bluuebunny/crossref_metadata_2025 huggingface dataset:
Utilization of Machine Learning and Explainable Artificial Intelligence (XAI) for Fault Prediction and Diagnosis in Wafer Transfer Robot
Authors: Jeon Jeong Eun , Hong Sang Jeen , Han Seung-Soo
Published: November 2024

Faults in the wafer transfer robots (WTRs) used in semiconductor manufacturing processes can significantly affect productivity. This study defines high-risk components such as bearing motors, ball screws, timing belts, robot hands, and end effectors, and generates fault data for each component based on Fluke’s law. A stacking classifier was applied for fault prediction and severity classification, and logistic regression was used to identify fault components. Additionally, to analyze the frequency bands affecting each failed component and assess the severity of faults involving two mixed components, a hybrid explainable artificial intelligence (XAI) model combining Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) was employed to inform the user about the component causing the fault. This approach demonstrated a high prediction accuracy of 95%, and its integration into real-time monitoring systems is expected to reduce maintenance costs, decrease equipment downtime, and ultimately improve productivity.
Efficient Analysis of Noise Induced in Low-Voltage Installations Placed Inside Buildings with Lightning Protection Systems
Authors: Noga Artur , Topa Tomasz , Stefański Tomasz P.
Published: November 2024

This paper describes an efficient approach to the broadband analysis of lightning protection systems (LPSs) using the method of moments (MoM) implemented in the frequency domain. The adaptive frequency sampling (AFS) algorithm, based on a rational interpolation of the relevant observable (e.g., voltage, current, electric or magnetic field) which describes the properties of the LPS, is employed to reduce the number of samples computed by the full-wave MoM. This improvement is achieved by the quick comparison of two interpolants with the use of the Stöer–Bulirsch algorithm, which provides the frequency location of the next MoM samples for computations. This algorithm allows for the efficient localization of resonant frequencies while reducing the number of samples computed over the entire frequency range. In the instances when the induced noise is determined in low-voltage installations protected by various types of LPSs, reductions in computational overhead equal to 47.9× and 72.1× in broadband LPS simulations are obtained. Hence, the proposed approach allows for a significant reduction in computational overhead in comparison to standard, uniformly sampled simulations.
Observer-Based Fixed-Time Consensus Tracking for a Class of Nonlinear Multi-Agent Systems
Authors: Cao Guanzhen , Xiong Guanghuan , Hong Xingkui , Tan Xiangmin
Published: November 2024

In this paper, the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems (MASs) with uncertainties and external disturbances is addressed. To solve this problem, a new fixed-time consensus tracking protocol with disturbance rejection abilities is proposed that consists of a fixed-time distributed observer (FTDO), a fixed-time disturbance observer (FDO), and a nonsmooth backstepping controller with lumped disturbance compensation (NBCDC). The fixed-time stabilities are analyzed correspondingly. Firstly, an FTDO is designed to estimate a leader’s output by using the topology of communication networks for each follower. Second, an FDO is developed to observe the lumped disturbances composed of model uncertainties and external disturbances. Finally, an NBCDC is proposed for improving the closed-loop performance. The simulation results show that the proposed method is effective.
A Novel Mathematical Approach for Inductor-Current Expressions Definition in Multilevel Dual-Active-Bridge Converters
Authors: Borrell-Pons Eudald , Esquius-Mas Oriol , Filba-Martinez Alber , Trilla Lluís
Published: November 2024

The study of multilevel dual-active-bridge (DAB) converters has garnered significant attention in recent years thanks to their advantages with respect to the conventional two-level (2L) DAB; namely, its greater performance and its capability to operate at higher voltage. The analysis of the converter high-frequency inductor current (iL) is crucial, for instance, to compute its root mean square (RMS) value, required to estimate the conduction losses in the converter. The mathematical expression of iL is piecewise and multiple variations, i.e., modes, exist depending on the modulation parameter values. This increases the complexity of converter performance analytical study. Thus, a more practical and generalizable expression of iL current is desirable. This paper proposes novel compact analytic expressions for the instantaneous and RMS inductor current in the 2L-NL DAB converter, leveraging binary functions to define the piecewise intervals and to identify the mode as a function of the modulation parameter values. The proposed method paves the way for more simple and computationally efficient DAB performance optimization software tools that allow exploring any given converter structures and modulation strategies.
Secure and Flexible Privacy-Preserving Federated Learning Based on Multi-Key Fully Homomorphic Encryption
Authors: Shen Jiachen , Zhao Yekang , Huang Shitao , Ren Yongjun
Published: November 2024

