bluuebunny/crossref_metadata_2025 huggingface dataset:
Few-Shot Learning in Wi-Fi-Based Indoor Positioning
Authors: Xie Feng , Lam Soi Hoi , Xie Ming , Wang Cheng
Published: September 2024

This paper explores the use of few-shot learning in Wi-Fi-based indoor positioning, utilizing convolutional neural networks (CNNs) combined with meta-learning techniques to enhance the accuracy and efficiency of positioning systems. The focus is on addressing the challenge of limited labeled data, a prevalent issue in extensive indoor environments. The study explores various scenarios, comparing the performance of the base CNN and meta-learning models. The meta-learning approach involves few-shot learning tasks, such as three-way N-shot, five-way N-shot, etc., to enhance the model’s ability to generalize from limited data. The experiments were conducted across various scenarios, evaluating the performance of the models with different numbers of samples per class (K) after filtering by cosine similarity (FCS) during both the stages of data preprocessing and meta-learning. The scenarios included both base classes and novel classes, with and without meta-learning. The results indicated that the base CNN model achieved varying accuracy levels depending on the scenario and the number of samples per class retained after FCS. Meta-learning performed acceptably in scenarios with fewer samples, which are the distinct datasets pertaining to novel classes. With 20 samples per class, the base CNN achieved an accuracy of 0.80 during the pre-training stage, while meta-learning (three-way one-shot) achieved an accuracy of 0.78 on a new small dataset with novel classes.
An Evaluation of the Technologies Used for the Real-Time Monitoring of the Risk of Falling from Height in Construction—Systematic Review
Authors: Pereira Filipa , González García María de las Nieves , Poças Martins João
Published: September 2024

The construction industry has the highest number of fatal accidents compared to other industries. However, manual safety compliance monitoring is complex and difficult for safety engineers, and more automated solutions need to be found. The main research objective was to review the state of the art of real-time monitoring technologies used to assess the risk of falling from height in the construction sector. A systematic review is proposed in order to summarise the technologies used for real-time monitoring in the construction sector, following the PRISMA methodology. Only studies that assessed the risk of falling in real time were selected. From an initial set of 1289 articles, 40 were classified as strictly relevant to addressing the research questions. Various technologies that use artificial intelligence have been designed to monitor workers in real time and to send alerts to workers at any time in the event of a risk situation, thus preventing accidents. This study showed that new technologies are being introduced to predict the risk of a fall in real time, changing the approach from reactive to proactive and allowing this monitoring to improve workplace surveillance and safety. Further research is needed to develop effective systems that are easy for people to use without compromising productivity.
Evaluation of Dynamic Mechanical Properties of Steel-Fiber-Reinforced Concrete Subjected to Freeze–Thaw Cycles
Authors: Wang Ruijun , Tian Nan , Liu Jun , Jin Ruibao , Liang Gang , Li Yan , Hu Jing , Zhou Hekuan , Jia Yaofei , Liu Yanxiong
Published: September 2024

This study investigates the structural characteristics of SFRC with different amounts of steel fibers following exposure to freeze–thaw cycles, while taking into account various levels of confinement pressure and rates of deformation. The focus of the research is to examine the dynamic mechanical properties of SFRC exposed to freeze–thaw cycles. The inclusion of steel fibers improves the strength of concrete during freeze–thaw cycles, with 1% steel fiber content being the most effective. Strain rate and confining pressure significantly impact the strength and failure mode of SFRC. The strength of concrete increases linearly with the strain rate. With no confining pressure, the cracks in the concrete specimen align with the direction of the applied stress. And with confining pressure, concrete exhibits diagonal shear failure. Microstructural analysis results from scanning electron microscopy are consistent with the macroscopic properties.
Field Study on Winter Thermal Comfort of Occupants of Nursing Homes in Shandong Province, China
Authors: Sun Ninghan , Ding Xin , Bi Jialin , Cui Yanqiu
Published: September 2024

The increasing population aging in China has led to a growing demand for nursing homes. The indoor thermal comfort of nursing homes affects the occupants’ quality of life, building energy consumption, and carbon emissions. This study used thermal comfort questionnaires, environment tests, and physiological parameter tests to conduct a field survey of 954 occupants (including the elderly and the adult staff) in nursing homes in Shandong Province, China, and analyzed the thermal comfort of occupants. Results showed that in Shandong Province, there was a significant difference in thermal sensation between the elderly and adults under the same conditions. The neutral temperatures for the elderly and adults were 21.7 and 20.5 °C, the comfort temperature ranges were 19.4–24.0 °C and 18.6–22.5 °C, and the preferred temperatures were 23.8 and 23.1 °C, respectively. The elderly prefer higher temperatures than adults. Personal clothing insulation was significantly negatively correlated with operative temperature. Occupants’ average skin temperature was significantly positively correlated with operative temperature and mean thermal sensation votes. Based on the simulation results of building energy consumption and carbon emissions, this paper proposes design strategies for nursing homes that balance thermal comfort and energy savings.
Train-Induced Vibration and Structure-Borne Noise Measurement and Prediction of Low-Rise Building
Authors: Chen Jialiang , Hou Sen , Zheng Bokai , Li Xuming , Peng Fangling , Wang Yingying , Chen Junjie
Published: September 2024

