bluuebunny/crossref_metadata_2025 huggingface dataset:
Virtual Reality Utilized for Safety Skills Training for Autistic Individuals: A Review
Authors: Liu Lili , Yao Xinyu , Chen Jingying , Zhang Kun , Liu Leyuan , Wang Guangshuai , Ling Yutao
Published: January 2024

In recent years, virtual reality technology, which is able to simulate real-life environments, has been widely used in the field of intervention for individuals with autism and has demonstrated distinct advantages. This review aimed to evaluate the impact of virtual reality technology on safety skills intervention for individuals with autism. After searching and screening three databases, a total of 20 pertinent articles were included. There were six articles dedicated to the VR training of street-crossing skills for individuals with autism, nine articles focusing on the training of driving skills for individuals with ASD, and three studies examining the training of bus riding for individuals with ASD. Furthermore, there were two studies on the training of air travel skills for individuals with ASD. First, we found that training in some complex skills (e.g., driving skills) should be selected for older, high-functioning individuals with ASD, to determine their capacity to participate in the training using scales or questionnaires before the intervention; VR devices with higher levels of immersion are not suitable for younger individuals with ASD. Second, VR is effective in training safety skills for ASD, but there is not enough evidence to determine the relationship between the level of VR immersion and intervention effects. Although the degree of virtual reality involvement has an impact on the ability of ASD to be generalized to the real world, it is important to ensure that future virtual reality settings are realistic and lifelike. Again, adaptive models that provide personalized training to individuals with ASD in VR environments are very promising, and future research should continue in this direction. This paper also discusses the limitations of these studies, as well as potential future research directions.
Carbon Neutrality in the Building Sector of the Global South—A Review of Barriers and Transformations
Authors: Ghasemi Elnaz , Azari Rahman , Zahed Mehdi
Published: January 2024

This work provides a review of economic, technical, sociocultural, political, and technological barriers that impede carbon neutrality in the building sector of countries in the Global South. These barriers include limitations in public and professional awareness, knowledge, and skills in the construction industry, the lack of ambitious energy codes and green building rating systems, the lack of green financing schemes and investment, costs of materials and technology, and limitations in regulations. Finally, this article recommends five transformations to address the critical barriers and enable net-zero emission status in the building sector of countries in the Global South. These transformations include environmental data collection, stringent building energy codes, and green building system certifications, lifecycle-based thinking and circular design, education enhancement and workforce development, and net-zero emission business practices.
Energy Consumption in Higher Education Institutions: A Bibliometric Analysis Focused on Scientific Trends
Authors: Laporte Juan P. , Cansino José M.
Published: January 2024

While universities are expected to exemplify sustainable practices, they often encounter high energy demands. This dichotomy highlights the necessity for research into their energy consumption. Through a Systematic Literature Review, we examined international research trends in this field and explored factors influencing energy consumption. The importance of this article stems from its novel approach to energy consumption in universities, addressed from a global and comprehensive perspective, offering generalizable insights. Additionally, it pioneers in the use of a market concentration indicator (Herfindahl–Hirschman index) to measure the level of diversity in various bibliometric aspects. The extended perspective of our approach helps to close knowledge gaps about scientific trends and common energy consumption factors. Our results show that this topic has been investigated with limited involvement of social sciences. Building function, research intensity, and disciplinary orientation are distinctive factors in energy consumption in this field. Most influential authors, countries, and journals in this area were identified. This analysis contributes academically, by mapping research trends and providing guidance for future studies; practically, by offering insights for educational administrators on common factors affecting energy consumption; and in terms of policy, by advocating for the promotion of social sciences-based investigations on the topic.
Rare Case of First Permanent Molar Primary Failure of Eruption with Agenesis of Premolars
Authors: Vinereanu Arina , Munteanu Aneta , Clauss Francois , Gorduza Eusebiu Vlad
Published: January 2024

Primary failure of eruption (PFE) is a rare non-syndromic condition involving total or partial non-eruption of posterior teeth in the absence of mechanical obstruction. This paper presents the case of a healthy girl referred at age 4 for asymmetry of the upper arch. Clinical examination confirmed a slight shift of the upper midline to the left, with no erupted teeth distal to the upper left canine and a left posterior open bite. Panoramic X-rays showed delayed intraosseous development of the lateral left upper teeth compared to the right side. Clinic and radiographic follow-up during the next 9 years showed that 26 had not erupted by almost 13 years of age, 27 had delayed development and an unusual shape, and there was an agenesis in 24 and 25. Genetic analysis using the PTH1R single-gene sequencing method did not detect any known disease-causing or rare pathogenic variants that could explain the patient’s phenotype. Even when detected early, PFE raises difficult problems with regard to diagnosis and ortho-surgical treatment planning due to the impossibility of accurately predicting its evolution. Tooth agenesis on the same arch worsens the prognosis and adds challenges to planning the treatment. Close long-term follow-up and timely adjustment of the treatment plan in accordance with the evolution of the case are needed.
Syndromic and Monogenic Obesity: New Opportunities Due to Genetic-Based Pharmacological Treatment
Authors: Kalinderi Kallirhoe , Goula Vasiliki , Sapountzi Evdoxia , Tsinopoulou Vasiliki Rengina , Fidani Liana
Published: January 2024

