bluuebunny/crossref_metadata_2025 huggingface dataset:
Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
Authors: Guo Wangyong , Liu Shijin , Weng Liguo , Liang Xingyu
Published: February 2025

Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine their complementary advantages and fail to fully capture the internal information in the load sequence, leading to a decrease in accuracy. To achieve accurate and efficient short-term load forecasting, this study proposes a novel power grid load forecasting model that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Multi-Head Self-Attention Mechanism (MHSA), Global Attention Mechanism (GAM), and Channel Attention Mechanism (CAM) to achieve efficient and precise short-term load forecasting. This model aims to address the issue in traditional methods where complex temporal features and important information in power grid load data are not fully captured. Firstly, the CNN module is used to extract high-dimensional spatial features from the load data, and a pooling layer is applied to reduce dimensionality while retaining key information. Then, the Multi-Head Self-Attention mechanism is employed to model the long-range dependencies of the sequence data, enhancing the ability to extract temporal features. Next, the LSTM layer further captures the time dependencies in the load sequence. Subsequently, the Global Attention mechanism helps the model focus more on the most relevant parts of the input sequence, improving the model’s performance and generalization ability. The Channel Attention module is then applied to weight different feature channels, highlighting important information and reducing redundancy. Finally, the flattened output layer produces the forecast results. Experimental validation shows that the proposed CNN-MHSA-LSTM-GAM-CAM model outperforms existing mainstream methods in terms of load forecasting accuracy, providing effective support for the optimized scheduling of smart grids.
Genetic Diversity in Apricot Orchards Across Key Growing Regions in Slovakia and Austria, Along with Cultivar Authentication of Apricot Genotypes Found in the Market
Authors: Hudcovicová Martina , Klčová Lenka , Gubišová Marcela , Gubiš Jozef , Zetochová Erika , Horacek Micha , Kraic Ján , Havrlentová Michaela , Ondreičková Katarína
Published: February 2025

This study was aimed at evaluating the genetic diversity and authentication of apricot cultivars from Slovak and Austrian orchards. Eleven highly polymorphic SSR markers were used to analyse 53 apricot genotypes. A high level of genetic diversity was observed, with a mean of 13 alleles per locus and a mean expected heterozygosity of 0.811. The most polymorphic locus, UDP97-402, exhibited a probability of identity of 0.025, indicating a high discriminatory power. Differentiation analyses revealed distinct groupings of cultivars based on geographical origin and pedigree. Central European cultivars, particularly those related to the landrace ‘Hungarian Best’, formed a distinct cluster. Modern Slovakian cultivars, influenced by the genetic background of cultivars from China and Central Asia, showed a higher level of genetic diversity. The Western European and North American cultivars formed a separate cluster. The developed SSR database proved to be effective in identifying apricot cultivars and detecting mislabelled or misidentified samples. A significant proportion (74%) of samples from commercial orchards were properly and correctly labelled, while 26% were mislabelled. Fifty-three per cent of samples from shops without a cultivar name were successfully identified, and their labels were verified. These results highlight the potential of SSR markers for genetic diversity assessment and cultivar identification in apricots. The results contribute to a better understanding of apricot genetic resources and provide valuable information for breeding programmes, germplasm conservation, and cultivar authentication.
Rapid Estimation of Vs30 Through Elitist Genetic Algorithm HVSR Inversion and Refraction Microtremor Data Analysis in the Greater Metro Manila Area and Leyte Province, Philippines
Authors: Grutas Rhommel N. , Serrano Andrew T. , Tan Jamie Mary Loise C. , Castro Rio Angela F.
Published: February 2025

Vs30, the average shear wave velocity in the uppermost 30 m, is a critical parameter in seismic hazard analysis. In the Philippines, the Refraction Microtremor (ReMi) survey is the standard method for Vs30 Estimation. This study evaluates the efficiency of using an elitist Genetic Algorithm (GA) to invert Horizontal-to-Vertical Spectral Ratio (HVSR) data as an alternative approach. Unlike ReMi surveys, which require geophone arrays, HVSR surveys use a single-unit three-component microtremor seismograph, enabling faster and broader data collection. Analysis of 174 HVSR and 52 ReMi datasets from the Greater Metro Manila Area (GMMA) and Leyte Province revealed strong correlations between estimated and measured Vs30 values. The overall match rates for soil profile classification under the National Structural Code of the Philippines (NSCP 2015) were 76% in GMMA and 81% in Leyte, with R-squared values of 0.885 and 0.806, respectively. Additionally, the relationship between the fundamental site period and estimated Vs30 values was explored. The R-squared values of 0.772 for GMMA and 0.707 for Leyte indicate a strong correlation and demonstrate the expected inverse relationship between the two variables. Given the Philippines’ high seismic activity, this method provides an efficient means to enhance seismic hazard mapping, improving earthquake preparedness and mitigation.
Image Augmentation Approaches for Building Dimension Estimation in Street View Images Using Object Detection and Instance Segmentation Based on Deep Learning
Authors: Hwang Dongjin , Kim Jae-Jun , Moon Sungkon , Wang Seunghyeon
Published: February 2025

