of randomized controlled studies, the evidence remains inconclusive as to the relative risk of VTE after laparoscopic bariatric surgery.


*1.2.1 Divination*

It is the spirtual healing process which is an act to contact between spirtual world and the mundane world for getting the guidance to heal [13]. The traditional healers were known as diviners. As per the belief systems, divination is a part of witchcraft and is a sign of metaphysical curses to block ones living energy. The local tribes in Africa have strong belief in metaphysical healing systems incompare to western medicine with respect to some ailments which are not clearly understood by the allopathy system of medicine. These type of ailments were considered as spiritual illnesses. The healing protocols in divination includes following of ancestor instructions and sacrifices to the spirtual world. Sometimes the diagnosis in divination protocol includes dream interpretations like apperance of ancestors, nightmares, omens, owls in diseased patients. The traditional healers through the secret knowledge of divination and interprets the dreams through the communication to spirtual world and the healing process will be done as per the spirtual communication by the healer which includes secret recipes of herbal bath, herbal decoctions, sacrifices, incantations and wearing of herbal parts as a protective medicine in form of talisman [14, 15]. However clear scientific validation over divination healing is still unclear and lack of scientific evidences. The traditional healers never expose their herbo-magical remedies to the other people. The different procedures in the divination includes tarot card and readings, celetic ogham, Norse Runes, tasseography or tasseomancy, Pendulum readings, osteomancy, lithomancy, fullmoon water scrying, pshycic automatic writing. To understand divination, it is always better to watch a documented video (Video 1; Available from (can be viewed at) https://www.youtube.com/watch?v=yZpqdICyq UM.).


**A Combined Field and Numerical Modeling Study to Assess the Longitudinal Channel Slope Evolution in a Mixed Alluvial and Soft Bedrock Stream**

**Kuowei Wu 1,\*, Keh-Chia Yeh 1 and Yong G. Lai 2**

Received: 26 February 2019; Accepted: 7 April 2019; Published: 9 April 2019

**Abstract:** A study approach is developed to assess the longitudinal channel slope under the equilibrium condition as well as the transient evolution of a mixed alluvial-soft-bedrock stream. Both the historical field data and 2D mobile-bed numerical modeling are adopted. The proposed approach is applied to a 14 km reach downstream of the Ji-Ji Weir, Chuo-Shui River, Taiwan, where continuous maintenance works have been carried out to stabilize this reach. In this study, the temporal evolution of the longitudinal channel profile is assessed numerically with three spatial scales: The large (the entire study reach), the medium (four sub-reaches), and the local (cross-sections) scale. The large scale analysis is the approach for purely alluvial streams and is shown to be difficult to use to characterize mixed alluvial-bedrock streams. The local scale analysis shows that the soft-bedrock incision has a widely fluctuating slope, reflecting the compound environmental forcing and complex riverbed setting. With the medium scale analysis, the longitudinal channel profile is found to follow a predictive trend if the reach is partitioned into four distinctive sub-reaches. Characteristics of the dynamic channel slope evolution in different spatial scales are computed and presented. The study results can be used to select the proper locations and types of the engineering stabilizing structures in a mixed alluvial and soft bedrock stream.

**Keywords:** soft bedrock; SRH-2D; head-cutting; mixed transition; knickpoint; spatial scales; river morphology


*3.3.1 Olivines*

In the caldera of the Mount Bambouto, olivine is present in the basalts with an average size of 0.5 × 3 mm. It is automorphic to subautomorphic. Their section is traversed by numerous cracks along which one notes the beginning of iddingsitization and serpentinization. Some sections have a core and borders corroded by mesostase. The olivine in the caldera of the Mount Bambouto is globally magnesian with forsterite contents between Fo57 and Fo75 (**Figure 6**).


**Luminescence Nanomaterials and Applications**

Editors **Wei Chen Derong Cao**

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**1. Introduction**

The field of biosensing has exploded into nearly all areas of research, from medical applications [1] to environmental monitoring [2]. Some of the greatest appeals of biosensors are their specificity and sensitivity. These properties are primarily due to bioreceptors, which are selected for their inherent specificities such as enzymes [3], antibodies [4], and aptamers [5]. However, the very aspect that makes biosensors so specific and sensitive can also limit the sensor stability due to the degradation of the bioreceptor [6]. Additionally, as the bioreceptor is specific to an individual analyte, the particular sensor's scope is limited to the specific analyte to which the bioreceptor can bind.

To obviate these issues, many nature-inspired sensors have emerged that are bioreceptorfree. Some of the most notable examples that have made great progress include the electronic nose (Enose) [7–11] and electronic tongue (Etongue) [12–16]. Additionally, surface enhanced Raman spectroscopy (SERS)-based sensors have demonstrated incredible chemosensing ability [17–21]. Without a bioreceptor, however, there is the risk of significantly compromised biosensor performance including the limit of detection (LOD) and specificity. Researchers have introduced machine learning (ML) to bioreceptor-free biosensors to bridge this trade-off gap, improving the LOD and specificity [22]. In a sense, ML can be used to take the place of a bioreceptor by reintroducing specificity during data analysis. This is made possible by powerful ML techniques capable of detecting subtle patterns in sensor responses.

