bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing Outcomes of Open and Robot-Assisted Inguinal Lymphadenectomy for the Treatment of cN2 Squamous Cell Carcinoma of the Penis: A Retrospective Single-Center Analysis
Authors: Brassetti Aldo , Pallares-Mendez Rigoberto , Bove Alfredo M. , Misuraca Leonardo , Anceschi Umberto , Tuderti Gabriele , Mastroianni Riccardo , Licari Leslie C. , Bologna Eugenio , Cartolano Silvia , D’Annunzio Simone , Ferriero Mariaconsiglia , Flammia Rocco S. , Proietti Flavia , Leonardo Costantino , Simone Giuseppe
Published: November 2024

Background: Inguinal lymph node (LN) dissection (iLND) is mandatory in cN2 penile squamous cell carcinoma (PSCC). Open iLND (OIL) is often omitted due to the high rate of complications. A minimally invasive approach may reduce morbidity; however, evidence supporting its role to treat bulky nodes is limited. This study aimed to present the outcomes of the largest European single-center series of robot-assisted iLND (RAIL) for the treatment of cN2 PSCC and to compare the surgical and survival outcomes of this approach with the standard of care. Methods: A retrospective analysis was conducted on men with cT1-4N2M0 PSCC undergone either OIL or RAIL at our institution from January 2014 onwards. Baseline demographics, perioperative data, and oncologic outcomes were analyzed. Results: Overall, 47 patients were included; 38 (81%) underwent OIL. Median age was 59 years, with 23 men (48%) presenting with a ≥4 Charlson comorbidity index. Operation time was significantly longer in the robotic cohort (212 min vs. 145 min; p < 0.001), while the length of stay (p = 0.09) and time to inguinal drainage removal (p = 0.08) were not. Estimated blood loss favored the robotic approach (60 mL vs. 300 mL; p < 0.001). Post-operative complications rates were comparable in the two groups (25% vs. 47%; p = 0.17): four major complications were observed overall, and these were all in the OIL cohort. Median LN yield was comparable between the two groups (18 vs. 25; p = 0.05). Final pathology reports showed no significant differences in tumor stage distribution between the cohorts (p = 0.54). Kaplan–Meier analysis did not reveal any significant differences in RFS probabilities between the two treatment groups (Log Rank = 0.99). Conclusions: RAIL demonstrated comparable perioperative and oncologic outcomes to OIL for cN2 PSCC, with the benefit of reduced estimated blood loss. RAIL is a feasible option for cases where a minimally invasive approach is preferred, offering comparable perioperative safety and oncological outcomes.
Neo-RAS Wild Type or RAS Conversion in Metastatic Colorectal Cancer: A Comprehensive Narrative Review
Authors: Pesola Guido , Epistolio Samantha , Cefalì Marco , Trevisi Elena , De Dosso Sara , Frattini Milo
Published: November 2024

The management of metastatic colorectal cancer in patients harboring RAS mutations primarily involves chemotherapy, often combined with bevacizumab, as a standard first-line treatment. However, emerging evidence suggests that tumors in a subset of these patients may experience a conversion from RAS-mutant status to RAS wild type (wt) during or after chemotherapy, a process referred to as “RAS conversion” or “neo-RAS wt”. Understanding the mechanisms driving the neo-RAS wt phenomenon is crucial for its application in personalized medicine. Hypotheses suggest that selective pressure from chemotherapy may lead to a decrease in the number of mutant RAS clones or an outgrowth of pre-existing RAS wt clones. Further research is needed to validate these mechanisms and understand the impact of the neo-RAS wt phenomenon on long-term outcomes, such as overall survival and progression-free survival. This review provides a comprehensive overview of the current understanding of the neo-RAS wt phenomenon, including its incidence, potential mechanisms, and clinical implications.
Flexible Humidity Sensor Based on Chemically Reduced Graphene Oxide
Authors: Laera Anna Maria , Cassano Gennaro , Burresi Emiliano , Protopapa Maria Lucia , Penza Michele
Published: November 2024

The accurate measurement of moisture content in pure gases and in gas mixtures, such as air, has great relevance in many industrial processes. In the present study, graphene oxide reduced through a mild alkaline treatment was used as a humidity sensing material to fabricate a flexible chemiresistive device operating at room temperature. The active layer was deposited by solution casting on a substrate of bimatted polyester, previously coated with inkjet-printed interdigitated electrodes made of silver. Structural investigations were performed by means of X-ray diffraction, Raman spectroscopy, and FTIR spectroscopy, while the optical properties were investigated using UV-VIS absorption and photoluminescence excitation spectroscopy. With increasing relative hu-midity from 0 to 80%, the electrical resistance decreased from about 1.4 GΩ to 2.5 MΩ. The ex-traordinarily large range of resistance values highlights the ultrahigh humidity sensitivity of re-duced graphene oxide, which acquires a fair amount of electrical conductivity after physisorption of water molecules but results in a highly resistive material in dry air. The high sensitivity at room temperature, the response’s repeatability, the wide relative humidity range detected, and the fast response time are the main advantages of the proposed humidity sensor, while the presence of some hysteresis, mainly at low relative humidity, and the recovery time need further improve-ment. Finally, the sensing mechanisms are briefly discussed.
Reverse Mutations in Pigmentation Induced by Sodium Azide in the IR64 Rice Variety
Authors: Chen Hsian-Jun , Sawasdee Anuchart , Lin Yu-Ling , Chiang Min-Yu , Chang Hsin-Yi , Li Wen-Hsiung , Wang Chang-Sheng
Published: November 2024