There are numerous applications for building dimension data, including building performance simulation and urban heat island investigations. In this context, object detection and instance segmentation methods—based on deep learning—are often used with Street View Images (SVIs) to estimate building dimensions. However, these methods typically depend on large and diverse datasets. Image augmentation can artificially boost dataset diversity, yet its role in building dimension estimation from SVIs remains under-studied. This research presents a methodology that applies eight distinct augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, translation augmentation, and a combined “sum of all” approach—to train models in two tasks: object detection with Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) and instance segmentation with You Only Look Once (YOLO)v10. Comparing the performance with and without augmentation revealed that contrast augmentation consistently provided the greatest improvement in both bounding-box detection and instance segmentation. Using all augmentations at once rarely outperformed the single most effective method, and sometimes degraded the accuracy; shearing augmentation ranked as the second-best approach. Notably, the validation and test findings were closely aligned. These results, alongside the potential applications and the method’s current limitations, underscore the importance of carefully selected augmentations for reliable building dimension estimation.
Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model
Authors: Li Kunping , Liu Jianhua , Zhuang Cunbo
Published: February 2025

In the context of the accelerating new technological revolution and industrial transformation, the issue of talent supply and demand matching has become increasingly urgent. Precise matching talent supply and demand is a critical factor in expediting the implementation of technological innovations. However, traditional methods relying on interpersonal networks for talent ability collection, demand transmission, and matching suffer from inefficiency and are often influenced by the subjective intentions of intermediaries, posing significant limitations. To address this challenge, we propose a novel approach named TSDM for talent supply and demand matching. TSDM leverages prompt learning with pre-trained large language models to extract detailed expressions of talent ability and demand from unstructured documents while utilizing the powerful text comprehension capabilities of pre-trained models for feature embedding. Furthermore, TSDM employs talent-specific and demand-specific encoding networks to perform deep learning on talent and demand features, capturing their comprehensive representations. In a series of comparative experiments, we validated the effectiveness of the proposed model. The results demonstrate that TSDM significantly enhances the accuracy of talent supply and demand matching, offering a promising approach to optimize human resource allocation.
Pan-Cancer Characterization Identifies SLC19A1 as an Unfavorable Prognostic Marker and Associates It with Tumor Infiltration Features
Authors: Pan Yimin , Liu Zhichen , Wu Changwu
Published: February 2025

Background: Recent studies have identified solute carrier family 19 member 1 (SLC19A1) as a second messenger transporter that regulates massive immune-related signaling cascades, but current studies provide limited information. This study aims to evaluate its role and the potential mechanisms across various cancers. Methods: We analyzed multi-omics data from a pan-cancer cohort to evaluate SLC19A1 expression and its association with multiple features, including prognosis, tumor stemness, genome instability, and immune infiltration. Immunofluorescence staining was performed to validate SLC19A1 expression in tumor tissues and its relationship M2 macrophages. In addition, we used web tools such as ROCplotter to evaluate the association between SLC19A1 and response to chemotherapy and immunotherapy. Results: SLC19A1 was found to be overexpressed in multiple cancer types compared to normal tissues, correlating with poor prognosis. High SLC19A1 levels were associated with increased genomic instability and immune suppression. In addition, SLC19A1 was negatively correlated with CD8+ T-cell infiltration and positively correlated with M2 macrophage infiltration. The association of SLC19A1 with M2 macrophages was confirmed in multiple immunofluorescence staining. Finally, SLC19A1 was associated with the response to chemotherapy and immunotherapy in a variety of tumors. Conclusions: Our findings position SLC19A1 as a novel unfavorable prognostic marker in cancer, closely linked to immune suppression and genomic instability. This study highlights the need for further exploration of SLC19A1 as a therapeutic target and its implications in cancer treatment strategies.
Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors
Authors: Zou Xue , Wang Xiaohong , Tu Jinchun , Chen Delun , Cao Yang
Published: February 2025

The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most existing multi-mode sensors rely on the separate analysis of each mode signal, which can easily lead to sensor failure when the deviation between different mode results is too large. In this study, we propose a multi-mode sensor based on Prussian Blue (PB) for ascorbic acid (AA) detection. We innovatively integrate back-propagation artificial neural networks (BP ANNs) to comprehensively process the three collected signal data sets, which successfully solves the problem of sensor failure caused by the large deviation of signal detection results, and greatly improves the prediction accuracy, detection range, and anti-interference of the sensor. Our findings provide an effective solution for optimizing the data analysis of multi-modal sensors, and show broad application prospects in bioanalysis, clinical diagnosis, and related fields.
Red- and Black-Phosphorus-Based Materials for Photocatalytic Water Disinfection
Authors: He Dongyang , Dong Jingyuan , Zhang Ya-Nan , Zhang Sui , Zhang Yi-Nan , Qu Jiao
Published: February 2025