**Citation:** Schackart, K.E., III; Yoon, J.-Y. Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors. *Sensors* **2021**, *21*, 5519. https://doi.org/10.3390/s21165519

Academic Editors: Zihuai Lin and Wei Xiang

Received: 15 July 2021 Accepted: 13 August 2021 Published: 17 August 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

While this approach has demonstrated success, there are still several challenges that these systems must overcome. A major challenge being faced is model generalizability. Since many models rely on subtle patterns in the data, they can be quite sensitive to underlying data changes. This can make the models susceptible to error when faced with sensor drift or replacing parts of the system [14].

Since the scope of this review is quite large and covers all bioreceptor-free biosensors that utilize ML, there are a few points to clarify. Many subsets of our scope have received thorough attention and review. For instance, the use of ML for Enose and Etongue [23–27] and SERS-based biosensors [28] have previously been described. Since the literature is rich in these areas, we realize that all recent original research cannot be adequately covered here. Rather, our intent is to provide a unified discussion of the relevant methods and challenges to give a bigger picture. We also would like to acknowledge that there is a complementary review in the literature discussing the use of ML in biosensing in general [29], but not for biosensors that are bioreceptor-free.

In this review, we will give the current state of using ML to enhance the performance of bioreceptor-free biosensors. Section 2 briefly introduces the types of biosensors that have most benefited from ML. Section 3 provides some background on machine learning algorithms and how their performance can be assessed. Section 4 covers electrochemical biosensors, with particular emphasis on Enose and Etongue. Successful methods are discussed as well as some of the challenges and how they are being addressed with ML. Section 5 discusses optical biosensors, notable for imaging- and SERS-based biosensors. Additional considerations and future perspectives are discussed in Section 6 including what currently prevents many of these systems from being commercialized and what directions may be taken. We also present some considerations on best practices for ML in biosensing, especially regarding communication of methods and reproducibility.

#### **2. How Biosensors Can Benefit from Machine Learning**

Biosensors in the classic definition are sensors that utilize a bioreceptor such as antibody, enzyme, peptide, nucleic acid, etc. A bioreceptor binds to a target biological molecule and generates a signal when coupled with a transducer. Biosensors have evolved to a wide range of transducer types including electrochemical, optical, and spectroscopic biosensors. Traditionally, it is the bioreceptor that provides specificity and sensitivity to the biosensor. Increasingly, however, researchers are developing biosensors that lack a specific bioreceptor. A typical example is a semi-specific chemical sensor array, termed Enose (from gas), or Etongue (in solution). Since such a sensor's specificity is not provided by the bioreceptor, a fingerprinting technique is used to recognize signal patterns indicative of a particular analyte. Frequently, machine learning techniques are employed to detect these patterns and provide specificity.

The use of machine learning to enhance the performance (e.g., specificity, sensitivity, and LOD) of bioreceptor-free biosensors is not limited to chemical sensor arrays. It has been employed in various biosensor mechanisms. Some of the most famous examples aside from Enose and Etongue are imaging-based biosensors and SERS-based biosensors. Additionally, the use of machine learning for biosensors is not limited to those that lack bioreceptors. Cui et al. [29] cover several examples of traditional biosensors employing machine learning to enhance performance.

Table 1 provides an overview of the tasks for which machine learning has been applied, the specific algorithms used, and the relevant papers. More information on the algorithms themselves can be found in Section 3. Additionally, Table 2 gives a comparison of each of the major biosensing mechanisms including data type and appropriate feature engineering and ML methods. All information in Table 2 comes from Table 1 and serves as a higher-level summary.

#### **Table 1.** Machine learning tasks and algorithms used in biosensing.

**Table 1.** *Cont.*

Note. CV = cyclic voltammetry; ANN = artificial neural network; LSTM = Long short-term memory; PCA = principal component analysis; DT = decision tree; RF = random forest; SVM = support vector machine; SVR = support vector regression; BPNN = back-propagation neural network; JDA = joint distribution adaptation; DTBLS = domain transfer broad learning system; GBM = gradient boost machine; ELM = extreme learning machine; EIS = electrical impedance spectroscopy; EIT = electrical impedance tomography; *k*-NN = k-nearest neighbor; RBFN = radial basis function network; CNN = convolutional neural network; *t*-SNE = *t*-distributed stochastic neighbor embedding; Si-CARS-PLS = synergy interval partial least square with competitive adaptive reweighted sampling; FTIR = Fourier transform infrared; VOC = volatile organic compound; GAN = generative adversarial network; DNN = deep neural network; TLC = thin layer chromatography; SERS = surface enhance Raman spectroscopy; PLSR = partial least squares regression; PCR = principal component regression; LDA = linear discriminant analysis; HCA = hierarchical cluster analysis; KPCA = kernel principal component analysis.

#### **3. A Brief Tour of Machine Learning**

In simple terms, machine learning aims to learn patterns in data to make predictions on new data. Generally, this prediction is either categorical classification (into one of a set of classes) or regression (continuous numerical output). In machine learning terms, the data used for prediction (i.e., biosensor data) are termed features or predictors. The set of features associated with one "observation" (e.g., biosensor data from one sample) is termed the feature vector.


**1. Introduction**

In horticulture and indoor farming, LEDs have several advantages e.g., they save energy, emit less heat and have a long lifetime [1,2]. A spectrum can be designed depending on the response of the specific crop and the production aim. However, to fully exploit the spectral flexibility of LED lighting an increased knowledge of the spectral effects on plant morphology and growth is required [3]. Energy consumption can also be considered during spectral optimization as this can vary between spectra depending on the LED types [4]. A higher energy consumption of red than of blue LEDs has been reported [4,5], but theoretically the energy consumption of blue LEDs is higher than of red LEDs due to the higher energy level per photon of shorter than of longer wavelengths [6].