The advancement of urban rail transit is increasingly confronted with environmental challenges related to vibration and noise. To investigate the critical issues surrounding vibration propagation and the generation of structure-borne noise, a two-story frame building was selected for on-site measurements of both vibration and its induced structure-borne noise. The collected data were analyzed in both the time and frequency domains to explore the correlation between these phenomena, leading to the proposal of a hybrid prediction method for structural noise that was subsequently compared with measured results. The findings indicate that the excitation of structure-borne noise produces significant waveforms within sound signals. The characteristic frequency of the structure-borne noise is 25–80 Hz, as well as that of the train-induced vibration. Furthermore, there exists a positive correlation between structural vibration and structure-borne noise, whereby increased levels of vibration correspond to more pronounced structure-borne noise; additionally, indoor distribution patterns of structure-borne noise are non-uniform, with corner wall areas exhibiting greater intensity than central room locations. Finally, a hybrid prediction methodology that is both semi-analytical and semi-empirical is introduced. The approach derives dynamic response predictions of the structure through analytical solutions, subsequently estimating the secondary noise within the building’s interior using a newly formulated empirical equation to facilitate rapid predictions regarding indoor building vibrations and structure-borne noises induced by subway train operations.
A Two-Level Machine Learning Prediction Approach for RAC Compressive Strength
Authors: Qi Fei , Li Hangyu
Published: September 2024

Through the use of recycled aggregates, the construction industry can mitigate its environmental impact. A key consideration for concrete structural engineers when designing and constructing concrete structures is compressive strength. This study aims to accurately forecast the compressive strength of recycled aggregate concrete (RAC) using machine learning techniques. We propose a simplified approach that incorporates a two-layer stacked ensemble learning model to predict RAC compressive strength. In this framework, the first layer consists of ensemble models acting as base learners, while the second layer utilizes a random forest (RF) model as the meta-learner. A comparative analysis with four other ensemble learning models demonstrates the superior performance of the proposed stacked model in effectively integrating predictions from the base learners, resulting in enhanced model accuracy. The model achieves a low mean absolute error (MAE) of 2.599 MPa, a root mean squared error (RMSE) of 3.645 MPa, and a high R-squared (R2) value of 0.964. Additionally, a Shapley (SHAP) additive explanation analysis reveals the influence and interrelationships of various input factors on the compressive strength of RAC, aiding design and construction professionals in optimizing raw material content during the RAC design and production process.
Seismic Response of Multi-Story Buildings Subjected to Luding Earthquake 2022, China Considering the Deformation Saturation Theory
Authors: Dong Xiaoyao , Guo Xun , A Lata , Luo Ruofan , Yan Cheng
Published: September 2024

Frequent seismic events have demonstrated that building collapse is primarily caused by the loss of load-bearing capacity in vertical structural members. In response to this risk, various national design codes have been established. This study conducted field investigations at an earthquake site in Luding County, Sichuan Province, which was struck at a magnitude of 6.8 on 5 September 2022. In this case, the lower x-direction load-bearing wall of the Tianyi Hotel suffered severe shear damage, and the building was on the verge of collapse. However, no obvious damage was seen in the elementary school dormitory. Numerical simulation analysis revealed that during the earthquake, the buildings primarily experienced y-direction displacement in the x-direction, with significant differences in the stress state among different axes. In the model of Tianyi Hotel, the x-direction load-bearing walls suffered shear damage, while the frame columns were still in the elastic stage. At this point, the shear force of the walls was 6–9 times that of the frame columns. Comparing the damage characteristics of the two buildings during the earthquake, it was found that different structural forms lead to different internal force distributions. This phenomenon is further interpreted through the principle of “deformation saturation”, with core structural components being modeled and tested using quasi-static experiments. The results indicated substantial differences in material properties among different structural forms, including variations in lateral stiffness, ultimate load-bearing capacity, and maximum displacement. Moreover, at the same floor level, components with smaller ultimate displacements are decisive of the overall structural stability. To ensure seismic resilience and stability, it is essential to consider not only the load-bearing capacity but also the rational arrangement and cooperative interactions between different components to achieve a balanced distribution of overall stiffness. This approach significantly enhances the building’s resistance to collapse.
Ferroptosis in Renal Cancer Therapy: A Narrative Review of Drug Candidates
Authors: Yu Lingyan , Qiu Yuyueyang , Tong Xiangmin
Published: September 2024

Renal cancer is a common and serious malignant tumor of the urinary system. While surgery effectively treats early-stage renal cancer, advanced cases pose a significant challenge due to poor treatment outcomes and chemotherapy resistance. Therefore, there is an urgent need to develop alternative therapeutic strategies. Ferroptosis is a newly defined form of programmed cell death characterized by the accumulation of iron-dependent lipid peroxides, which plays a critical role in tumor progression and drug resistance. Recent studies have shown that ferroptosis is involved in the occurrence and development of renal cancer, and ferroptosis-related genes can induce cell apoptosis and can be used as potential biomarkers for early diagnosis of renal cancer and participate in drug resistance of renal cancer chemotherapy. With the continuous improvement of the mechanism of ferroptosis, drugs targeting ferroptosis for the treatment of renal cancer are emerging in an endless stream. Based on the theoretical basis of the occurrence of ferroptosis, this paper reviewed drug-induced ferroptosis in renal cancer cells from the aspects of herbal medicine, natural compounds, drug resistance mechanisms, and nanomaterials, and delves into the clinical application potential of ferroptosis-related drugs in the treatment of renal cancer.
The Signature of Climate in Annual Burned Area in Portugal
Authors: DaCamara Carlos C.
Published: September 2024