Federated learning avoids centralizing data in a central server by distributing the model training process across devices, thus protecting privacy to some extent. However, existing research shows that model updates (e.g., gradients or weights) exchanged during federated learning may still indirectly leak sensitive information about the original data. Currently, single-key homomorphic encryption methods applied in federated learning cannot solve the problem of privacy leakage that may be caused by the collusion between the participant and the federated learning server, whereas existing privacy-preserving federated learning schemes based on multi-key homomorphic encryption in semi-honest environments have deficiencies and limitations in terms of security and application conditions. To this end, this paper proposes a privacy-preserving federated learning scheme based on multi-key fully homomorphic encryption to cope with the potential risk of privacy leakage in traditional federated learning. We designed a multi-key fully homomorphic encryption scheme, mMFHE, that encrypts by aggregating public keys and requires all participants to jointly participate in decryption sharing, thus ensuring data security and privacy. The proposed privacy-preserving federated learning scheme encrypts the model updates through multi-key fully homomorphic encryption, ensuring confidentiality under the CRS model and in a semi-honest environment. As a fully homomorphic encryption scheme, mMFHE supports homomorphic addition and homomorphic multiplication for more flexible applications. Our security analysis proves that the scheme can withstand collusive attacks by up to N−1 users and servers, where N is the total number of users. Performance analysis and experimental results show that our scheme reduces the complexity of the NAND gate, which reduces the computational load and improves the efficiency while ensuring the accuracy of the model.
The Current Status, Challenges, and Future of China’s Photovoltaic Industry: A Literature Review and Outlook
Authors: Wang Feng , Liu Weiwei
Published: November 2024

This paper reviews the transformative shifts within China’s photovoltaic (PV) industry against the backdrop of a global pivot from fossil fuels to renewable energies, a transition underscored by the pressing demands of climate change mitigation. By systematically analyzing existing literature, this study captures the rapid advancements and dominant role of China in the global PV market, spurred by robust governmental support and technological innovation. It also identifies persistent challenges such as technological gaps, supply chain instability, and evolving regulatory frameworks. Key findings highlight the industry’s significant contributions to national energy security and its pivotal role in achieving China’s carbon neutrality goals. This research underscores the critical importance of the PV industry in steering global sustainable energy policies and practices.
A Fast Variance Reduction Technique for Efficient Radiation Shielding Calculations in Nuclear Reactors
Authors: Jo Seungjae , Kim Sanghwan , Cho Jaehyun
Published: November 2024

The increasing demand for cleaner and more sustainable energy sources has sparked significant interest in small modular reactors (SMRs). Due to their compact and modular design, SMRs pose unique challenges in radiation shielding, requiring a more refined approach. This study focuses on developing a new variance reduction technique (VRT) for radiation shielding analysis, specifically tailored for SMRs, to address the limitations of traditional methods such as surface source write/surface source read (SSW/SSR). The proposed VRT supports multi-threading and enhances computational efficiency by redefining source particles using a two-step method. The analysis is conducted using the Monte Carlo radiation transport code, MCNP6, and the effectiveness of the new VRT is evaluated through sensitivity analyses across various energy and directional divisions.
Cleaning of Abnormal Wind Speed Power Data Based on Quartile RANSAC Regression
Authors: Zhang Fengjuan , Zhang Xiaohui , Xu Zhilei , Dong Keliang , Li Zhiwei , Liu Yubo
Published: November 2024

The combined complexity of wind turbine systems and harsh operating conditions pose significant challenges to the accuracy of operational data in Supervisory Control and Data Acquisition (SCADA) systems. Improving the precision of data cleaning for high proportions of stacked abnormalities remains an urgent problem. This paper deeply analyzes the distribution characteristics of abnormal data and proposes a novel method for abnormal data cleaning based on a classification processing framework. Firstly, the first type of abnormal data is cleaned based on operational criteria; secondly, the quartile method is used to eliminate sparse abnormal data to obtain a clearer boundary line; on this basis, the Random Sample Consensus (RANSAC) algorithm is employed to eliminate stacked abnormal data; finally, the effectiveness of the proposed algorithm in cleaning abnormal data with a high proportion of stacked abnormalities is verified through case studies, and evaluation indicators are introduced through comparative experiments to quantitatively assess the cleaning effect. The research results indicate that the algorithm excels in cleaning effectiveness, efficiency, accuracy, and rationality of data deletion. The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection.
Ecological Gate Water Control and Its Influence on Surface Water Dynamics and Vegetation Restoration: A Case Study from the Middle Reaches of the Tarim River
Authors: Wu Jie , Gao Fan , He Bing , Sheng Fangyu , Xu Hailiang , Liu Kun , Zhang Qin
Published: November 2024

