bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Future Climate in the Upper Awash Basin (UASB) with Selected Climate Model Outputs under CMIP6
Authors: Balcha Yonas Abebe , Malcherek Andreas , Alamirew Tena
Published: November 2022

Climate change makes the climate system of a given region unpredictable and increases the risk of water-related problems. GCMs (global climate models) help in understanding future climate conditions over a given region. In this study, 12 GCMs from the CMIP6 (coupled model intercomparison project six) were evaluated and ranked based on their abilities to describe the historical observed series. The ensemble mean of bias-adjusted best five models of average annual precipitation showed an increment with an uncertainty range of (2.0–11.9) and change in the mean of 6.4% for SSP2-4.5 and (6.1–16.1) 10.6% for SSP5-8.5 in 2040–2069 relative to the historical period. Similarly, for 2070–2099, increments of (2.2–15.0) 7.9% and (11.8–29.4) 19.7% were predicted for the two scenarios, respectively. The average annual maximum temperature series showed increments of (1.3–2.0) 1.6 °C for SSP2-4.5 and (1.7–2.3) 2.0 °C for SSP5-8.5 in 2040–2069. At the same time, increments of (1.7–2.3) 2.0 °C and (2.8–3.2) 3.0 °C were predicted for 2070–2099. Furthermore, it was predicted that the average annual minimum temperature series will have increments of (1.6–2.3) 2.0 °C and (2.2–2.9) 2.5 °C for 2040–2069 and (2.1–2.7) 2.4 °C and (3.7–4.2) 4.0 °C for 2070–2099 for the two scenarios, respectively. An increase in precipitation with increased land degradation in the sub-basin results in a higher risk of flood events in the future. Improved soil and water conservation practices may minimize the adverse impacts of future climate change on the loss of agricultural productivity.
Nondestructive Testing Technologies for Rail Inspection: A Review
Authors: Gong Wendong , Akbar Muhammad Firdaus , Jawad Ghassan Nihad , Mohamed Mohamed Fauzi Packeer , Wahab Mohd Nadhir Ab
Published: November 2022

Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestructive testing techniques. With the development of information technology, communication technology, and sensor technology, rail health monitoring systems have been evolving, and have become equally significant and challenging because they can achieve real-time detection and give a risk warning forecast. This paper provides an in-depth review of traditional nondestructive techniques for rail inspection as well as the development of using machine learning approaches, combined nondestructive techniques, and rail health monitoring systems.
A Novel Protection Method for Carbonate Stone Artifacts with Gypsum Weathering Crusts
Authors: Lu Ruicong , He Lu , Li Ting , Yang Fuwei , Liu Yan , Zhang Kun , Chen Xinnan
Published: November 2022

An innovative method using a methanol solution of barium hydroxide-urea as a protective agent was investigated for the conservation of stone artifacts with harmful gypsum weathering crusts. In this method, the methanol solution of barium hydroxide-urea and water were introduced into the gypsum crust in sequence by surface spraying. By doing so, the harmful gypsum crust is directly converted into a barium sulfate—calcium carbonate composite protective layer. The properties of the composite layer were characterized by SEM-EDX, XRD, ATR-FTIR, IC, water solubility, wetting angle, color difference, open porosity, capillary water absorption, and surface hardness. The results of the morphological and composition characterization (SEM-EDX, XRD, ATR-FTIR) indicate that the added urea can promote the carbonization reaction effectively. In addition, the methanol solution of barium hydroxide-urea can penetrate deep into the gypsum crust. The results of the physical properties characterization denote that the water stability of the specimens was significantly increased after the protection treatment; an approximate ten-fold stronger water resistance ability was achieved. Meanwhile, the intrinsic physical properties of gypsum crust, such as pore structure and original appearance, could basically be maintained. The presented conservative method has high facility and controllability and satisfying conservation effect, which means it has potential in the conservation of surface weathering carbonate stone artifacts.
A Prognostic Model Generated from an Apparent Diffusion Coefficient Ratio Reliably Predicts the Outcomes of Oral Tongue Squamous Cell Carcinoma
Authors: Cai Lingling , Li Xiaoguang , Wu Lizhong , Wang Bocheng , Si Mingjue , Tao Xiaofeng
Published: November 2022