Pigmentation in rice is due mainly to the accumulation of anthocyanins. Five color mutant lines, AZ1701, AZ1702, AZ1711, AZ1714, and AZ1715, derived from the sodium azide mutagenesis on the non-pigmented IR64 variety, were applied to study inheritance modes and genes for pigmentation. The mutant line AZ1711, when crossed with IR64, displays pigmentation in various tissues, exhibiting a 3:1 pigmented to non-pigmented ratio in the F2 progeny, indicating a single dominant locus controlling pigmentation. Eighty-four simple sequence repeat (SSR) markers were applied to map the pigment gene using 92 F2 individuals. RM6773, RM5754, RM253, and RM2615 markers are found to be linked to the color phenotype. RM253 explains 78% of the phenotypic variation, implying linkage to the pigmentation gene(s). Three candidate genes, OsC1 (MYB), bHLH, and 3GT, as anthocyanin biosynthesis-related genes, were identified within a 0.83 Mb region tightly linked to RM253. PCR cloning and sequencing revealed 10 bp and 72 bp insertions in the OsC1 and 3GT genes, respectively, restoring pigmentation as in wild rice. The 72 bp insertion is highly homologous to a sequence of Ty1-Copia retrotransposon and shows a particular secondary structure, suggesting that it was derived from the transposition of Ty1-Copia in the IR64 genome.
Analyzing Temperature, Precipitation, and River Discharge Trends in Afghanistan’s Main River Basins Using Innovative Trend Analysis, Mann–Kendall, and Sen’s Slope Methods
Authors: Akhundzadah Noor Ahmad
Published: November 2024

Afghanistan, a nation already challenged by geopolitical and environmental pressure, faces severe climate change impacts, evident through rising temperatures, decreasing precipitation, and reduced river discharge. These changes profoundly affect the country’s water resources, agriculture, ecosystems, and well-being. This study analyzes trends in mean annual temperature, precipitation, and river discharge across all five of Afghanistan’s river basins from 1980 to 2022, utilizing an innovative trend analysis (ITA), the Mann–Kendall (MK) test, and Sen’s slope (SS) estimator. Climate data were derived from the CRU TS.v4 and TerraClimate gridded datasets, while river discharge data were obtained from GloFAS-ERA5 datasets. The results reveal significant climate shifts, including a notable 1.5 °C rise in mean annual temperature, significantly higher than the global average of 1.3 °C, a 1.2 mm decrease in mean annual precipitation, and a −128 m3/s reduction in river discharge across all basins since 1980. Climate change impacts were particularly severe in the western part of the country. These findings underscore the strain on Afghanistan’s vulnerable water resources, with critical implications for agriculture and water management, highlighting the urgent need for adaptive strategies to mitigate climate-induced risks.
Investigation of the Microhardness, Morphology, and Wear Resistance of A7075 Subjected to Machine Hammer Peening
Authors: Liu Yu , Wang Yefu , Yu Lu , Yang Ying , Nie Ning , Wang Xingxing , Shen Yifu
Published: November 2024

In industrial production, 7075 aluminum alloy (A7075) is prized for its strength and light weight. However, heat treatment can reduce its hardness and wear resistance. Therefore, proper surface treatments are often necessary to optimize its mechanical properties. In this work, a hammering tool attached to a robotic arm was employed to impact the surface of A7075 using different impact energies, and the surface hardness, morphology, roughness, and frictional characteristics of samples subjected to machine hammer peening (MHP) treatment were analyzed to explore the strengthening mechanism of MHP. The results indicate that the hardness increased to a maximum value of 235 HV with rising impact energy, whereas the depth of influence (2 mm) was almost unaffected by the impact energy. Microstructural analysis revealed significant grain refinement, especially at 2.7 J. The surface roughness increased significantly to about 7.2 μm, then dropped to around 3.7 μm when the impact energy increased to 2.7 J. Finally, the roughness decreased to ~6.8 μm. In addition, the samples that were strengthened by MHP demonstrated low friction coefficients (about 0.27) and wear volume (minimum value of 7.67/10−4 mm3), implying that MHP can effectively improve the wear resistance of A7075. Observation by SEM revealed that the corresponding wear mechanism is mainly attributable to mild oxidative wear and three-body wear.
Integrating Few-Shot Learning and Multimodal Image Enhancement in GNut: A Novel Approach to Groundnut Leaf Disease Detection
Authors: Qureshi Imran
Published: November 2024