The advantages of LED lighting can be used in speed breeding, a breeding system developed particularly for growth chambers. The aim of a speed breeding system is to grow many generations per year to shorten the time for developing new cultivars. For instance, in a speed breeding system for several cereals, pea and chickpea six generations can be grown per year [7]. For a more efficient use of space, plants can be grown in a multi-layer system. For these systems, short plants are desirable to increase the number of layers of plants and hereby the possibility to include more genotypes at the same time. Therefore, a spectrum for speed breeding should not delay seed setting (many generations) and induce a shorter plant height to cultivate in more layers (many genotypes). These requirements

deviate from other indoor plant productions aiming to increase resource use efficiency considering other properties, such as yield and nutritional value [5]. Recently, a speed breeding protocol for soybean was developed using LED lighting. Red and blue light was found not to influence flowering time and was recommended to induce short compact plants. However, only two ratios of red and blue light (1:1 and 2:1) were studied [8].

The spectral light environment is perceived by the plant photoreceptors, which in a natural environment induce morphological changes such as those that express shade adaptations [9]. Shaded plants experience a reduced red to far-red ratio perceived by phytochrome [10] and a reduced photosynthetic photon flux density (PPFD). The latter is associated with a reduced blue photon flux density (BPFD) perceived by cryptochrome [11]. Typical shade responses of soybean are elongated internodes and petioles, increased specific leaf area (SLA) and decreased biomass and internode diameter [12–14]. Under LED lighting, BPFD can be reduced by lowering the ratio of blue light in the spectrum without a simultaneous reduction of PPFD. By reducing BPFD, some morphological shade responses, e.g., increased height, can be triggered also under constant PPFD [15,16]. For soybean, earlier studies found an increased plant height with decreasing BPFD [17–19] showing that high BPFD can be applied to induce short soybean plants, but these studies used a broad spectrum and included only one treatment [17] with a blue light ratio over 30%. None of these studies derived a response function to BPFD for soybean height under LED lighting with narrow peaks and none focused on blue light ratios between 15–78%. Earlier studies in other species than soybean also explored relatively low BPFD ratios (<50%) with the aim of avoiding extreme elongation under sole red LED lighting [15] or explored an intermediate BPFD to maximize biomass [20]. The aim in the present study was to reduce plant height to its minimum under a high BPFD.

Beside the influences on plant growth through the perceptions of photoreceptors, the light spectra can also influence the photosynthetic rate. Whereby, carbon assimilation can differ depending on the spectrum even under a constant PPFD. Photosynthetic pigments of plants absorb light mainly within the range of wavelength from 400 to 700 nm. The photosynthetic most effective part is considered to be the light within the red range (600–700 nm) due to a better balance of excitation between photosystem I and II and due to a more effective transfer between the red light absorbing chlorophylls than from the blue light absorbing carotenoids to chlorophyll [21]. Despite this, several studies measuring photosynthesis on plants grown under different light spectra found similar rates of photosynthesis under spectra with different ratios of light within the blue range (400–500 nm) [22–24].

The optimization of light spectrum for a specific crop and production system is very time-consuming given the many aspects that have to be considered, e.g., light quality, intensity and day length. Also, the transfer of knowledge between studies and into practice can be impaired by variability in several factors, e.g., plant density, type of light source and dimensions of the climate chambers. In this context, functional structural plant (FSP) modeling can assist as a tool for optimization of crop production and understanding of plant responses to its environment. An FSP model simulates plant growth and development, while considering its architectural appearance, by responding to the experienced environment on the individual organ level [25,26]. Hereby, responses can be related to the actually perceived spectrum of individual organs. The perceived spectral light environment can differ from the environment above the canopy and between phytomers due to self-shading and light reflection, as other studies found focusing on PPFD [27–29] or the red to far red ratio [30–34].

Earlier FSP models using artificial light sources for indoor plant production addressed the light regime for greenhouse production [35–37], while only one study used an FSP model with LEDs being the only light source [38]. An FSP model within an LED chamber can be a tool to reduce the amount of necessary experiments for spectral optimization and assist in the understanding of the plant response to the indoor environment and in the transfer of knowledge between studies and into practice.

The aim of this study was to find an optimal BPFD inducing short soybean plants under a narrow peaked red and blue LED spectrum, also considering energy consumption. We hypothesize that an optimum BPFD for minimum plant height, not influencing flowering time, could be determined with a

combination of experiments and FSP modelling. The objectives were to (i) examine the influence of different levels of BPFD under constant PPFD on soybean biomass, photosynthesis and morphology and (ii) calibrate an FSP model of soybean and integrate a response function to BPFD for internode length and (iii) to find by simulation the minimum BPFD to reduce plant height and energy consumption. optimum BPFD for minimum plant height, not influencing flowering time, could be determined with a combination of experiments and FSP modelling. The objectives were to (i) examine the influence of different levels of BPFD under constant PPFD on soybean biomass, photosynthesis and morphology and (ii) calibrate an FSP model of soybean and integrate a response function to BPFD for internode length and (iii) to find by simulation the minimum BPFD to reduce plant height and energy


**3.1 Antenna performance using conventional materials and techniques**

We base the hybrid substrate-shared aperture antenna on the shorted annular ring and concentric patch geometry explored by Dorsey and Zaghloul [15–17]. **Figure 1** (left) shows the geometry of the dual-band antenna on two nested dielectric substrates, and **Figure 1** (right) shows the layout of the nested substrates themselves. **Table 1** gives the values in millimeters for the antenna dimensions illustrated in **Figure 1**. We shrink the overall footprint of the dual-band antenna by 30% by increasing the dielectric constant of the substrate under the annular ring from *εr1* = 2.33 to *εr1* = 6.15.