This study aimed to develop an apparent diffusion coefficient (ADC) ratio-based prognostic model to predict the recurrence and disease-free survival (DFS) of oral tongue squamous cell carcinoma (OTSCC). A total of 188 patients with cT1-2 oral tongue squamous cell carcinoma were enrolled retrospectively. Clinical and laboratory data were extracted from medical records. The ADC values were measured at the regions of interest of the tumor and non-tumor tissues of the MRI images, and the ADC ratio was used for comparison between the patient with recurrence (n = 83 case, 44%) and patients without recurrence (n = 105 cases, 56%). Cox proportional hazards models were generated to analyze the risk factors of cancer recurrence. A nomogram was developed based on significant risk factors to predict 1-, 5- and 10-year DFS. The receiver operator characteristic (ROC) curves of predictors in the multivariable Cox proportional hazards prognostic model were generated to predict the recurrence and DFS. The integrated areas under the ROC curve were calculated to evaluate discrimination of the models. The ADC ratio, tumor thickness and lymph node ratio were reliable predictors in the final prognostic model. The final model had a 71.1% sensitivity and an 81.0% specificity. ADC ratio was the strongest predictor of cancer recurrence in prognostic performance. Discrimination and calibration statistics were satisfactory with C-index above 0.7 for both model development and internal validation. The calibration curve showed that the 5- and 10-year DFS predicted by the nomogram agreed with actual observations.
Anianabacter salinae gen. nov., sp. nov. ASV31T, a Facultative Alkaliphilic and Extremely Halotolerant Bacterium Isolated from Brine of a Millennial Continental Saltern
Authors: Azpiazu-Muniozguren Maia , García Minerva , Laorden Lorena , Martinez-Malaxetxebarria Irati , Seoane Sergio , Bikandi Joseba , Garaizar Javier , Martínez-Ballesteros Ilargi
Published: November 2022

During a prokaryotic diversity study in Añana Salt Valley, a new Rhodobacteraceae member, designated ASV31T, was isolated from Santa Engracia spring water. It was extremely halotolerant, tolerating up to 23% NaCl, and facultatively alkaliphilic, growing at pH 6.5–9.5 (optimum at 7.0–9.5). The isolate was a Gram-negative, rod-shaped, aerobic and non-motile bacterium that formed beige-to-pink colonies on marine agar. According to a 16S rRNA gene-based phylogenetic analysis, strain ASV31T forms a distinct branch of the family Rhodobacteraceae, with Thioclava pacifica DSM 10166T being its closest type strain (95.3%). This was confirmed with a phylogenomic tree and the values of ANI (73.9%), dDDH (19.3%), AAI (63.5%) and POCP (56.0%), which were below the genus/species level boundary. Additionally, an ability to degrade aromatic compounds and biosynthesise secondary metabolites was suggested by the genome of strain ASV31T. Distinguishing fatty acid profiles and polar lipid content were also observed. The genome size was 3.6 Mbp, with a DNA G+C content of 65.7%. Based on the data obtained, it was considered that strain ASV31T (=CECT 30309T = LMG 32242T) represents a new species of a new genus in the family Rhodobacteraceae, for which the name Anianabacter salinae gen. nov., sp. nov. is proposed.
Are South African Wild Foods the Answer to Rising Rates of Cardiovascular Disease?
Authors: Sadgrove Nicholas J.
Published: November 2022

The rising burden of cardiovascular disease in South Africa gives impetus to managerial changes, particularly to the available foods in the market. Since there are many economically disadvantaged groups in urban societies who are at the forefront of the CVD burden, initiatives to make healthier foods available should focus on affordability in conjunction with improved phytochemical diversity to incentivize change. The modern obesogenic diet is deficient in phytochemicals that are protective against the metabolic products of sugar metabolism, i.e., inflammation, reactive oxygen species and mitochondrial fatigue, whereas traditional southern African food species have high phytochemical diversity and are also higher in soluble dietary fibres that modulate the release of sugars from starches, nurture the microbiome and produce digestive artefacts that are prophylactic against cardiovascular disease. The examples of indigenous southern African food species with high horticultural potential that can be harvested sustainably to feed a large market of consumers include: Aloe marlothii, Acanthosicyos horridus, Adansonia digitata, Aloe ferox, Amaranthus hybridus, Annesorhiza nuda, Aponogeton distachyos, Bulbine frutescens, Carpobrotus edulis, Citrullus lanatus, Dioscorea bulbifera, Dovyalis caffra, Eleusine coracana, Lagenaria siceraria, Mentha longifolia, Momordica balsamina, Pelargonium crispum, Pelargonium sidoides, Pennisetum glaucum, Plectranthus esculentus, Schinziophyton rautanenii, Sclerocarya birrea, Solenostemon rotundifolius, Talinum caffrum, Tylosema esculentum, Vigna unguiculata and Vigna subterranea. The current review explains the importance of phytochemical diversity in the human diet, it gives a lucid explanation of phytochemical groups and links the phytochemical profiles of these indigenous southern African foods to their protective effects against cardiovascular disease.
Detection of Fish Pathogens in Freshwater Aquaculture Using eDNA Methods
Authors: Bohara Kailash , Yadav Amit K. , Joshi Pabitra
Published: November 2022