Given the growing threat of pathogens in drinking water, exploring novel semiconductor photocatalysts for effective water disinfection has become a critical area of research. Recently, red and black phosphorus have attracted significant attention due to their unique physicochemical properties, making them well suited for photocatalytic water disinfection. Particularly, recent studies have demonstrated that red and black phosphorus can be combined with other materials to improve the photocatalytic disinfection performance of the resulting composites. Herein, we present the first review of red- and black-phosphorus-based materials for photocatalytic water disinfection. This article begins by presenting the fundamental principle of photocatalytic water disinfection before reviewing the latest developments in the application of red- and black-phosphorus-based photocatalysts for this purpose. In conclusion, it provides a summary and proposes potential avenues for future research in this field.
Research Progress on the Relationship Between PRPF8 and Cancer
Authors: Huang Guoqing , Wang Dandan , Xue Jiaying
Published: February 2025

Alternative splicing (AS) plays a crucial role in regulating gene expression and protein diversity, influencing both normal cellular function and pathological conditions, including cancer. Protein pre-mRNA processing factor 8 (PRPF8), a core component of the spliceosome, is integral to the splicing process, ensuring accurate gene transcription and spliceosome assembly. Disruptions in PRPF8 function are linked to a variety of cancers, as mutations in this gene can induce abnormal splicing events that contribute to tumorigenesis, metastasis, and drug resistance. This review provides an in-depth analysis of the mechanisms by which PRPF8 regulates tumorigenesis through AS, exploring its role in diverse cancer types, including breast, liver, myeloid, and colorectal cancers. Furthermore, we examine the molecular pathways associated with PRPF8 dysregulation and their impact on cancer progression. We also discuss the emerging potential of targeting PRPF8 in cancer therapy, highlighting challenges in drug development.
Metastasis of Squamous Cell Carcinoma of the Larynx to the Right Adrenal Gland—A Case Report
Authors: Działach Eliza Działach , Simlot Michał Simlot , Osowiecka Karolina Osowiecka , Nowara Elżbieta , Markowski Jarosław Markowski , Grajek Mateusz Grajek
Published: February 2025

Background/Objectives: Malignant adrenal tumors comprise both primary adrenal neoplasms and metastatic lesions, with the latter being significantly more common. Squamous cell carcinoma (SCC) of the larynx is a prevalent head and neck cancer that typically spreads to the cervical lymph nodes, with distant metastases being rare. Among such metastases, adrenal gland involvement is particularly uncommon, presenting unique diagnostic and therapeutic challenges. The study aimed to explore the progression, diagnostic process, and therapeutic management of a rare case of SCC of the larynx metastasizing to the adrenal gland, highlighting the role of advanced diagnostic imaging and a multidisciplinary approach in patient care. Methodology: A 66-year-old male with grade 3 SCC of the larynx underwent total laryngectomy, selective cervical lymphadenectomy, and radiotherapy with a dose of 70 Gy. Chemotherapy was discontinued due to hematological complications. Post-treatment monitoring included CT and PET-CT imaging, leading to the detection of a large adrenal mass. Surgical biopsy confirmed metastatic SCC in the adrenal gland, as resection was not feasible due to extensive invasion. Diagnostic imaging and histopathological examination were complemented by biochemical evaluations to assess hormonal activity. Results: The adrenal mass was identified as a metastasis from the laryngeal SCC. Imaging studies provided detailed insights into the lesion’s size, metabolic activity, and non-functional status. Despite comprehensive efforts, the tumor was deemed unresectable, highlighting the aggressive nature of the disease and the limitations of current therapeutic modalities. Conclusions: This rare case emphasizes the importance of early detection, advanced imaging techniques, and interdisciplinary collaboration in managing complex metastatic presentations. It underscores the critical need for further research into systemic treatments, such as immunotherapy, and the development of standardized protocols for rare metastatic patterns. The study contributes to the growing body of literature on the management of uncommon cancer metastases, advocating for individualized patient care and innovation in treatment strategies.
Tobacco Residues Deposition at the Surface of Cobalt–Chromium Dental Alloys and the Effect of Cigarette Smoke Extract on Human Mesenchymal Stem Cells: An In Vitro Study
Authors: Uriciuc Willi-Andrei , Bosca Bianca Adina , Tertis Mihaela , Țigu Adrian-Bogdan , Moldovan Radu-Cristian , Suciu Maria , Barbu-Tudoran Lucian , Topală Tamara Liana , Crisan Liana , Popa Cătălin Ovidiu , Ilea Aranka
Published: February 2025