Two pairs of orthogonal microstrip pin feeds control the different operational modes of the antenna by exciting either S- or X-band frequencies and either vertical or horizontal polarization. **Figure 2** gives the locations of the pin feeds with respect to the annular ring and concentric patch antennas respectively. **Figure 2** also shows a shorting wall grounding the inner perimeter of the annular ring to diminish surface waves. Suppression of surface waves on the dielectric substrates helps

#### **Figure 1.**

*Top view geometry of the dual-band antenna (left), layout of the nested substrates (center), and top view of the prototype dual-band antenna.*

> increases isolation between the excitation ports of the dual-band antenna. The antenna comprises a top metal layer, a hybrid Rogers 3006/Rogers 5870 substrate layer, and a bottom metal ground layer. All metal layers are 0.1 mm thick, and the

*Measured and simulated normalized radiation pattern at S-band horizontal port.*

*Comparison of simulated return loss to measured return loss of the horizontal port at S-band.*

*3D view of the dual-band antenna and pin feed network. The outer dielectric layer is transparent for clarity.*

**Figures 3** and **4** show the measured versus simulated return loss and normalized radiation patterns of the dual-band antenna at S-band. **Figures 5** and **6** show the

dielectric substrate layer is 5.05 mm thick.

**Figure 2.**

*Additive Manufacturing for Antenna Applications DOI: http://dx.doi.org/10.5772/intechopen.92363*

**Figure 3.**

**Figure 4.**

**193**

#### **Table 1.**

*Antenna dimensions in millimeters of Figure 1.*

*Additive Manufacturing for Antenna Applications DOI: http://dx.doi.org/10.5772/intechopen.92363*

**Figure 2.**

**3. Hybrid material dual-band and dual-polarization antenna design**

*Advanced Radio Frequency Antennas for Modern Communication and Medical Systems*

shelf materials and fabrication techniques, to the same antenna geometry

**3.1 Antenna performance using conventional materials and techniques**

newfound complexities of emerging multifunction antenna designs.

from *εr1* = 2.33 to *εr1* = 6.15.

**Figure 1.**

**Table 1.**

**192**

*prototype dual-band antenna.*

*Antenna dimensions in millimeters of Figure 1.*

manufactured using AM materials and 3D printing manufacturing processes. The idea being that the versatility allowed via AM of antennas can help navigate the

We base the hybrid substrate-shared aperture antenna on the shorted annular ring and concentric patch geometry explored by Dorsey and Zaghloul [15–17]. **Figure 1** (left) shows the geometry of the dual-band antenna on two nested dielectric substrates, and **Figure 1** (right) shows the layout of the nested substrates themselves. **Table 1** gives the values in millimeters for the antenna dimensions illustrated in **Figure 1**. We shrink the overall footprint of the dual-band antenna by 30% by increasing the dielectric constant of the substrate under the annular ring

Two pairs of orthogonal microstrip pin feeds control the different operational modes of the antenna by exciting either S- or X-band frequencies and either vertical or horizontal polarization. **Figure 2** gives the locations of the pin feeds with respect to the annular ring and concentric patch antennas respectively. **Figure 2** also shows a shorting wall grounding the inner perimeter of the annular ring to diminish surface waves. Suppression of surface waves on the dielectric substrates helps

*Top view geometry of the dual-band antenna (left), layout of the nested substrates (center), and top view of the*

*L1 L2 L3 L4 D εr1 εr2* 36.7 22.57 10.23 7.08 5.07 6.15 2.33

As the world continues to move to fully integrated multifunction antennas, the design of new multiband and multimode antenna geometries continues to yield structures that are more complicated to manufacture. However, current challenges in maintaining, upgrading, and networking an ever-increasing number of antenna nodes are alleviated through adapting these types of multifunction antennas and other RF devices. The low profile and lightweight designs of references [15–18] show great promise for multifunction antenna applications. This chapter compares the performance of a multifunction antenna prototyped using traditional off-the-

*3D view of the dual-band antenna and pin feed network. The outer dielectric layer is transparent for clarity.*

**Figure 3.** *Comparison of simulated return loss to measured return loss of the horizontal port at S-band.*

**Figure 4.** *Measured and simulated normalized radiation pattern at S-band horizontal port.*

increases isolation between the excitation ports of the dual-band antenna. The antenna comprises a top metal layer, a hybrid Rogers 3006/Rogers 5870 substrate layer, and a bottom metal ground layer. All metal layers are 0.1 mm thick, and the dielectric substrate layer is 5.05 mm thick.