Groundnut is a vital crop worldwide, but its production is significantly threatened by various leaf diseases. Early identification of such diseases is vital for maintaining agricultural productivity. Deep learning techniques have been employed to address this challenge and enhance the detection, recognition, and classification of groundnut leaf diseases, ensuring better management and protection of this important crop. This paper presents a new approach to the detection and classification of groundnut leaf diseases by the use of an advanced deep learning model, GNut, which integrates ResNet50 and DenseNet121 architectures for feature extraction and Few-Shot Learning (FSL) for classification. The proposed model overcomes groundnut crop diseases by addressing an efficient and highly accurate method of managing diseases in agriculture. Evaluated on a novel Pak-Nuts dataset collected from groundnut fields in Pakistan, the GNut model achieves promising accuracy rates of 99% with FSL and 95% without it. Advanced image preprocessing techniques, such as Multi-Scale Retinex with Color Restoration and Adaptive Histogram Equalization and Multimodal Image Enhancement for Vegetative Feature Isolation were employed to enhance the quality of input data, further improving classification accuracy. These results illustrate the robustness of the proposed model in real agricultural applications, establishing a new benchmark for groundnut leaf disease detection and highlighting the potential of AI-powered solutions to play a role in encouraging sustainable agricultural practices.
Peloids in Skin Care and Cosmeceuticals
Authors: Mourelle M. Lourdes , Gómez Carmen P. , Legido José L.
Published: November 2024

Peloids are mixtures of clays, sediments, or peat with mineral–medicinal water or seawater, or salt-lake water used in spa therapy for different treatments, including dermatological ones. The origin of peloids can be natural; that is, they are formed in situ at the place where the thermal water emerges or on the shores of the sea or salt lake, or they are prepared ad hoc from high-quality materials, such as clays or peat. Peloids are also used as cosmeceuticals in skin care to treat different skin disorders and/or conditions, such as psoriasis, eczema, and other scaly disorders, sensitive skin, and acne. This review reports all available scientific data concerning the effects and specific activities of peloids in skin care and cosmeceuticals, providing a better understanding of the clinical and cosmetic benefits. Finally, the safety and regulation of peloids are also discussed.
Novel Alternative Ni-Based Binder Systems for Hardmetals
Authors: Spalden Mathias von , Pötschke Johannes , Michaelis Alexander
Published: November 2024

WC-Ni hardmetals, especially with the addition of Cr, are the first choice for wear parts in a corrosive environment. Despite Ni being studied as a metallic binder matrix in hardmetals for as long as Co, the mechanical properties achieved have consistently fallen behind those of their cobalt-containing counterparts. Due to the rapidly increasing demand for Co, its substitution is of increasing importance. In this study, various alloying elements that do not form strong carbides were systematically investigated as part of a binary Ni-based binder system for hardmetals. Solid and liquid phase sintering were compared by using field assisted sintering and a conventional SinterHIP furnace. The obtained hardmetals were analysed in terms of their microstructure, phases, sintering behaviour, and mechanical properties. The metals manganese, iron, and copper, as well as the metalloids silicon and germanium, were evaluated as additional binder constituents. Hardmetals with a binary Ni-based binder alloy were successfully prepared. The combination with Mn or Si showed the potential to significantly lower the necessary sintering temperature. In particular, Mn proved to be the most effective grain growth inhibitor among the investigated alloying elements.
A Case of Basal Cell Carcinoma Exacerbated with Akatsuki Disease
Authors: Ohara Yuji , Kido Issei , Nakai Kozo
Published: November 2024

Akatsuki disease (also known as pomade crust) is characterized by skin lesions resulting from inadequate skin hygiene. It is sometimes influenced by underlying psychological factors. Akatsuki disease sometimes mimics cutaneous horn or skin cancer. However, there are no previous reports of skin cancer accompanied with Akatsuki disease. Herein, we report a 79-year-old woman who was referred to our department with a tumor on her left cheek. Before performing a biopsy, we recommended that her family assist with regular facial cleansing. Two months later, the scales and crusts on her entire face had disappeared and the tumor on the left cheek had reduced. Skin biopsy was performed, and histological examination revealed ulcerative basaloid lobules consisting of cells with a small cytoplasm and large hyperchromatic nuclei. Peripheral palisading and tumor-stroma clefting were observed. A diagnosis of basal cell carcinoma was made.
RETRACTED: Monda et al. Left Ventricular Non-Compaction in Children: Aetiology and Diagnostic Criteria. Diagnostics 2024, 14, 115
Authors: Monda Emanuele , De Michele Gianantonio , Diana Gaetano , Verrillo Federica , Rubino Marta , Cirillo Annapaola , Fusco Adelaide , Amodio Federica , Caiazza Martina , Dongiglio Francesca , Palmiero Giuseppe , Buono Pietro , Russo Maria Giovanna , Limongelli Giuseppe
Published: November 2024

The journal retracts the article, titled “Left Ventricular Non-Compaction in Children: Aetiology and Diagnostic Criteria” [...]
Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images
Authors: Cheng Yi-Ching , Hung Yi-Chieh , Huang Guan-Hua , Chen Tai-Been , Lu Nan-Han , Liu Kuo-Ying , Lin Kuo-Hsuan
Published: November 2024

Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based object detection methods for automatically identifying and annotating abnormal regions in CXR images. Methods: We developed and tested our models using disease-labeled CXR images and location-bounding boxes from E-Da Hospital. Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. To address the issue of limited examples for certain diseases, we also investigated few-shot object detection techniques. We compared convolutional neural networks (CNNs) and Transformer-based models to determine the most effective architecture for medical image analysis. Results: The findings show that background image proportions greatly influenced model inference. Moreover, schemes incorporating binary classification consistently improved performance, and CNN-based models outperformed Transformer-based models across all scenarios. Conclusions: We have developed a more efficient and reliable system for the automated detection of disease labels and location bounding boxes in CXR images.
The Prevalence of Gingival Recession According to the Cairo Classification in a Population from the North of Portugal
Authors: Moura Beatriz , Salazar Filomena , Costa Rosana , Cabral Cristina , Reis Cátia
Published: November 2024

Background/Objectives: Gingival recession (GR) has a multifactorial etiology, resulting from the interaction of various factors. It affects aesthetics and comfort, and has negative consequences for quality of life. The main objective of this study is to investigate the prevalence of gingival recession in a sample of the population in northern Portugal, according to the classification system proposed by Cairo. The secondary objective is to investigate possible risk factors for the prevalence of gingival recession. Methods: This observational study analyzed 50 patients who met all our inclusion criteria. Participants underwent a clinical periodontal examination and completed a questionnaire. Results: On the lingual/palatine side, recession type 2 (RT2) was the most prevalent (39.1%), and on the buccal side, recession type 3 (RT3) was the most prevalent (37.7%). Statistically significant differences were found in the occurrence of RT3 on both sides, buccal and lingual/palatine, which was higher in patients over 65. Buccal side RT2 and buccal and lingual/palatine side RT3 were more prevalent in males. Buccal side RT2 was more prevalent in ex-smokers compared to nonsmokers. RT3 was more prevalent in ex-smokers compared to smokers and nonsmokers. Most patients have gingival recession with loss of interproximal attachment. Conclusions: Older age, male gender, high plaque levels, and smoking habits are considered risk factors.
UAV Visual Object Tracking Based on Spatio-Temporal Context
Authors: He Yongxiang , Chao Chuang , Zhang Zhao , Guo Hongwu , Ma Jianjun
Published: November 2024

To balance the real-time and robustness of UAV visual tracking on a single CPU, this paper proposes an object tracker based on spatio-temporal context (STCT). STCT integrates the correlation filter and Siamese network into a unified framework and introduces the target’s motion model, enabling the tracker to adapt to target scale variations and effectively address challenges posed by rapid target motion, etc. Furthermore, a spatio-temporal regularization term based on the dynamic attention mechanism is proposed, and it is introduced into the correlation filter to suppress the aberrance of the response map. The filter solution is provided through the alternating direction method of multipliers (ADMM). In addition, to ensure efficiency, this paper proposes the average maximum response value-related energy (AMRE) for adaptive tracking state evaluation, which considers the time context of the tracking process in STCT. Experimental results show that the proposed STCT tracker can achieve a favorable balance between tracking robustness and real-time performance for UAV object tracking while running at ∼38 frames/s on a low-cost CPU.
Does Participatory Forest Management Reduce Deforestation and Enhance Forest Cover? A Comparative Study of Selected Forest Sites in Adaba-Dodola, Ethiopia
Authors: Tiki Lemma , Abdallah Jumanne M. , Marquardt Kristina , Tolera Motuma
Published: November 2024

Although extensive interventions are being made to protect forests, many developing countries, including Ethiopia, face persistent forest conservation challenges, particularly where local communities heavily rely on forests for their livelihoods. Recognizing the urgency of this issue, the government of Ethiopia introduced Participatory Forest Management (PFM) and devolved forest management responsibilities to enhance forest conservation. Therefore, investigating the impacts of PFM on forest covers is important. To this end, our research is based on an analysis of the land use/land cover changes (LULCCs) over the last 23 years in selected forest sites of Adaba–Dodola and their implications for the implementation of REDD+. This study examines the difference in forest cover changes between PFM and non-PFM sites within and between the study periods. Landsat images from 2000, 2012, and 2023 were analysed to detect LULCCs. Overall, the results from the comparison analysis indicate that in the period of 2000–2023, forest lands decreased by 5.22% in non-PFM sites, while they increased by 5.89% in PFM sites. On the other hand, agricultural lands experienced a notable increase of 9.64% in non-PFM sites but decreased by 1.65% in PFM sites. The increase in the forest cover is attributed to the effectiveness of PFM in halting deforestation and promoting forest conservation compared to non-PFM sites. Thus, the PFM approach is a tool for preserving forest ecosystems and mitigating the adverse effects of deforestation and forest degradation; therefore, this strategy could be used as a driving wheel for the implementation of REDD+.
Portuguese Validation of the Youth Sport Environment Questionnaire (YSEQ) for the Physical Education Context
Authors: Bessa Cristiana , Mesquita da Silva Sara , Farias Cláudio , Mesquita Isabel
Published: November 2024