The current study began with the following question: Is smoking a balanced factor between human body systems? One of the particular features of the oral cavity is its localization at the gateway of respiratory and digestive. Morphologically, the oral cavity encompasses a complex association of soft tissues, hard tissues, salivary glands, and taste receptors. The main purpose of this study was to analyze the tobacco residues (TAR) deposited on dental materials and the alterations of artificial saliva that comes into contact with tobacco smoke, by obtaining a solution of cigarette smoke extracts (CSE) after 5, 10, 15, and 20 tobacco cigarettes. According to LC-MS analysis and FT-IR spectra, carbonyl compounds, phenols, and carboxylic acids are present in CSE, which could explain the pH decrease and acid characteristic. Moreover, the CSE solution was added to the culture medium of Mesenchymal Stem Cells (MSCs) to evaluate the cytotoxicity. The MTT study revealed decreased MSC viability; morphological changes and cell death were more intense at higher doses of CSE added to the culture medium. Scanning Electron Microscopy (SEM) indicated cellular ruffling and irregular cell surface under higher concentrations of CSE-15 and CSE-20 in culture media, which is a characteristic feature demonstrating the membrane stress. In conclusion, the present study, with its limitations, showed the negative cellular effects of tobacco cigarette smoking and the impact of this habit on the oral cavity homeostasis.
Drug Discovery for SARS-CoV-2 Utilizing Computer-Aided Drug Design Approaches
Authors: Guo Jiao , Bai Yang , Guo Yan , Wang Meihua , Ji Xinxin , Wang Yang
Published: February 2025

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense RNA virus with an unusually large genome of approximately 30 kb. It is highly transmissible and exhibits broad tissue tropism. The third most pathogenic of all known coronaviruses, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for the clinical manifestation known as coronavirus disease 2019 (COVID-19), which has resulted in the loss of millions of lives on a global scale. This pandemic has prompted significant efforts to develop therapeutic strategies that target the virus and/or human proteins to control viral infection. These efforts include the testing of hundreds of potential drugs and thousands of patients in clinical trials. Although the global pandemic caused by the SARS-CoV-2 virus is approaching its end, the emergence of new variants and drug-resistant mutants highlights the need for additional oral antivirals. The appearance of variants and the declining effectiveness of booster shots are resulting in breakthrough infections, which continue to impose a significant burden on healthcare systems. Computer-aided drug design (CADD) has been widely utilized for predicting drug–target interactions and evaluating drug safety; it is regarded as an effective tool for identifying promising drug candidates to combat SARS-CoV-2. The CADD approach aids in the discovery of new drugs or the repurposing of United States Food and Drug Administration (FDA)-approved drugs, whose safety and side effects are already well established, thus making the process more viable. This review summarizes potential therapeutic agents that target SARS-CoV-2 or host proteins critical for viral pathogenesis, as identified using CADD approaches. Additionally, this study provides insights into the common in silico methods used in CADD and their current applications in the SARS-CoV-2 drug discovery process.
The Physiatrist in Intensive Care: Role, Tasks, and Critical Issues in a Clinical Case Report Analysis
Authors: Magro Valerio Massimo , Sorbino Andrea , Manocchio Nicola , Ljoka Concetta , Foti Calogero
Published: February 2025

Background: Disorders of Consciousness (DoC) following acute brain injuries, such as intracerebral hemorrhage, present significant clinical challenges in intensive care and rehabilitation settings. Early multidisciplinary interventions, including physiatric care, are critical in optimizing recovery trajectories. However, evidence regarding the timing and intensity of rehabilitation interventions remains limited. This case report highlights the role of physiatrists in managing a critically ill patient with a DoC in an Intensive Care Unit (ICU), focusing on early rehabilitation strategies and individualized care planning. Case presentation: A 63-year-old male with a history of hypertension and cardiac disease presented with a left hemispheric hemorrhage and quadriventricular intraventricular hemorrhage. The patient was admitted to the ICU in a comatose state (Glasgow Coma Scale [GCS] 5). Initial physiatric evaluation revealed a critical condition precluding immediate initiation of an Individual Rehabilitation Project (IRP). Over subsequent weeks, clinical improvements were observed, including an increased GCS and Coma Recovery Scale-Revised (CRS-R) score. A tailored IRP was implemented, emphasizing passive mobilization to prevent complications such as muscle atrophy, joint contractures, and pressure ulcers. The patient demonstrated gradual progress, transitioning to a Minimally Conscious State (MCS) and achieving improved joint mobility and reduced peripheral edema. Discussion and Conclusions: This case underscores the pivotal role of physiatrists in ICU settings, particularly for patients with DoC. Early physiatric interventions, even in critically ill patients, can prevent secondary complications and facilitate functional recovery. Close collaboration with ICU teams and infectious disease specialists ensured the safe implementation of rehabilitation strategies despite the patient’s severe condition. The observed clinical improvements highlight the potential benefits of early mobilization and individualized care plans, both in terms of survival (quoad vitam) and quality of life (quoad valetudinem). This report emphasizes the need for further research to refine rehabilitation practices for patients with DoC, bridging gaps between acute care and neurorehabilitation.
An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification
Authors: Alruwaili Madallah , Mohamed Mahmood
Published: February 2025

