bluuebunny/crossref_metadata_2025 huggingface dataset:
EUS-Guided Radiofrequency Ablation Therapy for Pancreatic Neoplasia
Authors: Rimbaș Mihai , Dumitru Andra-Cristiana , Tripodi Giulia , Larghi Alberto
Published: September 2024

Radiofrequency ablation (RFA) under endoscopic ultrasound (EUS) guidance has been developed and utilized over the last decade to provide the loco-regional treatment of solid and cystic pancreatic neoplastic lesions. The advantage of this approach relies on the close proximity of the EUS transducer to the target pancreatic lesion, which, coupled with the development of specifically designed RFA ablation devices, has made the procedure minimally invasive, with a clear reduction in adverse events as compared to the high morbidity of the surgical approach. EUS-RFA has been applied so far to pancreatic functional and non-functional neuroendocrine neoplasms, pancreatic ductal adenocarcinoma or metastases to the pancreas, and pancreatic neoplastic cysts. Excluding neuroendocrine tumors, for other indications, most of these procedures have been performed in patients who refused surgery or were at high surgical risk. More studies evaluating EUS-RFA in selected patients, not at surgical risk, are gradually becoming available and will pave the road to extend the indications for this therapeutic approach, also in association with other oncological therapies. The present manuscript will critically review the available evidence in the field of the EUS-guided RFA of solid and cystic pancreatic neoplasms.
Application of Digital Analysis for Assessment of Coronary Sub-Occlusions in Autopsy Pathology: It Is Time to Move beyond Histology Alone
Authors: D’Abbronzo Giuseppe , Franco Renato , Salzillo Cecilia , Campobasso Carlo Pietro , Municinò Maurizio , Feola Alessandro , Ronchi Andrea
Published: September 2024

Background: Coronary artery disease (CAD) underlies most cases of myocardial infarction (MI), causing or at least contributing to oxygen supply–demand mismatch and myocardial injury, so a careful and reliable evaluation of the main coronary arteries and large branches is a key moment of autopsy in order to establish the cause of death. The aim of this study is to evaluate the application of digital image analysis in the assessment of coronary artery sub-occlusions. Methods: A total of 50 coronary sections sampled during 11 consecutive autopsies, regardless of the cause of death, were analyzed. The ideal lumen and the percentage of the residual lumen were evaluated by digital pathology using QuPath v 4.3 and by an expert pathologist. The evaluations performed were compared using Lin’s concordance correlation coefficient. Results: The Lin agreement index between the two evaluation methods for all measurements showed an excellent agreement rate [0.923, with confidence interval (0.866, 0.956)]. However, in the case of critical stenosis, from 60% to 80% and from 65% to 75%, the Lin agreement index between the two evaluation methods was, respectively, 0.798 [0.603, 0.904], corresponding to good agreement, and 0.516 [0.071, 0.725], corresponding to slight agreement. The digital system has superior performance in cases where lumen occlusion falls between 60% and 80% and provides an objective assessment of the residual lumen area. Conclusions: According to the widespread availability and ease of use of these technologies, we suggest that image analysis should be considered a routine tool and established as the diagnostic gold standard in this field.
Thioredoxin System Protein Expression in Carcinomas of the Pancreas, Distal Bile Duct, and Ampulla in the United Kingdom
Authors: Al-Hadyan Khaled S. , Storr Sarah J. , Zaitoun Abed M. , Lobo Dileep N. , Martin Stewart G.
Published: September 2024

Background: Poor survival outcomes in periampullary cancer highlight the need for improvement in biomarkers and the development of novel therapies. Redox proteins, including the thioredoxin system, play vital roles in cellular antioxidant systems. Methods: In this retrospective study, thioredoxin (Trx), thioredoxin-interacting protein (TxNIP), and thioredoxin reductase (TrxR) protein expression was assessed in 85 patients with pancreatic ductal adenocarcinoma (PDAC) and 145 patients with distal bile duct or ampullary carcinoma using conventional immunohistochemistry. Results: In patients with PDAC, high cytoplasmic TrxR expression was significantly associated with lymph node metastasis (p = 0.033). High cytoplasmic and nuclear Trx expression was significantly associated with better overall survival (p = 0.018 and p = 0.006, respectively), and nuclear Trx expression remained significant in multivariate Cox regression analysis (p < 0.0001). In distal bile duct and ampullary carcinomas, high nuclear TrxR expression was associated with vascular (p = 0.001) and perineural (p = 0.021) invasion, and low cytoplasmic TxNIP expression was associated with perineural invasion (p = 0.025). High cytoplasmic TxNIP expression was significantly associated with better overall survival (p = 0.0002), which remained significant in multivariate Cox regression analysis (p = 0.013). Conclusions: These findings demonstrate the prognostic importance of Trx system protein expression in periampullary cancers.
Assessing the Impact of IOS Scanning Accuracy on Additively Manufactured Occlusal Splints
Authors: Anitua Eduardo , Lazcano Asier , Eguia Asier , Alkhraisat Mohammad Hamdan
Published: September 2024