Group cohesion is a prominent feature of sport involvement and small group work. Despite long-standing research interest and varied conceptualizations across fields, studies have primarily focused on sports. Research in educational contexts, particularly in physical education (PE), has been limited, even though PE offers an environment conducive to fostering diverse opportunities that promote group cohesion. This study sought to adapt and validate the Portuguese version of the Youth Sport Environment Questionnaire (YSEQ) using confirmatory factor analysis. The participants were 460 Portuguese-speaking PE students (58.9% girls, aged 14–19) who completed the YSEQ. After removing four items, the confirmatory factor analysis validated the two-factor structure of the YSEQ, supporting its appropriateness for the Portuguese context. This adapted 14-item YSEQ showed metric invariance across gender, and its two dimensions exhibited good internal consistency and convergent and discriminant validity. Significant correlations between the YSEQ and social affiliation scores confirmed the YSEQ’s concurrent validity. These findings suggest that the adapted Portuguese YSEQ is a reliable and valid instrument for measuring group cohesion in physical education. This instrument allows teachers, researchers, and sports psychologists to better understand group dynamics, assess task and social cohesion in middle and secondary PE classes, and identify potential difficulties in this area.
RETRACTED: Lei et al. Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory. Electronics 2021, 10, 2700
Authors: Lei Zhaoyu , Guo Jianyi , Tian Yingfu , Yang Jiemin , Xiong Yinwu , Zhang Jie , Shang Ben , Fan Youping
Published: November 2024

The journal retracts the article titled “Thyristor Aging-State-Evaluation Method Based on State In-formation and Tensor Domain Theory” [...]
Networking 3 K Two-Qubit Logic Gate Quantum Processors to Approach 1 Billion Logic Gate Performance
Authors: Guidotti Daniel , Ma Xiaoli , Chang Gee-Kung
Published: November 2024

Outlined is a proposal designed to culminate in the foundry fabrication of arrays of singly addressable quantum dot sources deterministically emitting single pairs of energy-time entangled photons at C-band wavelengths, each pair having negligible spin-orbit fine structure splitting, each pair being channeled into single mode pig-tail optical fibers. Entangled photons carry quantum state information among distributed quantum servers via I/O ports having two functions: the unconditionally secure distribution of decryption keys to decrypt publicly distributed, encrypted classical bit streams as input to generate corresponding qubit excitations and to convert a stream of quantum nondemolition measurements of qubit states into a classical bit stream. Outlined are key steps necessary to fabricate arrays of on-demand quantum dot sources of entangled photon pairs; the principles are (1) foundry fabrication of arrays of isolated quantum dots, (2) generation of localized sub-surface shear strain in a semiconductor stack, (3) a cryogenic anvil cell, (4) channeling entangled photons into single-mode optical fibers, (5) unconditionally secure decryption key distribution over the fiber network, (6) resonant excitation of a Josephson tunnel junction qubits from classical bits, and (7) conversion of quantum nondemolition measurements of qubit states into a classical bit.
Explainable Safety Argumentation for the Deployment of Automated Vehicles
Authors: Weissensteiner Patrick , Stettinger Georg
Published: November 2024

With over 1.6 million traffic deaths in 2016, automated vehicles equipped with automated driving systems (ADSs) have the potential to increase traffic safety by assuming human driving tasks within the operational design domain (ODD). However, safety validation is challenging due to the open-context problem. Current strategies, such as pure driving and requirement-based testing, are insufficient. Scenario-based testing offers a solution but necessitates appropriate scenario selection, testing methods, and evaluation criteria. This paper builds upon a method to calculate the covered ODD using tested scenarios generated from logical scenarios, considering parameter discretisation uncertainty. Acceptance criteria for the safety argumentation are proposed based on parameter space coverage and variance introduced via discretisation, thus contributing to quantifying the residual risks of safety validation. The approach is demonstrated through two logical scenarios with probability density functions of the parameters generated using a trajectory dataset. These criteria can serve as risk acceptance criteria, providing comparability and explainable results. By developing a robust scenario-based testing approach, ADS safety can be validated, leading to increased traffic safety and reduced fatalities. Since ADSs incorporate AI models, this proposed validation strategy can be extended to AI systems across multiple domains for the respective assurance argument required for deployment.
Multitasking Feature Selection Using a Clonal Selection Algorithm for High-Dimensional Microarray Data
Authors: Wang Yi , Luo Dan , Yao Jian
Published: November 2024

Effective gene feature selection is critical for enhancing the interpretability and accuracy of genetic data analysis, particularly in the realm of disease prediction and precision medicine. Most evolutionary feature selection algorithms tend to become stuck in local optima and incur high computational costs, particularly when dealing with the complex and high-dimensional nature of genetic data. To address these issues, this study proposes a multitasking feature selection method based on clone selection for high-dimensional microarray data, which identifies optimal features by transferring useful knowledge across two related tasks derived from the same microarray dataset. First, a dual-task generation strategy is designed, where one task selects features based on the Relief-F method, and the other task is generated from the original features. Second, a new mutation operator is introduced to share useful information between the multiple tasks. Finally, an improved clonal selection algorithm is proposed to strengthen the global and local search abilities. The experimental results on six high-dimensional microarray datasets demonstrate that our method significantly outperforms four state-of-the-art feature selection methods, highlighting its effectiveness and efficiency in tackling complex feature selection problems.
Dynamic Resource Aggregation Method Based on Statistical Capacity Distribution
Authors: Wang Yuexin , You Jiali , Li Yang
Published: November 2024