Background: Medical diagnosis for skin diseases, including leukemia, early skin cancer, benign neoplasms, and alternative disorders, becomes difficult because of external variations among groups of patients. A research goal is to create a fusion-level deep learning model that improves stability and skin disease classification performance. Methods: The model design merges three convolutional neural networks (CNNs): EfficientNet-B0, EfficientNet-B2, and ResNet50, which operate independently under distinct branches. The neural network model uses its capability to extract detailed features from multiple strong architectures to reach accurate results along with tight classification precision. A fusion mechanism completes its operation by transmitting extracted features to dense and dropout layers for generalization and reduced dimensionality. Analyses for this research utilized the 27,153-image Kaggle Skin Diseases Image Dataset, which distributed testing materials into training (80%), validation (10%), and testing (10%) portions for ten skin disorder classes. Results: Evaluation of the proposed model revealed 99.14% accuracy together with excellent precision, recall, and F1-score metrics. Conclusions: The proposed deep learning approach demonstrates strong potential as a starting point for dermatological diagnosis automation since it shows promise for clinical use in skin disease classification.
Discrepancy in the Histological Diagnoses of Oral Lichen Planus Based on WHO Criteria Versus the Newly Proposed Diagnostic Set of the American Academy of Oral and Maxillofacial Pathology
Authors: Mutafchieva Maria Zaharieva , Tashkova Desislava Atanasova
Published: February 2025

Background/Objectives: The diagnosis of oral lichen planus (OLP) is challenging because many other oral diseases demonstrate similar clinical and microscopic features. Clinicopathological discrepancy and inter- and intraobserver variability in the histological assessment of OLP have been shown in the literature, as there are no unified diagnostic criteria for the disease. In 2016, the American Academy of Oral and Maxillofacial Pathology (AAOMP) proposed a new diagnostic set for OLP. The aim of the study was to assess the reliability of the AAOMP histological criteria in diagnosing OLP. Methods: In this retrospective study, 34 histological sections, once diagnosed as OLP, were revised by a second pathologist using the WHO criteria. Then, all specimens were analyzed for the presence (P) or absence (A) of the criteria listed in the AAOMP diagnostic set. The reproducibility of the histological diagnosis of OLP when applying the different sets of diagnostic criteria was assessed. Results: From the AAOMP diagnostic criteria, hydropic degeneration was found in 35.2%, lymphocytic exocytosis in 32.3%, mild epithelial dysplasia in 2.9%, verrucous epithelial architectural change in 0% and band-like lymphocytic infiltrate, confined to the epithelium-lamina propria interface in 55.8% of the samples. Reproducibility of the histological diagnosis of OLP was achieved in only 19.3% of the cases when applying the 1978 WHO criteria versus the newly proposed AAOMP criteria. Conclusions: A large number of OLP cases failed to meet the AAOMP histological criteria in the present study. Further studies are needed to assess the validity of the proposed diagnostic set.
Advanced Doppler Ultrasound Insights: A Multicenter Prospective Study on Healthy Skin
Authors: Giavedoni Priscila , Romaní Jorge , de Cabo Francisco , García-Martínez Francisco Javier , Quintana-Codina Mónica , Roè-Crespo Esther , Fuertes de Vega Irene , Soria-Gili Xavier , Aguayo-Ortiz Rafael , Garbayo-Salmons Patricia , Castillo Gonzalo , Vidal-Sarró David , Mollet Jordi , Serra Laura , Gonzalez Carlos , López-Trujillo Emilio , Just Miquel , Combalia Marc , Podlipnik Sebastian , Malvehy Josep , Wortsman Ximena
Published: February 2025

Background: There have been multiple studies on the use of Doppler ultrasound to define skin inflammation, but the visible vessels of healthy skin have yet to be described. Objective: This study aimed to evaluate the visible vessels of healthy skin using Doppler ultrasound. Methods: Prospective multicenter study using Doppler ultrasound to analyze healthy skin. The color percentage, flow velocity, and maximum vessel diameter were calculated. Results: 943 images from 152 patients were recorded. The most frequently used mode was color Doppler (40.6%), followed by power Doppler (30.4%). Visible vessels were detected in 18.23%; in positive Doppler images, color occupied less than 5%. The malar region exhibited the highest visible vessels. The 22 MHz probe detected smaller vessels with slower flows than the 18 MHz probe. Spectral Doppler showed peak systolic values of less than 10 cm/s and a vessel diameter of less than 1 mm. In most of the participating centers, the operators had less than 10 years of experience in performing skin ultrasound examinations. Sensitivity of the Doppler may vary according to the device. Conclusions: With the used ultrasound equipment, it was uncommon to visualize vessels in healthy skin. When seen, they covered less than 5% of the image with low flow and small size.
Development of a High-Reliability Hybrid Data Transmission System for Unmanned Surface Vehicles Under Interference Conditions
Authors: Kurdiuk Sergiy , Dremliuk Vadym , Melnyk Oleksiy , Onishchenko Oleg , Fomin Oleksij , Píštěk Václav , Kučera Pavel
Published: February 2025

