bluuebunny/crossref_metadata_2025_split huggingface dataset:
Power regulation of variable speed multi rotor wind systems using fuzzy cascaded control
Authors: Benbouhenni Habib , Colak Ilhami , Bizon Nicu , Mosaad Mohamed I. , Tella Teshome Goa 
Published: July 2024

AbstractPower quality is a crucial determinant for integrating wind energy into the electrical grid. This integration necessitates compliance with certain standards and levels. This study presents cascadedfuzzy power control (CFPC) for a variable-speed multi-rotor wind turbine (MRWT) system. Fuzzy logic is a type of smart control system already recognized for its robustness, making it highly suited and reliable for generating electrical energy from the wind. Therefore, the CFPC technique is proposed in this work to control the doubly-fed induction generator (DFIG)-based MRWT system. This proposed strategy is applied to the rotor side converter of a DFIG to improve the current/power quality. The proposed control has the advantage of being model-independent, as it relies on empirical knowledge rather than the specific characteristics of the DFIG or turbine. Moreover, the proposed control system is characterized by its simplicity, high performance, robustness, and ease of application. The implementation of CFPC management for 1.5 MW DFIG-MRWT was carried out in MATLAB environment considering a variable wind speed. The obtained results were compared with the direct power control (DPC) technique based on proportional-integral (PI) controllers (DPC-PI), highlighting that the CFPC technique reduced total harmonic distortion by high ratios in the three tests performed (25%, 30.18%, and 47.22%). The proposed CFPC technique reduced the response time of reactive power in all tests by ratios estimated at 83.76%, 65.02%, and 91.42% compared to the DPC-PI strategy. Also, the active power ripples were reduced by satisfactory proportions (37.50%, 32.20%, and 38.46%) compared to the DPC-PI strategy. The steady-state error value of reactive power in the tests was low when using the CFPC technique by 86.60%, 57.33%, and 72.26%, which indicates the effectiveness and efficiency of the proposed CFPC technique in improving the characteristics of the system. Thus this control can be relied upon in the future.
Noninvasive reconstruction of internal heat source in biological tissue using adaptive simulated annealing algorithm
Authors: Ye Fuli , Shi Diwen , Xu Cheng , Li Kaiyang , Lin Minyue , Shi Guilian 
Published: July 2024

AbstractThe heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. This paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the adaptive simulated annealing (ASA) algorithm is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the sum of absolute values of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.
Seismic performance and cost comparison of RC moment resisting and dual frames using UBC 97 and IBC 2021
Authors: Shakeel Sarmad , Khan Saadan Hussain , Saqib Syed Aayan , Khan Muhammad Awais , Moiz Muhammad Abdul 
Published: July 2024

AbstractThe transition from the Uniform Building Code (UBC-97) to the International Building Code (IBC-21) marked a major shift in the definition of seismic hazard. The term “seismic hazard” in the form of peak ground acceleration (PGA) is replaced by spectral acceleration. This paper investigates the effect of using new seismic hazards on the structural performance of reinforced concrete (RC) buildings. It also looks into the financial impact on the capital costs of new buildings. Useful insights are made to understand the structural performance and financial impact of adopting IBC 21 for structural design in contrast to UBC 97. This study was carried out from the perspective of a developing country, Pakistan. Reinforced concrete moment resisting and dual frames are used as the main structural system of a typical 7-story residential building to investigate the aforementioned effect. The frames are assumed to be located in two locations with high and low seismic hazards. The effect on structural performance is investigated via nonlinear pushover analysis. Financial impact is judged mainly through cost estimation for steel and concrete. A detailed discussion is also presented on the seismic design guidelines in both codes.
Causality of genetically determined blood metabolites on inflammatory bowel disease: a two-sample Mendelian randomization study
Authors: Long Xiongquan , Zhang Yuyang , Liu Mingzhu , Liu Zihao , Xia Lvzhou , Xu Xiaoping , Wu Minghao 
Published: July 2024