The computing network is a novel architecture that enables resource matching through the network. In distributed computing networks, computing resource management devices collect resource information and report it to network nodes. These nodes then broadcast the information to guide resource matching. One challenge is efficiently aggregating and disseminating computing resource information, as directly reporting fully multi-dimensional data can cause excessive overhead, while overly simplified aggregation may reduce matching accuracy. Existing aggregation methods typically rely on static resource information, overlooking the heterogeneity and dynamics of computing resources that arise from variations in resource capabilities and fluctuations over time, leading to suboptimal matching decisions. In response, this study proposes a dynamic resource aggregation method based on statistical capacity distribution. By modeling the capacity distribution of computing nodes, this method captures dynamic resource information, enabling more precise resource matching. Additionally, constructing resource groups and calculating representative distributions effectively compress the volume of data announcements. Experiments and data analysis demonstrate that, compared to static resource matching methods, the proposed method improves matching accuracy by 48%. Furthermore, it reduces announcement overhead by approximately 77.1% compared to existing dynamic resource allocation methods. These findings provide an efficient solution for resource aggregation in distributed computing networks.
Parallel Fusion of Graph and Text with Semantic Enhancement for Commonsense Question Answering
Authors: Zong Jiachuang , Li Zhao , Chen Tong , Zhang Liguo , Zhan Yiming
Published: November 2024

Commonsense question answering (CSQA) is a challenging task in the field of knowledge graph question answering. It combines the context of the question with the relevant knowledge in the knowledge graph to reason and give an answer to the question. Existing CSQA models combine pretrained language models and graph neural networks to process question context and knowledge graph information, respectively, and obtain each other’s information during the reasoning process to improve the accuracy of reasoning. However, the existing models do not fully utilize the textual representation and graph representation after reasoning to reason about the answer, and they do not give enough semantic representation to the edges during the reasoning process of the knowledge graph. Therefore, we propose a novel parallel fusion framework for text and knowledge graphs, using the fused global graph information to enhance the semantic information of reasoning answers. In addition, we enhance the relationship embedding by enriching the initial semantics and adjusting the initial weight distribution, thereby improving the reasoning ability of the graph neural network. We conducted experiments on two public datasets, CommonsenseQA and OpenBookQA, and found that our model is competitive when compared with other baseline models. Additionally, we validated the generalizability of our model on the MedQA-USMLE dataset.
A Fusion Model for Predicting the Vibration Trends of Hydropower Units
Authors: Liu Dong , Pi Youchun , Tang Zhengyang , Hua Hongpeng , Wang Xiaopeng
Published: November 2024

Hydropower units are essential to the safe, stable, and efficient operation of modern power systems, particularly given the current expansion of renewable energy systems. To enable timely monitoring of unit performance, it is critical to investigate the trends in vibration signals, to enhance the accuracy and reliability of vibration trend prediction models. This paper proposes a fusion model for the vibration signal trend prediction of hydropower units based on the waveform extension method empirical mode decomposition (W-EMD) and long short-term memory neural network (LSTMNN). The fusion model first employed a waveform matching extension method based on parameter ergodic optimization to extend the original signal. Secondly, EMD was used to decompose the extended signal sequence and reconstruct the decomposition components by the extreme point division method, and the reconstructed high- and low-frequency components were used as LSTMNN inputs for component prediction. Finally, the component prediction results were superimposed with equal weights to obtain the predicted value of the vibration signal trend of the hydropower unit. The experimental results showed that the W-EMD signal decomposition method can effectively suppress the endpoint effect problem in the traditional EMD algorithm, improving the quality of EMD decomposition. Furthermore, through a case study of the upper guide X direction swing signal on the 16F unit of a domestic hydropower station, it was found that the proposed fusion model successfully predicted anomalies in the unit’s swing signals; compared with SVR, KELM, LSTMNN, and EMD + LSTMNN, the prediction accuracy was improved by 78.94%, 66.67%, 55.56%, and 42.86%, respectively.
Enhanced Pool Boiling Heat Transfer on Hybrid Wettability Downward-Facing Surfaces: Impact of Interfacial Phenomena and Rewetting Characteristics
Authors: Li Xiaojia , Long Qingyun , Xue Jingtao , Liang Zhiguang , Yan Binghuo , Wang Laishun
Published: November 2024