Obesity is a significant health problem with a continuously increasing prevalence among children and adolescents that has become a modern pandemic during the last decades. Nowadays, the genetic contribution to obesity is well-established. For this narrative review article, we searched PubMed and Scopus databases for peer-reviewed research, review articles, and meta-analyses regarding the genetics of obesity and current pharmacological treatment, published in the English language with no time restrictions. We also screened the references of the selected articles for possible additional articles in order to include most of the key recent evidence. Our research was conducted between December 2022 and December 2023. We used the terms “obesity”, “genetics”, “monogenic”, “syndromic”, “drugs”, “autosomal dominant”, “autosomal recessive”, “leptin-melanocortin pathway”, and “children” in different combinations. Recognizing the genetic background in obesity can enhance the effectiveness of treatment. During the last years, intense research in the field of obesity treatment has increased the number of available drugs. This review analyzes the main categories of syndromic and monogenic obesity discussing current data on genetic-based pharmacological treatment of genetic obesity and highlighting the necessity that cases of genetic obesity should follow specific, pharmacological treatment based on their genetic background.
Compensatory Interactions between Corneal and Internal Astigmatism despite Lifestyle Changes
Authors: Liang Yuanyuan , Kang Byung-Soo , Kee Chea-Su , Leung Tsz-Wing
Published: January 2024

This study explores whether children’s refractive errors and visual behaviors reverted to pre-COVID-19 levels a year after normal schooling resumed in Hong Kong as well as the impact of corneal and internal astigmatism on refractive astigmatism development. Vision survey data and questionnaire results collected in 2022 (n = 119) and 2020 (n = 173) were compared. Cross-sectional data showed similar proportions of astigmatism (cylindrical power ≥ 0.75 D) in the 2020 (49.1%) and 2022 cohorts (55.5%). Despite a 0.28 D increase in corneal astigmatism, a compensatory 0.24 D increase in internal astigmatism of opposite direction kept refractive astigmatism relatively stable. The questionnaire data showed that children spent an additional 0.5 h/day outdoors on weekends post-resumption of normal schooling but engaged in more near-work activities, especially non-screen near-work, by approximately 1 h/day on both weekdays and weekends. These findings were supported by longitudinal data from 72 children who participated in both surveys. This study highlights the significant role of corneal and internal astigmatism in refractive astigmatism changes. Despite the return to in-person classes, children’s total near-work time increased and astigmatism remained high. These findings underscore the need for comprehensive strategies to reduce the high environmental risks for refractive error development in children.
Damage Location Determination with Data Augmentation of Guided Ultrasonic Wave Features and Explainable Neural Network Approach for Integrated Sensor Systems
Authors: Polle Christoph , Bosse Stefan , Herrmann Axel S.
Published: January 2024

Machine learning techniques such as deep learning have already been successfully applied in Structural Health Monitoring (SHM) for damage localization using Ultrasonic Guided Waves (UGW) at various temperatures. However, a common issue arises due to the time-consuming nature of collecting guided wave measurements at different temperatures, resulting in an insufficient amount of training data. Since SHM systems are predominantly employed in sensitive structures, there is a significant interest in utilizing methods and algorithms that are transparent and comprehensible. In this study, a method is presented to augment feature data by generating a large number of training features from a relatively limited set of measurements. In addition, robustness to environmental changes, e.g., temperature fluctuations, is improved. This is achieved by utilizing a known temperature compensation method called temperature scaling to determine the function of signal features as a function of temperature. These functions can then be used for data generation. To gain a better understanding of how the damage localization predictions are made, a known explainable neural network (XANN) architecture is employed and trained with the generated data. The trained XANN model was then used to examine and validate the artificially generated signal features and to improve the augmentation process. The presented method demonstrates a significant increase in the number of training data points. Furthermore, the use of the XANN architecture as a predictor model enables a deeper interpretation of the prediction methods employed by the network.
Growth and Characterization of Yb: CALYGLO Crystal for Ultrashort Pulse Laser Applications
Authors: Wang Zebin , Liu Jian , Chen Peng , Liu Peng , Ma Jie , Xu Xiaodong , Wei Yong , Lebbou Kheirreddine , Xu Jun
Published: January 2024