Ecological sluices were constructed along the Tarim River to supplement the ecosystem’s water supply. However, the impact of water regulation by these sluices on the surface water area (SWA) and its relationship with the vegetation response remain unclear. To increase the efficiency of ecological water use, it is crucial to study the response of SWA to water control by the ecological gates and its relationship with vegetation restoration. We utilized the Google Earth Engine (GEE) cloud platform, which integrates Landsat-5/7/8 satellite imagery and employs methods such as automated waterbody extraction via mixed index rule sets, field investigation data, Sen + MK trend analysis, mutation analysis, and correlation analysis. Through these techniques, the spatiotemporal variations in SWA in the middle reaches of the Tarim River (MROTR) from 1990–2022 were analyzed, along with the relationships between these variations and vegetation restoration. From 1990–2022, the SWA in the MROTR showed an increasing trend, with an average annual growth rate of 12.47 km2 per year. After the implementation of ecological gates water regulations, the SWA significantly increased, with an average annual growth rate of 28.8 km2 per year, while the ineffective overflow within 8 km of the riverbank notably decreased. The NDVI in the MROTR exhibited an upward trend, with a significant increase in vegetation on the northern bank after ecological sluice water regulation. This intervention also mitigated the downward trend of the medium and high vegetation coverage types. The SWA showed a highly significant negative correlation with low-coverage vegetation within a 5-km range of the river channel in the same year and a significant positive correlation with high-coverage vegetation within a 15-km range. The lag effect of SWA influenced the growth of medium- and high-coverage vegetation. These findings demonstrated that the large increase in SWA induced by ecological gate water regulation positively impacted vegetation restoration. This study provides a scientific basis for water resource regulation and vegetation restoration in arid regions globally.
Optimization of the Culture Medium of Beauveria bassiana and Spore Yield Using Response Surface Methodology
Authors: Lou Hanxiao , Luo Qizheng , Guo Qingqing , Su Runshen , Liu Jianfeng , He Hongli , Cheng Yunqing
Published: November 2024

Beauveria bassiana has distinct nutrient requirements depending on the growth stage, with the mycelial growth stage being particularly nutrient-intense. When nutrients are exhausted, B. bassiana produces numerous spores that re-germinate and release visible hyphae under suitable environmental conditions. This study aimed to use low-cost agricultural by-products for developing an optimized sporulation medium. Single-factor experiments revealed that potato, wheat bran, and wheat had the greatest influence on sporulation after 14 d of culture. Wheat, wheat bran, and corncob had the strongest effect on spore production. Using a mathematical model and Box–Behnken design, the maximum predicted sporulation using the stock medium was 5.55 × 106/mL. The optimized formula comprised 54.82 g potato, 77.74 g wheat bran, and 101.48 g wheat. The actual stock medium produced a spore yield of 5.52 × 106/mL. Similarly, the maximum predicted sporulation was 15.66 × 106/mL for the culture medium. The optimized formula comprised 50.81 g wheat, 99.6 g wheat bran, and 112.59 g corncob, with a spore yield of 15.69 × 106/mL. The high consistency between predicted and experimental values indicated that our model can accurately determine the effect of medium on sporulation.
Enhanced Fumagillin Production by Optimizing Fermentation and Purification Techniques
Authors: Feng Zili , Du Pengfei , Sheng Yunzhi , Wang Bin , Chen Wang , Peng Hao
Published: November 2024

Fumagillin is a complex biomolecule. Nowadays, the separation and purification techniques of fumagillin still need to be optimized. In this study, the yield of fumagillin was examined in relation to temperature, pH, inoculation volume, rotation speed, and liquid volume. By using single-factor optimization, a factorial design, and response surface methodology, the fumagillin yield increased from 10~15% to 30~35%. After scaling up the fermentation process, the fumagillin yield remained stable at 30–35%. In addition, fumagillin was prepared via high-performance preparative liquid chromatography; the yield of the preparation was 77.29%, and the purity was greater than 99%. To sum up, these findings demonstrated that the issue of low fumagillin yield can be resolved by optimizing the fermentation process and introducing the high-performance liquid chromatographic method for the rapid preparation of fumagillin with high recoveries. The findings of this study may serve as a foundation for the large-scale production of fumagillin.
Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure
Authors: Khatiwoda Naba Raj , Dawadi Babu Ram , Joshi Sashidhar Ram
Published: November 2024

With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper planning procedures are to be adopted to provide cost-effective and quality telecommunication services. In this paper, we planned 5G network deployment in two frequency ranges, 3.5 GHz and 28 GHz, using a mixed cell structure. We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. The comparative analysis of metaheuristic algorithms shows that the proposed network is efficient in providing an average data rate of 50 Mbps, can meet the coverage requirements of at least 98%, and meets quality-of-service requirements. We carried out the case study for an urban area and another suburban area of Kathmandu Valley, Nepal. We analyzed the outcomes of 5G greenfield deployment and 5G deployment using existing 4G infrastructure. Deploying 5G networks using existing 4G infrastructure, resources can be saved up to 33.7% and 54.2% in urban and suburban areas, respectively.
Development of Whole-Grain Rice Lines Exhibiting Low and Intermediate Glycemic Index with Decreased Amylose Content
Authors: Chaichoompu Ekawat , Ruengphayak Siriphat , Wattanavanitchakorn Siriluck , Wansuksri Rungtiwa , Yonkoksung Usa , Suklaew Phim On , Chotineeranat Sunee , Raungrusmee Sujitta , Vanavichit Apichart , Toojinda Theerayut , Kamolsukyeunyong Wintai
Published: November 2024