Introduction: Digital workflow and intraoral scanners (IOSs) are used to clinically obtain data for a wide range of applications in restorative dentistry. The study aimed to compare two different IOSs with inexperienced users in the digital workflow of oral split manufacturing. Material and Methods: Anonymous stone models of upper and lower dentate patients were used. Both models were scanned with a desktop 3D scanner 3Shape D2000 to obtain the reference models (STLR). Ten inexperienced operators scanned each model three times with each IOS system (3Shape TRIOS 3 and Carestream CS 3800). Finally, 20 intraoral scanners were randomly chosen from the obtained dataset (10 per IOS system) to design and manufacture 20 nightguards. All the nightguards were scanned. Trueness and precision were calculated and compared between the two IOS systems. Results: All the mean errors both for trueness and precision were below 40 µm, more than acceptable for the design and manufacturing of intraoral devices such as nightguards. All the mean errors (except one) for trueness between the inner part of the nightguards and the upper control model were below 100 µm, less than a printed layer height. For inexperienced operators, both IOSs are suitable for a digital workflow of manufacturing occlusal splints.
Multi-UAV Assisted Air–Ground Collaborative MEC System: DRL-Based Joint Task Offloading and Resource Allocation and 3D UAV Trajectory Optimization
Authors: Wang Mingjun , Li Ruishan , Jing Feng , Gao Mei
Published: September 2024

In disaster-stricken areas that were severely damaged by earthquakes, typhoons, floods, mudslides, and the like, employing unmanned aerial vehicles (UAVs) as airborne base stations for mobile edge computing (MEC) constitutes an effective solution. Concerning this, we investigate a 3D air–ground collaborative MEC scenario facilitated by multi-UAV for multiple ground devices (GDs). Specifically, we first design a 3D multi-UAV-assisted air–ground cooperative MEC system, and construct system communication, computation, and UAV flight energy consumption models. Subsequently, a cooperative resource optimization (CRO) problem is proposed by jointly optimizing task offloading, UAV flight trajectories, and edge computing resource allocation to minimize the total energy consumption of the system. Further, the CRO problem is decoupled into two sub-problems. Among them, the MATD3 deep reinforcement learning algorithm is utilized to jointly optimize the offloading decisions of GDs and the flight trajectories of UAVs; subsequently, the optimal resource allocation scheme at the edge is demonstrated through the derivation of KKT conditions. Finally, the simulation results show that the algorithm has good convergence compared with other algorithms and can effectively reduce the system energy consumption.
An Elite Wolf Pack Algorithm Based on the Probability Threshold for a Multi-UAV Cooperative Reconnaissance Mission
Authors: Zhang Hanrui , Lv Xiao , Ma Chao , Cui Liangzhong
Published: September 2024

In the task assignment problem of multi-UAV collaborative reconnaissance, existing algorithms have issues with inadequate solution accuracy, specifically manifested as large spatial spans and knots of routes in the task execution of UAVs. To address the above challenges, this paper presents a multi-UAV task assignment model under complex conditions (MTAMCC). To efficiently solve this model, this paper proposes an elite wolf pack algorithm based on probability threshold (EWPA-PT). The EWPA-PT algorithm combines the wandering behavior in the traditional wolf pack algorithm with the genetic algorithm. It introduces an ordered permutation problem to calculate the adaptive wandering times of the detective wolves in a specific direction. During the calling phase of the algorithm, the fierce wolves in the wolf pack randomly learn the task assignment results of the head wolf. The sieging behavior introduces the Metropolis criterion from the simulated annealing algorithm to replace the distance threshold in traditional wolf pack algorithms with a probability threshold, which dynamically changes during the iteration process. The wolf pack updating mechanism leverages the task assignment experience of the elite group to reconstruct individual wolves, thereby improving the individual reconstruction’s efficiency. Experiments demonstrate that the EWPA-PT algorithm significantly improves solution accuracy compared to typical methods in recent years.
Preanalytic Integrity of Blood Samples in Uncrewed Aerial Vehicle (UAV) Medical Transport: A Comparative Study
Authors: Stierlin Noel , Loertscher Fabian , Renz Harald , Risch Lorenz , Risch Martin
Published: September 2024