Organisms release their nucleic acid in the environment, including the DNA and RNA, which can be used to detect their presence. eDNA/eRNA techniques are being used in different sectors to identify organisms from soil, water, air, and ice. The advancement in technology led to easier detection of different organisms without impacting the environment or the organism itself. These methods are being employed in different areas, including surveillance, history, and conservation. eDNA and eRNA methods are being extensively used in aquaculture and fisheries settings to understand the presence of different fish species and pathogens in water. However, there are some challenges associated with the reliability of results because of the degradation of nucleic acid by several factors. In aquaculture, there are several diseases and parasites detected with these methods. In this review, we discuss different aquaculture diseases and parasites detected with eDNA/eRNA approach and the fate of these nucleic acids when subjected to different water quality and environmental parameters. This review intends to help the researcher with the potential of eDNA/eRNA-based detection of pathogens in aquaculture; this will be useful to predict a potential outbreak before it occurs. Along with that, this paper intends to help people understand several factors that degrade and can hamper the detection of these nucleic acids.
Comparative Analysis of In-House RT-qPCR Detection of SARS-CoV-2 for Resource-Constrained Settings
Authors: Bello-Lemus Yesit , Anaya-Romero Marco , Gómez-Montoya Janni , Árquez Moisés , González-Torres Henry J. , Navarro-Quiroz Elkin , Pacheco-Londoño Leonardo , Pacheco-Lugo Lisandro , Acosta-Hoyos Antonio J.
Published: November 2022

We developed and standardized an efficient and cost-effective in-house RT-PCR method to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated sensitivity, specificity, and other statistical parameters by different RT-qPCR methods including triplex, duplex, and simplex assays adapted from the initial World Health Organization- (WHO) recommended protocol. This protocol included the identification of the E envelope gene (E gene; specific to the Sarvecovirus genus), RdRp gene of the RNA-dependent RNA polymerase (specific for SARS-CoV-2), and RNase P gene as endogenous control. The detection limit of the E and the RdRp genes were 3.8 copies and 33.8 copies per 1 µL of RNA, respectively, in both triplex and duplex reactions. The sensitivity for the RdRp gene in the triplex and duplex RT-qPCR tests were 98.3% and 83.1%, respectively. We showed a decrease in sensitivity for the RdRp gene by 60% when the E gene acquired Ct values > 31 in the diagnostic tests. This is associated with the specific detection limit of each gene and possible interferences in the protocol. Hence, developing efficient and cost-effective methodologies that can be adapted to various health emergency scenarios is important, especially in developing countries or settings where resources are limited.
A Pilot Analysis of Circulating cfRNA Transcripts for the Detection of Lung Cancer
Authors: Seneviratne Chamindi , Shetty Amol Carl , Geng Xinyan , McCracken Carrie , Cornell Jessica , Mullins Kristin , Jiang Feng , Stass Sanford
Published: November 2022

Lung cancers are the leading cause of cancer-related deaths worldwide. Studies have shown that non-small cell lung cancer (NSCLC), which constitutes the majority of lung cancers, is significantly more responsive to early-stage interventions. However, the early stages are often asymptomatic, and current diagnostic methods are limited in their precision and safety. The cell-free RNAs (cfRNAs) circulating in plasma (liquid biopsies) offer a non-invasive detection of spatial and temporal changes occurring in primary tumors since the early stages. To address gaps in the current cfRNA knowledge base, we conducted a pilot study for the comprehensive analysis of transcriptome-wide changes in plasma cfRNA in NSCLC patients. Total cfRNA was extracted from archived plasma collected from NSCLC patients (N = 12), cancer-free former smokers (N = 12), and non-smoking healthy volunteers (N = 12). Plasma cfRNA expression levels were quantified by using a tagmentation-based library preparation and sequencing. The comparisons of cfRNA expression levels between patients and the two control groups revealed a total of 2357 differentially expressed cfRNAs enriched in 123 pathways. Of these, 251 transcripts were previously reported in primary NSCLCs. A small subset of genes (N = 5) was validated in an independent sample (N = 50) using qRT-PCR. Our study provides a framework for developing blood-based assays for the early detection of NSCLC and warrants further validation.
Express Diagnostics of Proteolytic Activity of Periodontopathogens—Methodological Approach
Authors: Aronova Ekaterina , Dmitrienko Marina , Ivanova Anastasija , Gaykova Yulia , Kurochkina Anna , Blinova Alisa , Bazarnova Julia , Paponova Elizaveta
Published: November 2022