AbstractInflammatory bowel disease (IBD) is a chronic and recurrent inflammatory disease of the gastrointestinal tract, including two subtypes: Crohn’s disease (CD) and ulcerative colitis (UC). Metabolic disorders are important factors in the development of IBD. However, the evidence for the causal relationship between blood metabolites and IBD remains limited. A two-sample MR analysis was applied to evaluate relationships between 486 blood metabolites and IBD. The inverse variance weighted method was chosen as the primary MR analysis method. False discovery rate correction was used to control for false positives in multiple testing. Following complementary and sensitivity analyses were conducted using methods such as weight median, MR-egger, weighted mode, simple mode, Cochran Q test, and MR-PRESSO. Moreover, we performed replication, meta-analysis, Steiger test, and linkage disequilibrium score regression to enhance the robustness of the results. Additionally, we performed metabolic pathway analysis to identify potential metabolic pathways. As a result, we identified four significant causal associations between four blood metabolites and two IBD subtypes. Specifically, one metabolite was identified as being associated with the development of CD (mannose: odds ratio (OR) = 0.19, 95% confidence interval (CI) 0.08–0.43, P = 8.54 × 10–5). Three metabolites were identified as being associated with the development of UC (arachidonate (20:4n6): OR = 0.18, 95% CI 0.11–0.30, P = 2.09 × 10–11; 1, 5-anhydroglucitol: OR = 2.21, 95% CI 1.47–3.34, P = 1.50 × 10–4; 2-stearoylglycerophosphocholine: OR = 2.66, 95% CI 1.53–4.63, P = 5.30 × 10–4). The findings of our study suggested that the identified metabolites and metabolic pathways can be considered as useful circulating metabolic biomarkers for the screening and prevention of IBD in clinical practice, as well as candidate molecules for future mechanism exploration and drug target selection.
High-pressure phase transition in 3-D printed nanolamellar high-entropy alloy by imaging and simulation insights
Authors: Pope Andrew D. , Chen Wen , Chen Hangman , Cao Penghui , Yeghishyan Armenuhi , Zhukovskyi Maksym , Manukyan Khachatur , Vohra Yogesh K. 
Published: July 2024

AbstractWe report on the high-resolution imaging and molecular dynamics simulations of a 3D-printed eutectic high-entropy alloy (EHEA) Ni40Co20Fe10Cr10Al18W2 consisting of nanolamellar BCC and FCC phases. The direct lattice imaging of 3D-printed samples shows the Kurdjumov–Sachs (K–S) orientation relation {111} FCC parallel to {110} BCC planes in the dual-phase lamellae. Unlike traditional iron and steels, this alloy shows an irreversible BCC-to-FCC phase transformation under high pressures. The nanolamellar morphology is maintained after pressure cycling to 30 GPa, and nano-diffraction studies show both layers to be in the FCC phase. The chemical compositions of the dual-phase lamellae after pressure recovery remain unchanged, suggesting a diffusion-less BCC–FCC transformation in this EHEA. The lattice imaging of the pressure-recovered sample does not show any specific orientation relation between the two resulting FCC phases, indicating that many grain orientations are produced during the BCC–FCC phase transformation. Molecular dynamics simulations on phase transformation in a nanolamellar BCC/FCC in K–S orientation show that phase transformation from BCC to FCC is completed under high pressures, and the FCC phase is retained on decompression aided by the stable interfaces. Our work elucidates the irreversible phase transformation under static compression, providing an understanding of the orientation relationships in 3-D printed EHEA under high pressures.
Modeling pediatric antibiotic use in an area of declining malaria prevalence
Authors: Rautman Lydia Helen , Eibach Daniel , Boateng Felix Osei , Akenten Charity Wiafe , Hanson Henry , Maiga-Ascofaré Oumou , May Jürgen , Krumkamp Ralf 
Published: July 2024

AbstractIn malaria-endemic areas of Sub-Saharan Africa, overlap of clinical symptoms between malarial and non-malarial febrile illnesses can lead to empiric use of antibiotics among children. Our study aimed to illustrate the potential impact of decreasing malaria prevalence from malaria control efforts on antibiotic use. We constructed a probabilistic decision tree model representing antibiotic prescription in febrile children < 5 years. This model was used to predict change in absolute antibiotic use compared to baseline under levels of decreasing malaria prevalence. Model parameters were based on data from a hospital study in Ghana and validated via literature review. The baseline prevalence of malaria diagnoses was 52% among all hospitalized children. For our main results, we reported outcomes for a scenario representing a 50% decrease in malaria prevalence. Compared to baseline, absolute antibiotic prescription decreased from a baseline of 639 doses (95% CI 574–694) to 575 (95% CI 502–638). This reflected a 10% (95% CI 7%–13%) decrease in absolute antibiotic use. Our findings demonstrate that effective malaria control can reduce pediatric antibiotic use. However, until substantial progress is made in developing accurate diagnostics for non-malarial febrile illnesses, further reductions in antibiotic use will remain a challenge.
Associations between tinnitus and body composition: a cross-sectional study
Authors: Han Sang-Yoon , Lee Sang-Yeon , Suh Myung-Whan , Lee Jun Ho , Park Moo Kyun 
Published: July 2024

