bluuebunny/crossref_metadata_2025 huggingface dataset:
An Energy-Efficient Scheme Design for NOMA-Based UAV-Assisted MEC Systems
Authors: Wang Shanshan , Luo Zhiyong
Published: October 2024

UAV-assisted MEC networks provide extensive communication coverage and massive computation services for mobile terminals (MTs), which are considered a promising edge paradigm to support future air–ground integrated communications. In this paper, an energy-efficient scheme in NOMA-based UAV-assisted MEC systems is proposed to address the system’s energy constraints and its inability to support massive MT access. Our goal is to minimize system-weighted energy consumption by jointly optimizing the allocation of transmission power, computation resources, and UAV trajectory scheduling. As the formulated problem is non-convex and difficult to solve directly, we decompose it into two manageable sub-problems and propose an iterative algorithm based on successive convex approximations (SCA) to solve each sub-problem alternatively. Simulation results show that the proposed joint optimization algorithm achieves a significant performance improvement compared to other benchmark approaches.
A Deep Learning PM2.5 Hybrid Prediction Model Based on Clustering–Secondary Decomposition Strategy
Authors: Zeng Tao , Liu Ruru , Liu Yahui , Shi Jinli , Luo Tao , Xi Yunyun , Zhao Shuo , Chen Chunpeng , Pan Guangrui , Zhou Yuming , Xu Liping
Published: October 2024

Accurate prediction of PM2.5 concentration is important for pollution control, public health, and ecological protection. However, due to the nonlinear nature of PM2.5 data, the accuracy of existing methods suffers and performs poorly in both short-term and long-term predictions. In this study, a deep learning hybrid prediction model based on clustering and quadratic decomposition is proposed. The model utilizes the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the PM2.5 sequences into multiple intrinsic modal function components (IMFs), and clusters and re-fuses the subsequences with similar complexity by permutation entropy (PE) and K-means clustering. For the fused high-frequency sequences, a secondary decomposition is performed using the whale optimization algorithm (WOA) optimized variational modal decomposition (VMD). Finally, the nonlinear and temporal features are captured for prediction using the long- and short-term memory neural network (LSTM). Experiments show that this proposed model exhibits good stability and generalization ability. It does not only make accurate predictions in the short term, but also captures the trends in the long-term prediction. There is a significant performance improvement over the baseline models. Further comparisons with existing models outperform the current state-of-the-art models.
Prompt Update Algorithm Based on the Boolean Vector Inner Product and Ant Colony Algorithm for Fast Target Type Recognition
Authors: Zhou Quan , Shi Jie , Wang Qi , Kong Bin , Gao Shang , Zhong Weibo
Published: October 2024

In recent years, data mining technology has become increasingly popular, evolving into an independent discipline as research deepens. This study constructs and optimizes an association rule algorithm based on the Boolean vector (BV) inner product and ant colony optimization to enhance data mining efficiency. Frequent itemsets are extracted from the database by establishing BV and performing vector inner product operations. These frequent itemsets form the problem space for the ant colony algorithm, which generates the maximum frequent itemset. Initially, data from the total scores of players during the 2022–2024 regular season was analyzed to obtain the optimal lineup. The results obtained from the Apriori algorithm (AA) were used as a standard for comparison with the Confidence-Debiased Adversarial Fuzzy Apriori Method (CDAFAM), the AA based on deep learning (DL), and the proposed algorithm regarding their results and required time. A dataset of disease symptoms was then used to determine diseases based on symptoms, comparing accuracy and time against the original database as a standard. Finally, simulations were conducted using five batches of radar data from the observation platform to compare the time and accuracy of the four algorithms. The results indicate that both the proposed algorithm and the AA based on DL achieve approximately 10% higher accuracy compared with the traditional AA. Additionally, the proposed algorithm requires only about 25% of the time needed by the traditional AA and the AA based on DL for target recognition. Although the CDAFAM has a similar processing time to the proposed algorithm, its accuracy is lower. These findings demonstrate that the proposed algorithm significantly improves the accuracy and speed of target recognition.
Measurement Verification of a Developed Strategy of Inrush Current Reduction for a Non-Loaded Three-Phase Dy Transformer
Authors: Łukaniszyn Marian , Majka Łukasz , Baron Bernard , Kulesz Barbara , Tomczewski Krzysztof , Wróbel Krzysztof
Published: October 2024