Yb:CALYGLO crystals with a dopant concentration of 5 at.% were successfully grown using the Czochralski method. The crystal samples were extensively studied to analyze their structure, room temperature and low temperature spectra, and laser properties. The highest absorption cross-section at 977 nm was calculated to be 1.83 × 10−20 cm2 for σ polarization and 5.32 × 10−20 cm2 for π polarization. Similarly, the emission cross-section was determined to be 1.38 × 10−20 cm2 at 980 nm for σ polarization and 2.28 × 10−20 cm2 at 981 nm for π polarization, with a full width at half maximum (FWHM) of 50.3 nm and 89.5 nm, respectively. The fluorescence lifetime of the 5 at.% Yb:CALYGLO crystal at 2F5/2 was measured to be 1.10 ms. Additionally, gain cross-sections were calculated for different β values. In the continuous laser experiment, the crystal demonstrated a laser output of 20.15 W at 1057 nm, with a slope efficiency of 53.3%. These experimental findings indicate that the lattice of Y3+ in the crystal is partially replaced by Lu3+ and Gd3+, resulting in a broader spectrum. Consequently, this crystal shows promising potential as a gain medium for ultrashort pulse laser crystals.
Primary Diffuse Leptomeningeal Melanomatosis in a Child with Extracranial Metastasis: Case Report
Authors: Shahab Shubin W. , Patil Prabhumallikarjun , Fangusaro Jason R. , Patteson Brooke , Goldman-Yassen Adam , Eaton Bree R. , Boydston William , Schniederjan Matthew , Aguilera Dolly
Published: January 2024

Primary meningeal melanomatosis is an extremely rare tumor with very few documented responses to treatment. A 3-year-old male with a complex past medical history, including prematurity and shunted hydrocephalus, was diagnosed with primary meningeal melanomatosis with peritoneal implants. Molecular testing revealed an NRAS Q61R mutation. The patient received proton craniospinal radiation followed by immunotherapy with nivolumab (1 mg/kg) and ipilimumab (3 mg/kg) IV every 3 weeks and, upon progression, he was switched to a higher dose of nivolumab (3 mg/kg IV every 2 weeks) and binimetinib (24 mg/m2/dose, twice a day). The patient had significant improvement of CNS disease with radiation therapy and initial immunotherapy but progression of extracranial metastatic peritoneal and abdominal disease. Radiation was not administered to the whole abdomen. After two cycles of nivolumab and treatment with the MEK inhibitor binimetinib, he had radiographic and clinical improvement in abdominal metastasis and ascitis. He ultimately died from RSV infection, Klebsiella sepsis, and subdural hemorrhage without evidence of tumor progression. This is the first report of a child with primary meningeal melanomatosis with extracranial metastatic disease with response to a combination of radiation, immunotherapy and MEK inhibitor therapy.
An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms
Authors: Abdo Amani , Mostafa Rasha , Abdel-Hamid Laila
Published: January 2024

Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. It is possible to think about feature selection as an optimization problem. Swarm intelligence algorithms are promising techniques for solving this problem. This research paper presents a hybrid approach for tackling the problem of feature selection. A filter method (chi-square) and two wrapper swarm intelligence algorithms (grey wolf optimization (GWO) and particle swarm optimization (PSO)) are used in two different techniques to improve feature selection accuracy and system execution time. The performance of the two phases of the proposed approach is assessed using two distinct datasets. The results show that PSOGWO yields a maximum accuracy boost of 95.3%, while chi2-PSOGWO yields a maximum accuracy improvement of 95.961% for feature selection. The experimental results show that the proposed approach performs better than the compared approaches.
NSCLC Digital PCR Panel Returns Low-Input Sample Results Where Sequencing Fails
Authors: Herdt Leah Rowland , Berroteran Paige , Rajagopalan Malini , Brown Bradley A. , Schwartz Jerrod J.
Published: January 2024

Molecular diagnostics has drastically improved the survival rate of patients diagnosed with non-small cell lung cancer (NSCLC) over the last 10 years. Despite advancements in molecular testing, targeted therapies, and national guideline recommendations, more than half of NSCLC patients in the United States either never receive testing or patient care is not informed via molecular testing. Here, we sought to explore the relationship between DNA/RNA input, the molecular testing method, and test success rates. On a shared set of low-input reference test materials (n = 3), we ran both a hybrid capture-based, next-generation sequencing (NGS) assay and a multiplexed digital PCR (dPCR) panel. The dPCR panel was highly sensitive and specific for low-input samples in dilution studies ranging from 40 to 1 ng DNA and from 20 to 2.5 ng RNA, while NGS had up to an 86% loss in sensitivity as contrived sample inputs were serially diluted. The dPCR panel also demonstrated a high PPA (>95%) at diluted inputs as low as 15/7.5 ng DNA/RNA on 23 banked clinical samples with the same NGS hybrid capture assay at a high input. These data suggest that digital PCR is an accurate and effective way of identifying clinically relevant NSCLC mutations at low nucleotide input and quality.
The Multisystem Impact of Long COVID: A Comprehensive Review
Authors: Negrut Nicoleta , Menegas Georgios , Kampioti Sofia , Bourelou Maria , Kopanyi Francesca , Hassan Faiso Dahir , Asowed Anamaria , Taleouine Fatima Zohra , Ferician Anca , Marian Paula
Published: January 2024