The demand for rice varieties with lower amylose content (AC) is increasing in Southeast Asia, primarily due to their desirable texture and cooking qualities. This study presents the development of whole-grain rice lines with low to intermediate glycemic index (GI) and reduced AC. We selected six rice lines for in vivo GI assessment based on their starch properties. We successfully identified two lines with low AC that exhibited low and intermediate GI values, respectively. Our findings indicate that dietary fiber (DF) content may significantly influence rice GI. The selected whole-grain low-GI line showed a higher ratio of soluble dietary fiber (SDF) to insoluble dietary fiber (IDF) compared to control varieties, highlighting SDF’s potential positive role in lowering whole-grain rice’s GI. This study underscores the feasibility of developing rice varieties with desirable agronomic traits, nutritional traits, and culinary attributes, particularly for individuals managing their blood sugar levels. Additionally, we proposed the positive role of starch composition, especially DF content, in modulating the GI of rice. This study reinforces the importance of incorporating starch properties and DF content into rice breeding programs to produce more health-oriented and marketable rice varieties.
Enhancement of Nutrient, Trace Element, and Organic Selenium Contents of Ratooning Rice Grains and Straw Through Foliar Application of Selenite
Authors: Wu Wenjiang , Qi Deqiang , Chen Yalong , Wang Jiaqi , Wang Qinghua , Yang Yanjun , Niu Hongbin , Zhao Quanzhi , Peng Ting
Published: November 2024

Selenium (Se) is an essential trace element that has various beneficial effects for human healthy. However, the effects of different Se forms and concentrations on growth and development, photosynthetic characteristics and antioxidant capacity are still unclear with regard to the dual grain-and-feed dual-use of ratoon rice (RR). In this study, three concentrations of three different Se forms were applied to RR using the foliar spraying method, and the results showed that Se treatment can increase the Se content of rice grain and straw. All the Se treatments improved the photosynthetic indexes and activities of antioxidant enzymes. The Se and trace elements contents, and the percentages of organic Se and protein Se of brown rice were found to be similar in all three Se forms. A higher organic Se content was found in the grain by spraying sodium selenite and Se-Met, in which the resistant starch (RS) content was increased with the increase in amylose content in grains. The main Se species in the grain was SeMet and the SeMeCys was found only with SeMet treatments. The grain quality showed that all three Se forms increased the consistency of gelatinization. Our study indicated that exogenous Se could improve the nutritional quality of both grain and straw by improving photosynthetic traits and antioxidant enzyme activities, especially sodium selenite and Se-Met. These results underscore the potential of foliar biofortification to enhance the functional component contents of RR grains and provide an insight into the Se enrichment of ratoon rice.
Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models
Authors: Poudel Bishal , Dahal Dewasis , Banjara Mandip , Kalra Ajay
Published: November 2024

The rising frequency and severity of droughts requires accurate monitoring and forecasting to reduce the impact on water resources and communities. This study aims to investigate drought monitoring and categorization, while enhancing drought forecasting by using three machine learning models—Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF). The models were trained on the study region’s historic precipitation and temperature data (minimum and maximum) from 1960 to 2021. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were computed for a time scale of 3, 6 and 12 months. The monthly precipitation data were used for creating lag scenarios and were used as input features for the models to improve the models’ performance and reduce overfitting. Statistical parameters like the coefficient of determination (R2), Mean Absolute Error (MAE), Root mean square error (RMSE) and Nash–Sutcliffe Efficiency (NSE) were determined to evaluate the model accuracy. For forecasting, the SPEI3, ANN and SVM models show better performance (R2 > 0.9) than the RF models when the 3-month lag data were used as input features. For SPEI6 and SPEI12, the 6-month lag and 12-month lag data, respectively, were needed to increase the models’ accuracy. The models exhibited RMSE values of 0.27 for ANN, 0.28 for SVM, and 0.37 for RF for the SPEI3, indicating the superior performance of the former two. The models’ accuracy increases as the lag period increases for SPI forecasting. Overall, the ANN and SVM models outperformed the RF model for forecasting long-term drought.
Cathode Materials for Intermediate Temperature Solid Oxide Fuel Cells
Authors: Nisar Jamila , Kaur Gurpreet , Giddey Sarbjit , Bhargava Suresh , Jones Lathe
Published: November 2024

Intermediate temperature solid oxide fuel cell (SOFC) operation provides numerous advantages such as high combined heat and power (CHP) efficiency, potentially long-term material stability, and the use of low-cost materials. However, due to the sluggish kinetics of the oxygen reduction reaction at intermediate temperatures (500–700 °C), the cathode of SOFC requires an efficient and stable catalyst. Significant progress in the development of cathode materials has been made over recent years. In this article, multiple strategies for improving the performance of cathode materials have been extensively reviewed such as A- and B-site doping of perovskites, infiltration of catalytic active materials, the use of core-shell composites, etc. Emphasis has been given to intrinsic properties such as chemical and thermal stability and oxygen transport number. Furthermore, to avoid any insulating phase formation at the cathode/electrolyte interface, strategies for interfacial layer modifications have also been extensively reviewed and summarized. Based on major technical challenges, future research directions have been proposed for efficient and stable intermediate temperature solid oxide fuel cell (SOFC) operation.
Lidocaine-Loaded Thermoresponsive Gel for Accelerated Wound Healing in Dry Socket and Oral Wounds
Authors: Supachawaroj Nuttawut , Kerdmanee Kunchorn , Limsitthichaikoon Sucharat
Published: November 2024