**Figures 3** and **4** show the measured versus simulated return loss and normalized radiation patterns of the dual-band antenna at S-band. **Figures 5** and **6** show the

with the extrusion of high-dielectric filaments, discussed in Section 2.1, allow cus-

**Figure 7** shows a to-scale 3D printed prototype of the antenna shown in **Figures 1** and **2**. The antenna utilizes a microstrip stack of a printed copper layer, hybrid dielectric layer, and a copper ground layer. We printed all pieces separately and assembled by hand afterward. All conductive layers are 1.0 mm thick. The hybrid substrate layer is 5.05 mm thick. The total profile of the antenna is 6.05 mm

**Table 2** shows the dimensions of the geometries given in **Figure 2** as they pertain to **Figure 7**. We chose two AM filaments compatible with a Fuse Deposition Modeling (FDM) 3D printer. For the high dielectric, we chose Preperm ABS 650 with a reported dielectric constant of *εr* = 6.5 to mimic the Rogers 6010 material with *εr* = 6.15. However, after conducting waveguide measurements on a 3D printed sample, we found anisotropic values of *εrx* = *εry* = 5.7 and *εrz* = 5.3. We chose ABS to mimic the Rogers 5870 material with *εr* = 2.33. Measured values of the ABS were nearly isotropic with a value of *εr* = 2.38. The loss tangents of both materials were

. We printed the metal layers via the selective laser sintering (SLS) method allowing us to print actual copper as opposed to using a lower conductivity ink. Using a substrate of *εr1* = 5.3 instead of *εr1* = 6.15 under the S-band element shifts the resonances from 3.2 to 3.8 GHz. Changing the dimensions of the concentric slot and X-band patch, shown in **Figure 1** and **Table 2**, shifts the X-band

*L1 L2 L3 L4 D εr1 εr2* 37.08 25.36 11.03 7.38 7.05 5.3 2.38

*Antenna dimensions in millimeters of 3D printed antenna based on schematic in Figure 1.*

We choose a slightly modified version of the antenna design presented in Section 3.1. Due to the hybrid material design and the utilization of a fully embedded vertical shorting wall, we believe this is a good design to test capabilities of AM as it pertains to antennas. For this investigation, we design the dual-band antenna for S-band and X-band respectively. We perform all simulations using the finite

tomized dielectric constants not previously achievable [2].

*Additive Manufacturing for Antenna Applications DOI: http://dx.doi.org/10.5772/intechopen.92363*

due to printing thicker metal layers to prevent warping.

about 103

**Figure 7.**

**Table 2.**

**195**

frequency from 11.1 to 9.35 GHz.

*Fully assembled AM dual-band antenna.*

difference time domain (FDTD) solver of CST Studio Suite 2019.

**Figure 5.** *Measured and simulated return loss at X-band horizontal port.*

**Figure 6.** *Measured and simulated normalized radiation pattern at X-band horizontal port.*

same for at X-band. The radiation shows good agreement at both S-band and Xband. **Figures 3** and **5** show a return loss measurement of 17.5 dB at the resonant frequency of 3.2 GHz and measurement of 11.0 dB at the resonant frequency of 11.1 GHz. There is a second S-band resonance seen at a frequency of 2.7 GHz in **Figure 3**, but the radiation characteristics in **Figure 4** show that this is not the dominant S-band resonance. We attribute discrepancies in the depth of the return loss curve in **Figure 5** to manufacturing tolerances in the placement of the pin feeds with respect to the edges of the concentric patch. Since electrical length decreases with wavelength at X-band, smaller tolerance errors can have larger effects than at S-band on the impedance match quality. However, both the simulated and measured resonant frequency at X-band is the same as expected.

### **3.2 Antenna performance using additive manufacturing**

AM allows engineers to rethink the RF design space. AM facilitates complex designs that required properties not achievable by traditional manufacturing methods. Strides in AM produce robust structural and mechanical parts, but industry has yet to develop a full suite of electromagnetic properties for AM feedstocks. Low dielectric constants of commercial feedstocks limit current antenna designs, but recent research into the composition of high-dielectric feedstocks for AM opens the design space for antennas [12–14]. Additionally, space-filling algorithms paired

#### *Additive Manufacturing for Antenna Applications DOI: http://dx.doi.org/10.5772/intechopen.92363*

with the extrusion of high-dielectric filaments, discussed in Section 2.1, allow customized dielectric constants not previously achievable [2].

We choose a slightly modified version of the antenna design presented in Section 3.1. Due to the hybrid material design and the utilization of a fully embedded vertical shorting wall, we believe this is a good design to test capabilities of AM as it pertains to antennas. For this investigation, we design the dual-band antenna for S-band and X-band respectively. We perform all simulations using the finite difference time domain (FDTD) solver of CST Studio Suite 2019.

**Figure 7** shows a to-scale 3D printed prototype of the antenna shown in **Figures 1** and **2**. The antenna utilizes a microstrip stack of a printed copper layer, hybrid dielectric layer, and a copper ground layer. We printed all pieces separately and assembled by hand afterward. All conductive layers are 1.0 mm thick. The hybrid substrate layer is 5.05 mm thick. The total profile of the antenna is 6.05 mm due to printing thicker metal layers to prevent warping.

**Table 2** shows the dimensions of the geometries given in **Figure 2** as they pertain to **Figure 7**. We chose two AM filaments compatible with a Fuse Deposition Modeling (FDM) 3D printer. For the high dielectric, we chose Preperm ABS 650 with a reported dielectric constant of *εr* = 6.5 to mimic the Rogers 6010 material with *εr* = 6.15. However, after conducting waveguide measurements on a 3D printed sample, we found anisotropic values of *εrx* = *εry* = 5.7 and *εrz* = 5.3. We chose ABS to mimic the Rogers 5870 material with *εr* = 2.33. Measured values of the ABS were nearly isotropic with a value of *εr* = 2.38. The loss tangents of both materials were about 103 . We printed the metal layers via the selective laser sintering (SLS) method allowing us to print actual copper as opposed to using a lower conductivity ink. Using a substrate of *εr1* = 5.3 instead of *εr1* = 6.15 under the S-band element shifts the resonances from 3.2 to 3.8 GHz. Changing the dimensions of the concentric slot and X-band patch, shown in **Figure 1** and **Table 2**, shifts the X-band frequency from 11.1 to 9.35 GHz.