(1) Background: COVID-19 was responsible for the latest pandemic, shaking and reshaping healthcare systems worldwide. Its late clinical manifestations make it linger in medical memory as a debilitating illness over extended periods. (2) Methods: the recent literature was systematically analyzed to categorize and examine the symptomatology and pathophysiology of Long COVID across various bodily systems, including pulmonary, cardiovascular, gastrointestinal, neuropsychiatric, dermatological, renal, hematological, and endocrinological aspects. (3) Results: The review outlines the diverse clinical manifestations of Long COVID across multiple systems, emphasizing its complexity and challenges in diagnosis and treatment. Factors such as pre-existing conditions, initial COVID-19 severity, vaccination status, gender, and age were identified as influential in the manifestation and persistence of Long COVID symptoms. This condition is highlighted as a debilitating disease capable of enduring over an extended period and presenting new symptoms over time. (4) Conclusions: Long COVID emerges as a condition with intricate multi-systemic involvement, complicating its diagnosis and treatment. The findings underscore the necessity for a nuanced understanding of its diverse manifestations to effectively manage and address the evolving nature of this condition over time.
Correction: Pavičić, M. Non-Kochen–Specker Contextuality. Entropy 2023, 25, 1117
Authors: Pavičić Mladen
Published: January 2024

In the original publication [...]
Entropy of the Canonical Occupancy (Macro) State in the Quantum Measurement Theory
Authors: Spalvieri Arnaldo
Published: January 2024

The paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons. The probability distribution is obtained through two approaches: one involves tracing out the environment from a bosonic eigenstate of the combined environment and system of interest (the empirical approach), while the other involves tracing out the environment from the mixed state of the combined environment and system of interest (the Bayesian approach). In the thermodynamic limit, the two coincide and are equal to the multinomial distribution. Furthermore, the paper proposes to identify the physical entropy of the bosonic system with the Shannon entropy of the occupancy numbers, fixing certain contradictions that arise in the classical analysis of thermodynamic entropy. Finally, by leveraging an information-theoretic inequality between the entropy of the multinomial distribution and the entropy of the multivariate hypergeometric distribution, Bayesianism of information theory and empiricism of statistical mechanics are integrated into a common “infomechanical” framework.
Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power
Authors: Zhou Yan , Wei Fuzhen , Kuang Kaiyang , Mahfoud Rabea Jamil
Published: January 2024

An accurate method for predicting wind power is crucial in effectively mitigating wind energy fluctuations and ensuring a stable power supply. Nevertheless, the inadequacy of the stability of wind energy severely hampers the consistent functioning of the power grid and the reliable provision of electricity. To enhance the accuracy of wind power forecasting, this paper proposes an ensemble model named the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional bidirectional long short-term memory (CNN-BiLSTM), which incorporates a data preprocessing technique, feature selection method, deep ensemble model, and adaptive control. Initially, CEEMDAN is utilized to decompose wind speed and power sequences and hence obtain decomposed subsequences for further analysis. Subsequently, the CNN is used to extract features from each subsequence, whereas each subsequence is processed by BiLSTM to obtain an ultra-short-term deterministic prediction model. Additionally, the adaptive kernel density estimation (AKDE) method is employed to estimate the probabilistic distribution of prediction error, enabling ultra-short-term probabilistic wind power prediction. Finally, based on real datasets, the reliability of the model in probabilistic prediction is verified through the evaluation metrics of multi-step prediction intervals (PIs).
Improving Generation and Evaluation of Long Image Sequences for Embryo Development Prediction
Authors: Celard Pedro , Seara Vieira Adrián , Sorribes-Fdez José Manuel , Iglesias Eva Lorenzo , Borrajo Lourdes
Published: January 2024