This paper discusses modern approaches to the creation of a highly reliable data transmission system for unmanned surface vehicles (USVs) operating under interference conditions. In contrast to existing solutions, an improved communication algorithm is proposed to ensure uninterrupted transmission of video, telemetry, and control signals even in highly unstable environments. The study identifies the main technical requirements for data transmission and evaluates the key parameters of the communication channel. The proposed hybrid communication system utilizes adaptive channel switching, data compression, and equipment reconfiguration, improving data transmission stability and reducing latency. A comparative analysis of existing communication technologies reveals the limitations of acoustic, optical, and radio wave systems. A conceptual architecture combining these technologies provides optimal data transmission by adapting to the environment. Experimental results confirm the effectiveness of the system, demonstrating reliable operation even with 80% packet loss in public Internet networks. The system’s adaptability, low latency, and dynamic routing make it suitable for real-time USV operations, including environmental monitoring, scientific research, and search and rescue missions. Its potential extends to commercial and dual applications requiring sustained data transmission in challenging maritime environments.
Technical Progress and Sustainable Growth in the Manufacturing Sector of North American Countries, 1984–2022: A Stochastic Frontier Analysis
Authors: Navarro-Chávez César Lenin
Published: February 2025

This article presents an estimation of a stochastic frontier model using a translogarithmic production function to identify the impact of production factors—labor and capital—along with CO2 emissions and technical progress on the value added of the manufacturing sector in North American countries over the 1984–2022 period. The model also provides estimates for technical efficiency, scale efficiency, and technological change, allowing for a comparative analysis of these indicators’ evolution within the manufacturing sectors of Canada, Mexico, and the United States. The findings indicate that capital exerts the strongest influence on manufacturing value added, followed by labor. CO2 emissions exhibit the anticipated negative effect on the sector’s value added. Notably, the average technical efficiency of Mexico’s manufacturing sector is higher than that of Canada and the United States over the studied period. Regarding technological change, the United States demonstrates the highest values, followed by Canada, with both nations displaying an upward trend throughout the years, while Mexico shows a declining trend in this indicator.
Comparative Analysis of Offshore Wind Resources and Optimal Wind Speed Distribution Models in China and Europe
Authors: Chen Yanan , Zhao Ming , Liu Zhengxian , Ma Jianlong , Yang Lei
Published: February 2025

Offshore wind resources in China and Europe are systematically compared, focusing on wind speed characteristics and the selection of optimal wind speed probability distribution models. Using 20 years of data at 10 m and 100 m above sea level, seven unimodal wind speed probability distribution models were applied. The results point out that China’s offshore wind resources exhibit high spatial and temporal variability, influenced by monsoons and typhoons, while European seas are characterized by stable wind patterns. Among the models tested, the Weibull distribution is the most accurate one for wind speed fitting, while the Generalized Extreme Value and Gamma models perform better in regions with higher skewness and extreme wind events. This study highlights the importance of wind speed characteristics, such as skewness and kurtosis, in selecting the optimal model. These findings provide valuable guidance for the improvement of offshore wind energy assessments and the selection of appropriate models. Future research should explore advanced techniques, such as machine learning and hybrid models, to better capture complex wind patterns and enhance model accuracy.
Networked Microgrid Energy Management Considering Ownership and Control Structures: A Comparison
Authors: Liu Guodong , Starke Michael
Published: February 2025

With the growing deployment of microgrids, networked microgrids have emerged for their additional advantages of economy, reliability and resilience by coordinating the operation of multiple microgrids. As microgrids are operated with different ownership, objectives and functionalities, the formed networked microgrids show characteristics of mixed ownership, inconsistent objectives and various functionalities. To enable the coordinated operation of networked microgrids, three control structures, i.e., centralized, distributed and decentralized, have been constructed in the literature. However, the data sharing enabling these different paradigms and the resulted value propositions are not well defined, leading to poor resource management and resilience, etc. To solve this issue, a complete comparison of networked microgrid energy management under centralized, distributed and decentralized structures are performed. As a novel contribution, the required minimum data exchange of networked microgrid energy management under three control structures are identified, respectively. The value propositions are calculated and compared against each other by the results of case studies.
Research on Safety Domain Modeling of Low-Voltage Distribution Substations Based on High-Dimensional Safety Region Analysis
Authors: Guan Tianyi , Ma Zhuang , Ren Hao , Yu Qingshuai , Zhang Rongxing , Sun Zhenao
Published: February 2025

A low-voltage distribution substation is the last link before electricity transmission from the high-voltage grid to end-users. It is responsible for converting high-voltage electricity into low-voltage electricity suitable for domestic and commercial use and plays a central and critical role in the power system. The traditional modeling method is difficult to directly observe and solve the complete safety boundary expression in the high-dimensional state space, so the solution efficiency is greatly reduced. To address the above problems, this paper proposes a low-voltage distribution substation station safety domain (LVDS-SR) modeling method based on the high-dimensional safety domain definition method. In this paper, the concepts of safety work point, safety boundary, and safety domain are first defined. Then, the general mathematical model, edge points, and safety boundaries of the substation system are solved accurately by the high-dimensional safety domain definition and solution method, to obtain the safety domain model. The validity of the model and method is verified by arithmetic examples. Comparison with existing studies shows that the complete analytical formulation of the high-dimensional security domain is obtained for the first time in this paper, and the linearization method is used to improve the solution efficiency at the same time. This study provides a new analytical tool for the reliable and stable operation of low-voltage distribution substations, which has important theoretical and practical application value for the security assessment and optimization of power systems.
Wind Turbine Fault Diagnosis with Imbalanced SCADA Data Using Generative Adversarial Networks
Authors: Wang Hong , Li Taikun , Xie Mingyang , Tian Wenfang , Han Wei
Published: February 2025