Portugal is by far the country most affected by wildfires in Mediterranean Europe. The increase in frequency and severity of extreme years in the last two decades calls for a better understanding of the role played by climate variability and climate change. Using data covering a period of 44 years (1980–2023), it is shown that the distribution of annual burned area in Portugal follows a Rayleigh distribution whose logarithm of the scale parameter depends linearly on Cumulative Daily Severity Rate (CDSR). Defined for each year as the sum of the mean Daily Severity Rate over Portugal from 1 January to 31 December, CDSR is a measure of the dryness of dead fuels as induced by atmospheric conditions. Changes along the years of the modeled average explain 56% of the interannual variability of the annual burned area. When comparing the model for 30-year subperiods 1980–2009 and 1994–2023, large decreases are observed in return periods of annual burned area amounts, from 35% for amounts greater than 120 thousand hectares up to 49% for amounts greater than 200 thousand hectares. The proposed model is a useful tool for fire management under present and future climate conditions.
Characteristics, Management, and Outcomes of Acute Life-Threatening Asthma in Adult Intensive Care
Authors: Watson Adam J. R. , Roe Thomas , Arscott Oliver , Thomas Charlotte , Ward James , Beecham Ryan , Browning David , Saeed Kordo , Dushianthan Ahilanandan
Published: September 2024

Background: There is limited evidence regarding the management of acute life-threatening asthma in intensive care units (ICUs), and few guidelines have details on this. We aimed to describe the characteristics, management, and outcomes of adults with life-threatening asthma requiring ICU admission. Methods: In this single-centre retrospective observational study, we included consecutive adults with acute asthma requiring ICU admission between 1 January 2016 and 31 December 2023. Our primary outcome was requirement for invasive mechanical ventilation (IMV). Results: We included 100 patients (median age 42.5 years, 67% female). The median pH, PaCO2, and white cell count (WCC) on ICU admission were 7.37, 39 mmHg, and 13.6 × 109/L. There were 30 patients (30%) who required IMV, and the best predictors of IMV requirement were pH (AUC 0.772) and PaCO2 (AUC 0.809). In univariate analysis, IMV requirement was associated with both increasing WCC (OR 1.14) and proven bacterial infection (OR 8.50). A variety of respiratory support strategies were utilised, with 38 patients (38%) receiving only non-invasive respiratory support. Conclusions: Our data highlight key characteristics which may be risk factors for acute asthma requiring ICU admission and suggest that pH, PaCO2, and WCC are prognostic markers for disease severity. Our overall outcomes were good, with an IMV requirement of 30% and a 28-day mortality of 1%.
Deep Learning for Predicting Attrition Rate in Open and Distance Learning (ODL) Institutions
Authors: Ndunagu Juliana Ngozi , Oyewola David Opeoluwa , Garki Farida Shehu , Onyeakazi Jude Chukwuma , Ezeanya Christiana Uchenna , Ukwandu Elochukwu
Published: September 2024

Student enrollment is a vital aspect of educational institutions, encompassing active, registered and graduate students. All the same, some students fail to engage with their studies after admission and drop out along the line; this is known as attrition. The student attrition rate is acknowledged as the most complicated and significant problem facing educational systems and is caused by institutional and non-institutional challenges. In this study, the researchers utilized a dataset obtained from the National Open University of Nigeria (NOUN) from 2012 to 2022, which included comprehensive information about students enrolled in various programs at the university who were inactive and had dropped out. The researchers used deep learning techniques, such as the Long Short-Term Memory (LSTM) model and compared their performance with the One-Dimensional Convolutional Neural Network (1DCNN) model. The results of this study revealed that the LSTM model achieved overall accuracy of 57.29% on the training data, while the 1DCNN model exhibited lower accuracy of 49.91% on the training data. The LSTM indicated a superior correct classification rate compared to the 1DCNN model.
Possibility of Making Plastic Roof Tiles from Waste Plastic, Sand, and Fly Ash
Authors: Karedla Anil Babu , Schuster Jens , Shaik Yousuf Pasha
Published: September 2024