AbstractThe relationship between tinnitus and body composition in specific regions has not been extensively investigated. This study aimed to identify associations between tinnitus and body composition. Individuals with data on physical and otological examination findings, and bioelectrical impedance analysis were included from the ninth Korea National Health and Nutritional Examination Survey. They were divided into a tinnitus group and a non-tinnitus group. Participants with tinnitus were further classified into acute or chronic tinnitus group. The tinnitus group showed significantly higher body fat percentages in each region (arms: P = 0.014; legs: P = 0.029; trunk: P = 0.008; whole body: P = 0.010) and waist circumference (P = 0.007) than the non-tinnitus group, and exhibited lower leg muscle percentage (P = 0.038), total body fluid percentage (P = 0.010), and intracellular fluid percentage (P = 0.009) than the non-tinnitus group in men. Furthermore, men with chronic tinnitus showed a significantly higher trunk fat percentage (P = 0.015) and waist circumference (P = 0.043), and lower intracellular fluid percentage (P = 0.042) than their counterparts without tinnitus. No significant differences in body composition were observed among the groups in the female population. In men, body composition may be associated with tinnitus.
Predictive modelling of freeway space utilising clinical history, normalised muscle activity, dental occlusion, and mandibular movement analysis
Authors: Farook Taseef Hasan , Haq Tashreque Mohammed , Ramees Lameesa , Dudley James 
Published: July 2024

AbstractThis study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable. Six models were trained on synthetic data ranging from 500 to 25,000 observations, with 65 original observations reserved for testing: Linear Regression, Random Forest, CatBoost Regressor, XGBoost Regressor, Multilayer Perceptron Neural Network (MPNN), and TabNet. Explainable AI indicated that key predictors of freeway space included phonetics, resting temporalis muscle activity, mandibular muscle activity during clenching, body weight, mandibular hard tissue lateral displacements, and dental arch parameters. CatBoost excelled with a test error of 0.65 mm using 5000 synthetic data points, while a refined MPNN achieved the best performance with 25,000 synthetic data points and 121 unique predictors, yielding an absolute error of 0.43 mm on the 65 original observations.
WWP1 inhibition increases SHP2 inhibitor efficacy in colorectal cancer
Authors: Fan Hao , Hu Xuefei , Cao Fuao , Zhou Leqi , Wen Rongbo , Shen Hao , Fu Yating , Zhu Xiaoming , Jia Hang , Liu Zixuan , Wang Guimin , Yu Guanyu , Chang Wenjun , Zhang Wei 
Published: July 2024

AbstractProtein tyrosine phosphatase SHP2 activates RAS signaling, which is a novel target for colorectal cancer (CRC) therapy. However, SHP2 inhibitor monotherapy is ineffective for metastatic CRC and a combination therapy is required. In this study, we aimed to improve the antitumor efficacy of SHP2 inhibition and try to explore the resistance mechanism of SHP2 inhibitor. Results showed that WWP1 promoted the proliferation of CRC cells. Genetic or pharmacological inhibition of WWP1 enhanced the effect of SHP2 inhibitor in suppressing tumor growth in vitro and in vivo. WWP1 may mediate feedback reactivation of AKT signaling following SHP2 inhibition. Furthermore, nomogram models constructed with IHC expression of WWP1 and SHP2 greatly improved the accuracy of prognosis prediction for patients with CRC. Our findings indicate that WWP1 inhibitor I3C can synergize with SHP2 inhibitor and is expected to be a new strategy for clinical trials in treating advanced CRC patients.
Anomaly detection in network traffic with ELSC learning algorithm
Authors: Khan Muhammad Muntazir , Rehman Muhammad Zubair , Khan Abdullah , Abusham Eimad 
Published: July 2024

AbstractIn recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%.
Modular digital twin modelling method for 10 kV cable lines based on real‐time temperature field inversion
Authors: Huang Shijie , Niu Haiqing 
Published: July 2024

AbstractState perception of long‐span 10 kV cable lines is one of the critical issues in building intelligent distribution networks. Digital twin (DT), which can map the physical status of equipment in real‐time, has received extensive attention. However, traditional modelling methods cannot meet the requirement of long‐span complex DT's real‐time computation and scalability. In order to reduce the modelling cost and improve the calculation speed, a modular DT modelling method for cable lines based on real‐time temperature field inversion is proposed. The authors develop a modular reduced‐order modelling method that applies to long‐span 10 kV cable lines. Combined with the temperature field inversion, a DT inversion algorithm is proposed to evaluate the transient temperature field and contact resistance inside cable lines, which has been used to evaluate the emergency load capacity. Finally, temperature rise tests verified the DT inversion algorithm's correctness. The operating status of cable lines was evaluated through the modular DT, and the emergency time and emergency ampacity were calculated. The result shows that the new method meets the real‐time and high‐precision requirements of DT calculation in actual operation scenarios. It can be easily extended to cable lines of different voltage levels.
Low‐light visibility enhancement for improving visual surveillance in intelligent waterborne transportation systems
Authors: Liu Ryan Wen , Han Chu , Huang Yanhong 
Published: July 2024