Generating synthetic time series data, such as videos, presents a formidable challenge as complexity increases when it is necessary to maintain a specific distribution of shown stages. One such case is embryonic development, where prediction and categorization are crucial for anticipating future outcomes. To address this challenge, we propose a Siamese architecture based on diffusion models to generate predictive long-duration embryonic development videos and an evaluation method to select the most realistic video in a non-supervised manner. We validated this model using standard metrics, such as Fréchet inception distance (FID), Fréchet video distance (FVD), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE). The proposed model generates videos of up to 197 frames with a size of 128×128, considering real input images. Regarding the quality of the videos, all results showed improvements over the default model (FID = 129.18, FVD = 802.46, SSIM = 0.39, PSNR = 28.63, and MSE = 97.46). On the coherence of the stages, a global stage mean squared error of 9.00 was achieved versus the results of 13.31 and 59.3 for the default methods. The proposed technique produces more accurate videos and successfully removes cases that display sudden movements or changes.
Development of Single-Phase Synchronous Inverter for Single-Phase Microgrid
Authors: Yorino Naoto , Zoka Yoshifumi , Sasaki Yutaka , Sekizaki Shinya , Yokonuma Mitsuo , Himuro Takahiro , Kuroki Futoshi , Fujii Toshinori , Inoue Hirotaka
Published: January 2024

The work is based on a collaboration between Hiroshima University and Kure KOSEN College. This paper presents the design concept, hardware, and applications of a single-phase synchronous inverter (SSI), a specially designed grid-forming inverter (GFM) for single-phase micro-grid (SMGs). The SSI is designed for the conventional 100/200 V distribution network and is based on the concept of “Non-Interference Core (NIC) dynamic model”. Novel contributions of this paper are: (1) A root mean square (RMS) model of NIC-SSI was developed, combined with the conventional power system model, and verified through the comparison with the hardware-in-the-loop (HIL) simulation and SSI hardware experiments; (2) using the developed RMS simulation tool, the stabilization effect of the SSIs was investigated in condition under which the SSIs are massively installed in a distribution system; (3) off-grid SMG operations using SSIs under various ill-conditioned loads were demonstrated. The results show that the SSI has the considerable ability of grid stability enhancements for frequency, transient, and small-signal stabilities. The proposed SMG using SSIs is promising.
GPT-Driven Source-to-Source Transformation for Generating Compilable Parallel CUDA Code for Nussinov’s Algorithm
Authors: Palkowski Marek , Gruzewski Mateusz
Published: January 2024

Designing automatic optimizing compilers is an advanced engineering process requiring a great deal of expertise, programming, testing, and experimentation. Maintaining the approach and adapting it to evolving libraries and environments is a time-consuming effort. In recent years, OpenAI has presented the GPT model, which is designed for many fields like computer science, image processing, linguistics, and medicine. It also supports automatic programming and translation between programming languages, as well as human languages. This article will verify the usability of the commonly known LLM model, GPT, for the non-trivial NPDP Nussinov’s parallel algorithm code within the OpenMP standard to create a parallel equivalent of CUDA for NVIDIA graphics cards. The goal of this approach is to avoid creating any post-processing scripts and writing any lines of target code. To validate the output code, we compare the resulting arrays with the ones calculated by the optimized code for the CPU generated employing the polyhedral compilers. Finally, the code will be checked for scalability and performance. We will concentrate on assessing the capabilities of GPT, highlighting common challenges that can be refined during future learning processes. This will enhance code generation for various platforms by leveraging the outcomes from polyhedral optimizers.
Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition
Authors: Zhao Xueqing , Ren Fuquan , Sun Haibo , Qi Qinghong
Published: January 2024

Synthetic aperture radar (SAR) images are inevitably interspersed with speckle noise due to their coherent imaging mechanism, which greatly hinders subsequent related research and application. In recent studies, deep learning has become an effective tool for despeckling remote sensing images. However, preserving more texture details while removing speckle noise remains a challenging task in the field of SAR image despeckling. Furthermore, most despeckling algorithms are designed specifically for a specific look and seriously lack generalizability. Therefore, in order to remove speckle noise in SAR images, a novel end-to-end frequency domain decomposition network (SAR−FDD) is proposed. The method first performs frequency domain decomposition to generate high-frequency and low-frequency information. In the high-frequency branch, a mean filter is employed to effectively remove noise. Then, an interactive dual-branch framework is utilized to learn the details and structural information of SAR images, effectively reducing speckles by fully utilizing features from different frequencies. In addition, a blind denoising model is trained to handle noisy SAR images with unknown noise levels. The experimental results demonstrate that the SAR−FDD achieves good visual effects and high objective evaluation metrics on both simulated and real SAR test sets (peak signal-to-noise ratio (PSNR): 27.59 ± 1.57 and structural similarity index (SSIM): 0.78 ± 0.05 for different speckle noise levels), demonstrating its strong denoising performance and ability to preserve edge textures.
Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach
Authors: Yang Yanhua , Li Jiali , Chen Zhenbao , Liu Yong-Chao , Chen Kui , Liu Kai , Xin Dong-Li , Gao Guoqiang , Wu Guangning
Published: January 2024