The rapid increase in plastic usage today poses a significant threat to our environment and the planet. It contributes to global warming and negatively impacts biodiversity. Most plastic ends up in landfills, where it can take up to 1000 years to decompose. Shockingly, only 9% of the plastic produced annually is recycled, while an astounding 2 million plastic bags are used every minute worldwide. This paper highlights the primary goal of plastic recycling, with a particular focus on using plastic to manufacture roof tiles. The motivation behind this approach is that everyone deserves a decent roof over their heads. To achieve this, a well-balanced mixture of waste polypropylene (PP), quartz sand, and fly ash minerals was utilized in producing plastic roof tiles. The research employed a hot press process to prepare samples of all composite materials, and no cracks or fractures were observed on the surface of these samples. The results of this innovative process exceed the standards set for most building materials in terms of both mechanical and thermal properties, demonstrating a compressive strength of 99.8 MPa, a flexural strength of 35.6 MPa, and an impact energy absorption of 7.93 KJ/m2. Importantly, all samples exhibited zero percent water absorption, making these roof tiles ideal for insulation purposes. Additionally, the resulting roof tiles are lightweight and cost-effective compared to conventional options.
Editorial for Special Topics: Imaging-Based Diagnosis for Prostate Cancer—State of the Art
Authors: Mayer Rulon , Choyke Peter L. , Simone II Charles B.
Published: September 2024

This Special Topics Issue, “Imaging-based Diagnosis of Prostate Cancer—State of the Art”, of Diagnostics compiles 10 select articles [...]
B7H3 Immune Checkpoint Overexpression Is Associated with Decreased Complete Response Rates to Neoadjuvant Therapy in Locally Advanced Rectal Cancer
Authors: Curcean Sebastian , Hendea Raluca Maria , Buiga Rares , Tipcu Alexandru , Curcean Andra , Vlad Catalin , Fekete Zsolt , Muntean Alina-Simona , Martin Daniela , Irimie Alexandru
Published: September 2024

Background and Objectives: Rectal cancer accounts for approximately one-third of colorectal cancers, with over 340,000 deaths globally in 2022. Despite advancements in treatment, the five-year overall survival for locally advanced rectal cancer (LARC) remains at 74%, with significant morbidity. B7H3 (CD276), an immune checkpoint protein, plays a role in tumor progression and resistance to therapy, and correlates with poor prognosis in various cancers, including colorectal cancer. This study aims to evaluate the expression of B7H3 in LARC and its impact on overall complete response (oCR) rates to neoadjuvant therapy. Methods: A retrospective study was conducted on 60 patients with LARC who received neoadjuvant chemoradiation (nCRT) followed by total mesorectal excision (TME). B7H3 expression was assessed using immunohistochemistry on surgical specimens. Expression levels were categorized as high or low based on a composite score, and their association with oCR rates was analyzed. Results: High B7H3 expression was observed in 60% of patients, with 73.5% showing expression in more than 50% of tumor cells. Patients who achieved oCR had significantly lower B7H3 expression compared to those with residual disease (p < 0.001). No nuclear expression of B7H3 was detected. No significant correlation was found between B7H3 expression and other clinicopathological variables, except for a higher likelihood of non-restorative surgery in patients with elevated B7H3 levels (p = 0.049). Mucinous adenocarcinoma had high expression of B7H3. Conclusions: Elevated B7H3 expression is associated with reduced oCR rates in LARC, highlighting its potential role as a prognostic biomarker. Further studies with larger cohorts are warranted to validate these findings and explore B7H3-targeted therapies as a treatment strategy for LARC.
Joint Resource Scheduling of the Time Slot, Power, and Main Lobe Direction in Directional UAV Ad Hoc Networks: A Multi-Agent Deep Reinforcement Learning Approach
Authors: Liang Shijie , Zhao Haitao , Zhou Li , Wang Zhe , Cao Kuo , Wang Junfang
Published: September 2024

Directional unmanned aerial vehicle (UAV) ad hoc networks (DUANETs) are widely applied due to their high flexibility, strong anti-interference capability, and high transmission rates. However, within directional networks, complex mutual interference persists, necessitating scheduling of the time slot, power, and main lobe direction for all links to improve the transmission performance of DUANETs. To ensure transmission fairness and the total count of transmitted data packets for the DUANET under dynamic data transmission demands, a scheduling algorithm for the time slot, power, and main lobe direction based on multi-agent deep reinforcement learning (MADRL) is proposed. Specifically, modeling is performed with the links as the core, optimizing the time slot, power, and main lobe direction variables for the fairness-weighted count of transmitted data packets. A decentralized partially observable Markov decision process (Dec-POMDP) is constructed for the problem. To process the observation in Dec-POMDP, an attention mechanism-based observation processing method is proposed to extract observation features of UAVs and their neighbors within the main lobe range, enhancing algorithm performance. The proposed Dec-POMDP and MADRL algorithms enable distributed autonomous decision-making for the resource scheduling of time slots, power, and main lobe directions. Finally, the simulation and analysis are primarily focused on the performance of the proposed algorithm and existing algorithms across varying data packet generation rates, different main lobe gains, and varying main lobe widths. The simulation results show that the proposed attention mechanism-based MADRL algorithm enhances the performance of the MADRL algorithm by 22.17%. The algorithm with the main lobe direction scheduling improves performance by 67.06% compared to the algorithm without the main lobe direction scheduling.
A Qualitative-Content-Analytical Approach to the Quality of Primary Students’ Questions: Testing a Competence Level Model and Exploring Selected Influencing Factors
Authors: Schilling Yannick , Hillebrand Leonie , Kuckuck Miriam
Published: September 2024