Wind turbine fault diagnostics is essential for enhancing turbine performance and lowering maintenance expenses. Supervisory control and data acquisition (SCADA) systems have been extensively recognized as a feasible technology for the realization of wind turbine fault diagnosis tasks due to their capacity to generate vast volumes of operation data. However, wind turbines generally operate normally, and fault data are rare or even impossible to collect. This makes the SCADA data distribution imbalanced, with significantly more normal data than abnormal data, resulting in a decrease in the performance of existing fault diagnosis techniques. This article presents an innovative deep learning-based fault diagnosis method to solve the SCADA data imbalance issue. First, a data generation module centered on generative adversarial networks is designed to create a balanced dataset. Specifically, the long short-term memory network that can handle time series data well is used in the generator network to learn the temporal correlations from SCADA data and thus generate samples with temporal dependencies. Meanwhile, the convolutional neural network (CNN), which has powerful feature learning and representation capabilities, is employed in the discriminator network to automatically capture data features and achieve sample authenticity discrimination. Then, another CNN is trained to perform fault classification using the augmented balanced dataset. The proposed approach is verified utilizing actual SCADA data derived from a wind farm. The comparative experiments show the presented approach is effective in diagnosing wind turbine faults.
Analysis of Quality and Sensory Characteristics of Functional Fermented Products
Authors: Cosme Fernanda , Patarata Luís , Nunes Fernando M.
Published: February 2025

Fermentation has long been a basis of food processing, playing a critical role in enhancing nutritional value, sensory appeal and functional properties [...]
Self-Healing Hydrogels with Intrinsic Antioxidant and Antibacterial Properties Based on Oxidized Hydroxybutanoyl Glycan and Quaternized Carboxymethyl Chitosan for pH-Responsive Drug Delivery
Authors: Jeong Jae-pil , Kim Kyungho , Oh Eunkyung , Park Sohyun , Jung Seunho
Published: February 2025

In this study, self-healing hydrogels were created using oxidized hydroxybutanoyl glycan (OHbG) and quaternized carboxymethyl chitosan (QCMCS), displaying antioxidant and antibacterial properties for pH-responsive drug delivery. The structures of the modified polysaccharides were confirmed through 1H NMR analysis. Double crosslinking in the hydrogel occurred via imine bonds (between the aldehyde group of OHbG and the amine group of QCMCS) and ionic interactions (between the carboxyl group of OHbG and the quaternized group of QCMCS). The hydrogel exhibited self-healing properties and improved thermal stability with an increase in OHbG concentration. The OHbG/QCMCS hydrogel demonstrated high compressive strength, significant swelling, and large pore size. Drug release profiles varied between pH 2.0 (96.57%) and pH 7.4 (63.22%). Additionally, the hydrogel displayed antioxidant and antibacterial effects without compromising the polysaccharides’ inherent characteristics. No cytotoxicity was observed in any hydrogel samples. These findings indicate that the OHbG/QCMCS hydrogel is a biocompatible and stimuli-responsive drug carrier, with potential for various pharmaceutical, biomedical, and biotechnological applications.
Molecular Abnormalities and Carcinogenesis in Barrett’s Esophagus: Implications for Cancer Treatment and Prevention
Authors: de Melo Viana Thaís Cabral , Nakamura Eric Toshiyuki , Park Amanda , Filardi Kaique Flávio Xavier Cardoso , de Almeida Leite Rodrigo Moisés , Baltazar Luiz Fernando Sposito Ribeiro , Usón Junior Pedro Luiz Serrano , Tustumi Francisco
Published: February 2025

Background: Barrett’s esophagus (BE) is described by the transformation of the normal squamous epithelium into metaplastic columnar epithelium, driven by chronic gastroesophageal reflux disease (GERD). BE is a recognized premalignant condition and the main precursor to esophageal adenocarcinoma (EAC). Understanding the molecular mechanisms underlying BE carcinogenesis is crucial for improving prevention, surveillance, and treatment strategies. Methods: This narrative review examines the molecular abnormalities associated with the progression of BE to EAC. Results: This study highlights inflammatory, genetic, epigenetic, and chromosomal alterations, emphasizing key pathways and biomarkers. BE progression follows a multistep process involving dysplasia and genetic alterations such as TP53 and CDKN2A (p16) mutations, chromosomal instability, and dysregulation of pathways like PI3K/AKT/mTOR. Epigenetic alterations, including aberrant microRNA expression or DNA methylation, further contribute to this progression. These molecular changes are stage-specific, with some alterations occurring early in BE during the transition to high-grade dysplasia or EAC. Innovations in chemoprevention, such as combining proton pump inhibitors and aspirin, and the potential of antireflux surgery to halt disease progression are promising. Incorporating molecular biomarkers into surveillance strategies and advancing precision medicine may enable earlier detection and personalized treatments. Conclusions: BE is the primary preneoplastic condition for EAC. A deeper understanding of its molecular transformation can enhance surveillance protocols, optimize the management of gastroesophageal reflux inflammation, and refine prevention and therapeutic strategies, ultimately contributing to a reduction in the global burden of EAC.
First Description of Intergenic Sequences in Corydoradinae and Introducing the Complete Mitogenome of Hoplisoma concolor (Siluriformes: Callichthyidae)
Authors: Do Seong Duk , Rhee Jae-Sung
Published: February 2025