The nucleation and growth of bubbles on homogeneous wetting surfaces have been extensively studied, but the intricate dynamics on hybrid wetting surfaces remain under-explored. This research aims to elucidate the impact of hybrid wettability on pool boiling heat transfer efficiency, specifically under downward-facing heating conditions. To this end, a series of hybrid wettability surfaces with varying hydrophilic and hydrophobic configurations are meticulously fabricated and analyzed. The study reveals distinctive interfacial phenomena occurring at the boundary between hydrophilic and hydrophobic regions during the boiling process. Experimental results indicate that surfaces with a higher proportion of hydrophilic to hydrophobic interfaces exhibit reduced superheat requirements and enhanced boiling heat transfer coefficients for equivalent heat flux densities. Furthermore, the rewetting characteristics of hybrid wettability surfaces are identified as pivotal factors in determining their critical heat flux (CHF). This investigation underscores the potential of hybrid wettability surfaces to optimize pool boiling heat transfer, offering valuable insights for the design and en-hancement of heat exchangers and other thermal management systems.
Experimental Study on Heuristics Energy Management Strategy for Hybrid Energy Storage System
Authors: Ranjan Alok , Bodkhe Sanjay , Goyal Gaurav , Belge Archana , Tibude Sneha
Published: November 2024

The energy management strategy (EMS) is a decision-making algorithm for effective power allocation between storage devices in a hybrid energy storage system (HESS). Source voltages, state of charge (SOC), the terminal voltage of the load, and the rate of change in the battery current must be considered while implementing the EMS and, hence, they are termed as performance indicators. This research work focuses on the development of an EMS, designed to manage the performance indicators of the sources (terminal voltage and battery current rate) and ensure efficient power distribution through a shared bus topology. A shared bus topology employs individual converters for each source, offering efficient control over these sources. Rule-based fuzzy logic control ensures efficient power distribution between batteries and ultracapacitors. Additionally, hardware has been developed to validate the power allocation strategy and regulate the DC-link voltage in the energy management system (EMS). dSPACE MicroLabBox is utilized for the implementation of real-time control strategies. A battery and an ultracapacitor bank are utilized in a hybrid energy storage system. The simulation outcomes have been corroborated by experimental data, affirming the efficacy of the proposed energy management strategy. The proposed EMS achieves a 2.1% battery energy saving compared to a conventional battery electric vehicle over a 25 s duration under the same load conditions.
A Regression-Based Method for Monthly Electric Load Forecasting in South Korea
Authors: Lee Geun-Cheol
Published: November 2024

In this study, we propose a regression-based method for forecasting monthly electricity consumption in South Korea. The regression model incorporates key external variables such as weather conditions, calendar data, and industrial activity to capture the major factors influencing electricity demand. These predictor variables were identified through comprehensive data analysis. Comparative experiments were conducted with various existing methods, including univariate time series models and machine learning techniques like Holt–Winters, LightGBM, and Long Short-Term Memory (LSTM). Additionally, ensemble methods combining two or more of these existing methods were tested. In the empirical analysis, the proposed model was used to forecast monthly electricity demand for a 24-month period (2022–2023), achieving a mean absolute percentage error (MAPE) of approximately 2%. The results demonstrated that the proposed method consistently outperforms all benchmarks tested in this study.
A Survey on the Sustainability of Traditional and Emerging Materials for Next-Generation EV Motors
Authors: Lucchini Francesco , Torchio Riccardo , Bianchi Nicola
Published: November 2024

The transportation sector is experiencing a profound shift, driven by the urgent need to reduce greenhouse gas (GHG) emissions from internal combustion engine vehicles (ICEVs). As electric vehicle (EV) adoption accelerates, the sustainability of the materials used in their production, particularly in electric motors, is becoming a critical focus. This paper examines the sustainability of both traditional and emerging materials used in EV traction motors, with an emphasis on permanent magnet synchronous motors (PMSMs), which remain the dominant technology in the industry. Key challenges include the environmental and supply-chain concerns associated with rare earth elements (REEs) used in permanent magnets, as well as the sustainability of copper windings. Automakers are exploring alternatives such as REE-free permanent magnets, soft magnetic composites (SMCs) for reduced losses in the core, and carbon nanotube (CNT) windings for superior electrical, thermal, and mechanical properties. The topic of materials for EV traction motors is discussed in the literature; however, the focus on environmental, social, and economic sustainability is often lacking. This paper fills the gap by connecting the technological aspects with sustainability considerations, offering insights into the future configuration of EV motors.
Electrical Model Analysis for Bifacial PV Modules Using Real Performance Data in Laboratory
Authors: Becerra Valentina González , Valdivia-Lefort Patricio , Barraza Rodrigo , García Jesús García
Published: November 2024