The species spectrum of the oral microbiome is considered to be the key factor in the development and progression of periodontal inflammatory disorders. The “red complex” including Porphyromonas gingivalis, Tannerella forsythia and Treponema denticola has the highest pathogenic potential. These bacteria have several biochemical mechanisms that allow them to colonize and destroy periodontal tissues. Proteolytic enzymes play a crucial role in this process. Early diagnosis of pathological conditions induced by microbial contamination allows for the timely treatment of periodontitis. Otherwise, the development of the disease may lead to tooth loss. A total of 48 patients aged 18 to 65 years old who required professional oral hygiene were recruited for this clinical study. Microbial content analysis of dental plaque from the interdental space and the back of the tongue was performed using real-time PCR. To determine the proteolytic activity of oral bacteria, the new express diagnostic method was applied (diagnostic sensitivity, 0.875; specificity, 0.928). The results demonstrate a strong and significant correlation between the new method and the PCR analysis (r = 0.785, p < 0.001). These results show that the new express method can be valuable as an early diagnostic method for periodontal inflammatory disorders caused by the “red complex” bacteria.
Scale Enhancement Pyramid Network for Small Object Detection from UAV Images
Authors: Sun Jian , Gao Hongwei , Wang Xuna , Yu Jiahui
Published: November 2022

Object detection is challenging in large-scale images captured by unmanned aerial vehicles (UAVs), especially when detecting small objects with significant scale variation. Most solutions employ the fusion of different scale features by building multi-scale feature pyramids to ensure that the detail and semantic information are abundant. Although feature fusion benefits object detection, it still requires the long-range dependencies information necessary for small objects with significant scale variation detection. We propose a simple yet effective scale enhancement pyramid network (SEPNet) to address these problems. A SEPNet consists of a context enhancement module (CEM) and feature alignment module (FAM). Technically, the CEM combines multi-scale atrous convolution and multi-branch grouped convolution to model global relationships. Additionally, it enhances object feature representation, preventing features with lost spatial information from flowing into the feature pyramid network (FPN). The FAM adaptively learns offsets of pixels to preserve feature consistency. The FAM aims to adjust the location of sampling points in the convolutional kernel, effectively alleviating information conflict caused by the fusion of adjacent features. Results indicate that the SEPNet achieves an AP score of 18.9% on VisDrone, which is 7.1% higher than the AP score of state-of-the-art detectors RetinaNet achieves an AP score of 81.5% on PASCAL VOC.
A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network
Authors: Zhang Jie , Wang Zhongmin , Zhou Kexin , Bai Ruohan
Published: November 2022

With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category.
Orthogonal Time Frequency Space Modulation Based on the Discrete Zak Transform
Authors: Lampel Franz , Joudeh Hamdi , Alvarado Alex , Willems Frans M. J.
Published: November 2022

In orthogonal time frequency space (OTFS) modulation, information-carrying symbols reside in the delay-Doppler (DD) domain. By operating in the DD domain, an appealing property for communication arises: time-frequency (TF) dispersive channels encountered in high-mobility environments become time-invariant. OTFS outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios, making it an ideal waveform candidate for 6G. Generally, OTFS is considered a pre- and postprocessing step for OFDM. However, the so-called Zak transform provides the fundamental relation between the DD and time domain. In this work, we propose an OTFS system based on the discrete Zak transform (DZT). To this end, we discuss the DZT and establish the input–output relation for time-frequency (TF) dispersive channels solely by the properties of the DZT. The presented formulation simplifies the derivation and analysis of the input–output relation of the TF dispersive channel in the DD domain. Based on the presented formulation, we show that operating in the DD incurs no loss in capacity.
The Impacts of Poverty, Unemployment, and Divorce on Child Abuse in Malaysia: ARDL Approach
Authors: Yob Zaidi , Shaari Mohd Shahidan , Esquivias Miguel Angel , Nangle Benjamin , Muhamad Wan Zuki Azman Wan
Published: November 2022

The increasing number of reported child abuse cases in Malaysia is alarming, with more than 1000 cases reported every year. If this trend continues, it may have detrimental effects on children’s mental development, and far-reaching negative effects for wider society. Examining whether there is some relationship between divorce, unemployment, poverty, inflation, economic growth, and child abuse in Malaysia may help shed some light on the issue and any potential solutions. This study employs the ARDL approach by using data from 1989 to 2019. The results reveal that unemployment, inflation, and economic growth have significant relationships with reported child abuse cases in the long run. However, divorce and poverty do not affect the number of reported child abuse cases in the long run. In the short run, the results show that divorce, economic growth, and unemployment can positively affect the number of reported child abuse cases. Poverty, on the other hand, has a significant and negative relationship with the number of reported child abuse cases in the short run. Family stress originating from economic and social distress and the potential inability of couples to manage stress may exacerbate the risk of child abuse in Malaysia. Social programs are likely needed to help couples handle stress at home, in the form of state-sponsored counselling, educational programs for parents, the provision of social support for an increasing number of dual-career couples, assistance for spouses dealing with divorce, and the protection of children from hostile environments at home, as well as general approaches to the alleviation of poverty.
Integrating IoT Technologies into the CS Curriculum at PVAMU: A Case Study
Authors: Ahmed Ahmed Abdelmoamen , Bellam Kiranmai , Yang Yonggao , Preuss Michael
Published: November 2022

