Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability Analysis of Screen-printed Tags with Low-power Electronics on Flexible Substrates

The additive manufacturing of RFID smart tags typically involves printing of antennas using electrically conductive materials along with the hybrid integration of the off-the-shelf low-power electronic components. In this case, the conductivity of printed material could significantly influence the reliable working of electronics as the electromagnetic performance of the antenna depends on it. In this research, we demonstrate the effect of conductive materials for printed antenna and show how their reliable operation could be attained by using suitable number of coatings. The printed antenna with its low-power electronics circuit is also compared with the conventional copper etched rigid and flexible tags to show the challenges regarding the electromagnetic performance. The printed tags are further subjected to different bending cycles to investigate their mechanical stability under varying strain conditions.
Optimal Placement of Distributed Generation and D-STATCOM in Radial Distribution Network

Abstract Analytical approach-based technique was used in this work for finding the optimized size and location for distributed generations (DG) and Distribution Static Compensator (D-STATCOM) placement in a distribution network. Optimum placement of DG and D-STATCOM mitigated the power losses and improved the voltage profile in a radial distribution system. This technique was tested on a 33-bus radial distribution system through a voltage stability index and loss sensitivity factor, thereby determining the best possible size and optimal location for DG and D-STATCOM. The changes in voltage profile and active power losses were compared before and after installation of these devices in the distribution system. Analytical approach based technique was used in this work for finding the optimized size and location for DG and D-STATCOM placement in a distribution network. Optimum placement of DG and D-STATCOM mitigated the power losses and improved the voltage profile in a radial distribution system. This technique was tested on a 33-bus radial distribution system through a voltage stability index and loss sensitivity factor, thereby determining the best possible size and optimal location for DG and D-STATCOM. The changes in voltage profile and active power losses were compared before and after installation of these devices in the distribution system.
Noncooperative Equilibrium-Seeking in Distributed Energy Systems Under AC Power Flow Nonlinear Constraints

Power distribution grids are commonly controlled through centralized approaches, such as the optimal power flow. However, the current pervasive deployment of distributed renewable energy sources and the increasing growth of active players, providing ancillary services to the grid, have made these centralized frameworks no longer appropriate. In this context, we propose a novel noncooperative control mechanism for optimally regulating the operation of power distribution networks equipped with traditional loads, distributed generation, and active users. The latter, also known as prosumers, contribute to the grid optimization process by leveraging their flexible demand, dispatchable generation capability, and/or energy storage potential. Active users participate in a noncooperative liberalized market designed to increase the penetration of renewable generation and improve the predictability of power injection from the high-voltage grid. The novelty of our game-theoretical approach consists in incorporating economic factors as well as physical constraints and grid stability aspects. Finally, by integrating the proposed framework into a rolling-horizon approach, we show its effectiveness and resiliency through numerical experiments.
Locating Anomaly Clues for Atypical Anomalous Services: An Industrial Exploration

Continuity and steadiness are vital for services with massive users, which requires the anomalies of services should be detected and resolved in a timely manner. Our previous work proposed a tool, namely <italic>ImpAPTr (Impact Analysis based on Pruning Tree)</italic>, to identify the combination of multiple dimensional attributes as the clues leading to the root cause of service anomalies. However, <italic>ImpAPTr</italic> applies a threshold driven strategy, i.e., it needs to be triggered by a <inline-formula><tex-math notation="LaTeX">$\geq 0.05\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>≥</mml:mo><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>05</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq1-3181143.gif"/></alternatives></inline-formula> drop of the success rate of the service calls (abbr. <italic>SRSC</italic>), which may face problems in an atypical yet pervasive situation in field application. For example, the combination of trivial anomalies (i.e., each causes a drop less than 0.05% to <italic>SRSC</italic>) can lead to a far more than 0.05% drop on <italic>SRSC</italic>. Besides, a suitable threshold is usually hard to be determined, etc. To address these problems, we propose a new method, namely <italic>ImpAPTr+</italic> in this paper to free the constraint of the 0.05% threshold. The basic idea is to involve time dimension and identify clues across multiple time intervals of data. We performed evaluation on three typical methods (i.e., <italic>ImpAPTr+</italic>, <italic>R-Adtributor</italic> and <italic>Squeeze</italic>) with both production environment dataset and simulation dataset. The former dataset is directly retrieved from the service monitoring data in <italic>Meituan</italic>, one of the largest on-line service providers worldwide. The latter dataset is fabricated also using the monitoring data from the same company. The results indicate: (1) <italic>ImpAPTr+</italic> outperforms previous approaches to a large degree in terms of accuracy. (2) Both <italic>ImpAPTr+</italic> and <italic>R-Adtributor</italic> are able to find proper clues within seconds. (3) <italic>ImpAPTr+</italic> tends to find proper clues with shorter time intervals (i.e., less data), which implies that the method is more suitable for near real-time monitoring scenarios.
Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