Dry socket, also known as alveolar osteitis, presents significant challenges in oral surgery because of severe pain and delayed wound healing. This study aims to address these challenges by developing and evaluating a lidocaine-loaded polyelectrolyte complex thermoresponsive gel (LG) designed to enhance wound healing and provide effective pain management in oral wounds. The thermoresponsive gel transitions from a liquid to a gel at body temperature, ensuring sustained contact with the wound site and prolonged release of lidocaine. The in vitro assessments, including cytotoxicity and wound scratch assays, demonstrated the biocompatibility and therapeutic potential of the LG formulation. Following this, palatal wounds were induced in rats, with healing monitored over a 14-days period. Histological analyses were conducted to assess tissue regeneration and inflammation. The results indicated that the LG formulation significantly improved wound closure rates, reduced inflammation, and accelerated epithelialization compared with control groups, primarily because of the high content of hyaluronic acid (HA). The synergistic effects of HA combined with the thermoresponsive properties of the gel facilitated faster healing. These findings suggest that LG is a promising therapeutic option for enhancing oral wound healing and effectively managing pain, particularly in conditions such as dry socket.
Development and Quality Enhancement of Fried Fish Cake Prototype with Transglutaminase, Trehalose, and Herbal Oil for Room Temperature Distribution
Authors: Kim Ye Youl , Bashir Khawaja Muhammad Imran , Kang Seonyeong , Park Yongjoon , Sohn Jae Hak , Choi Jae-Suk
Published: November 2024

This study focuses on developing a fried fish cake prototype with improved quality and extended shelf-life, enabling room-temperature distribution through an innovative high-temperature and high-process retort method. Surimi-based products typically necessitate cold storage and a refrigerated distribution system, affecting their physical properties and flavor while escalating costs. By incorporating Transglutaminase (TGase), trehalose, and herbal oils, and optimizing the heating process using the response surface methodology, this research addresses challenges related to changes in physical properties, color, and off-flavors during high-temperature and high-pressure treatment. The addition of 0.37% ACTIVA-K TGase significantly enhanced gel strength by promoting protein cross-linking, while 0.75% trehalose improved color stability by suppressing browning, thus enhancing visual appeal. A 0.1% concentration of bay oil effectively enhanced the flavor profile by masking undesirable odors without compromising the sensory quality. Optimized processing conditions maximized DPPH radical scavenging activity, whiteness, and gel strength, ensuring superior product quality and safety. Nutritional analysis confirmed a balanced composition of moisture, protein, essential amino acids, and minerals, in accordance with Korean national standards for acid values. Microstructural examination revealed a uniform network structure, contributing to excellent texture and sensory evaluations. Shelf-life predictions indicated a storage duration of approximately 19 months, surpassing commercially available products and offering a competitive edge. This novel approach allows surimi-based products to be stored and distributed at room temperature, while also providing the potential for increased profitability.
Deep Learning for Urban Tree Canopy Coverage Analysis: A Comparison and Case Study
Authors: Morgan Grayson R. , Zlotnick Danny , North Luke , Smith Cade , Stevenson Lane
Published: November 2024

Urban tree canopy (UTC) coverage, or area, is an important metric for monitoring changes in UTC over large areas within a municipality. Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. This study seeks to compare three urban tree canopy cover classifiers by testing a deep learning U-Net convolutional neural network (CNN), support vector machine learning classifier (SVM) and a random forests machine learning classifier (RF) on cost-free 2012 aerial imagery over a small southern USA city and midsize, growing southern USA city. The results of the experiment are then used to decide the best classifier and apply it to more recent aerial imagery to determine canopy changes over a 10-year period. The changes are subsequently compared visually and statistically with recent urban heat maps derived from thermal Landsat 9 satellite data to compare the means of temperatures within areas of UTC loss and no change. The U-Net CNN classifier proved to provide the best overall accuracy for both cities (89.8% and 91.4%), while also requiring the most training and classification time. When compared spatially with city heat maps, city periphery regions were most impacted by substantial changes in UTC area as cities grow and the outer regions get warmer. Furthermore, areas of UTC loss had higher temperatures than those areas with no canopy change. The broader impacts of this study reach the urban forestry managers at the local, state/province, and national levels as they seek to provide data-driven decisions for policy makers.
Assessing the Use of Electrical Resistivity for Monitoring Crude Oil Contaminant Distribution in Unsaturated Coastal Sands Under Varying Salinity
Authors: Adeniran Margaret A. , Oladunjoye Michael A. , Doro Kennedy O.
Published: November 2024