The integration of unmanned aerial vehicles or uncrewed aerial vehicles (UAVs)—commonly known as drones—into medical logistics offers transformative potential for the transportation of sensitive medical materials, such as blood samples. Traditional car transportation is often hindered by traffic delays, road conditions, and geographic barriers, which can compromise timely delivery. This study provides a comprehensive analysis comparing high-speed drone transportation with traditional car transportation. Blood samples, including EDTA whole blood, serum, lithium-heparin plasma, and citrate plasma tubes, were transported via both methods across temperatures ranging from 4 to 20 degrees Celsius. The integrity of the samples was assessed using a wide array of analytes and statistical analyses, including Passing–Bablok regression and Bland–Altman plots. The results demonstrated that drone transportation maintains blood sample integrity comparable to traditional car transportation. For serum samples, the correlation coefficients (r) ranged from 0.830 to 1.000, and the slopes varied from 0.913 to 1.111, with minor discrepancies in five analytes (total bilirubin, calcium, ferritin, potassium, and sodium). Similar patterns were observed for EDTA, lithium-heparin, and citrate samples, indicating no significant differences between transportation methods. Conclusions: These findings highlight the potential of drones to enhance the efficiency and reliability of medical sample transport, particularly in scenarios requiring rapid and reliable delivery. Drones could significantly improve logistical operations in healthcare by overcoming traditional transportation challenges.
Parallel Currencies under Free Floating Exchange Rates: A Model Setting Out the Conditions for Stable Currency Competition
Authors: Castañeda Juan E. , Damrich Sebastian , Schwartz Pedro
Published: September 2024

We use a theoretical model to set up the conditions for a country to attain monetary stability by allowing for two freely tradable currencies to circulate in parallel. For this parallel system to function properly, confidence in the good behavior of the monetary authorities in charge of the two currencies is key. Our model shows how a floating exchange rate between the two can keep the issuers of the local currency in check. The results from our model show the conditions under which a parallel currency system disciplines the issuers of the currencies and thus maintains their purchasing power. In non-volatile economies, it also discourages governments (or private issuers) from inflating one of the currencies as a means to raise seigniorage, as this policy results in the displacement of the currency from the market. When foreign payments shortfall—such as in Greece and Cyprus during the ‘euro crisis’ in the mid-2010s, or intractable hyperinflation—leave the country without a medium of exchange, our model shows how currency choice can restore monetary circulation and offer a path to achieving and maintaining monetary stability.
The Social Motivational Orientations in Sport Scale (SMOSS): Validation for Portuguese Physical Education Students
Authors: Bessa Cristiana , Mesquita da Silva Sara , Farias Cláudio , Mesquita Isabel
Published: September 2024

Social goals are increasingly seen as motivational factors for youth sports participation and can strongly motivate participation and engagement, not only in structured sports contexts but also in physical education (PE), given the opportunities for social interaction with peers and the presence of skills like communication, cooperation, and competition within groups. The Social Motivational Orientations in Sport Scale (SMOSS) measures three types of social goals in sports participation: affiliation, status, and recognition. The current study aimed to adapt and validate the SMOSS for the Portuguese context, using a sample of 460 PE students (14–19 years old, 58.9% female). The confirmatory factor analysis results supported the three-factor model (affiliation, recognition, and status), after excluding two items. This adapted 13-item SMOSS demonstrated invariance across genders and showed good internal consistency across its three dimensions. It also exhibited convergent, discriminant, and concurrent validity. These findings indicate that the Portuguese version of the SMOSS is a valid and reliable instrument. It is now well suited for use by schools, teachers, and psychologists to effectively assess PE students’ social goals. Additionally, the SMOSS can assist in evaluating intervention programs aimed at enhancing students’ social motivation, thus contributing to more effective educational and developmental strategies in PE settings.
Transitioning towards Tomorrow’s Workforce: Education 5.0 in the Landscape of Society 5.0: A Systematic Literature Review
Authors: Shahidi Hamedani Sharareh , Aslam Sarfraz , Mundher Oraibi Bha Aldan , Wah Yap Bee , Shahidi Hamedani Shervin
Published: September 2024