With the coming of intelligent vehicles and vehicular communication, Intelligent Transportation Systems (ITS) of connected vehicles are emerging and now evolving to Cooperative-ITS (C-ITS), as service platforms for smart cities. Considering new service properties, the autonomous cooperation of such vehicles has exhibited novel QoS features that imply new requirements: guaranteeing the traffic efficiency of any emergent vehicle while trying to promote the throughput at an intersection. So, after analyzing the classic reservation-based cooperation mechanisms, new QoS-oriented cooperation methods and policies are studied in this work. Concretely, several models of related traffic objects we have proposed are firstly introduced briefly. Then, the scheduling policies of vehicles approaching an intersection have been presented, including three existing policies (FAFP-SV, FAFP-SQ, and HQEP-SV) and five new polices (FAFP-SQ-SV, FAFP-MQ, HWFP-SQ, HWFP-SQ-SV, HWFP-MQ). These policies combine two major factors: vehicular priority for scheduling and concurrency in traffics. The first one includes the vehicular arrival-time, priority mapped to QoS, and the weight of reserved vehicles on a lane etc. In addition, the second refers to schedule a platoon rather than single vehicle each time, or platoons on different lanes instead of one platoon on only one lane. All these policies have been implemented, and further, verified within the parameter-configurable traffic simulator QoS-CITS (v2.1) we designed and developed with C#. Abundant experiments have been conducted with configured typical traffic scenes, and experimental results show that HWFP-SQ-SV and HWFP-MQ can guarantee both the QoS of emergent vehicles and traffic throughput better than other six policies.
Criteria for the Classification of the Interradicular Septum Shape in Maxillary Molars with Clinical Importance for Prosthetic-Driven Immediate Implant Placement

The aim of this study was to use cone-beam computed tomography (CBCT) to evaluate the morphometric properties of the interradicular septum (IRS) in the maxillary molar region that may be indicative for prosthetic-driven implant placement. Following the repetitive algorithm based on the visual identification of IRS shapes, we described the following IRS shapes: arrow, boat, drop, and palatal and buccal convergence. The incidence of IRS shapes showed significant differences for the first and second maxillary molars (the highest frequency for the arrow shape, and the lowest for the drop shape) with no significant difference between the molars. The most prominent width indicative for implant placement was observed in the palatal convergence shape, whereas the height criteria were the most satisfying in the buccal convergence-shaped IRS for both molars. Apart from the parameters in the coronal view, the image analysis in the axial view revealed that IRS surface area, required for the implant placement, was the most prominent in the palatal convergence shape for the first, and boat shape for the second molars. Our results showed the benefits of CBCT diagnostics in posterior maxilla morphometric analysis. IRS shape classification may be helpful in achieving more rapid and accurate planning for interventions in this region.
Inactivation of Osteoblast PKC Signaling Reduces Cortical Bone Mass and Density and Aggravates Renal Osteodystrophy in Mice with Chronic Kidney Disease on High Phosphate Diet