This article presents the measurement verification of a novel strategy for inrush current reduction in an unloaded three-phase Dy transformer. The strategy combines appropriate pre-magnetization of transformer cores with an original control switching system using initial phase values of the supply voltage as control variables. Measurements were recorded for primary voltages and currents as well as secondary voltages during transient states at start-up under no-load conditions. Various inrush scenarios were examined across the full angular spectrum of initial phase angles, both polarities, and in regard to different pre-magnetization current values. A detailed analysis of the inrush currents was performed using proprietary automated software based on the recorded data. A comparative study with a nonlinear mathematical model of the transformer was also conducted. Additionally, key technical aspects of the designed system for implementing the proposed pre-magnetization strategy with controlled voltage energization are discussed.
AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning
Authors: Wei Qian , Sun Hongjun , Fan Jingjing , Li Guojun , Zhou Zhiguang
Published: October 2024

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.
Environmentally Friendly Biofuel Production
Authors: Frankowski Jakub , Czekała Wojciech
Published: October 2024

Changes in the environment and climate conditions, as well as economic growth and the increase in energy consumption, have led to an intensification of research on alternative fuels to replace fossil fuels [...]
Development of an Experimental Acoustic Noise Characterization Setup for Electric Motor Drive Applications
Authors: Masoumi Moien , Selliah Abeka , Bilgin Berker
Published: October 2024

This paper presents the development of an experimental setup for acoustic noise characterization of electric motors. It describes the sound measurement microphones utilized in the setup and discusses the application of octave bands and A-weighting in noise measurement. Various methods for acoustic noise measurement and sound power calculation, including those based on sound pressure and sound intensity, are also covered. Given the relatively noisy test environment and restricted access around the test setup, discrete point sound intensity measurement is selected for sound power calculation. Initially, a stationary probe-holding fixture is designed and fabricated for sound intensity measurements. To enhance the fixture’s flexibility and the accuracy of the measurements, a transportable fixture is subsequently designed and fabricated. The necessary hardware and software settings for acoustic noise characterization are then developed. Finally, the setup is used to conduct acoustic noise characterization of an IPM motor, validating the application of the transportable probe-holding fixture.
Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty
Authors: Haj Issa Haala , Abedini Moein , Hamzeh Mohsen , Anvari-Moghaddam Amjad
Published: October 2024

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.
Energy Recovery from Municipal Sewage Sludge: Combustion Kinetics in a Varied Oxygen–Carbon Dioxide Atmosphere
Authors: Bień Jurand , Bień Beata
Published: October 2024

Energy from municipal sewage sludge can be obtained by applying a thermal conversion method. In this study, the combustion kinetics of municipal sewage sludge were analyzed in an O2/CO2 atmosphere. Studies were conducted in different gaseous atmospheres consisting of varying proportions of oxygen and carbon dioxide. The participation of oxygen was as follows: 20, 40, 60, 80 and 100% vol. The experimental temperatures varied from 873 to 1273 K. The experimentally obtained results helped establish the basic kinetic parameters, such as the reaction order n, factor Ko and activation energy Ea of sludge grains. The values of the activation energy Ea and Ko were, respectively, 46 kJ/mol and 0.0127 mg/m2sPa. They show that the limiting factor of combustion is the diffusion of oxygen and that combustion reactions take place in the outer layer of the unreacted core. Therefore, sludge is promising for energy recovery because it has quite a high net calorific value (NCV) and a high gross calorific value (GCV). The GCV was 14.1 MJ/kg and the NCV was 12.8 MJ/kg. The experimental studies with a wide range of process parameters helped to create an array of apparent reaction rates as a function of the temperature and oxygen concentration, showing the significant effect of oxygen on the apparent reaction rate, in contrast to the effect of temperature.
Application of Artificial Neural Networks in Predicting the Thermal Performance of Heat Pipes
Authors: Pereira Thomas Siqueira , Machado Pedro Leineker Ochoski , Veitia Barbara Dora Ross , Biglia Felipe Mercês , dos Santos Paulo Henrique Dias , Tadano Yara de Souza , Siqueira Hugo Valadares , Antonini Alves Thiago
Published: October 2024