Globalization and technology are presently impacting every aspect of life, with digital technologies helping to set the trend. Human-centricity and the ethical use of technology are integral components of Society 5.0, which emphasizes quality of life, productivity, social responsibility, and sustainability. The purpose of this study was to develop a deeper understanding of how Higher Education 5.0 empowers the future workforce in the landscape of Society 5.0. A systematic literature review was conducted using 18 full-text research papers from the Scopus database. It shows that the number of studies in this area is limited. The results revealed the critical role of education in cultivating the digital skills necessary for upcoming professionals. In order to transition from higher education to the workplace, fresh graduates need academic and practical readiness to meet the demands of a future-ready workforce. The contribution of the future workforce will also be crucial in building a harmonious human-centric society that will use technology to improve the quality of life towards sustainable living as envisioned by the United Nations’ SDGs. Additionally, we identify the current gaps in the research on Education 5.0 in the landscape of Society 5.0 and subsequently provide recommendations for the future.
Exploring the Literacy Experiences of Preservice Early Childhood Teachers: Social and Contextual Influences, and Implications for Teacher Education
Authors: Chen Jennifer J. , Ren Yonggang , O’Neill Shirley
Published: September 2024

Considering that early literacy (reading and writing) is critical for later literacy and academic success, this study investigated the social and contextual influences on the early and current literacy experiences of 70 preservice early childhood teachers in the United States through a questionnaire. Analyses of both quantitative and qualitative data revealed eight key findings: First, the strongest influence on these individuals’ early literacy development was parents, followed by teachers. Second, their current literacy experiences were shaped primarily by professors/academic work followed by self-motivation. Third, nearly half of the preservice teachers regarded themselves as average readers and writers. While the majority of them enjoyed reading and more than half also enjoyed writing, those who lacked such enjoyment provided various reasons, especially citing insufficient intrinsic motivation, limited time for reading, and inadequate writing skills or ideas. Fourth, most of them prioritized their literacy time to fulfill academic requirements. Fifth, most devoted a limited amount of time to literacy activities. Sixth, there was a relatively strong positive correlation between reading fluency and writing fluency. Seventh, a positive correlation was found between years in college and writing fluency. Eighth, there was a negative correlation between grade point average and time spent writing per week.
Path Planning in Complex Environments Using Attention-Based Deep Deterministic Policy Gradient
Authors: Chen Jinlong , Jiang Yun , Pan Hongren , Yang Minghao
Published: September 2024

The traditional Deep Deterministic Policy Gradient (DDPG) algorithm frequently exhibits a notable reduction in success rate when transferred to new environments after being trained in complex simulation settings. To address these issues, this paper adopts a Multi-Environment (Multi-Env) parallel training approach and integrates Multi-Head Attention (MHA) and Prioritized Experience Replay (PER) into the DDPG framework, optimizing the reward function to form the MAP-DDPG algorithm. This approach enhances the algorithm’s generalization capability and execution efficiency. Through comparative training and testing of the DDPG and MAP-DDPG algorithms in both simulation and real-world environments, the experimental results demonstrate that MAP-DDPG significantly improves generalization and execution efficiency over the DDPG algorithm. Specifically, in simulation environment tests, the MAP-DDPG algorithm achieved an average 30% increase in success rate and reduced the average time to reach the target point by 23.7 s compared to the DDPG algorithm. These results indicate that the MAP-DDPG algorithm significantly enhances path planning generalization and execution efficiency, providing a more effective solution for path planning in complex environments.
Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency
Authors: Arévalo Paul , Ochoa-Correa Danny , Villa-Ávila Edisson
Published: September 2024

Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.
Multimodal Emotion Recognition Based on Facial Expressions, Speech, and Body Gestures
Authors: Yan Jingjie , Li Peiyuan , Du Chengkun , Zhu Kang , Zhou Xiaoyang , Liu Ying , Wei Jinsheng
Published: September 2024