There is a consensus on the importance of students’ questions in educational contexts due to diverse potentials to promote learning. Engaging with students’ questions in primary school is highly relevant as it fosters critical thinking skills, encourages curiosity, and cultivates a deeper understanding of subject matter. At the same time, research findings agree that students’ questions about the subject matter are rare. Research on the quality of students’ questions in the classroom mostly focuses on secondary or higher education. However, when it comes to the quality of students’ questions in primary schools, there is a research gap, although it is possible to use questions in primary school lessons to improve learning processes. Against this background, the present study takes up a competence level model for assessing the quality of students’ questions in General Studies and evaluates its use in a qualitative–explorative setting on the questions from a non-probabilistic random sample (n = 477). The results of the analysis are further used to look for indications of the influences of the grade level and the subject matter on competence levels. Further, they also allow conclusions to be drawn for primary school teacher education. The competence level model in modified form turns out to be a reliable instrument for assessing the competence levels of questions. In addition, a weak positive correlation was found between the level of competence levels and the students’ grade level. The conclusion is that there is a need for tailored support across different grade levels. The detected lack of consistent connection with the subject matter highlights the importance of diverse instructional approaches.
Spiking PointCNN: An Efficient Converted Spiking Neural Network under a Flexible Framework
Authors: Tao Yingzhi , Wu Qiaoyun
Published: September 2024

Spiking neural networks (SNNs) are generating wide attention due to their brain-like simulation capabilities and low energy consumption. Converting artificial neural networks (ANNs) to SNNs provides great advantages, combining the high accuracy of ANNs with the robustness and energy efficiency of SNNs. Existing point clouds processing SNNs have two issues to be solved: first, they lack a specialized surrogate gradient function; second, they are not robust enough to process a real-world dataset. In this work, we present a high-accuracy converted SNN for 3D point cloud processing. Specifically, we first revise and redesign the Spiking X-Convolution module based on the X-transformation. To address the problem of non-differentiable activation function arising from the binary signal from spiking neurons, we propose an effective adjustable surrogate gradient function, which can fit various models well by tuning the parameters. Additionally, we introduce a versatile ANN-to-SNN conversion framework enabling modular transformations. Based on this framework and the spiking X-Convolution module, we design the Spiking PointCNN, a highly efficient converted SNN for processing 3D point clouds. We conduct experiments on the public 3D point cloud datasets ModelNet40 and ScanObjectNN, on which our proposed model achieves excellent accuracy. Code will be available on GitHub.
Study on the Generation and Comparative Analysis of Ethnically Diverse Faces for Developing a Multiracial Face Recognition Model
Authors: Park Yeongje , Baek Junho , Kim Seunghyun , Jeong Seung-Min , Seo Hyunsoo , Lee Eui Chul
Published: September 2024

Despite major breakthroughs in facial recognition technology, problems with bias and a lack of diversity still plague face recognition systems today. To address these issues, we created synthetic face data using a diffusion-based generative model and fine-tuned already-high-performing models. To achieve a more balanced overall performance across various races, the synthetic dataset was created by following the dual-condition face generator (DCFace) resolution and using race-varied data from BUPT-BalancedFace as well as FairFace. To verify the proposed method, we fine-tuned a pre-trained improved residual networks (IResnet)-100 model with additive angular margin (ArcFace) loss using the synthetic dataset. The results show that the racial gap in performance is reduced from 0.0107 to 0.0098 in standard deviation terms, while the overall accuracy increases from 96.125% to 96.1625%. The improved racial balance and diversity in the synthetic dataset led to an improvement in model fairness, demonstrating that this resource could facilitate more equitable face recognition systems. This method provides a low-cost way to address data diversity challenges and help make face recognition more accurate across different demographic groups. The results of the study highlighted that more advanced synthesized datasets, created through diffusion-based models, can also result in increased facial recognition accuracy with greater fairness, emphasizing that these should not be ignored by developers aiming to create artificial intelligence (AI) systems.
YOD-SLAM: An Indoor Dynamic VSLAM Algorithm Based on the YOLOv8 Model and Depth Information
Authors: Li Yiming , Wang Yize , Lu Liuwei , An Qi
Published: September 2024

Aiming at the problems of low positioning accuracy and poor mapping effect of the visual SLAM system caused by the poor quality of the dynamic object mask in an indoor dynamic environment, an indoor dynamic VSLAM algorithm based on the YOLOv8 model and depth information (YOD-SLAM) is proposed based on the ORB-SLAM3 system. Firstly, the YOLOv8 model obtains the original mask of a priori dynamic objects, and the depth information is used to modify the mask. Secondly, the mask’s depth information and center point are used to a priori determine if the dynamic object has missed detection and if the mask needs to be redrawn. Then, the mask edge distance and depth information are used to judge the movement state of non-prior dynamic objects. Finally, all dynamic object information is removed, and the remaining static objects are used for posing estimation and dense point cloud mapping. The accuracy of camera positioning and the construction effect of dense point cloud maps are verified using the TUM RGB-D dataset and real environment data. The results show that YOD-SLAM has a higher positioning accuracy and dense point cloud mapping effect in dynamic scenes than other advanced SLAM systems such as DS-SLAM and DynaSLAM.
A Review of State-of-the-Art Multiphase and Hybrid Electric Machines
Authors: Gholamian Mahzad , Beik Omid , Arshad Muhammad
Published: September 2024