The loss of energy by heat is a common problem in almost all areas of industry, and heat pipes are essential to increase efficiency and reduce energy waste. However, in many cases, they have complex theoretical equations with high percentages of error, limiting their development and causing dependence on empirical methods that generate a waste of time and material, resulting in significant expenses and reducing the viability of their use. Thus, Artificial Neural Networks (ANNs) can be an excellent option to facilitate the construction and development of heat pipes without knowledge of the complex theory behind the problem. This investigation uses experimental data from previous studies to evaluate the ability of three different ANNs to predict the thermal performance of heat pipes with different capillary structures, each of them in various configurations of the slope, filling ratio, and heat load. The goal is to investigate results in as many different scenarios as possible to clearly understand the networks’ capacity for modeling heat pipes and their operating parameters. We chose two classic ANNs (the most used, Multilayer Perceptron (MLP) network, and the Radial Basis Function (RBF) network) and the Extreme Learning Machine (ELM), which has not yet been applied to heat pipes studies. The ELM is an Unorganized Machine with a fast training process and a simple codification. The ANN results were very close to the experimental ones, showing that ANNs can successfully simulate the thermal performance of heat pipes. Based on the RMSE (error metric being reduced during the training step), the ELM presented the best results (RMSE = 0.384), followed by MLP (RMSE = 0.409), proving their capacity to generalize the problem. These results show the importance of applying different ANNs to evaluate the system deeply. Using ANNs in developing heat pipes is an excellent option for accelerating and improving the project phase, reducing material loss, time, and other resources.
Recent Development and Future Perspective of Wind Power Generation
Authors: Jung Christopher
Published: October 2024

The expansion of wind energy has progressed rapidly in recent years [...]
Research on Carbon Footprint Reduction During Hydrogen Co-Combustion in a Turbojet Engine
Authors: Ciupek Bartosz , Brodzik Łukasz , Frąckowiak Andrzej
Published: October 2024

The paper presents experimental studies on the effect of co-combustion of aviation kerosene with hydrogen in the GTM400 turbojet engine on the change in the carbon footprint generated by the engine in relation to its standard operation without hydrogen in the fuel. This research is in line with current research and development trends carried out in the EU, linking them to the issues of the European Green Deal, the Fit for 55 directive and current environmental trends in aviation and energy. The main objective of the research was to check the effect of hydrogen co-combustion in a turbojet engine on the change of the carbon footprint, while a secondary objective was to verify the impact of higher exhaust gas temperatures generated by the new, high-calorific fuel on the secondary generation of nitrogen oxides (NOx), especially in the thermal mechanism, as an undesirable effect. The research shows that the co-combustion of hydrogen with aviation kerosene in a turbojet engine reduces the carbon footprint (reduction of CO2 maximum of 15% and CO emissions maximum of 24%), but also increases the emission of nitrogen oxides (NOx) maximum of 58%, including those generated in the thermal mechanism (significant increase in the temperature of exhaust gases), moreover, the increase in nitrogen oxide emissions is proportional to the amount of co-combusted hydrogen, which is directly related to the stoichiometry of the combustion process. The main conclusion of the research is that technologies for the combustion or co-combustion of hydrogen in turbojet engines require further research and development, mainly on the side of the use of excess exhaust gas temperature generated during combustion and methods of reducing secondary nitrogen oxides.
Research Progress on CO2 as Geothermal Working Fluid: A Review
Authors: Cong Lianghan , Lu Shuaiyi , Jiang Pan , Zheng Tianqi , Yu Ziwang , Lü Xiaoshu
Published: October 2024

With the continuous increase in global greenhouse gas emissions, the impacts of climate change are becoming increasingly severe. In this context, geothermal energy has gained significant attention due to its numerous advantages. Alongside advancements in CO2 geological sequestration technology, the use of CO2 as a working fluid in geothermal systems has emerged as a key research focus. Compared to traditional water-based working fluids, CO2 possesses lower viscosity and higher thermal expansivity, enhancing its mobility in geothermal reservoirs and enabling more efficient heat transfer. Using CO2 as a working fluid not only improves geothermal energy extraction efficiency but also facilitates the long-term sequestration of CO2 within reservoirs. This paper reviews recent research progress on the use of CO2 as a working fluid in Enhanced Geothermal Systems (EGS), with a focus on its potential advantages in improving heat exchange efficiency and power generation capacity. Additionally, the study evaluates the mineralization and sequestration effects of CO2 in reservoirs, as well as its impact on reservoir properties. Finally, the paper discusses the technological developments and economic analyses of integrating CO2 as a working fluid with other technologies. By systematically reviewing the research on CO2 in EGS, this study provides a theoretical foundation for the future development of geothermal energy using CO2 as a working fluid.
Lightweight Implementation of the Signal Enhancement Model for Early Wood-Boring Pest Monitoring
Authors: Li Juhu , Li Xue , Ju Mengwei , Zhao Xuejing , Wang Yincheng , Yang Feng
Published: October 2024