The research of multimodal emotion recognition based on facial expressions, speech, and body gestures is crucial for oncoming intelligent human–computer interfaces. However, it is a very difficult task and has seldom been researched in this combination in the past years. Based on the GEMEP and Polish databases, this contribution focuses on trimodal emotion recognition from facial expressions, speech, and body gestures, including feature extraction, feature fusion, and multimodal classification of the three modalities. In particular, for feature fusion, two novel algorithms including supervised least squares multiset kernel canonical correlation analysis (SLSMKCCA) and sparse supervised least squares multiset kernel canonical correlation analysis (SSLSMKCCA) are presented, respectively, to carry out efficient facial expression, speech, and body gesture feature fusion. Different from the traditional multiset kernel canonical correlation analysis (MKCCA) algorithms, our SLSKMCCA algorithm is a supervised version and is based on the least squares form. The SSLSKMCCA algorithm is implemented by the combination of SLSMKCCA and a sparse item (L1 Norm). Moreover, two effective solving algorithms for SLSMKCCA and SSLSMKCCA are presented in addition, which use the alternated least squares and augmented Lagrangian multiplier methods, respectively. The extensive experimental results on the popular public GEMEP and Polish databases show that the recognition rate of multimodal emotion recognition is superior to bimodal and monomodal emotion recognition on average, and our presented SLSMKCCA and SSLSMKCCA fusion methods both obtain very high recognition rates, especially for the SSLSMKCCA fusion method.
Cross-Task Rumor Detection: Model Optimization Based on Model Transfer Learning and Graph Convolutional Neural Networks (GCNs)
Authors: Jiang Wen , Yan Facheng , Ren Kelan , Zhang Xiong , Wei Bin , Zhang Mingshu
Published: September 2024

With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent need. Given the challenges of rumor detection tasks, including data scarcity, feature complexity, and difficulties in cross-task knowledge transfer, this paper proposes a BERT–GCN–Transfer Learning model, an integrated rumor detection model that combines BERT (Bidirectional Encoder Representations from Transformers), Graph Convolutional Networks (GCNs), and transfer learning techniques. By harnessing BERT’s robust text representation capabilities, the GCN’s feature extraction prowess on graph-structured data, and the advantage of transfer learning in cross-task knowledge sharing, the model achieves effective rumor detection on social media platforms. Experimental results indicate that this model achieves accuracies of 0.878 and 0.892 on the Twitter15 and Twitter16 datasets, respectively, significantly enhancing the accuracy of rumor detection compared to baseline models. Moreover, it greatly improves the efficiency of model training.
Memory Grouping for the Built-In Self-Test of Three-Dimensional Integrated Circuits
Authors: Huang Shou-Yi , Huang Shih-Hsu
Published: September 2024

As the complexity of circuit design continues to grow, the development of three-dimensional (3D) integrated circuit (IC) technology has become increasingly vital. While 3D ICs offer faster signal transmission speeds and lower power consumption compared with traditional two-dimensional (2D) ICs, they also pose greater challenges in manufacturing and testing. In memory testing, traditional 2D ICs require only a single testing stage, whereas 3D ICs involve both prebond and postbond testing stages, complicating the memory grouping process. Most existing memory grouping algorithms focus on testing 2D ICs. While one study addressed the memory grouping problem for 3D IC testing, it did not consider the impact of test scheduling. In contrast, our approach incorporates test scheduling into the memory grouping process, resulting in a reduction in BIST area overhead. Experimental results demonstrate that our method reduces built-in self-test circuit area overhead by an average of 10.28% compared with those in the existing literature.
YOLOv8s-DDA: An Improved Small Traffic Sign Detection Algorithm Based on YOLOv8s
Authors: Niu Meiqi , Chen Yajun , Li Jianying , Qiu Xiaoyang , Cai Wenhao
Published: September 2024

In the realm of traffic sign detection, challenges arise due to the small size of objects, complex scenes, varying scales of signs, and dispersed objects. To address these problems, this paper proposes a small object detection algorithm, YOLOv8s-DDA, for traffic signs based on an improved YOLOv8s. Specifically, the C2f-DWR-DRB module is introduced, which utilizes an efficient two-step method to capture multi-scale contextual information and employs a dilated re-parameterization block to enhance feature extraction quality while maintaining computational efficiency. The neck network is improved by incorporating ideas from ASF-YOLO, enabling the fusion of multi-scale object features and significantly boosting small object detection capabilities. Finally, the original IoU is replaced with Wise-IoU to further improve detection accuracy. On the TT100K dataset, the YOLOv8s-DDA algorithm achieves mAP@0.5 of 87.2%, mAP@0.5:0.95 of 68.3%, precision of 85.2%, and recall of 80.0%, with a 5.4% reduction in parameter count. The effectiveness of this algorithm is also validated on the publicly available Chinese traffic sign detection dataset, CCTSDB2021.
Municipal Solid Waste as a Renewable Energy Source: Advances in Thermochemical Conversion Technologies and Environmental Impacts
Authors: Kasiński Sławomir , Dębowski Marcin
Published: September 2024

