**4.3 Ireland**

In 2013, the CBA had in Ireland the strongest positive result, especially for the fast rollout scenario with the combination of energy statement with In-Home display (IHD) and variable tariff. Regarding the additional benefits from reduced emissions of greenhouse gases, all the considered scenarios were positive. However, in 2018 the trend changed and the CBA was reviewed with a slightly negative result that was considered neutral by the European Commission. The new deadline for the large-scale rollout has been set up in 2024 [12].

**Figure 4.** *Percentage of smart meter gas installed in Italy 2013-2020 (Source: [17]).*

The company Gas Networks Ireland7 (GNI) currently supports the National Smart Metering program, which is focused on both gas and electricity metering devices. The program includes the use of smart metering technology, variable time of use, and in-home displays according to the CBA results. A share communication infrastructure has been implemented for gas meters and electricity meters in order to leverage the costs. In future, the same infrastructure could be used also for water smart meters.


*3.1. Data Pre-Processing Algorithm*

The field data acquisition system collects real-time machine-state data continuously. In order to avoid the interference of invalid data to the power-feeding model, only the data collected during the silage harvesting period should be selected as the original data input of the model.

#### 3.1.1. Effective Data Filtering

The shredding roller and throwing blower are the key operating components of the silage harvester, which have more than 60% of the power consumption of the whole machine and have more significant data variation characteristics. Therefore, the monitoring data of these two components are used as the screening basis, and combined with the time-lag analysis model based on material flow, the screening of all measurement data is realized. The main process is as follows.

(1) Define a collection of data categories,D={*d1*, *d2*, *d3*, *d4*} = {1, 2, 3, 0}. Where *d1*, *d2*, *d3*, and *d4* represent pre-acceleration data, field harvesting data, harvesting stopped data, and non-experimental suspended data, respectively.

(2) Preliminary classification is carried out depending on the rotation speed of the shredding roller, where the experimental and non-experimental data segments are obtained.

$$D\_i = \begin{cases} 0 & n\_c < 0.5n\_{cc} \\ 1,2,3 & n\_c \ge 0.5n\_{cc} \end{cases} \tag{5}$$

where *Di* is the data category of the *ith* sampling. *nc and nce* are the real-time shredding speed and rated shredding speed, respectively.

(3) The Mann-Kendall algorithm is used to detect the boundary points of field harvesting data [21]. Mann-Kendall is a non-parametric time-series rank test method which has good applicability to field data with anomalous interference and unknown sample distribution. However, it is not appropriate for multiple mutation detection. Thus, the experiment samples in the original torque detection sequence are segmented into two data subsets, which are independently used to detect the loaded mutation boundary points and the unloaded mutation boundary points. In the case of a subset *X* = {*x*1, *x*2, *x*3 ... ,*x*n} with *n* torque detection sequences, first, a forward rank sequence *Sk* is constructed by forwarding traversal and calculation of the cumulative count of torque values at the *ith* time greater than that at the *jth* time in the sequence, as shown in Formula (6).

$$\begin{aligned} \mathcal{S}\_k &= \sum\_{i=1}^k p\_k \mid k = 2, 3, \dots, n \mid \\ p\_k &= \sum\_{j=1}^k m\_{kj} \mid j = 1, 2, \dots, k \mid \\ m\_{kj} &= \begin{cases} 1, \ge \boldsymbol{x}\_k > \boldsymbol{x}\_j \\ 0, \boldsymbol{x}\_k \le \boldsymbol{x}\_j \end{cases} \end{aligned} \tag{6}$$

Secondly, based on the sequence mean *E*(*Sk*) and sequence variance *var*(*Sk*), the forwarding statistics sequence *UFk* is calculated by Formula (7).

$$\begin{array}{l} UF\_k = \begin{cases} 0 & (k=1) \\ \frac{(S\_k - E(S\_k))}{\sqrt{var(S\_k)}} \left(k=2, 3, \cdots, n\right) \\ E(S\_k) = \frac{k(k-1)}{4} \\ var(S\_k) = \frac{k(k-1)(2k+5)}{72} \end{cases} \tag{7}$$

Thirdly, the reverse statistic sequence *UBk* is calculated by reconstructing the reverse detection sequence *XB* = {*xn*, *xn-*1, ... , *x*1}, repeating the above traversal process, and taking the negative. Then, under the assumption that the statistic follows the standard normal distribution [22], the solution in Equation (8) under the *U0.05* significance level constraint is the rising or falling boundary of the effective data segment.