This paper presents a machine learning-based approach to identify and separate partial discharge (PD) and two typical pulse interference (PI) signals in the vehicle-mounted cable terminations of high-speed electric multiple units (EMUs). First, a test platform was established to capture PD and two typical PI signals in these terminations. The acquired signals were then processed using the square envelope method to extract feature parameters, such as the rise time proportion, the left–right symmetry, and the upper–lower symmetry. PD signal classification was carried out on these signals, utilizing waveform parameters derived from a hierarchical clustering algorithm. The results validate that the extracted feature components effectively classify and separate PD and two typical PI signals in the vehicle-mounted cable terminations of high-speed EMUs.
Performance Comparison and Optimization of a PMSM Based on Hybrid-Type Permanent Magnet with Two Kinds of Rotor Topology
Authors: Yang Kai , Zhang Lu , Wang Mengyao , Du Chunyu
Published: January 2024

This study focuses on designing and optimizing Permanent Magnet Synchronous Motors (PMSMs) using hybrid rare earth and ferrite materials. Two distinctive rotor topologies of the Hybrid-Type Permanent Magnet Motor (HTPMM) are proposed: series and parallel magnetic circuits. Initially, the rotor topology and magnetic circuit principles of both the prototype and the designed HTPMM are introduced. Subsequently, a multi-objective genetic algorithm is employed to optimize the two HTPMMs, determining the final optimized parameters. Thise study further analyzes the cost advantage of HTPMMs from the perspective of permanent magnet materials, and detailed finite element analysis is conducted to evaluate the electromagnetic performance, including the air-gap flux density, no-load back electromotive force, cogging torque, load torque characteristics, and demagnetization properties. A comparative analysis of the prototype and two designed motors reveals that the HTPMM exhibits similar performance to the prototype, effectively reducing the usage of rare earth materials and significantly lowering the manufacturing costs. This research validates the feasibility of reducing rare earth material usage while maintaining a similar performance and provides a new perspective for the design of permanent magnet motors.
A Review on Flow Boiling Enhancement on Textured Surfaces
Authors: Mertens Frederik , Castagne Sylvie , Vetrano Maria Rosaria
Published: January 2024

It is widely established that flow boiling, being a direct cooling technique also employing the latent heat of the fluid, has the potential to be more efficient than being useful in single-phase conventional cooling methods. This results in considerable potential for thermal management in many fields like microelectronics, space technology, thermal power plants, etc. The increasing demand for heat dissipation, consequent to component miniaturization, has pushed the development of new strategies for enhancing heat transfer efficiency, such as employment of functionalized surfaces. This review aims to describe in detail the current status of technology related to flow boiling heat transfer enhancement via micro/nanoscale surface functionalization. Key objectives are an increased nucleation site density and enhanced bubble dynamics. The vast majority of findings show favorable heat transfer performance, evidenced by an earlier onset of boiling (ONB), an improved flow boiling heat transfer coefficient (HTC), and an ameliorated critical heat flux (CHF). Increased pressure drop is a serious concern in certain application cases. Nanoscale textures mainly enhance capillary wicking to nucleation sites, thus being more effective in combination with microscale textures that define fixed nucleation sites. Degradation effects need to be more thoroughly and systematically characterized for application cases. Extra effects related to the manufacturing process can be easily overlooked, but one should be aware of their possible existence when drawing conclusions. Finally, the implementation of enhanced surfaces in mainstream applications is hindered by the absence of general predictive design tools for different channel configurations/materials, fluids, and operating conditions. A more universal understanding of the basic mechanisms involving texture geometry is needed in this aspect.
Key Performance Indicators for Decision Support in Building Retrofit Planning: An Italian Case Study
Authors: Abbà Ilaria , Crespi Giulia , Vergerio Giulia , Becchio Cristina , Corgnati Stefano Paolo
Published: January 2024

To achieve climate and energy goals in the building sector, the current pace of renovating existing structures must double, overcoming prevailing barriers. Key Performance Indicators play a pivotal role in science-based decision making, emphasizing both direct and indirect benefits of building retrofits. The authors aim to contribute to proper metric identification for multi-perspective building performance assessment and formulate a methodology supporting energy planning decisions. They introduce the Global Cost per Emission Savings (GCES), an aggregated indicator encompassing both public (CO2 emissions) and private (global cost) perspectives of diverse retrofit technologies for building HVAC systems. Applied to the Italian residential building stock via the Reference Building approach, the methodology is tested using condensing gas boilers, biomass boilers, and electric heat pumps, revealing diverse environmental and economic performances. Addressing the establishment of effective decision-support tools for policymakers, the paper explores the potential impact of various policies on the favorability of technologies. Different policy scenarios are delineated to analyze how distinct approaches may influence the attractiveness of technologies. Notably, in the baseline scenario, biomass boilers hold an advantage over heat pumps according to the GCES index. However, scenarios involving technology-specific incentives or a greenhouse gases emission tax failed to alter the technological ranking, leaving heat pumps financially uncompetitive. In contrast, the TXPM scenario positions heat pumps as the most financially appealing option, penalizing biomass boilers for high particulate matter emissions.
Environmental Assessment of Hydrothermal Treatment of Wet Bio-Residues from Forest-Based and Agro-Industries into Intermediate Bioenergy Carriers
Authors: Ugolini Marco , Recchia Lucia , Wray Heather E. , Dijkstra Jan Wilco , Nanou Pavlina
Published: January 2024