#### **Figure 7.**

same for at X-band. The radiation shows good agreement at both S-band and Xband. **Figures 3** and **5** show a return loss measurement of 17.5 dB at the resonant frequency of 3.2 GHz and measurement of 11.0 dB at the resonant frequency of 11.1 GHz. There is a second S-band resonance seen at a frequency of 2.7 GHz in **Figure 3**, but the radiation characteristics in **Figure 4** show that this is not the dominant S-band resonance. We attribute discrepancies in the depth of the return loss curve in **Figure 5** to manufacturing tolerances in the placement of the pin feeds with respect to the edges of the concentric patch. Since electrical length decreases with wavelength at X-band, smaller tolerance errors can have larger effects than at S-band on the impedance match quality. However, both the simulated and mea-

*Advanced Radio Frequency Antennas for Modern Communication and Medical Systems*

AM allows engineers to rethink the RF design space. AM facilitates complex designs that required properties not achievable by traditional manufacturing methods. Strides in AM produce robust structural and mechanical parts, but industry has yet to develop a full suite of electromagnetic properties for AM feedstocks. Low dielectric constants of commercial feedstocks limit current antenna designs, but recent research into the composition of high-dielectric feedstocks for AM opens the design space for antennas [12–14]. Additionally, space-filling algorithms paired

sured resonant frequency at X-band is the same as expected.

*Measured and simulated normalized radiation pattern at X-band horizontal port.*

**Figure 5.**

**Figure 6.**

**194**

*Measured and simulated return loss at X-band horizontal port.*

**3.2 Antenna performance using additive manufacturing**

*Fully assembled AM dual-band antenna.*

#### **Table 2.**

*Antenna dimensions in millimeters of 3D printed antenna based on schematic in Figure 1.*

We show the measured versus simulated return loss and realized gain of the dual-band antenna at S-band in **Figures 8** and **9** as well as at X-band in **Figures 10** and **11**. We took all realized gain versus frequency measurements at boresight to the antenna. We see general agreement at both bands for all measurements. One

discrepancy is in the S-band realized gain where measurements are up to 2.0 dB lower than expected from 3.25 to 3.6 GHz. The resonance at S-band is also about 100 MHz or 2.6% off. The X-band resonance and realized gain curves show better agreement than those at S-band. The return loss at X-band is even better than that predicted by simulation and this shows up in **Figure 11** where the measured realized

We attribute measured differences in the return loss to manufacturing toler-

This chapter describes both the benefits and current challenges facing AM for antennas and RF devices. The forefront of which is a lack of commercially available high-dielectric materials that are compatible with filament fed 3D printers. As a comparison, we fabricated two similar antenna designs utilizing multiple nested dielectrics and an embedded shorting wall within the dielectrics to compare performance between traditional materials and manufacturing methods versus those of AM. Due to restricted access to an AM feedstock with the proper dielectric constant, direct comparisons of the two antennas is not the preferred way of comparison. However, experimental results for both prototypes agree well with the simulation data. There also seems to be no degradation in the performance of the AM prototype over the traditional prototype in terms of the agreement with the respective antenna models. With new methods of extruding higher dielectric filaments for FDM 3D printers, AM seems to be a good fit for future work in antenna design especially as antenna and RF front ends grow increasingly complex and more fully

ances since the pin fed patch is an extremely resonant type of feed.

gain is higher than simulation at resonance.

*Realized gain to boresight at S-band horizontal polarization port.*

*Additive Manufacturing for Antenna Applications DOI: http://dx.doi.org/10.5772/intechopen.92363*

**4. Conclusions**

**Figure 11.**

integrated.

**197**

**Figure 8.** *S-parameters at S-band horizontal polarization port.*

**Figure 9.** *Realized gain to boresight at S-band horizontal polarization port.*

**Figure 10.** *S-parameters at X-band horizontal polarization port.*

*Additive Manufacturing for Antenna Applications DOI: http://dx.doi.org/10.5772/intechopen.92363*

**Figure 11.** *Realized gain to boresight at S-band horizontal polarization port.*

discrepancy is in the S-band realized gain where measurements are up to 2.0 dB lower than expected from 3.25 to 3.6 GHz. The resonance at S-band is also about 100 MHz or 2.6% off. The X-band resonance and realized gain curves show better agreement than those at S-band. The return loss at X-band is even better than that predicted by simulation and this shows up in **Figure 11** where the measured realized gain is higher than simulation at resonance.

We attribute measured differences in the return loss to manufacturing tolerances since the pin fed patch is an extremely resonant type of feed.