The new PV technologies, such as bifacial modules, bring the challenge of analyzing the response of numerical models and their fit to actual measurements. Thus, this study explores various models available in the literature for simulating the IV curve behavior of bifacial photovoltaic modules. The analysis contains traditional models, such as single and double-diode models, and empirical or analytical methodologies. Therefore, this paper proposes and implements a model performance assessment framework. This framework aims to establish a common basis for comparison and verify the applicability of each model by contrasting it with experimental data under controlled conditions of irradiance and temperature. The study utilizes bifacial modules of PERC+, HJT, and n-PERT technologies, tracing IV curves using a high-precision A+A+A+ solar simulator and conducting two sets of laboratory illumination measurements: single-sided and double-sided. In the first case, each face of the module is illuminated separately, while in the latter, the incident frontal illuminating light is reflected on a reflective surface. Experimental data obtained from these measurements are used to evaluate three different approximations for bifacial IV curve models in the case of double-sided illumination. The employed model for single-sided illumination is a single-diode model. The evaluation of various models revealed that shadowing from frames and junction boxes contributes to an increase in the error of modeled IV curves. However, among the three evaluated bifacial electrical models, one exhibited superior performance, with current errors approaching approximately 20%. To mitigate this discrepancy, a proposed methodology highlighted the significance of accurately estimating Io, suggesting its potential to reduce errors. This research provides a foundation for comparing electrical models to identify their strengths and limitations, paving the way for the development of more accurate modeling approaches tailored to bifacial modules. The insights gained from this study are crucial for enhancing the precision of IV curve predictions under various illumination conditions, which is essential for optimizing bifacial module performance in real-world applications.
From Conquests to Epidemics in 18th-Century South America: A Reflection on Social Resilience and Reconstruction: Review of the Literature
Authors: Villafañe Jorge Hugo
Published: November 2024

Background/Objectives: This narrative review examines resilience and social reconstruction strategies implemented during the 1742–1743 plague along the Royal Road between Buenos Aires and Lima. The study explores how colonial authorities managed the epidemic and its long-term effects, providing insights into historical crisis management and public health governance. Methods: A systematic analysis of primary and secondary historical records was conducted to identify public health measures, such as quarantines, hospital construction, and administrative reforms. Sources were retrieved from archives and databases, focusing on resilience strategies and institutional responses to the epidemic. Results: The findings highlight key public health interventions designed to contain the epidemic and mitigate its impacts. These included the establishment of quarantines, the construction of temporary hospitals, and administrative adaptations. Religious practices, such as novenas and community prayers, complemented institutional responses. The study underscores the role of colonial governance in adapting under epidemic pressures, illustrating an emergent institutional resilience. Conclusions: The 1742–1743 plague along the Royal Road serves as a case study for understanding the intersection of health crises and institutional adaptability. The review emphasizes the importance of coordinated public health measures and governance in addressing pandemics, offering lessons on resilience and social reconstruction applicable to contemporary health crises. This historical perspective enriches current discussions on crisis management and public health policy.
Study on the Water Mechanism of Sparse Grassland Decline of Ulmus pumila L.
Authors: Xia Tianbo , Zhang Ping , Ma Jinluo , Zhao Yuan , Yang Xiaohui , Wu Hao , Feng Xuejuan , Jin Lei , Zhang Kaifang
Published: November 2024

Ulmus pumila L. occupies an important niche in arid ecosystems. This study aimed to investigate the sap flow characteristics of declining Ulmus pumila L. in arid regions and its relationship with environmental factors. During the 2023 growing season (June to October), continuous sap flow monitoring was conducted using thermal dissipation probes (TDPs) on Ulmus pumila L., along with measurements of soil moisture, air temperature, relative humidity, solar radiation, wind speed, and vapor pressure deficit (VPD). The results showed that when the sap flow rate of elm individuals reached 0.92 mL/cm2/h, the trees entered an extremely severe decline stage. Sap flow rates were significantly positively correlated with net solar radiation, relative humidity, VPD, and soil moisture, but negatively correlated with wind speed and real-time rainfall. VPD was identified as the key factor influencing sap flow across different decline stages, while solar radiation was critical in assessing the severity of decline. A weakened correlation between sap flow and solar radiation marked the onset of severe decline. Additionally, soil moisture exhibited a significant positive effect on sap flow rates overall. These findings not only advance our theoretical understanding of plant ecology in arid areas but also offer practical insights for managing Ulmus pumila L. decline, thus contributing to more sustainable resource management and environmental protection strategies.
Incidence, Level of Damage and Identification of Insect Pests of Fruits and Leaves of Ziziphus Tree Species in Ethiopia
Authors: Alle Tigabu R. , Gure Abdella , Karlsson Miriam F. , Andrew Samora M.
Published: November 2024

The Ziziphus tree species offer valuable socio-economic and ecological benefits but experience significant damage from insect pests. In Ethiopia, there is limited knowledge of the insects attacking Ziziphus fruits, and a study aimed to identify these pests, assess their impact and understand how different land use types (LUTs) affect them was conducted. Sampling involved collecting fifty fruits and ten leaves from each of ten randomly chosen Ziziphus trees per LUT within each agroecological zone from August to December in 2022 and 2023. Samples were visually assessed for incidence and infestation levels, and the five morphotypes were identified using molecular techniques through phylogenetic analysis. Fruit pest incidence varied during the season, yet a positive correlation (r = 0.84) was observed among the months and years when assessment took place. Most fruits showed low to medium infestation levels (5%–50%), while severe infestations (>75%) were predominant in the lowland agroecological zone. The insects that had caused the damage were identified as Carpomya incompleta Becker, 1903; Drosophila hydei Sturtevant, 1921; D. simulans Sturtevant, 1919 and Zaprionus indianus Gupta, 1970. Fruits showed higher incidence and infestation levels than leaves, indicating significant yield and income losses. Thus, implementing effective management strategies is vital to minimize these losses and achieve sustainable production in Ethiopia.