Hydrothermal carbonization (HTC) of low quality, wet biogenic residues into intermediate bioenergy carriers can potentially contribute to a more flexible and stable renewable energy system and reduce environmental impacts compared to current residue disposal practices. This study quantifies the environmental impacts via life cycle assessment (LCA) of a novel hydrothermal process for the treatment on an industrial scale of application of three wet biogenic residues (paper bio-sludge, olive pomace, and orange peel) into bioenergy carriers, i.e., solid pellets and biogas. A comprehensive attributional cradle-to-gate life cycle assessment (LCA) was conducted; the life cycle impact assessment (LCIA) utilised the ReCiPe impact assessment method. A selection of 10 significant impact categories was prioritised. Reliability of this categorization was also ensured through a sensitivity analysis carried out using Monte Carlo simulation. Climate change, particulate matter formation and terrestrial acidification impact categories showed the highest reliability, while for freshwater ecotoxicity and freshwater eutrophication impact categories in the study suggest the need for more robust data and further investigation. The climate change impact category presents the following values, as kg CO2eq/tresidue: pulp and paper bio-sludge (PPB), 17.9; olive pomace (OP), −1290; orange peel (ORP), −1301. The LCA study compared electricity yields of the hydrothermal treatment process with conventional treatment processes for each of the target residue streams. The environmental performance of the proposed hydrothermal treatment benefits significantly from the combination of intermediate bioenergy carriers (pellets) from the solid fraction with biogas production from the liquid fraction. Avoided emissions due to the heat recovery provide further environmental benefits. The LCIA results show that the carbon footprint of the F-CUBED production system, as kgCO2eq/kWhe, accounts for –4.56, −0.63, and −0.25 for paper bio-sludge, olive pomace and orange peel, respectively.
Investigating Pressure Patterns in Transformer Tanks after an Interturn Short Circuit: A Finite Element Approach
Authors: Han Mingming , Zhu Mengzhao , Gu Zhaoliang , Zhang Qizhao , Zhao Ruxue , Liu Hongshun
Published: January 2024

When an inter-turn short-circuit fault occurs during the operation of a transformer, the arc generates energy that causes the temperature in the tank to rise. This in turn increases the temperature of the insulating oil, vaporizing it, and the rising pressure in the tank acts on the tank such that it can easily explode. The arc energy is related to the initial pressure of the gas and its production in the tank. The pressure wave propagates in insulating oil, and the transient pressure at any point in the path of the pressure wave is the superposition of vectors of forward- and backward-traveling waves. The authors of this study applied a finite element simulation software to establish a model of the transformer tank and used it to analyze the changes in pressure in the tank and on the wall as well as the factors influencing this phenomenon. The results show that the wall pressure of the transformer increases with time after the failure of the interturn short circuit. The pressure wave travels from the initial position of the arc to the periphery and decreases with diffusion effects. The influence of pressure on the transformer tank can be reduced by selecting an appropriate location for the pressure-release valve and can in turn prevent the tank from rupturing due to the impact of rising pressure.
Research on the New Hydrostatic Transmission System of Wheel Loaders Based on Fuzzy Sliding Mode Control
Authors: Wang Xin , Wang Zhongyu , Xie Lei , Wang Songlin , Wang Zhongshan , Ma Wenxing
Published: January 2024

To improve the control performance and energy efficiency of the hydrostatic transmission (HST) system of wheel loaders, this study proposes a novel variable structure dual-motor hydrostatic transmission system, accompanied by a multivariable control strategy based on fuzzy sliding mode control. By constructing a simulation model of the transmission system, the performance of the novel transmission system is verified, and the control effects of common control strategies are compared. The simulation results show that the proposed control strategy and system have good matching and can adapt to disturbances actively. Field experiment results show that the proposed solution can achieve smooth automatic shifting of wheel loaders, while effectively adjusting system pressure, flow, and power. Compared with the traditional hydrodynamic torque converter loader transmission system, its transmission efficiency is effectively increased by 30%, and the efficiency is increased by 8% compared with the traditional hydrostatic transmission system This achievement is of great significance for improving the working efficiency of wheel loaders and reducing energy consumption. The future research focus is to further improve the multivariable control strategy, achieve intelligent operation, and improve the efficiency and reliability of the transmission system.
Comparative of IEC 60891 and Other Procedures for Temperature and Irradiance Corrections to Measured I–V Characteristics of Photovoltaic Devices
Authors: Piliougine Michel , Sánchez-Friera Paula , Spagnuolo Giovanni
Published: January 2024