In the realm of electric machines, there has been an increasing interest in multiphase (greater than three-phase) and hybrid excited machines. The benefits of multiphase machines include improved power density, efficiency, reliability, and fault tolerance, while for hybrid electric machines, the literature offers a variety of topologies, each with its own advantages and disadvantages. In essence, the term hybrid for electric machines is used when there is more than one source of excitation, e.g., permanent magnet (PM) excitation combined with or assisted by wound field (WF) excitation. This paper presents an extensive review of the latest topologies in hybrid machines. It explores fundamental principles, multiphase winding, and the advantage of multiphase over three-phase, as well as a comparison of ripple in the DC link for different numbers of phase winding. Additionally, this review discusses applications across industries, including automotive, aerospace, marine, and renewable energy systems. This paper later studies the motoric and generator modes of hybrid machines while considering the machine characteristics in both of these modes.
Pectin–Chitosan Hydrogel Beads for Delivery of Functional Food Ingredients
Authors: Morales Eduardo , Quilaqueo Marcela , Morales-Medina Rocío , Drusch Stephan , Navia Rodrigo , Montillet Agnès , Rubilar Mónica , Poncelet Denis , Galvez-Jiron Felipe , Acevedo Francisca
Published: September 2024

A common challenge in hydrogel-based delivery systems is the premature release of low molecular weight encapsulates through diffusion or swelling and reduced cell viability caused by the low pH in gastric conditions. A second biopolymer, such as chitosan, can be incorporated to overcome this. Chitosan is usually associated with colonic drug delivery systems. We intended to formulate chitosan-coated pectin beads for use in delaying premature release of the encapsulate under gastric conditions but allowing release through disintegration under intestinal conditions. The latter is of utmost importance in delivering most functional food ingredients. Therefore, this study investigated the impact of formulation and process conditions on the size, sphericity, and dissolution behavior of chitosan-coated hydrogel beads prepared by interfacial coacervation. The size and sphericity of the beads depend on the formulation and range from approximately 3 to 5 mm and 0.82 to 0.95, respectively. Process conditions during electro-dripping may be modulated to tailor bead size. Depending on the voltage, bead size ranged from 1.5 to 4 mm. Confocal laser scanning microscopy and scanning electron microscopy confirmed chitosan shell formation around the pectin bead. Chitosan-coated beads maintained their size and shape in simulated gastric fluid but experienced structural damage in simulated intestinal fluid. Therefore, they represent a novel delivery system for functional food ingredients.
The Accurate and Exclusive Quantification of Somatic Cells in Raw Milk with an OPD-Cu2+ System-Based Colorimetric Method
Authors: Xie Menghui , Wang Meng , Liu Siyuan , Liu Yingying , Wang Ziquan , Zhou Guoping , Sui Zhiwei
Published: September 2024

The somatic cell count (SCC) refers to the number of somatic cells present in each milliliter of raw milk and serves as a crucial indicator of dairy cow udder health and raw milk quality. Traditional SCC detection methods are often time-consuming, expensive, and susceptible to bacterial interference, rendering them unsuitable for the rapid and unbiased assessment of raw milk quality. Consequently, there is an urgent need for a low-cost, accurate, and user-friendly SCC quantification method. Here, a method based on an OPD-Cu2+ system for SCC quantification was developed. It was found that OPD oxidation signals exhibited a linear correlation with SCC. Following optimization, the detection system was established with a Cu2+ concentration of 25 μM, an OPD concentration of 2 mM, and an incubation time of 15 min. Furthermore, the method demonstrated significant resistance to bacterial interference, though it produced weaker signals in response to bacteria. The somatic cell recovery rate in milk after pretreatment was 88.9%, and SCC was quantified accurately within 45 min, with a linear range of 104–106 cells/mL. In summary, the method developed is cost-effective, straightforward, and facilitates precise somatic cell quantification, offering significant practical value and a new approach for SCC detection in raw milk.
Inactivation of Hepatitis A Virus and Feline Calicivirus on Model Food Contact Surfaces by Ultraviolet Light (UV-C) Systems
Authors: Polen Breanna , Pendyala Brahmaiah , Patras Ankit , D’Souza Doris H.
Published: September 2024