AbstractUnder low‐light imaging conditions, visual scenes captured by intelligent waterborne transportation systems often suffer from low‐intensity illumination and noise corruption. The visual quality degradation would lead to negative effects in maritime surveillance, e.g., vessel detection, positioning and tracking, etc. To restore the low‐light images, we develop an effective visibility enhancement method, which contains a coarse‐to‐fine framework of spatially‐smooth illumination estimation. In particular, the refined illumination is effectively generated by optimizing a novel structure‐preserving variational model on the coarse version, estimated through the Max‐RGB method. The proposed variational model has the capacity of suppressing the textural details while preserving the main structures in the refined illumination map. To further boost imaging performance, the refined illumination is adjusted through the Gamma correction to increase brightness in dark regions. We then estimate the refined reflection map by implementing the joint denoising and detail boosting strategies on the original reflection. In this work, the original reflection is yielded by dividing the input image using the refined illumination. We finally produce the enhanced image by multiplying the adjusted illumination and the refined reflection. Experiments on synthetic and realistic datasets illustrate that our method can achieve comparable results to the state‐of‐the‐art techniques under different imaging conditions.
Guest Editorial: Unfolding the potential of 5G technologies for future wireless networks
Authors: Jeon Gwanggil , Ahmad Awais , Chehri Abdellah , Wen Shiping 
Published: July 2024

AbstractWith the rapid advancements in mobile Internet and smartphones, data traffic in current mobile communication systems is growing exponentially. At the same time, demands for lower latency, increased robustness, and higher energy efficiency are becoming more stringent. In response, 5G technology promises to meet these demands and is currently garnering extensive research interest from both industry and academia. 5G is not just an incremental improvement over its predecessors; it is a transformative technology designed to revolutionise mobile communications. By offering significantly higher speeds, reduced latency, and the ability to connect a massive number of devices simultaneously, 5G stands to impact a wide range of applications from autonomous vehicles to smart cities, healthcare, and beyond. Significant progress has been made in the standardisation and field deployment of 5G networks. Organisations such as the 3rd Generation Partnership Project (3GPP) have been instrumental in developing the standards that define 5G technologies. Moreover, various pilot projects and commercial deployments have been initiated around the world, showcasing the practical capabilities of 5G in real‐world environments.
Heavy metal toxicological analysis of vegetables cultivated in sewage-irrigated soil in the Indian metropolis of Ahmedabad City, Gujarat
Authors: Mohanty Bibhabasu , Patel Yogesh , Dhamsaniya Meet 
Published: July 2024

Using wastewater for irrigation poses a significant health risk. We measured the concentration of (cobalt, chromium, copper, manganese, nickel, lead, and zinc) in wastewater, soil, and vegetation at 8 sites along a 60 km stretch of the Sabarmati River in Ahmedabad city. An evaluation of the potential danger from consuming contaminated vegetables was conducted using Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), and Target Cancer danger (TCR).
The average concentration of the metals Co, Cr, Cu, Mn, Ni, Pb, and Zn exceeds the maximum permitted limits for irrigation purposes as established by regulatory bodies. The heavy metals in the collected soil sample are ranked in descending order of concentration as follows: Zn > Mn > Cu > Cr > Ni > Pb > Co. The average metal concentrations in vegetables range from 0.10-11.3 µg g-1 for Co, 5.2-11.8 µg g-1 for Cr, 0.04-9.9 µg g-1 for Cu, 12.3-110 µg g-1 for Mn, 0.7-4.2 µg g-1 for Ni, 0.4-8.4 µg g-1 for Pb, and 4.4-44 µg g-1 for Zn. The mean content of heavy metals (µg g-1) in the collected vegetable samples is highest in spinach, followed by brinjal, cabbage, tomato, and cauliflower. The Hazardous Quotient (THQ) shows a high health risk for Pb (6.1) and Mn (1.02), and a medium health risk for Cr (0.9). The Target Cancer Risk (TCR) emphasized the cancer risk posed by Chromium (Cr) and Nickel (Ni), following with Lead (Pb). The study indicates a link between health risks and consuming vegetables grown in the study area. It recommends improving wastewater treatment facilities and monitoring heavy metal levels in vegetables grown in soil irrigated with wastewater at regular intervals.