With sensors becoming increasingly ubiquitous, there is tremendous potential for innovative Internet of Things (IoT) applications across a wide variety of domains, including healthcare, agriculture, entertainment, environmental monitoring, and transportation. The rapid growth of IoT applications has increased the demand for experienced professionals with strong IoT hands-on skills. However, undergraduate students in STEM education still lack experience in how to use IoT technologies to develop such innovative applications. This is in part because the current computing curricula do not adequately cover the fundamental concepts of IoT. This paper presents a case study from integrating innovative IoT technologies into the Computer Science (CS) curriculum at Prairie View A&M University (PVAMU). This paper presents a set of IoT learning modules that can be easily integrated into existing courses of CS curriculum to engage students in smart-IoT. The modules developed have been used to introduce a new project-based course in the CS department at PVAMU that focuses on intelligent IoT technologies. Findings from external evaluation of the curricular change are also presented. These note positive impacts on student interest in and learning about IoT across multiple courses and semesters.
An Integrated Achievement and Mentoring (iAM) Model to Promote STEM Student Retention and Success
Authors: Santangelo Jessica , Elijah Rosebud , Filippi Lisa , Mammo Behailu , Mundorff Emily , Weingartner Kristin
Published: November 2022

The Integrated Achievement and Mentoring (iAM) Program responds to the challenge of STEM student retention. The iAM Program provides access to the hidden curriculum (the unwritten, implicit skills critical for academic success) and uses legitimate peripheral participation to structure resources. Three essential (integrated support services, mentoring, and responsive program structure) and two adaptable components (STEM writing and metacognition seminar, and scholarships) are intended as mechanisms of inclusivity that build community and promote belonging. Retention of iAM Scholars was 18.3% higher relative to peers who were eligible but did not join the program. The Scholars’ four-year graduation rate was 26% higher than that of their STEM peers. A cost/benefit analysis revealed a net revenue benefit and suggests less-quantifiable benefits to the institution such as increased reputation. While the essential components of an iAM-based program should be consistent across institutions, the adaptable components can be implemented in ways that address local challenges and opportunities across international contexts.
On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout
Authors: Melo Elvis , Silva Ivanovitch , Costa Daniel G. , Viegas Carlos M. D. , Barros Thiago M.
Published: November 2022

The school dropout problem has been recurrent in different educational areas, which has reinforced important challenges when pursuing education objectives. In this scenario, technical schools have also suffered from considerable dropout levels, even when considering a still increasing need for professionals in areas associated to computing and engineering. Actually, the dropout phenomenon may be not uniform and thus it has become urgent the identification of the profile of those students, putting in evidence techniques such as eXplainable Artificial Intelligence (XAI) that can ensure more ethical, transparent, and auditable use of educational data. Therefore, this article applies and evaluates XAI methods to predict students in school dropout situation, considering a database of students from the Federal Institute of Rio Grande do Norte (IFRN), a Brazilian technical school. For that, a checklist was created comprising explanatory evaluation metrics according to a broad literature review, resulting in the proposal of a new explainability index to evaluate XAI frameworks. Doing so, we expect to support the adoption of XAI models to better understand school-related data, supporting important research efforts in this area.
A Novel MOGNDO Algorithm for Security-Constrained Optimal Power Flow Problems
Authors: Pandya Sundaram B. , Visumathi James , Mahdal Miroslav , Mahanta Tapan K. , Jangir Pradeep
Published: November 2022

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor—driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system’s overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.
Evaluation Index for IVIS Integration Test under a Closed Condition Based on the Analytic Hierarchy Process
Authors: Chen Jialiang , Li Huizhe , Hu Zhaozheng , Liu Kai , Hou Yue
Published: November 2022

The intelligent vehicle infrastructure system (IVIS) requires systematic testing before being put into large-scale applications. IVIS testing under closed conditions includes stress tests for typical scenarios and extreme scenario strength testing. To extract IVIS integration test indicators under closed conditions, this article constructed a hierarchical framework of IVIS’s evaluation indexes in the stress tests and the strength tests. The hierarchical framework of IVIS stress test evaluation indicators reflect the highway construction area under typical scenarios, and the hierarchical framework of IVIS strength test evaluation indicators reflect the highway merging area under extreme scenarios. Both are based on the test requirements of the stress test and strength test, with safety as the evaluation objective. Second, the analytic hierarchy process (AHP) was used to calculate the weights of the test evaluation indicators of the two scenarios. Finally, the activity-based classification (ABC) method was used after ranking the weight results in order to extract the key factors that have the maximum impact on safety in the scenarios. In this paper, we proved the practicality and feasibility of the AHP-ABC extraction method in the IVIS integration testing evaluation index and guided the development and testing of the IVIS.
Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network
Authors: Li Chao , Guo Quanjie , Shao Lei , Li Ji , Wu Han
Published: November 2022

Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but the nonlinear load increases the complexity of forecasting. In order to solve the problem of modal aliasing in historical data, and fully explore the relationship between time series characteristics in load data, this paper proposes a gated cyclic network model (SSA–GRU) based on sparrow algorithm optimization. Firstly, the complementary sets and empirical mode decomposition (EMD) are used to decompose the original data to obtain the characteristic components. The SSA–GRU combined model is used to predict the characteristic components, and finally obtain the prediction results, and complete the short-term load forecasting. Taking the real data of a company as an example, this paper compares the combined model CEEMD–SSA–GRU with EMD–SSA–GRU, SSA–GRU, and GRU models. Experimental results show that this model has better prediction effect than other models.
Research on Lightning Overvoltage Characteristics of High-Voltage Diode Rectifier
Authors: Peng Guoping , Fu Chuanming , Yang Ping , Shi Ben , Wang Hongzhan , Zeng Jun , Liu Yangbo
Published: November 2022

Failure of the high-voltage diode rectifier caused by lightning will cause huge losses. The traditional analysis of overvoltage induced by the high-voltage diode rectifier shell under lightning stroke cannot adapt to the overvoltage process caused by lightning stroke-induced conduction invading the inside of the high-voltage diode rectifier. Therefore, this paper proposes to establish a high-frequency equivalent model of the core components of the high-voltage diode rectifier, including diodes, reactors, transformers, and overhead lines. On this basis, a lightning overvoltage model of lightning-induced conduction into the high-voltage diode rectifier is built, and the transient process of diode lightning overvoltage under the constraint of reverse recovery charge is analyzed. Then, we describe the transient distribution of overvoltage in high-voltage diode rectifiers caused by lightning stroke. The transient distribution of overvoltage induced by lightning in series diodes under different diode equivalent models is analyzed by simulation. The simulation results show that the inconsistent parameters of series diodes can easily lead to diode damage due to uneven voltage distribution when lightning strikes. Therefore, this paper puts forward a scheme to reduce lightning damage, including selecting diodes with the same parameters and adding fast-melting fuses at the transformer’s secondary side and in front of the series diode bridge arm. The simulation shows that the scheme proposed in this paper can effectively prevent the high-voltage diode rectifier from being damaged by lightning strikes.
A Multi-Attribute Decision-Making Approach for the Analysis of Vendor Management Using Novel Complex Picture Fuzzy Hamy Mean Operators
Authors: Hussain Abrar , Ullah Kifayat , Pamucar Dragan , Vranješ Đorđe
Published: November 2022

Vendor management systems (VMSs) are web-based software packages that can be used to manage businesses. The performance of the VMSs can be assessed using multi-attribute decision-making (MADM) techniques under uncertain situations. This article aims to analyze and assess the performance of VMSs using MADM techniques, especially when the uncertainty is of complex nature. To achieve the goals, we aim to explore Hany mean (HM) operators in the environment of complex picture fuzzy (CPF) sets (CPFSs). We introduce CPF Hamy mean (CPFHM) and CPF weighted HM (CPFWHM) operators. Moreover, the reliability of the newly proposed HM operators is examined by taking into account the properties of idempotency, monotonicity, and boundedness. A case study of VMS is briefly discussed, and a comprehensive numerical example is carried out to assess VMSs using the MADM technique based on CPFHM operators. The sensitivity analysis and comprehensive comparative analysis of the proposed work are discussed to point out the significance of the newly established results.
Production and Testing of Butyl and Methyl Esters as New Generation Biodiesels from Fatty Wastes of the Leather Industry
Authors: Kurczyński Dariusz , Wcisło Grzegorz , Leśniak Agnieszka , Kozak Miłosław , Łagowski Piotr
Published: November 2022