Background/Objectives: In this study, we report the complete mitochondrial genome sequence of Hoplisoma concolor Weitzman, 1961 (Siluriformes: Callichthyidae), a callichthyid catfish. Methods: DNA sequencing was performed to obtain its complete mitogenome using the HiSeq platform. To assess the phylogenetic relationships, maximum-likelihood and Bayesian inference phylogenetic trees were constructed using two ribosomal RNA (rRNA) genes and all protein-coding sequences (PCGs) concatenated from the H. concolor mitogenome, along with 31 other Siluriformes mitogenomes. Results: The complete mitogenome of H. concolor is 16,579 base pairs in length, with a nucleotide composition of 32.2% A, 26.0% T, 15.3% G, and 26.5% C. It contains 13 PCGs, 22 transfer RNA genes, and 2 rRNA genes. Phylogenetic analysis based on all PCGs and two rRNAs of the complete mitogenome confirms H. concolor as a sister species of H. panda within the subfamily Corydoradinae. In addition, intergenic sequences between atp6 and cox3 of 21 species of Corydoradinae provide further support for their phylogenetic relationship. Conclusions: Given the lack of detailed descriptions regarding the length and nucleotide composition of these intergenic sequences, our study contributes valuable insights into the genetic diversity and evolutionary complexity of Callichthyidae.
Healthcare Practitioners’ Perceptions of mHealth Application Barriers: Challenges to Adoption and Strategies for Enhancing Digital Health Integration
Authors: Alzghaibi Haitham
Published: February 2025

Background: Mobile health (mHealth) applications have transformed healthcare delivery by enhancing accessibility, patient monitoring, and clinician communication. Despite these advantages, significant barriers hinder their adoption among healthcare practitioners, limiting their effectiveness in primary care settings. Understanding these barriers is crucial for optimizing mHealth integration into healthcare systems. Aim: This study examines healthcare practitioners’ perceptions of barriers to mHealth application adoption, with a focus on the Sehaty app (version 1.3) in Saudi Arabia. It aims to identify key challenges, assess their impact on user engagement and system efficiency, and provide insights for enhancing digital health implementation. Methods: A cross-sectional survey was conducted among 409 primary healthcare practitioners using the Sehaty app. The study employed a structured questionnaire assessing ten major barriers to mHealth adoption, including technical, usability, privacy, and integration challenges. Descriptive statistics, ANOVA, t-tests, and correlation analyses were performed to examine differences across demographic groups and relationships among identified barriers. Results: Findings revealed that technical and usability challenges were the most significant barriers, with system compatibility (Mean = 3.64), slow performance (Mean = 3.43), and excessive task complexity (Mean = 3.45) among the most cited issues. Training and support limitations (Mean = 3.28) and workflow integration difficulties (Mean = 3.24) further hindered adoption. Correlation analysis indicated weak interdependencies among barriers, suggesting that targeted interventions addressing specific concerns may be more effective. ANOVA results showed that digital literacy significantly influenced perceptions of communication barriers (p = 0.046), while gender differences in usability and productivity constraints were marginally significant. Conclusions: The study underscores the necessity for improved system interoperability, user-centered design, and enhanced technical support to promote mHealth adoption. Addressing these challenges through strategic policy initiatives and infrastructure improvements is essential for fostering a more integrated and effective digital healthcare ecosystem.
Visual Narratives of Change: Examining Students’ Drawings and Teaching Transformations at MIT
Authors: Sousa Santos Rafael , Pimenta do Vale Clara , Bogoni Barbara , Kirkegaard Poul Henning
Published: February 2025

Since its inception, the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA) has preserved student drawings and design projects as educational tools and reference materials. This practice, modeled after the Fine Arts School tradition, led to the archiving of thousands of student thesis drawings during MIT’s first century. These drawings provide valuable data for documenting the development of architectural education at MIT, illustrating shifts in representation techniques, responses to various influences, and changing architectural conceptualizations. In this study, we examine a collection of thesis drawings produced between 1873 and 1968 using a systematic, data-driven approach that incorporates both qualitative and quantitative methods for collection, processing, and analysis. By comparing these drawings with course catalogs from the corresponding years, we aim to investigate the relationship between drawing practices and teaching methodologies, contributing to a better understanding of the evolution of architectural education at MIT and the role of drawing in that process.