**8. Conclusion**

Post-apartheid South Africa has probably put the wrong foot (entrepreneurship) in front of innovation, and yet the latter is fundamental. We argue that the emphasis should be innovation particularly disruptive and open innovation ahead of entrepreneurship. An emphasis on entrepreneurship is wrong because it implies defaulting to incremental innovation and hence competing with the pioneers of the product or service. It could be this lack of competitive edge that explains the high failure rate or discontinuance rate of South African new business ventures. The problem of innovation should be looked at in the light of the mathematics and science challenges the country is facing. Could it be that if we resolved this then innovation particularly disruptive innovation will also fall in place? Second, focusing on the product or service and neglecting innovation in the way we do business implies we can have an output but not the outcome. In this sense, the outcome should be commercialising the product or service arising out of innovation. In this case, then we need innovative business models that speak to the context. In this paper, we argue for disruptive innovation and innovative business models.


**Diagnosis and Treatment of Small Bowel Disorders**

Editors

**Anastasios Koulaouzidis Wojciech Marlicz**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Anastasios Koulaouzidis The Royal Infirmary of Edinburgh UK

Wojciech Marlicz Pomeranian Medical University Poland

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Journal of Clinical Medicine* (ISSN 2077-0383) (available at: https://www.mdpi.com/journal/jcm/ special issues/small bowel disorder).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

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**ISBN 978-3-03943-873-0 (Hbk) ISBN 978-3-03943-874-7 (PDF)**

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*3.2. Testosterone*

Testosterone (T) signaling is required for spermatogenesis to proceed beyond meiosis. Consequently, it has been postulated that patients with hypogonadism (serum T < 300 ng/dL) may have lower chances of SSR compared to patients with normal serum T levels. Indeed, a pooled estimate of six studies evaluating 2029 patients with NOA undergoing mTESE demonstrated that patients with normal T levels had a significantly higher chance of SSR compared to those with subnormal T levels (OR 1.63, 95% CI 1.08–2.45, *p* = 0.02) [41]. However, the available evidence has provided conflicting results. Reifsnyder et al. evaluated 736 men undergoing mTESE; 348 (47.3%) with baseline T level < 300 ng/dL and 388 (53%) with baseline testosterone levels greater than 300 ng/dL. Among patients with hypogonadism, 88% received hormonal treatment. SRR did not vary among men with low vs. normal baseline T levels; yet, the mean presurgical T level was normal in patients with previous low baseline T levels as the effect of hormonal treatment. Moreover, 18% of patients receiving hormonal treatment did not respond to treatment, but their SRR was comparable to that of responders to treatment [42]. Enatsu et al. evaluated 329 patients, of whom 65 had KS, and found that serum T levels did not differ among men with SSR (97) and SRF (232) (420 + 180 vs. 430 + 190 ng/dL; *p* = 0.42) [43]. Althakafi et al. evaluated 421 patients, of whom 181 had low baseline T levels, and found no difference in SRR between those with normal and low T levels (SRR 38.6% vs. 40.3%, *p* = 0.718). Fifty patients received hormonal treatment with clomiphene citrate (CC) or human chorionic gonadotropin (hCG) due to subnormal T levels: their SRR was comparable to that of patients with normal baseline T levels (36% vs. 38%, *p* = 0.736) [44]. Kizilkan et al. evaluated 860 patients and found that T levels were predictive of SSR in univariate, but not in multivariate, logistic regression [20]. On the other hand, Mehmood et al. and Çayan et al., evaluating 264 and 327 patients respectively, found that SRR was significantly lower in men with low baseline T levels compared to those with normal baseline T levels (40.6 vs. 57.25, *p* = 0.0068, and 40.5% vs. 65.9%, *p* < 0.0001

respectively) [15,45]. Accumulating evidence suggests that higher baseline T levels may be associated with a higher probability of SSR in men with KS [26,34].

It has been suggested that intratesticular testosterone (ITT) measurement could represent a more reliable way of assessing the role of testosterone on the probability of SSR in men with NOA. Due to the inherent risks of performing testicular aspiration to obtain a direct assessment of ITT level, a measurement of the circulating levels of 17-hydroxyprogesterone (17OHP) has been proposed as an indirect biomarker of ITT levels, since 17 OHP is likely to be of testicular and not adrenal origin in men. Indeed, serum 17 OHP levels were found to be undetectable in men receiving exogenous testosterone replacement therapy, and to increase after CC and hCG treatment [46]. Studies evaluating the predictive ability of serum 17 OHP on the probability of SSR in patients with NOA are needed to provide evidence in support or against such a hypothesis.

## **4. Testis Histology**

There is great consensus about the close relationship between different histopathological categories and mTESE outcome: patients with SCO have the lowest probability of SSR (22.5–41%), while patients with HS have the best chances of sperm retrieval (73–100%), and patients with late MA have better prognosis (SRR 27–86%) compared to those with early MA (SRR 27–40%) [47]. Indeed, a meta-analysis evaluating 19 articles showed that HS predicted SSR (pooled diagnostic odds ratio (DOR) 16.49, 95% CI: 9.63–28.23) with a sensitivity of 30% and specificity of 98%, AUC 0.6758; SCO had a negative predictive ability on SSR (AUC 0.27), while MA had a poor predictive accuracy (AUC 0.55) [25].

To obtain a realistic picture of the severity of spermatogenic dysfunction, the testicular specimen sent to the pathologist should be representative of the overall appearance of the testicular parenchyma. However, it is not uncommon for men with NOA to have more than one histopathological report. Very recently, Punjani et al. demonstrated that these patients may display up to four distinct histopathological subcategories, the increasing histopathological variety being associated with a higher probability of SSR (SRR was 33% in men with one histopathological subtype, compared to 94% in men with 4 subtypes) [48].