The photovoltaic literature contains a wide range of methods for translating the I–V curves of a solar device to other conditions of irradiance and cell temperature, different from those under which the measurements were performed. Some of these translation methods are included as part of the International Standard IEC 60891. In this paper, these techniques are classified, reviewed, and implemented to perform a deep comparative analysis between them and to discuss their suitability for converting the I–V curves of photovoltaic modules under different scenarios of irradiance and temperature. From the analysis conducted, it can be seen that the interpolation method proposed in IEC 60891 achieves accurate results when it is applied to correct small and medium irradiance and temperature gaps. If no interpolation is possible and for large irradiance corrections, other procedures described in IEC 60891 can be applied. However, certain explicit methods based on the single-diode model or on the double-diode model can overcome the most well-known approaches proposed by the standard.
Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions
Authors: Michailidis Panagiotis , Michailidis Iakovos , Gkelios Socratis , Kosmatopoulos Elias
Published: January 2024

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted.
A Eutectic Mixture of Calcium Chloride Hexahydrate and Bischofite with Promising Performance for Thermochemical Energy Storage
Authors: Li Bryan , Buisson Louise , Clark Ruby-Jean , Ushak Svetlana , Farid Mohammed
Published: January 2024

Thermochemical energy storage using salt hydrates is a promising method for the efficient use of energy. In this study, three host matrices, expanded vermiculite, expanded clay, and expanded natural graphite were impregnated with a eutectic mixture of CaCl2·6H2O and bischofite (MgCl2·6H2O). These composites were subjected to various humidity conditions (30–70% relative humidity) at 20 °C over an extended hydration period to investigate their cyclability. It was shown that only expanded natural graphite could contain the deliquescent salt at high humidity over 50 cycles. Hence, the expanded natural graphite composites containing either CaCl2·6H2O or CaCl2·6H2O/bischofite eutectic mixture were placed in a lab-scale open packed bed reactor, providing energy densities of 150 and 120 kWh/m3 over 20 h, respectively. The eutectic composite showed slightly lower temperature lift, water uptake rate, and power output but at reduced cost. Using the eutectic mixture also decreased the composite’s dehydration temperature at which the maximum mass loss rate occurred around 16.2 °C to 62.3 °C, allowing recharge using less energy-intensive heating methods. The cost of storing 1 kWh of energy with expanded natural graphite composites is only USD 0.08 due to its stability. This research leveraging cost-effective composites with enhanced stability, reaction kinetics, and high thermal energy storage capabilities benefits renewable energy, power generation, and the building construction research communities and industries by providing a competitive alternative to sensible heat storage technologies.
Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management
Authors: Lazaridis Charalampos Rafail , Michailidis Iakovos , Karatzinis Georgios , Michailidis Panagiotis , Kosmatopoulos Elias
Published: January 2024

The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a prominent solution to this challenge. However, the modern literature exhibits a plethora of RL methodologies, rendering the selection of the most suitable one a significant challenge. This work focuses on evaluating various RL methodologies for saving energy while maintaining adequate comfort levels in a residential setting. Five prominent RL algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Soft Actor-Critic (SAC)—are being thoroughly compared towards a baseline conventional control approach, exhibiting their potential to improve energy use while ensuring a comfortable living environment. The integrated comparison between the different RL methodologies emphasizes the subtle strengths and weaknesses of each algorithm, indicating that the best selection relies heavily on particular energy and comfort objectives.
Two-Wheeled Urban Vehicles—A Review of Emissions Test Regulations and Literature
Authors: Szymlet Natalia , Rymaniak Łukasz , Lijewski Piotr
Published: January 2024

Two-wheeled vehicles, due to their population, constitute a significant share of road vehicles in Europe. Therefore, this article presents an overview and analysis of the applicable legal regulations regarding two-wheeled vehicle engines in terms of toxic exhaust emission tests. For the correct interpretation of emission standards, the authors of this work made the necessary analysis of the categorization of two-wheeled vehicles based on Polish law and the criteria of European regulations. The presented analysis concerns not only the current regulations, but also their development trends over the years. These considerations are supplemented with a literature review, which includes the problems of the ecology, energy consumption and construction of the considered group of vehicles. The work presented in this article also concerns the assessment of the conditions for conducting tests on objects belonging to category L in laboratory conditions on chassis dynamometers. On this basis, considerations were made to evaluate the currently applicable WMTC (World Motorcycle Test Cycle) test by comparing it with the actual operation of two-wheeled vehicles. This resulted in the formulation of conclusions regarding the need to introduce procedures for testing pollutant emissions in road conditions in the approval process.