Food contact surfaces can harbor and transmit pathogens leading to outbreaks. Decontamination strategies that are user- and environmentally-friendly without toxic by-product formation are needed. Novel UV-C light-emitting diode (LED) technologies are being explored to deliver the required dose to inactivate viruses in food-processing environments. The objective of this study was to compare the effects of 279 nm UV-C LED to 254 nm UV-C against hepatitis A virus (HAV) and feline calicivirus (FCV, a cultivable human norovirus surrogate) on stainless-steel, ceramic, and glass surfaces. Viruses were surface spread on sterile stainless-steel or ceramic coupons (100 μL on 2 × 2 cm2), or glass discs (50 μL on 1 × 1 cm2), air-dried, and UV-C-treated for up to 3.75 min (surface dose = 0–49.2 mJ/cm2 for HAV and 0–24.6 mJ/cm2 for FCV). Each triplicate treatment was assayed in duplicate, and data were statistically analyzed. The D10-values for HAV treated with UV-C at 254 nm on stainless-steel, ceramic, and glass were 9.48 ± 0.34, 14.53 ± 2.52, and 6.91 ± 1.93 mJ/cm2, while with UV-C LED at 279 nm were 19.53 ± 2.45, 26.05 ± 0.60, and 8.77 ± 2.08 mJ/cm2, respectively. The D10-values for FCV treated with UV-C at 254 nm on stainless-steel, ceramic, and glass were 3.65 ± 0.06, 6.25 ± 1.90, and 4.69 ± 0.03 mJ/cm2, while with UV-C LED at 279 nm were 7.097 ± 2.11, 8.31 ± 2.12, and 7.88 ± 0.86 mJ/cm2, respectively. Higher 279 nm UV-C doses were needed to inactivate HAV and FCV compared to 254 nm UV-C on the tested surfaces. Novel UV-C LED systems using appropriate doses show promise to inactivate foodborne viruses on food contact surfaces.
Leveraging the Performance of Integrated Power Systems with Wind Uncertainty Using Fractional Computing-Based Hybrid Method
Authors: Albalawi Hani , Muhammad Yasir , Wadood Abdul , Khan Babar Sattar , Zainab Syeda Taleeha , Alatwi Aadel Mohammed
Published: September 2024

Reactive power dispatch (RPD) in electric power systems, integrated with renewable energy sources, is gaining popularity among power engineers because of its vital importance in the planning, designing, and operation of advanced power systems. The goal of RPD is to upgrade the power system performance by minimizing the transmission line losses, enhancing voltage profiles, and reducing the total operating costs by tuning the decision variables such as transformer tap setting, generator’s terminal voltages, and capacitor size. But the complex, non-linear, and dynamic characteristics of the power networks, as well as the presence of power demand uncertainties and non-stationary behavior of wind generation, pose a challenging problem that cannot be solved efficiently with traditional numerical techniques. In this study, a new fractional computing strategy, namely, fractional hybrid particle swarm optimization (FHPSO), is proposed to handle RPD issues in electric networks integrated with wind power plants (WPPs) while incorporating the power demand uncertainties. To improve the convergence characteristics of the Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the proposed FHPSO incorporates the concepts of Shannon entropy inside the mathematical model of traditional PSOGSA. Extensive experimentation validates FHPSO effectiveness by computing the best value of objective functions, namely, voltage deviation index and line loss minimization in standard power systems. The proposed FHPSO shows an improvement in percentage of 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, and 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, and FPSOGSA in case of traditional optimal reactive power dispatch(ORPD) for IEEE 30 bus system. Furthermore, the stability, robustness, and precision of the designed FHPSO are determined using statistical interpretations such as cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, and histograms.
Assessing Road Safety in Morocco’s Regions from 2014 to 2022: A DEA-MPI Benchmarking Analysis
Authors: Chorfi Zoubida , El Khalai Ibtissam
Published: September 2024

Assessing road safety performance in various regions of a country is crucial for improving overall road safety conditions and reducing the global mortality rate. This study employs the data-envelopment-analysis-based Malmquist productivity index (DEA-MPI) to comprehensively assess the progress of road safety performance in different regions of Morocco over time. Using a dataset spanning from 2014 to 2022, which contains data on road accidents, fatalities, injuries, the number of vehicles, and road traffic, this article evaluates the efficiency evolution across Morocco’s twelve regions. The study results show that the improvement of Morocco’s road safety performance during the studied period is unsatisfying and far from reaching the objectives of the current road safety strategy, which aims to reduce the number of fatalities by 50% by 2026. Moreover, the Malmquist productivity index (MPI) approach, which decomposes total factor productivity change into efficiency and technical changes, revealed that neither component shows a consistent trend throughout the studied period. This indicates that performance progress over time is insufficient and falls short of expectations, underscoring the immediate need for both technical and managerial improvements to address the current road safety challenges effectively.
Physical Activity, Sedentary Behavior, and Body Image among Young Adults in Hong Kong: A Cross-Sectional Study
Authors: Hung Maria Shuk Yu , Ng Winnie Wing Man , Choi Edward Kwok Yiu
Published: September 2024

The COVID-19 pandemic has considerably impacted the health and lifestyle of various populations worldwide, leading to decreased physical activity, increased sedentary behavior, and increased health problems. This study aimed to investigate Hong Kong young adults’ physical activity, related behaviors, and perceptions of body image during the pandemic. A cross-sectional online survey of young adults aged 18–29 was conducted from February to March 2021 using the well-validated Multidimensional Body Self-Relations Questionnaire (MBSRQ). Among 408 respondents, 52.5% had a normal weight in the BMI range, 45.1% engaged in ≥8 h of sedentary behavior daily, 77.2% engaged in no regular or inadequate physical activity (<2.5 h/week), and only 22.3% joined a fitness club. BMI, regular physical activity, and joining any fitness club/class were significantly associated with the most factors or additional subscales. A multiple linear regression model showed that the underweight respondents (reference group: normal) (β = −0.26, 95% CI: −0.41 to −0.12) were less likely to have high scores of fitness orientation. The respondents who engaged in regular physical activity <2.5 h/week (β = 0.42, 95% CI: 0.29 to 0.54), engaged in regular physical activity ≥2.5 h/week (β = 0.99, 95% CI: 0.84 to 1.14) (reference group: no regular physical activity), and joined a fitness club/class (β = 0.32, 95% CI: 0.18 to 0.45) were more likely to have a high score of fitness orientation. Local governments, non-governmental organizations, schools, and community centers should establish appropriate strategies and activities in order to sufficiently encourage and support young adults’ physical health and well-being.
The Economic Burden of Type 2 Diabetes Mellitus in Pakistan: A Cost of Illness Study
Authors: Arshad Muhammad Subhan , Alqahtani Faleh , Rasool Muhammad Fawad
Published: September 2024