Climate changes caused by the greenhouse effect make it necessary to look for new sources of energy. One of them is waste of a biological origin. They are often difficult to dispose of and such a process can be expensive. Increasingly, they are used to produce biofuels that can replace petroleum-based fuels. They are also an alternative to food-based biofuels. The aim of the work was to propose a method of using fatty waste generated in a plant dealing with tanning animal skins and to evaluate the properties of the produced biofuels. The authors decided to use this waste to produce biodiesel. A patented reactor and technology developed by one of the co-authors was used for this purpose. Two alcohols, butyl and methyl, were used to produce esters in the transesterification process. Animal fats butyl esters (AFBE) and animal fats methyl esters (AFME) have been produced. A high efficiency of the transesterification process was obtained. It amounted to 99.2 (wt.%) for AFME and 98.9 (wt.%) for AFBE. The physicochemical properties of AFBE and AFME biodiesels, diesel fuel, and mixtures of these biodiesels and diesel fuel were tested. Most of the tested properties of AFBE were more favourable than those of AFME. The produced AFBE in relation to AFME was characterized by better cetane number, heat of combustion and calorific value, density, dynamic viscosity, kinematic viscosity, and flash point. For example, the kinematic viscosity for AFBE was 3.6 mm2/s and for AFME 4.1 mm2/s. In contrast, the calorific value of AFBE biodiesel was 39.2 MJ/kg, and that of AFME biodiesel was 38.4 MJ/kg. The use of butanol from the point of view of the properties of the produced biodiesel turned out to be more advantageous in relation to methanol. Due to the fact that the production uses waste fat, the produced biodiesels can be regarded as second-generation biofuels. Producing biodiesel from waste is a way to utilize waste and is beneficial in terms of environmental protection. It can be a way to increase the share of biofuels in the energy balance of regions where large amounts of fat waste products of animal origin are available.
Dynamic Characteristic Improvement of Integrated On-Board Charger Using a Model Predictive Control
Authors: Bak Yeongsu
Published: November 2022

This paper proposes a dynamic characteristic improvement of an integrated on-board charger (OBC) using a model predictive control (MPC) method. The integrated OBC performs both battery charging and starter generator (SG) driving for engine starting in plug-in hybrid electric vehicles (PHEVs). If it performs battery charging, battery-side voltage and battery-side current are control objects which are usually controlled by using a proportional-integral (PI) controller. However, it has the disadvantage of undesirable dynamic characteristics, and gain tuning of the PI controller is necessary to properly control the voltage and current. Therefore, this paper proposes the MPC method for the dynamic characteristic improvement of integrated OBC. It can achieve not only dynamic characteristic improvement, but also robustness from the abrupt change of load impedance. By using the proposed MPC method for integrated OBC, the settling time to control the output voltage is decreased by 50% in the transient state compared to that by using the PI controller. The effectiveness of the proposed MPC method is verified by simulation and experimental results.
Passive Cooling Analysis of an Electronic Chipset Using Nanoparticles and Metal-Foam Composite PCM: An Experimental Study
Authors: Hassan Faisal , Hussain Abid , Jamil Furqan , Arshad Adeel , Ali Hafiz Muhammad
Published: November 2022

Thermal management of electronic components is critical for long-term reliability and continuous operation, as the over-heating of electronic equipment leads to decrement in performance. The novelty of the current experimental study is to investigate the passive cooling of electronic equipment, by using nano-enriched phase change material (NEPCM) with copper foam having porosity of 97%. The phase change material of PT-58 was used with graphene nanoplatelets (GNPs) and magnesium oxide (MgO) nanoparticles (NPs), having concentrations of 0.01 wt.% and 0.02 wt.%. Three power levels of 8 W, 16 W, and 24 W, with corresponding heating inputs of 0.77 kW/m2, 1.54 kW/m2 and 2.3 kW/m2, respectively, were used to simulate the heating input to heat sink for thermal characterization. According to results, at 0.77 kW/m2 heating input the maximum base temperature declined by 13.03% in 0.02 wt.% GNPs-NEPCM/copper foam case. At heating input of 1.54 kW/m2, the maximum base temperature reduction of 16% was observed in case of 0.02 wt.% GNPs-NEPCM/copper foam and 13.1% in case of 0.02 wt.% MgO-NEPCM/copper foam. Similarly, at heating input of 2.3 kW/m2, the maximum temperature of base lessened by 12.58% in case of 0.02 wt.% GNPs-NEPCM/copper foam. The highest time to reach the set point temperature of 50 °C, 60 °C, and 70 °C was in case of GNPs-NEPCM/copper foam composites, while at all power levels MgO-NEPCM/copper foam gave comparable performance to GNPs based composite. Similar trend was observed in the study of enhancement ratio in operation time. From the results, it is concluded that the copper foam incorporation in NEPCM is an effective measure to mitigate the heat sink base temperature and can provide best cooling efficiency at low and higher heating loads.
Performance Evaluation of a Solar Heat-Driven Poly-Generation System for Residential Buildings Using Various Arrangements of Heat Recovery Units
Authors: Alqaed Saeed , Fouda Ali , Elattar Hassan F. , Mustafa Jawed , Almehmadi Fahad Awjah , Refaey Hassanein A. , Alharthi Mathkar A.
Published: November 2022