Wood-boring pests are one of the most destructive forest pests. However, the early detection of wood-boring pests is extremely difficult because their larvae live in tree trunks and have high invisibility. Borehole listening technology is a new and effective method to detect the larvae of insect pests. It identifies infested trees by analyzing wood-boring vibration signals. However, the collected wood-boring vibration signals are often disturbed by various noises existing in the field environment, which reduces the accuracy of pest detection. Therefore, it is necessary to filter out the noise and enhance the wood-boring vibration signals to facilitate the subsequent identification of pests. The current signal enhancement models are all designed based on deep learning models, which have complex scales, a large number of parameters, high demands for storage resources, large computational complexity, and high time costs. They often run on resource-rich computers or servers, and they are difficult to deploy to resource-limited field environments to realize the real-time monitoring of pests; as well, they have low practicability. Therefore, this study designs and implements two model lightweight optimization algorithms, one is a pre-training pruning algorithm based on masks, and the other is a knowledge distillation algorithm based on the separate transfer of vibration signal knowledge and noise signal knowledge. We apply the two lightweight optimization algorithms to the signal enhancement model T-CENV with good performance outcomes and conduct a series of ablation experiments. The experimental results show that the proposed methods effectively reduce the volume of the T-CENV model, which make them useful for the deployment of signal enhancement models on embedded devices, improve the usability of the model, and help to realize the real-time monitoring of wood-boring pest larvae.
Photoinhibition Sensitivity of Abies sachalinensis Saplings Under Different Leaf Mass per Area Conditions: Insights into Acclimation to Current and Future Light Environments
Authors: Tsunoda Yuuki , Ohno Yasuyuki , Ishizuka Wataru
Published: October 2024

Photoinhibition, a common physiological obstacle in forest regeneration, results from rapid light elevations after removing upper-layer trees, thereby impeding sapling growth and survival. The leaf mass per area (LMA), which reflects the current light environment, may predict saplings’ responses to future elevations in light intensity. However, the relationship between the LMA and photoinhibition sensitivity in boreal forest evergreen conifers remains unclear. In this study, we explored whether the LMA was related to photoinhibition sensitivity in A. sachalinensis saplings. Saplings with a high LMA exhibited higher excitation pressure (1−qP) under the current light environment, leading to increased stress even at low relative light intensities of 3%–17%. A model illustrating the dependence of photosynthetic physiological parameters on the photosynthetic photon flux density (PPFD) revealed that 1−qP significantly decreased with an increasing LMA, while the electron transfer rate (ETR) and non-photochemical quenching (NPQ) significantly increased. Additionally, regardless of the LMA, 1−qP reached near saturation (≈1.0) at a PPFD of 1000 μmol m−2 s−1, likely owing to the inefficient consumption of excess energy by electron transfer, as indicated by the low maximum ETR (approximately 25 μmol m−2 s−1). These findings suggest that although A. sachalinensis exhibits high sensitivity to photoinhibition, a high LMA may reflect physiological acclimation to forthcoming elevations in light exposure, thereby reducing the susceptibility to photoinhibition at present and in the future.
Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
Authors: Jesús-Azabal Manuel , Zhang Zheng , Gao Bingxia , Yang Jing , Soares Vasco N. G. J.
Published: October 2024

5G Mobile Adhoc Networks (5G-MANETs) are a popular and agile solution for data transmission in local contexts while maintaining communication with remote entities via 5G. These characteristics have established 5G-MANETs as versatile communication infrastructures for deploying contextual applications, leveraging physical proximity while exploiting the possibilities of the Internet. As a result, there is growing interest in exploring the potential of these networks and their performance in real-world scenarios. However, the management and monitoring of 5G-MANETs are challenging due to their inherent characteristics, such as highly variable topology, unstable connections, energy consumption of individual devices, message routing, and occasional inability to connect to 5G. Considering these challenges, the proposed work aims to address real-time monitoring of 5G-MANETs using a connection-aware Digital Twin (DT). The approach provides two main functions: offering a live virtual representation of the network, even in scenarios where multiple nodes lack 5G connectivity, and estimating the performance of the infrastructure, enabling the specification of customized conditions. To achieve this, a communication architecture is proposed, analyzing its components and defining the involved processes. The DT is implemented and evaluated in a laboratory setting, assessing its accuracy in representing the physical network under varying conditions of topology and Internet availability. The results show 100% accuracy for the DT in fully connected topologies, with ultra-low latency averaging under 80 ms, and suitable performance in partially connected contexts, with latency averages below 3000 ms.
Antilisterial Properties of Selected Strains from the Autochthonous Microbiota of a Swiss Artisan Soft Smear Cheese
Authors: Roetschi Alexandra , Baumeyer Alexandra , Berthoud Hélène , Braillard Lauriane , Gschwend Florian , Guisolan Anne , Haldemann John , Hummerjohann Jörg , Joller Charlotte , Loosli Florian , Meola Marco , Naskova Javorka , Oberhänsli Simone , Shani Noam , von Ah Ueli , Arias-Roth Emmanuelle
Published: October 2024