Monitoring crude oil spills in coastal areas is challenging due to limitations in traditional in situ methods. Electrical resistivity imaging (ERI) offers a high-resolution approach to monitoring the subsurface spatial distribution of crude oil, but its effectiveness in highly-resistive, unsaturated coastal sands with varying salinity remains unexplored. This study assessed the effectiveness of ERI for monitoring crude oil spills in sandy soil using a 200 × 60 × 60 cm 3D sandbox filled with medium-fine-grained sand under unsaturated conditions. Two liters of crude oil were spilled under controlled conditions and monitored for 48 h using two surface ERI transects with 98 electrodes spaced every 2 cm and a dipole–dipole electrode array. The influence of varying salinity was simulated by varying the pore-fluid conductivities at four levels (0.6, 20, 50, and 85 mS/cm). After 48 h, the results show a percentage resistivity increase of 980%, 280%, 142%, and 70% for 0.6, 20, 50, and 85 mS/cm, respectively. The crude oil migration patterns varied with porewater salinity as higher salinity enhanced the crude oil retention at shallow depth. High salinity produces a smaller resistivity contrast, thus limiting the sensitivity of ERI in detecting the crude oil contaminant. These findings underscore the need to account for salinity variations when designing remediation strategies, as elevated salinity may restrict crude oil migration, resulting in localized contaminations.
Refining Heterogeneities near the Core–Mantle Boundary Beneath East Pacific Regions: Enhanced Differential Travel-Time Analysis Using USArray
Authors: Ko Yenting (Justin) , Hu Kai-Jie
Published: November 2024

Recent advancements in seismic data analysis have enhanced our grasp of the seismic heterogeneities near the core–mantle boundary (CMB). Through seismic tomography, persistent lower-mantle structures like the large low shear velocity provinces (LLSVPs) beneath the Pacific and South Africa have been identified. However, variations in the finer-scale features across different models raise questions about their origins. This study utilizes differential travel-time measurements from the USArray, operational across the contiguous United States from 2007 to 2014, to examine the impact of upper-mantle heterogeneities on tomographic models. By averaging the P-wave travel times and calibrating them with diffracted P-waves at the same stations, we mitigate the effects of shallow heterogeneities. The findings confirm that this method accurately maps the seismic anomalies beneath the USArray, consistent with other North American studies. Calibrated Pdiff travel-time data indicate significant anomalies in the mid-Pacific and Bering Sea and lesser anomalies in the northern Pacific, aligning with the global tomographic images. Moreover, the study highlights sharp travel-time variations over short distances, such as those across the northern boundary of the mid-Pacific anomaly, suggesting a chemically heterogeneous Pacific LLSVP.
An Overview of Family and Community Nurse Specialists’ Employment Situation in Spain: A Qualitative Study
Authors: Sánchez-Muñoz Francisca , Fernández-Medina Isabel María , Ventura-Miranda María Isabel , Ortega-Galán Ángela María , Jiménez-Lasserrotte María del Mar , Ruíz-Fernández María Dolores
Published: November 2024

Background: Family and Community Nurse specialists are advocates of a holistic model of care in multidisciplinary primary care teams. This study aims to describe the experiences and perceptions of nurses specialising in Family and Community Nursing regarding their working conditions in primary care in Spain. Methods: A qualitative descriptive study was conducted. Eighteen family and community specialist nurses from different autonomous communities in Spain participated. Individual interviews and a focus group were conducted. Results: The results identified two main themes: The current work situation of the Family and Community Nursing specialist and Support network and system of rejection with four sub-themes highlighting the lack of social and work recognition, the advantages of working with Family and Community Nursing specialists, systematic ambivalence towards Family and Community Nursing, and the need for institutional support. The inclusion of Family and Community Nursing specialists in primary care teams favours the nurse–patient bond, increases and/or maintains the quality of life of patients, and strengthens their empowerment; however, there is an absence of specific job vacancies. Conclusions: The institutional and social lack of awareness about the roles of Family and Community nurse practitioners and their impact on health care systems limits the quality of patient care in primary care.
A Comparative Study on Patient Safety Awareness Between Medical School Freshmen and Age-Matched Individuals
Authors: Kohara Suguru , Miura Kentaro , Sasamori Chie , Hase Shuho , Shu Akihito , Kasai Kenji , Yokoshima Asuka , Fujishiro Naofumi , Otaki Yasuhiro
Published: November 2024