This review examines the potential of municipal solid waste (MSW) as a renewable energy source, focusing on recent advances in thermochemical conversion technologies and their environmental impacts. The exponential growth of urban populations has led to a surge in MSW, necessitating sustainable waste management solutions. Traditional disposal methods, such as landfilling and incineration, have significant environmental drawbacks. However, advancements in waste-to-energy (WtE) technologies, including incineration, pyrolysis, and gasification, offer promising alternatives for energy recovery and resource utilization. This review explores the composition of MSW, its classification as a renewable resource, and the thermochemical conversion technologies that transform waste into energy. The environmental impacts of these technologies, particularly emissions and air quality concerns, are critically analyzed. The review highlights the evolving regulatory landscape and the implementation of advanced emission reduction systems. The findings underscore the importance of integrating innovative waste management strategies to promote a circular economy and achieve sustainable development goals.
Offshore Wind Power Growth and Industrial Development in Emerging Markets
Authors: González Mario , Santiso Andressa , Jones Dylan , Akbari Negar , Vasconcelos Rafael , Melo David
Published: September 2024

Offshore wind has developed significantly over the past decade, and promising new markets are emerging, such as Brazil, South Africa, India, Poland, and Turkey. As logistic transport activities increase complexities, developing regional supply chains can help to reduce costs and enhance the sector’s competitiveness. This article proposes a framework for the industrial development of the offshore wind supply chain in new markets. This study is grounded in a systematic literature review and is validated through a multi-case study, identifying key variables and factors influencing industrial growth. Adopting a process-based approach, factors and variables were modeled into a framework, encompassing the following four phases: (1) demand assessment of a new sector, (2) sectorial and industrial planning, (3) industrial development and maturity, and (4) sectorial and industrial renewal or decline. Each phase brings together a group of policies. Our findings show the policies’ interrelations. These results complement the few studies that have examined the industrial development process, providing a clear guide as to the process for the development of the offshore wind industry in specific regions. Thus, the framework provides elements that contribute as a valuable tool to the debate, structuring, and development of public policies for the industrial development of a new sector.
Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables
Authors: Shahcheraghian Amir , Ilinca Adrian
Published: September 2024

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.
Research on Temperature-Rise Characteristics of Motor Based on Simplified Lumped-Parameter Thermal Network Model
Authors: Liang Jinguang , Liang Kaijie , Shao Zhengri , Niu Yihong , Song Xiaobei , Sun Ping , Feng Jincheng
Published: September 2024

The thermal management of a driving motor is related to the performance and economy of the vehicle. The lumped-parameter thermal network (LPTN) method can provide a model basis for motor temperature-rise control and effectively shorten the development cycle of motor thermal performance design. In this study, an 80 kw water-cooled permanent magnet synchronous motor was used and the accurate thermal model of a motor was built using the LPTN. On the basis of the accurate thermal model, the simplified thermal model of a motor was obtained by reducing the complexity of the model. The temperature-rise test of the end winding and magnetic steel of the motor was carried out under some working conditions. The test conditions were selected according to continuous external characteristics and operating characteristics of the motor. Compared with the experimental results, the temperature-rise error of the two thermal models was less than 5%. The temperature-rise error of the simplified thermal model was less than 3% compared with the accurate thermal model. Therefore, the simplified thermal model can be used to quickly predict the temperature rise of the motor.
Preliminary Techno-Economic Study of Optimized Floating Offshore Wind Turbine Substructure
Authors: Ojo Adebayo , Collu Maurizio , Coraddu Andrea
Published: September 2024

Floating offshore wind turbines (FOWTs) are still in the pre-commercial stage and, although different concepts of FOWTs are being developed, cost is a main barrier to commercializing the FOWT system. This article aims to use a shape parameterization technique within a multidisciplinary design analysis and optimization framework to alter the shape of the FOWT platform with the objective of reducing cost. This cost reduction is then implemented in 30 MW and 60 MW floating offshore wind farms (FOWFs) designed based on the static pitch angle constraints (5 degrees, 7 degrees and 10 degrees) used within the optimization framework to estimate the reduction in the levelized cost of energy (LCOE) in comparison to a FOWT platform without any shape alteration–OC3 spar platform design. Key findings in this work show that an optimal shape alteration of the platform design that satisfies the design requirements, objectives and constraints set within the optimization framework contributes to significantly reducing the CAPEX cost and the LCOE in the floating wind farms considered. This is due to the reduction in the required platform mass for hydrostatic stability when the static pitch angle is increased. The FOWF designed with a 10 degree static pitch angle constraint provided the lowest LCOE value, while the FOWF designed with a 5 degree static pitch angle constraint provided the largest LCOE value, barring the FOWT designed with the OC3 dimension, which is considered to have no inclination.
Technical Analysis of the Possibility of Burning Hydrogen in Furnaces of the Metallurgical Sector
Authors: Gołdasz Andrzej , Sztekler Karol , Mika Łukasz
Published: September 2024