Poly-generation systems are a feasible alternative to conventional energy production techniques in buildings. A poly-generation system allows for the concurrent production of electricity, heat, cold, and fresh water, with considerable advantages regarding technology, finances, energy recovery, and the environment. In the present study, the organic Rankine cycle (ORC), the humidification–dehumidification desalination system (HDH), and the desiccant cooling system (DCS) are merged with three unique solar-driven poly-generation systems (BS, IS-I, and IS-II) and numerically examined. The proposed options provide energy, space cooling, domestic heating, and potable water to buildings of small/medium scale. Using n-octane ORC working fluid, the impact of operational circumstances on system productivity and execution characteristics was considered. The findings show that (i) the suggested poly-generation systems can provide electrical power, conditioned space cooling, local heating, and fresh water, whereas keeping the conditioned area pleasant, (ii) the IS-I system achieves the best system performance among all compared arrangements (BS and IS-II); (iii) the attained extreme values of W˙net, m˙fresh, Q˙cooling, Q˙heating, and TGOR are 102.0 kW (all systems), 214.70 kg/h (IS-II), 29.940 kW (IS-II), 225.6 kW (IS-I), and 0.6303 (IS-I), respectively.
Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin
Authors: Nath Fatick , Asish Sarker Monojit , Ganta Deepak , Debi Happy Rani , Aguirre Gabriel , Aguirre Edgardo
Published: November 2022

Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes two wells for training and the remaining well was used for testing. The selected well input logs include compressional wave slowness, resistivity, gamma-ray, porosity, and bulk density to predict shear wave slowness. The results using RF and Bi-LSTM show a promising prediction of geomechanical properties for Permian Basin wells. RF algorithm performed superior for both single and grouped well prediction. The single-well prediction method using the RF algorithm provided the highest accuracy of 99.90% whereas Bi-LSTM gave 93.60%. The best accuracy for a grouped well prediction was achieved employing Bi-LSTM and RF models, i.e., 96.01% and 93.80%. The average prediction including RF and Bi-LSTM algorithms demonstrated that accuracy for single well and cross well prediction is 96% and 94% respectively with an error below 7%. These outcomes show the astonishing capability of artificial intelligence (AI) models trained to create a realistic prediction to unlock unconventional potential when datasets are inadequate. Given adequate training data, operators could leverage these efficient tools by utilizing them to examine fracture interpretations with reduced cost and time when datasets are incomplete and thus increase the hydrocarbon recovery potential.
Experimental Study on Flow Boiling Characteristics in Continuous and Segmented Microchannels with Vapor Venting Membrane
Authors: Li Shanwei , Wei Min
Published: November 2022

Flow boiling in microchannels is one of the promising techniques for heat dissipation occurred in micro devices. However, the rapid bubble growth must be suppressed, which leads to serious boiling instabilities, high pressure drop, and low heat transfer coefficient. The addition of porous hydrophobic membrane has proven an effective method to remove the vapor in-site in the literature. However, the effects of heat sink’s topological structures on the vapor venting are still a research gap. The present study experimentally investigates the influence of Polytetrafluoroethylene (PTFE) membrane on fluid flow pattern, pressure drop, vapor venting performance, and heat transfer characteristics of flow boiling in the continuous and segmented heat sinks. Results show that the vapor venting membrane can reduce the pressure drop and increase the heat transfer coefficient effectively by decreasing the exit vapor quality, especially in combination with the segmented structures. The interconnection area as a space for bubble growth and coalescence is beneficial for vapor venting due to increased vapor pressure and quantity. Following the enhanced vapor discharge, the fluctuation of pressure drop is further weakened, which is conducive for the safe operation of heat sink.
Hyperparameter Tuning of OC-SVM for Industrial Gas Turbine Anomaly Detection
Authors: Kang Hyun-Su , Choi Yun-Seok , Yu Jun-Sang , Jin Sung-Wook , Lee Jung-Min , Kim Youn-Jea
Published: November 2022

Gas turbine failure diagnosis is performed in this work based on seven types of tag data consisting of a total of 7976 data. The data consist of about 7000 normal data and less than 500 abnormal data. While normal data are easy to extract, failure data are difficult to extract. So, this study mainly is composed of normal data and a one-class support vector machine (OC-SVM) is used here, which has an advantage in classification accuracy performance. To advance the classification performance, four hyperparameter tuning (manual search, grid search, random search, Bayesian optimization) methods are applied. To analyze the performance of each technique, four evaluation indicators (accuracy, precision, recall, and F-1 score) are used. As a result, about 54.3% of the initial failure diagnosis performance is improved up to 64.88% through the advanced process in terms of accuracy.