Testis histology has been found to be predictive of SRR also in men undergoing salvage mTESE after a failed surgical attempt. Despite previous surgery possibly harming the blood supply of the testis with a potential risk of testicular tissue damage, Tsujimura [49] and Kalsi et al. [50] found comparable SRRs in patients undergoing salvage mTESE after failed cTESE stratified according to testis histology (39% and 40% in SCO, 41.7% and 36% in MA, and 100% and 75% in HS, respectively). Data from Xu et al. [51] confirmed that HS associates with high SRR (85%) even in patients with previous sperm retrieval attempts, but found lower SRR in patients with SCO (5.5%) and MA (25%) compared to previous reports. Very recently, our group found that early and late MA were associated with the lowest probability of SSR (8.7 and 11.1%, respectively), while sperm was retrieved in 85% of men with HS; SRRs in patients with SCO differed significantly according to the presence (focal SCO) or not (complete SCO) of residual areas of HS (SRR 100% vs. 24.4%, respectively) [52].

The obvious limit of testis histology is that it may be obtained only after surgery, therefore it may be used to counsel patients about the probability of having their testicular sperm retrieved in further surgical attempts. In occasional situations, however, testicular histology may be available when a diagnostic testicular biopsy has been done prior to microTESE, and there may be of help in the counselling of patients with NOA.

#### **5. Molecular Markers Expression in the Seminal Plasma**

Given the limited accuracy of hormonal and clinical parameters in predicting the probability of SSR in patients with NOA prior to surgery, researchers have sought to evaluate the feasibility of using the expression of some molecular markers in the seminal plasma as markers of residual spermatogenesis in such patients.

The evaluation of germ cell-specific mRNAs as predictors of SSR in patients with NOA has brought conflicting results. Following the demonstration that the testicular expression of ESX1, an X-linked homeobox gene, was restricted to germ cells, particularly the spermatogonia/preleptotene spermatocytes and round spermatids, and correlated with SSR [53], a group of researchers found that the seminal plasma levels of ESXI were significantly lower in men with NOA compared to normozoospermic subjects (*p* < 0.0001), and predicted SSR in men with NOA with a sensitivity of 84%, but with a specificity of 28% [54]. However, in a further study, the seminal plasma of ESXI was found to be comparable among men with NOA and normozoospermic men [55]; on the other hand, the seminal plasma levels of protamine-1 (PRM1) were found to predict SSR with a sensitivity of 89%, and a specificity of 90%. In another study, however, seminal plasma of PRM1, together with PRM2, DAZ and AKAP4, although being undetectable in patients with SCO, could not predict SSR [56]. Finally, several studies have evaluated the predictive ability of seminal DDX4 mRNA expression on SSR, but again, with conflicting results [reviewed in [57].

Seminal plasma also contains high concentrations of extracellular vesicles that are consistent with exosomes, which originate from the male reproductive tract, and contain coding and noncoding RNAs that vary according to their origin, enabling them to (hypothetically) reflect the pathophysiological conditions of the organ of origin. Some microRNAs (miRNAs) have been found to be preferentially expressed and localized to spermatocytes and spermatids (miR-34b/c and miR-449) or late-stage male germ cells (miR-122), and to be differentially expressed in testis biopsies of patients with and without elongated spermatids (miR-449a, miR-34c-5p and miR-122) [58]. A study evaluated the expression of exosomal miRNAs in the seminal plasma of infertile men with NOA or obstructive azoospermia, demonstrating that three miRNAs, miR-31-5p, miR-539-5p and miR-941, were downregulated in patients with obstructive azoospermia compared to men with NOA. The further evaluation of 12 patients with NOA with (*N* = 8) or without (*N* = 4) SSR showed that the association of the expression values of miR-539-5p and the miR-941 was predictive of SSR [59]. However, due to the very small sample size of such a study, further studies are warranted to provide conclusive results. Indeed, another study found that miR-539-5p was not predictive of SSR, nor could it discriminate normozoospermic, oligozoospermic, and azoospermic men from each other [60].

Long noncoding RNAs (lncRNAs) have been found to play a critical role in spermatogenesis: specifically, they have been implicated in regulating protein-coding genes at the epigenetic level, and it has been speculated that these germ-specific lncRNAs may be involved in epigenetic regulation during spermatogenesis [61]. Many of them display restricted expression in the testis, thus enabling their use as noninvasive biomarkers of spermatogenesis in men with NOA. A recent study investigated the predictive ability of extracellular vesicle long noncoding RNAs (exlncRNAs) in patients with NOA: after having selected 16 exlncRNAs on the basis of their different expression in normozoospermic and azoospermic patients, the Authors evaluated their diagnostic accuracy in predicting SSR in 30 patients with NOA who had (*N* = 18) or not (*N* = 12) their testicular sperm retrieved by mTESE. The Authors built a prediction model based on 9 exlncR-NAs (LOC100505685, SPATA42, CCDC37-DT, GABRG3-AS1, LOC440934, LOC101929088, LOC101929088, LINC00343 and LINC00301) and found that it predicted the probability of SSR with a sensitivity of 88.9% and a specificity of 100%, AUC 0.986. The model was then validated on 66 patients with NOA, with a resulting AUC of 0.960 [60]. Further studies are, however, warranted to validate the findings of the present study, and to confirm or challenge the predictive ability of other molecular markers expressed in the seminal plasma.