This article analyses the possibility of using hydrogen as fuel in furnaces used in the metallurgical industry. The research was conducted for a selected continuous furnace. For this purpose, based on actual measurements, a heat balance of the furnace was prepared to determine its energy indicators. These values were used to verify the developed numerical model in IPSEpro 7.0 software. Numerical calculations were performed for three variants: pure natural gas; 30% hydrogen, 70% natural gas; and 100% hydrogen. The determined values of gas and combustion air streams allowed for achieving the assumed charge temperature in the heating technology. Calculations of the impact of the excess combustion air ratio on process parameters were also carried out. It was found that no changes are required in the exhaust gas removal system, but verification of the fan supplying air to cool the exhaust gases before the recuperator is necessary. The amount of hydrogen required to fuel the continuous furnace also increases significantly (nearly threefold), which may also affect operating costs. At the same time, the emission of carbon dioxide into the atmosphere is completely reduced, which may be an important criterion when considering modernization options for heating furnaces in the metallurgical industry.
Predicting UK Domestic Electricity and Gas Consumption between Differing Demographic Household Compositions
Authors: Sewell Gregory , Gauthier Stephanie , James Patrick , Stein Sebastian
Published: September 2024

This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap.
Overview of the Recent Findings in the Perovskite-Type Structures Used for Solar Cells and Hydrogen Storage
Authors: Kuo Meng-Hsueh , Neykova Neda , Stachiv Ivo
Published: September 2024

Perovskite-type structures have unique crystal architecture and chemical composition, which make them highly attractive for the design of solar cells. For instance, perovskite-based solar cells have been shown to perform better than silicon cells, capable of adsorbing a wide range of light wavelengths, and they can be relatively easily manufactured at a low cost. Importantly, the perovskite-based structures can also adsorb a significant amount of hydrogen atoms into their own structure; therefore, perovskite holds promise in the solid-state storage of hydrogen. It is widely expected by the scientific community that the controlled adsorption/desorption of the hydrogen atoms into/from perovskite-based structures can help to overcome the main hydrogen storage issues such as a low volumetric density and the safety concerns (i.e., the hydrogen embrittlement affects strongly the mechanical properties of metals and, as such, the storage or transport of the gaseous hydrogen in the vessels is, especially for large vessel volumes, challenging). The purpose of this review is to provide an updated overview of the recent results and studies focusing on the perovskite materials used for both solar cells and hydrogen storage applications. Particular attention is given to (i) the preparation and the achievable efficiency and stability of the perovskite solar cells and (ii) the structural, thermodynamic, and storage properties of perovskite hydrides and oxides. We show that the perovskite materials can not only reach the efficiency above current Si-based solar cells but also, due to good stability and reasonable price, can be preferable in the solid-state storage of hydrogen. Then, the future trends and directions in the research and application of perovskite in both solar cells and hydrogen storage are also highlighted.
Engineering Microgrids Amid the Evolving Electrical Distribution System
Authors: Sharma Smriti , O’Donnell John , Su Wencong , Mueller Richard , Roald Line , Rehman Khurram , Bernstein Andrey
Published: September 2024

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system’s resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries.
Research on the Influence of Matrix Shape on Percolation Threshold Values for Current Flow Conducted Using the Monte Carlo Simulation Method
Authors: Bondariev Vitalii , Okal Pawel , Rogalski Przemyslaw , Pogrebnjak Alexander , Zukowski Pawel
Published: September 2024

In this study, in order to determine the effect of matrices’ shape on the percolation threshold values, computer simulations were performed using the Monte Carlo method for a 200 × 200 square-shaped matrix and rectangular matrices containing the same number of nodes as the square matrix. Based on the simulations, the average values of the percolation thresholds and standard deviations for the current flow along and across the matrices were determined. It was determined that for a square-shaped matrix, the average values of the percolation thresholds in both directions of the current flow were the same. Extending the rectangular matrix while reducing its height causes the average value of the percolation threshold in the direction of the current flow along the matrix to increase from 0.592740 to 0.759847, while in the transverse direction, it decreases from 0.592664 to 0.403614. The values of the classical asymmetry coefficients of the probability distributions of the percolation thresholds for both directions of the current flow were determined. Histograms of the probability distributions of the percolation threshold values for a square-shaped matrix and rectangular matrices were made and compared with the normal distributions. It was found that the occurrence of two percolation thresholds in rectangular layers should be considered when analyzing the electrical conductivity measurements of nanocomposite thin films.
Optimization of Pyrolysis Parameters by Design of Experiment for the Production of Biochar from Sewage Sludge
Authors: Cedrone Giacomo , Bracciale Maria Paola , Cafiero Lorenzo , Langone Michela , Mattioli Davide , Scarsella Marco , Tuffi Riccardo
Published: September 2024