Chronic kidney disease (CKD) frequently leads to hyperphosphatemia and hyperparathyroidism, mineral bone disorder (CKD-MBD), ectopic calcifications and cardiovascular mortality. PTH activates the osteoanabolic Gαs/PKA and the Gαq/11/PKC pathways in osteoblasts, the specific impact of the latter in CKD-MBD is unknown. We generated osteoblast specific Gαq/11 knockout (KO) mice and established CKD-MBD by subtotal nephrectomy and dietary phosphate load. Bone morphology was assessed by micro-CT, osteoblast function by bone planar scintigraphy at week 10 and 22 and by histomorphometry. Osteoblasts isolated from Gαq/11 KO mice increased cAMP but not IP3 in response to PTH 1-34, demonstrating the specific KO of the PKC signaling pathway. Osteoblast specific Gαq/11 KO mice exhibited increased serum calcium and reduced bone cortical thickness and mineral density at 24 weeks. CKD Gαq/11 KO mice had similar bone morphology compared to WT, while CKD Gαq/11-KO on high phosphate diet developed decreased metaphyseal and diaphyseal cortical thickness and area, as well as a reduction in trabecular number. Gαq/11-KO increased bone scintigraphic tracer uptake at week 10 and mitigated tracer uptake in CKD mice at week 22. Histological bone parameters indicated similar trends. Gαq/11-KO in osteoblast modulates calcium homeostasis, bone formation rate, bone morphometry, and bone mineral density. In CKD and high dietary phosphate intake, osteoblast Gαq/11/PKC KO further aggravates mineral bone disease.
MRI Assessment of Cardiac Function and Morphology in Adult Patients With Growth Hormone Deficiency: A Systematic Review and Meta-Analysis

Background Adult GH deficiency (GHD) has been described as a heterogeneous condition characterized by many clinical modifications, such as metabolic alterations, impaired quality of life, and increased mortality. The clinical relevance of cardiac involvement remains, however, only partially elucidated. Methods PubMed/Medline, EMBASE, Cochrane library, OVID and CINAHL databases were systematically searched until February 2022 for studies evaluating cardiac function and morphology by magnetic resonance imaging in adult patients with GHD. Effect sizes were pooled through a random-effect model. Results Four studies were considered in the meta-analysis. With respect to the left ventricle, GHD patients were characterized by a lower stroke-volume-index (-3.6 ml/m2, standardized mean difference (SMD) -0.60, 95%CI [-1.15,-0.05], p=0.03), lower end-diastolic-volume-index (-6.2 ml/m2, SMD -0.54, 95%CI [-0.97,-0.10], p=0.02) and, after accounting for possible biases, lower mass-index (-15.0 g/m2, SMD -1.03, 95%CI [-1.89,-0.16], p=0.02). With respect to the right ventricle, a lower end-diastolic-volume-index (-16.6 ml/m2, SMD -1.04, 95%CI [-2.04,-0.03], p=0.04) and a borderline-significant lower stroke-volume-index (-5.0 ml/m2, SMD -0.84, 95%CI [-1.77,0.08], p=0.07) could be observed. Data about the effect of GH replacement therapy highlighted a significant increase in left ventricular mass-index after treatment initiation (+3.7 g/m2, 95%CI [1.6,5.7], p<0.01). Conclusion With respect to the left ventricle, our results confirmed those retrieved by echocardiographic studies. In addition, significant alterations were demonstrated also for the right ventricle, for which echocardiographic data are nearly absent. This supports the thesis of a biventricular cardiac involvement in patients with GHD, with a similar pattern of morphological and functional alterations in both ventricles.
An Optimal Compensation Method of Shunt Active Power Filters for System-Wide Voltage Quality Improvement

Conventional control strategy of a shunt active power filter (SAPF) for harmonic and unbalance mitigation is to locally compensate the harmonic and unbalance component of load current, which may not be economical enough for a multinode system with distributed nonlinear loads (NLLs) and unbalanced loads (UBLs). Actually the SAPF will influence voltage at neighboring nodes, therefore it is promising to optimize SAPF control method from a system perspective. For this purpose, first this paper analyzes the drawbacks of a local compensation method and proposes the optimization problem for the system-wide control of SAPF in a simple single-phase system. Next, the idea is extended to a complex multinode three-phase system, and a matrix model for harmonic and unbalance analysis is established. Finally, a matrix-based least square method is utilized to solve the optimization problem, thus a generalized SAPF optimal compensation method for system-wide voltage quality improvement is obtained. Based on the proposed method, a small amount of SAPFs can be scientifically utilized to improve the overall voltage quality of a multinode system with a large amount of distributed NLLs and UBLs, with fast response, good performance, and reduced cost. Simulations and experimental results are provided to verify the effectiveness of the proposed method.
Soil organic carbon estimation along an altitudinal gradient of chir pine forests in the Garhwal Himalaya, India: A field inventory to remote sensing approach