$$\begin{cases} \, \text{lIF}\_k(i) - \text{lIB}\_k(i) = 0\\ \, \text{s.t.} \, \text{lIF}\_k(i) \in \mathcal{U}\_{0.05}, i = 2, 3, \dots, n \end{cases} \tag{8}$$

(4) Through the steps above, the data screening for shedding rollers and throwing blowers can be achieved. However, as for header and feeding parts, the data variation characteristics are not sufficient to enable precise boundary extraction. Thus, we locate the harvesting data segment of these two parts by the material's time-delay model in the silage harvester, as shown in Figure 4.

**Figure 4.** Time lag analysis of silage flow in the harvester. 1: header; 2: conveying roller; 3: feeding rollers; 4: shedding rollers; 5: self-sharpening unit; 6: self-sharpening unit; 7: kernel-processing unit.

As the silage is fed into the harvester at the time *th*, there is almost no time delay for header torque change; After the collection of the header, the silage then reaches the feeding conveying rollers at the time *tf*, while a time lag of Δ*t*1 exists for the feeding power. After the silage is compacted and conveyed by the feeding rollers, it arrives at the shedding rollers at the time *tc*, and there is a time lag of (Δ*t*1 + Δ*t*2) for the shredding torque change. Finally, after the crop is shredded, it reaches the blower at the time tb, while the blower load data has a time delay of (Δ*t*1 + Δ*t*2 + Δ*t*3). Δ*t*1 and Δ*t*2 are calculated by Formula (9).

$$\begin{array}{l} \Delta t\_1 + \Delta t\_2 = \frac{S\_1}{\text{120}\pi n\_k R\_h} + \frac{S\_2}{\text{120}\pi n\_f R\_f} = \frac{S\_1 n\_f R\_f + S\_2 n\_h R\_h}{\text{120}\pi n\_f n\_h R\_f R\_h} \\ \Delta t\_2 = \frac{S\_2}{\text{120}\pi n\_f R\_f} \end{array} \tag{9}$$

Thus, the effective data segments of the header, feeding unit, shredding rollers, and throwing blows are *Xh*, *Xf*, *Xc*, and *Xb* respectively, as shown in the Formula (10).

$$\begin{aligned} \mathbf{X}\_{h} &= \left[ \mathbf{x}(t\_{\rm cs} - \Delta t\_{1} - \Delta t\_{2}), \mathbf{x}(t\_{\rm cs} - \Delta t\_{1} - \Delta t\_{2} + T), \dots, \mathbf{x}(t\_{\rm cs} - \Delta t\_{1} - \Delta t\_{2} + iT), \dots, \mathbf{x}(t\_{\rm c\varepsilon} - \Delta t\_{1} - \Delta t\_{2}) \right] \\ \mathbf{X}\_{f} &= \left[ \mathbf{x}(t\_{\rm cs} - \Delta t\_{2}), \mathbf{x}(t\_{\rm cs} - \Delta t\_{2} + T), \dots, \mathbf{x}(t\_{\rm cs} - \Delta t\_{2} + iT), \dots, \mathbf{x}(t\_{\rm c\varepsilon} - \Delta t\_{2}) \right] \\ \mathbf{X}\_{c} &= \left[ \mathbf{x}(t\_{\rm cs}), \mathbf{x}(t\_{\rm cs} + T), \dots, \mathbf{x}(t\_{\rm cs} + iT), \dots, \mathbf{x}(t\_{\rm c\varepsilon}) \right] \\ \mathbf{X}\_{b} &= \left[ \mathbf{x}(t\_{\rm bs}), \mathbf{x}(t\_{\rm bs} + T), \dots, \mathbf{x}(t\_{\rm bs} + iT), \dots, \mathbf{x}(t\_{\rm bc}) \right] \end{aligned} \tag{10}$$

where *S*1 and *S*2 are the moving distance of the crop at the header and the feeder, respectively. *Rh* and *Rf* are the radii of the header cutting disk and the upper feed roller, respectively. *nh* and *nf* are the rotation speed of the header and the upper feed roller, respectively. *tcs* and *tce* denote the rising edge and descending edge of the filtered data in the shedder. *tbs* and *tbe* express the rising edge and descending edge of the filtered data in the blower.