Sewage sludge management is a key concern in today’s world. Improper disposal can lead to various environmental issues including air, water and soil pollution. Among the available technologies, thermal treatments, particularly pyrolysis, are gaining interest for their ability to reduce sewage sludge volume and to recover materials and energy from it. This study explored the influence of some relevant parameters in the thermal pyrolysis process. The design of experiment, named central composite design, was accounted to optimize temperature, heating rate and residence time in order to maximize the biochar yield and its CO2 adsorption capacity. A two-factor interaction model provided a satisfactory interpretation of the results. Within the studied ranges, maximum values of 47.8 wt% and 0.514 mol CO2/kg were obtained for the yield and CO2 adsorption capacity, respectively. Two significant experiments were repeated in a different pyrolysis system highlighting how other factors (e.g., reactor geometry, granulometry, etc.) can influence the quantity and the quality of produced biochar. The biochar obtained under the best pyrolysis conditions was characterized by a surface area of 124 m2/g and an ash content of 61 wt%. Lastly, the theoretical energy balance showed that the drying process is the main energy-intensive step in the pyrolysis of sewage sludge.
A Review of Chitosan as a Coagulant of Health-Related Microorganisms in Water and Wastewater
Authors: Coleman Collin Knox , Oza Hemali H. , Bailey Emily S. , Sobsey Mark D.
Published: September 2024

The coagulation and flocculation properties of chitosan, an organic biopolymer derived from chitin, have been researched as an alternative to synthetic polymers and inorganic metal salt coagulants currently used in water and wastewater treatment. In an effort to encourage further research into the practical uses of chitosan as green chemistry in water and wastewater treatment and to promote the efficacious removal of microbial contaminants in drinking and wastewater, we have summarized the current state of research pertaining to the treatment of microorganisms in water and wastewater. A search of PubMed revealed 720 possible titles and abstracts, of which 44 full-text articles were identified as matching the eligibility criteria for inclusion in this systematic review. Results are presented based on the type of water matrix treated (i.e., drinking water, wastewater, and recreational waters) and a summary table providing details on the types and forms of chitosan utilized and the treatment mechanisms and processes described in the study. We find chitosan to be an effective coagulant, flocculant, and adsorbent for removing microbes from water and wastewater; some modified forms of chitosan can inactivate microbes and achieve disinfection, such as those containing metals like silver and antimicrobial chemicals like quaternary ammonium compounds or other strong oxidants, and use with filtration or electrochemical processes can achieve extensive reductions in microbes to meet performance targets of the World Health Organization.
Ameliorative Effects of Camel Milk and Fermented Camel Milk on Acute Alcoholic Liver Injury
Authors: Zhu Chunxia , Sun Wancheng , Luo Yihao
Published: September 2024

Probiotics, which are prevalent in camel milk (CM) and naturally fermented camel milk (FCM), can regulate the intestinal ecological structure to alleviate alcoholic liver disease (ALD) through the “gut–liver” axis. The protective effects and mechanisms of CM and FCM interventions on alcohol-induced acute liver injury were investigated by combining the behavior observed in rats following alcohol exposure. The results revealed that CM and FCM effectively controlled the increased levels of alcohol-induced ALT, AST, TG, MDA, and proinflammatory cytokines. Alcohol-induced oxidative depletion of hepatic CAT, GPX, GSH, and ALDH was reversed, diminishing lipid accumulation, ameliorating severe pathological damage, increasing antioxidant capabilities, and postponing oxidative stress. Additionally, the abundance of the phylum Bacteroidota (which reduces the F/B ratio); the family Prevotellaceae; the genera Clostridia_vadinBB60_group, parabacteroides, Alloprevotella, and Prevotellaceae_UC_G001; the gastrointestinal barrier; and the microbiological environment was increased. The steroid hormone biosynthesis pathway was altered to reduce alcohol-induced predominant steroid metabolites such as 17-hydroxyprogesterone, cortisol, and dehydroepiandrosterone, preventing alcoholic liver impairment. Taken together, CM could be a therapeutic dietary supplement for preventing alcoholic liver injury by ameliorating the intestinal ecology and hepatic metabolism.