Background: To provide more effective pregraduate patient safety education, understanding medical students’ perceptions of patient safety before pregraduate patient safety education is necessary. Therefore, we conducted this study to examine patient safety awareness among medical students at the time of admission and compare it with that among controls. Methods: In the 2019 academic year, 132 medical school freshmen enrolled at Teikyo University and 166 age-matched, non-medical students enrolled at an affiliated institution within the Teikyo University organization were surveyed using an anonymous and self-administered questionnaire. The questionnaire divided patient safety awareness into three categories: perception, knowledge, and attitude, which were evaluated on a 5-point Likert scale (Cronbach’s alpha coefficient was 0.77). To assess overall patient safety awareness, the total scores were calculated for the item groups on “perception”, “knowledge”, and “attitude” and compared these scores between the two groups. Results: The total scores (mean ± SD) were 104.2 ± 10.2 for medical students and 88.8 ± 9.6 for controls (p < 0.001). In the “perception” and “attitude” item groups, a higher proportion of medical students provided a positive response than controls. In particular, medical students were more motivated to learn about patient safety than the controls. In the “knowledge” item group, neither medical students nor controls provided a high proportion of positive responses. Conclusions: Medical students demonstrated a higher awareness of patient safety than controls and showed a strong sensitivity to patient safety from the time of enrollment.
Single-Vessel PCI Versus CABG: Navigating Single-Vessel Disease Treatment
Authors: Go Yun Yun , Lancellotti Patrizio
Published: November 2024

Clinical equipoise remains regarding the optimal revascularization strategy for single-vessel disease, specifically isolated left anterior descending (LAD) artery disease, owing to the scarcity of contemporary randomized controlled trials [...]
Spatial-Temporal Changes in Ecosystem Service Value and Its Overlap with Coal Mining Intensity in the Yellow River Basin, China, During 2000–2030
Authors: Yang Yongjun , Gong Renjie , Wu Qinyu , Chen Fu
Published: November 2024

Understanding the ecosystem services and their interaction with coal resource development is crucial for formulating sustainable development policies. In this study, we focused on the Yellow River Basin, characterized by both rich coal resources and ecological fragility. The key findings are that (1) the ecosystem service value (ESV) in the Yellow River Basin exhibited significant spatial heterogeneity during 2000–2030, decreasing from the southeast to northwest, and decreasing the most notably in the southern part of the upper reaches of the river basin; (2) the high-high clustering area of the ESV shifted from the upper-middle reaches in 2000 to the middle-lower reaches in 2020, while the low-low clustering area remained within Inner Mongolia. By 2030, the high-high clustering area is expected to stabilize in southern Shaanxi Province, and the low-low area will potentially spread eastward; (3) the overall ESV is low, and it experienced a significant decline from 2000 to 2020, with water supply emerging as a major limiting factor, although some policy-supported counties had better ecological service values and trends. (4) From 2000 to 2020, the coal mining intensity (CMI) was concentrated in the upper and middle reaches, particularly at the junctions of Shanxi, Shaanxi, and Inner Mongolia, and the pattern remained stable, but local areas experienced increased mining intensity; (5) the overlap of the CMI and ESV primarily exhibited a low-high clustering pattern in the middle and upper reaches of the Yellow River Basin and eastern Ordos City, and a high-high clustering pattern in the middle reaches of the basin in Shanxi Province, which remained stable and slightly expanded from 2000 to 2030; (6) the trade-off between the ecosystem services in the overlap area intensified, especially between the provisioning and support services, and was significantly impacted by the coal mining activities. The findings indicate that the area that overlaps with the coal mining area in the Yellow River Basin has expanded and has had an increasing negative impact on the ESV. It is also essential to address the trade-offs between the provisioning and support services and to implement ecological restoration measures to mitigate the risk of ESV loss. Future efforts should focus on the regions where the CMI and ESV overlap and have poor coordination and the adverse effects of resource extraction on ecosystem services are becoming more pronounced. The results of this study demonstrate that spatial overlap analysis is effective in identifying the hotspots and provides a foundation for developing sustainable and high-quality policies for ecologically fragile basins.
Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches
Authors: Patne Akshata Yashwant , Dhulipala Sai Madhav , Lawless William , Prakash Satya , Mohapatra Shyam S. , Mohapatra Subhra
Published: November 2024

The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based drug discovery, particularly in small-molecule approaches. The Simplified Molecular Input Line Entry System (SMILES), which translates a chemical compound’s three-dimensional structure into a string of symbols, is now widely used in drug design, mining, and repurposing. Utilizing ML and natural language processing techniques, SMILES has revolutionized lead identification, high-throughput screening and virtual screening. ML models enhance the accuracy of predicting binding affinity and selectivity, reducing the need for extensive experimental screening. Additionally, deep learning, with its strengths in analyzing spatial and sequential data through convolutional neural networks (CNNs) and recurrent neural networks (RNNs), shows promise for virtual screening, target identification, and de novo drug design. Fragment-based approaches also benefit from ML algorithms and techniques like generative adversarial networks (GANs), which predict fragment properties and binding affinities, aiding in hit selection and design optimization. Structure-based drug design, which relies on high-resolution protein structures, leverages ML models for accurate predictions of binding interactions. While challenges such as interpretability and data quality remain, ML’s transformative impact accelerates target-based drug discovery, increasing efficiency and innovation. Its potential to deliver new and improved treatments for various diseases is significant.
Metabolic Flux Analysis of Xanthomonas oryzae Treated with Bismerthiazol Revealed Glutathione Oxidoreductase in Glutathione Metabolism Serves as an Effective Target
Authors: Yu Hai-Long , Liang Xiao-Long , Ge Zhen-Yang , Zhang Zhi , Ruan Yao , Tang Hao , Zhang Qing-Ye
Published: November 2024