Background: Type 2 Diabetes Mellitus (T2DM) is a highly prevalent disease with a chronic nature and poses a significant health burden worldwide, with no exception in Pakistan. Hence, this study aimed to explore the financial burden of T2DM in Pakistan through cost of illness analysis. Methods: A prevalence-based, cross-sectional study was conducted using a structured data collection tool from the patient’s perspective. Through structured interviews by trained data collectors, the data regarding direct medical costs, direct non-medical costs, and indirect costs were collected and further verified through prescriptions and bills. After testing the normality of data, mean and median with interquartile range were used to present cost data, while non-parametric tests, i.e., the Mann–Whitney U test and the Kruskal–Wallis test, were used to assess factors associated with costs, as cost data were not normally distributed. Results: The study included 522 participants, with a majority being female (54%) and aged between 41 and 60 years (64%). The mean annual total cost per patient was USD 235.1 (median = USD 162.8), comprising direct medical costs, 93.2% (mean = USD 219.2; median = USD 150.0), direct non-medical costs, 5.3% (mean = USD 12.4; median = USD 7.1), and indirect costs, 1.5% (mean = USD 3.5; median = USD 1.9). Costs were significantly higher for patients with advanced age, high literacy, higher household incomes, duration of diabetes, more than one complication, and using combination therapy. Conclusions: The economic burden of T2DM in Pakistan is substantial, with medication costs being the largest component. Effective management strategies and policy interventions are crucial to mitigate this burden and improve the economic and health outcomes for diabetic patients.
A Futures Perspective on Learning and Teaching in Higher Education: An Essay on Best and Next Practices
Authors: Jarni Nada , Gurr David
Published: September 2024

Higher education is a sector that can be slow to change, yet there are significant pressures on universities and other providers to change. Learning and teaching are central to what higher education does, and pressures, such as the switch to remote learning during the pandemic and the increasing use of generative AI, are causing a reconsideration about good learning and teaching. This essay provides a futures framework to explore best and next practices in learning and teaching in higher education. Four important and influential papers and reviews are used to consider past and current views of good teaching and learning in higher education. From these, six evidence-informed teaching practices are described as examples of current best-practice views, and then these are developed into possible, plausible, probable, and preferred next practices. This essay provides a stimulus for practitioners and researchers to adopt a futures mindset for thinking about the development of teaching and learning in higher education.
Scale- and Resolution-Adapted Shaded Relief Generation Using U-Net
Authors: Farmakis-Serebryakova Marianna , Heitzler Magnus , Hurni Lorenz
Published: September 2024

On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) relates to the neural network process and the maps used for training. Currently, there is no clear guidance on which DEM resolution to use to generate relief shading at specific scales. To address this gap, we trained the U-Net models on swisstopo manual relief shadings of Switzerland at four different scales and using four different resolutions of SwissALTI3D DEM. An interactive web application designed for this study allows users to outline a random area and compare histograms of varying brightness between predictions and manual relief shadings. The results showed that DEM resolution and output scale influence the appearance of the relief shading, with an overall scale/resolution ratio. We present guidelines for generating relief shading with neural networks for arbitrary areas and scales.
Performance of Zeolite-Based Soil–Geopolymer Mixtures for Geostructures under Eccentric Loading
Authors: Al-Rkaby Alaa H. J.
Published: September 2024

Although soil stabilization with cement and lime is widely used to overcome the low shear strength of soft clay, which can cause severe damage to the infrastructures founded on such soils, such binders have severe impacts on the environment in terms of increasing emissions of carbon dioxide and the consumption of energy. Therefore, it is necessary to investigate soil improvement using sustainable materials such as byproducts or natural resources as alternatives to conventional binders—cement and lime. In this study, the combination of cement kiln dust as a byproduct and zeolite was used to produce an alkali-activated matrix. The results showed that the strength increased from 124 kPa for the untreated clay to 572 kPa for clay treated with 30% activated stabilizer agent (activated cement kiln dust). Moreover, incorporating zeolite as a partial replacement of the activated cement kiln dust increased the strength drastically to 960 and 2530 kPa for zeolite ratios of 0.1 and 0.6, respectively, which then decreased sharply to 1167 and 800 kPa with further increasing zeolite/pr to 0.8 and 1.0, respectively. The soil that was improved with the activated stabilizer agents was tested under footings subjected to eccentric loading. The results of large-scale loading tests showed clear improvements in terms of increasing the bearing capacity and decreasing the tilt of the footings. Also, a reduction occurred due to the eccentricity decreasing as a result of increasing the thickness of the treated soil layer beneath the footing.