**Author details**

Antonio Marte Department of Woman, Child and General and Specialist Surgery, University of Campania "Luigi Vanvitelli", Naples, Italy

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© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**Author details**

far greater degrees of HLA mismatch, CBT is still associated with lower incidences and severity of acute and chronic GvHD than HSCT from adult donors [124]. According to the recent study of 143 patients in 2010, the rate of grade II or higher GvHD is only 9% among HLA-matched (n = 60) and 50% among HLA-mismatched (n = 18) patients [125]. Being less mature than stem cells from adult grafts, CB stem cells show lower alloreactivity and immunogenicity without increasing long-term relapse incidences [126]. Moreover, CB has other advantages including low contamination rate, easy storage and immediate accessibility via cryopreservation [125].

206 Umbilical Cord Blood Banking for Clinical Application and Regenerative Medicine

Together with its relatively lenient requirement for HLA matching, CB has been used commonly as an alternative source for transplantation. Indeed, one of the first cases to treat AD with CBT was performed due to lack of HLA-matched, appropriate adult stem cell source [127]. Raetz and colleagues performed myeloablative conditioning HSCT on a 5-year-old boy with severe Evans syndrome, which consists of immune thrombocytopenia and Coombspositive hemolytic anemia. The graft was a CB from an HLA-matched sibling with 3.85 × 107

remission. Acute GvHD prophylaxis regimen was with cyclosporine, with G-CSF initiated on day +1. The patient engrafted with an absolute neutrophil count (ANC) greater than 0.5 × 109

l on day +16. The following day, he developed symptoms of acute GvHD, with temperatures of up to 40°C, skin rash that on biopsy was consistent with GvHD and severe pulmonary insufficiency. He was intubated for 2 days and treated with high-dose steroids, with rapid resolution of symptoms. Platelet engraftment was delayed, with sustained platelets greater

from day +210. Reevaluation of his RBC antibody status revealed a DAT that was only microscopically positive by day +20 and negative on day +286. Anti-platelet antibodies were negative on day +115 and day +176. He had no evidence of response with cyclosporine pretransplant and no evidence of disease recurrence 2 months after discontinuation of cyclosporine and 4 months after discontinuation of prednisone.  However, the patient died unexpectedly from acute hepatic failure 9 months after transplantation [127].

Itamura et al. reported another case for a 48-year-old female patient with RA [128], who was subsequently diagnosed as having autoimmune hemolytic anemia (AIHA). Four months after the diagnosis of AIHA, she suddenly developed hemophagocytic syndrome (HPS) with disseminated intravascular coagulation (DIC) and *Staphylococcus aureus* bacteremia. Her HPS transiently improved after treatment, but relapsed 3 weeks later. Without an immediately available related adult donor, an unrelated donor CB graft was used for HSCT. The patient had HLA antibodies against multiple HLA class I antigens; however, the CB unit did not express HLA antigens reactive to her HLA antibodies and contained sufficient cell dose. HLA type of the CB graft was A\*02:06/A\*11:01, B\*40:06/B\*67:01, DRB1\*09:01/DRB1\*16:02 and that of the recipient was A\*11:01/A\*26:02, B\*40:06/B\*67:01, DRB1\*09:01/DRB1\*16:02. Four months after the onset of HPS, she received a CBT following a conditioning regimen of melphalan (40

was used for the prophylaxis of GvHD. Engraftment of neutrophils ANC500 and platelets 20K occurred on days 14 and 32, respectively. Bone marrow examination on day 60 showed

/l by day +170. He was platelet independent from day +240 and RBC independent

CD34+ cells/kg patient infused, leading to complete

/day 5 days) and total-body irradiation. Tacrolimus

/

nucleated cells/kg patient and 0.96 × 105

than 30 × 109

mg/m2

/day 2 days), fludarabine (25 mg/m2

Christine Chow, Tracie Dang, Vincent Guo, Michelle Chow, Qingyu Li, Delon Te-Lun Chow, Elizabeth Rao, Tony Zeng, Baixiang Wang and Robert Chow\*

\*Address all correspondence to<EMAIL_ADDRESS>
CyteTherapeutics, Inc., Irvine, CA, USA & Beijing, People's Republic of China