Chir pine (Pinus roxburghii, Sarg.) forests are dominant in the Indian Himalayan region and act as a huge carbon (C) sink. However, measuring the C sink in soil is complex and time‐intensive, and therefore the present study attempts to estimate the soil organic carbon (SOC) through a remote sensing (RS) approach. We estimated SOC stock of chir pine forests along an altitudinal gradient at three soil depths (0–30, 30–60 and 60–100 cm) in the Garhwal Himalaya, Uttarakhand. Fourteen forest stands at four altitudes, viz., <1000 m above sea level (m asl), 1001–1400 m asl, 1401–1800 m asl and >1801 m asl were surveyed and served for data collection. A model for predicting SOC was developed through stepwise regression analysis based on vegetation information and altitude as independent variables with the field data on SOC. For vegetation information, we used the normalized difference vegetation index (NDVI) measured through remote sensing (RS). The mean SOC stock up to 100 cm depth was increased with increasing altitude and were in the order of 69.66 ± 19.86, 85.27 ± 17.53, 95.68 ± 7.90 and 148.41 ± 71.39 million g ha−1 (million gram per hectare) for <1000, 1001–1400, 1401–1800 and >1801 m_asl, respectively. The result showed that NDVI was a good predictor for SOC estimation. The model predicted SOC stock between 57 and 152 million g ha−1 with a mean of 93 million g ha−1, which was close to the SOCs from field inventory. Therefore, RS could be used to precisely map the SOC stock in the chir pine forests of the Himalayas through NDVI and provide information to policymakers for forest management.
A Novel High-Step-Up Coupled-Inductor DC–DC Converter With Reduced Power Device Voltage Stress

In this paper, a novel high-step-up coupled-inductor dc–dc converter is presented. The proposed converter has an excellent feature, i.e., the closer the turn ratio to 1, the higher the gain of the converter, also the lower the voltage stress of the switch. Duty cycle and the turn ratio of the coupled inductor together determine the voltage gain of the converter; thus, it can achieve any wanted voltage without the circuit to operate at the extreme duty cycle. In addition, the proposed converter can realize higher voltage gain with a small duty cycle. And it is suitable for many types of new energy source, especially photovoltaic generation systems, which has a common ground and a continuous input current switch with great benefits for the life of the input power supply. The power density of the system improves for the lack of isolation transformer. Due to the introduction of the clamp circuit, the voltage spikes across the switch are limited. Therefore, we can choose a switch with lower voltage levels that would cut costs and reduce conduction losses. As the energy of leakage energy is absorbed by the circuit, the efficiency is further improved. The operational principle and the steady-state analysis of the proposed converter are presented in this paper. Finally, the experiment with an input 48-V voltage and an output voltage of 380 V is implemented, verifying the validity of this paper.
The Impact of Nutrition Labelling on Customer Buying Intention and Behaviours in Fast Food Operations: Some Implications for Public Health

This research examines customers’ intention to buy depending on their use of nutrition labelling (NL) in fast food operations (FFOs) and their intention to visit and recommend these FFOs with nutrition-labelled menus. The research model draws on the theory of planned behaviour (TPB) to examine customers’ intentions to buy from nutrition-labelled menus and their behaviour of visiting and recommending to others FFOs with nutrition-labelled menus. To achieve this purpose, a self-administrated questionnaire was distributed to and collected from a random sample of customers at FFOs in Greater Cairo, Egypt, i.e., McDonald’s and Subway. The results from the structural equation modelling (SEM) using AMOS software indicated positive and direct significant paths from the constructs of the TPB, except for customers’ attitude, to customer intention to buy nutrition-labelled menu items. The results also showed a positive significant impact of customers’ intention on their behaviour of visiting and recommending FFOs featuring nutrition-labelled menus. The findings showed that there is an awaking of nutritional awareness among fast-food customers and that providing nutritional information on fast-food menus will affect their purchasing intention in the future by encouraging them to make healthy food choices. Theoretical implications for scholars and managerial implications for FFOs, especially in relation to public health in general and healthy food choices in particular, are explained and discussed.
Predictive Abilities of the Frailty Phenotype and the Swiss Frailty Network and Repository Frailty Index for Non-Home Discharge and Functional Decline in Hospitalized Geriatric Patients