Bacterial blight (BB) of rice caused by Xanthomonas oryzae pathovar oryzae (Xoo) is a serious global rice disease. Due to increasing bactericide resistance, developing new inhibitors is urgent. Drug repositioning offers a potential strategy to address this issue. In this study, we integrated transcriptional data into a genome-scale metabolic model (GSMM) to screen novel anti-Xoo targets. Two RNA-seq datasets (before and after bismerthiazol treatment) were used to constrain the GSMM and simulate metabolic processes. Metabolic fluxes were calculated using parsimonious flux balance analysis (pFBA) identifying reactions with significant changes for target screening. Glutathione oxidoreductase (GSR) was selected as a potential anti-Xoo target and validated through antibacterial experiments. Virtual screening based on the target identified DB12411 as a lead compound with the potential for new antibacterial agents. This approach demonstrates that integrating metabolic networks and transcriptional data can aid in both understanding antibacterial mechanisms and discovering novel drug targets.
Preoperative Protein Profiling Among Postoperative Cognitive Dysfunction (POCD) Patients Following Open-Heart Surgery: A Systematic Review and Integrated Bioinformatic Analysis
Authors: Elias Marjanu Hikmah , Abdul Hamid Nazefah , Maluin Sofwatul Mokhtarah , Aris Shamsir , Kadiman Suhaini , Muhammad Hafidz Kamilah , Juliana Norsham
Published: November 2024

The inability to accurately predict the occurrence of postoperative cognitive dysfunction (POCD) among open-heart surgery patients leads to concerning increases in POCD cases. Preoperative circulating biomarkers are important to identify as they are non-invasive and could provide an early prediction of POCD development, allowing for earlier and more strategized interventions. However, to date, no robust circulating biomarkers have proven effective for preoperative POCD prediction. This systematic review aims to synthesize current evidence on preoperative protein profiling among POCD patients following open-heart surgery. Thus, a thorough literature search employing PubMed, EBSCOhost, Scopus, and Science Direct was carried out. This combination of keywords was used as part of the search strategy: (“Postoperative cognitive decline” OR “Postoperative cognitive disorders” OR “Postoperative cognitive dysfunction” OR “Postoperative cognitive complications”) AND (“Thoracic Surgery” OR “Cardiac Surgery” OR “Heart Surgery”) AND (“Protein expression” OR proteomic OR “Protein profiling”). Eight hundred and twenty-nine studies were retrieved and only clinical studies reporting the circulating preoperative differentially expressed Proteins (DEPs) in the POCD patients were selected. Six studies were selected following the inclusion and exclusion criteria. Only one preoperative DEP and four immediate postoperative DEPs were extracted from the studies. All four proteins were selected for analysis using DAVID, STRING, and Cytoscape software. Due to the very low number of proteins, no clusters have been identified. This systematic review demonstrates the lack of POCD preoperative biomarkers for open-heart-surgery patients. Thus, it is suggested that more studies can be conducted to fill this gap.
High-Throughput Tear Proteomics via In-Capillary Digestion for Biomarker Discovery
Authors: Xiao James , Frenia Kyla , Garwood Kathleen C. , Kimmel Jeremy , Labriola Leanne T.
Published: November 2024

Tear fluid has emerged as a valuable resource for biomarker discovery; however, the limited sample volume, the dynamic composition, and the variability introduced by collection methods all present significant challenges to the analysis and interpretation of the results. A majority of tear proteomic studies have utilized Schirmer strips for tear fluid collection; however, microcapillary collection can provide a superior collection method for proteomic studies when analysis procedures are optimized. We developed a novel, high-throughput in-capillary trypsin digestion workflow that requires as little as 0.5 μL of tear fluid for bottom–up shotgun proteomics. The use of a single microcentrifuge tube for both tear collection and sample processing simplifies sample handling and minimizes both the sample loss and experimental errors associated with sample transfers. This streamlined approach also reduces sample processing time to under 2 h before overnight trypsin digestion, compared to the 5–8 h required by the other methods. Our method uses liquid chromatography–tandem mass spectrometry (LC–MS/MS) to identify more proteins with greater efficiency than the existing techniques. With this workflow, we identified 500–800 proteins per 0.5 μL sample without peptide fractionation, allowing for at least three technical replicates. The results showed a four-fold increase in the number of proteins identified in the samples. This approach validates the use of microcapillary tear collection, and the innovative processing technique significantly increases the throughput of tear proteomics for biomarker discovery.