Frailty is increasingly applied as a measure to predict clinical outcomes, but data on the predictive abilities of frailty measures for non-home discharge and functional decline in acutely hospitalized geriatric patients are scarce. The aim of this study was to investigate the predictive ability of the frailty phenotype and a frailty index currently validated as part of the ongoing Swiss Frailty Network and Repository Study based on clinical admission data for non-home discharge and functional decline in acutely hospitalized older patients. Prospective cohort study. Data were analyzed from 334 consecutive hospitalized patients of a tertiary acute care geriatric inpatient clinic admitted between August 2020 and March 2021. We assessed frailty using 1) the frailty phenotype and 2) the Swiss Frailty Network and Repository Study (SFNR) frailty index based on routinely available clinical admission data. Predictive abilities of both frailty measures were analyzed for the clinical outcomes of non-home discharge and functional decline using multivariate logistic regression models and receiver operating characteristic curves (ROC). Mean age was 82.8 (SD 7.2) years and 55.4% were women. Overall, 170 (53.1%) were frail based on the frailty phenotype and 220 (65.9%) based on the frailty index. Frail patients based on the frailty phenotype were more likely to be discharged non-home (55 (32.4%) vs. 26 (17.3%); adjusted OR 2.4 (95% CI, 1.4, 5.1)). Similarly, frail patients based on the frailty index were more likely to be discharged non-home compared to non-frail patients (76 (34.6%) vs. 9 (7.9%); adjusted OR, 5.5 (95% CI, 2.6, 11.5)). Both, the frailty phenotype and the frailty index were similarly associated with functional decline (adjusted OR 2.7 (95% CI, 1.5, 4.9); adjusted OR 2.8 (95% CI 1.4, 5.5)). ROC analyses showed best discriminatory accuracy for the frailty index for non-home discharge (area under the curve 0.76). Frailty using the SFNR-frailty index and the frailty phenotype is a promising measure for prediction of non-home discharge and functional decline in acutely hospitalized geriatric patients. Further study is needed to define the most valid frailty measure.
E-Commerce Precision Marketing and Consumer Behavior Models Based on IoT Clustering Algorithm

This article aims to study e-commerce precision models and consumer behavior models based on clustering algorithms, and at the same time conduct detailed research on the Gaussian mixture distribution algorithm, consumer behavior and model construction, and precision marketing strategies in the clustering algorithm. First, a lot of analysis and demonstration of precision marketing strategies and the construction of consumer behavior models are carried out, and then the clustering algorithm-based electronic some experiments were carried out on the application of commercial precision marketing methods and consumer behavior models. The experimental results show that the precision marketing method using the clustering algorithm is more in line with the development of modern e-commerce. The application of the algorithm in the precision marketing methods of enterprises and consumer behavior models has promoted the vigorous development of enterprises, making the sales volume of enterprises reach 9.8 %.
Brain image pathological segmentation method based on a deep network

The automation of the medical industry has become one of the necessary conditions in today's medical field. Radiologists or physicians need this automation technology for accurate diagnosis and treatment. Automatic segmentation of tumor parts from magnetic resonance (MR) brain images is a challenging task. Several methods have been developed to improve the segmentation efficiency of automation systems. However, in the process of medical image segmentation, there is always room for improvement. This paper proposes a brain tumor image segmentation method based on deep learning, which includes the concepts of stationary wavelet transform (SWT) and growing convolutional neural network (GCNN). The vital goal of this work is to improve the accuracy of traditional systems. In this paper, the support vector machine (SVM) and convolutional neural network (CNN) are compared and analyzed. The experimental results show that this method is superior to SVM and CNN inaccuracy, peak signal-to-noise ratio, mean square error, and other performance indicators.