High incidences of the foodborne pathogen Listeria monocytogenes have been reported on smear cheeses, and despite increased hygiene efforts, this incidence has remained stable in recent years. Applying antilisterial strains may increase the safety of smear cheeses. To find and test antilisterial strains, we inoculated fresh soft cheeses from nine dairies with the surrogate species Listeria innocua and assessed its growth under standardized ripening conditions. Acetic acid at day 23 (r = −0.66), lactose in fresh cheese (r = −0.63), and glucose at day 10 (r = −0.62), as well as seven amplicon sequence variants (ASVs), were negatively correlated with L. innocua growth. Two of these ASVs were assigned to the genus Leuconostoc of Lactobacillaceae (r = −0.82 and −0.71). Isolates from this family, from Aerococcaceae, and Carnobacteriaceae were characterized according to their inhibitory properties, and those showing antilisterial properties were applied as protective cultures in challenge tests. The combined application of strains of Leuconostoc mesenteroides, Aerococcaceae, and Carnobacteriaceae successfully eliminated low levels of L. innocua in the final products. This is likely explained by antimicrobial compounds, including mesentericin Y105 and acetate, and competition for carbon sources and iron. This study shows a promising way to improve the safety of soft smear cheeses by applying defined protective cultures.
Production of Anthocyanin-Enriched Juices by Electrodialysis with Filtration Membrane Process: The Influence of Duration on Juice Composition, Process Efficiency, and Membrane Fouling
Authors: Revellat Eva , Bazinet Laurent
Published: October 2024

The Electrodialysis with Filtration Membrane (EDFM) system has shown promise in juice enrichment, but further optimization is needed. This study evaluated the effect of processing duration (3 and 6 h) on juice composition, process efficiency, and membrane fouling. Results demonstrated a significant impact of processing time on juice composition, especially anthocyanin and mineral content. Two anthocyanin-depleted juices (−18.94% and −30.70%) and two anthocyanin-enriched juices (26.21% and 44.21%) were produced. Similar energy (1512.13 Wh/g of anthocyanins) was required to migrate equivalent amounts of anthocyanins over both time periods, with no impediment due to fouling observed, although the system’s resistance increased (2.5-fold after 3 h, 3.2-fold after 6 h). Membrane fouling was characterized through conductivity, thickness, ATR-FTIR, SEM-EDX, and foulant identification. Minimal anthocyanin accumulation occurred on cation-exchange membranes (CEM), while anthocyanins and PACs concentrated within the filtering layer of filtration membranes (FM). However, fouling did not increase with longer processing. Structural alterations were noted in anion-exchange membranes (AEMs), suggesting instability under high electric fields. Overall, EDFM effectively enriched cranberry juice with anthocyanins, but further research is necessary to address AEM degradation under limiting current density conditions.
Pea Albumin Alleviates Oleic Acid-Induced Lipid Accumulation in LO2 Cells Through Modulating Lipid Metabolism and Fatty Acid Oxidation Pathways
Authors: Fang Bing , Luo Jie , Cui Zhengwu , Liu Rong , Wang Pengjie , Zhang Jian
Published: October 2024

Excessive lipid accumulation in the liver can cause NAFLD, leading to chronic liver injury. To relieve liver lipid accumulation by dietary proteins, this study used oleic acid (OA) induction to establish a stable in vitro LO2 cell lipid accumulation model. This model was used to explore the mechanism by which pea albumin (PA) regulates lipid levels in LO2 cells. PA has been shown to ameliorate OA-induced lipid accumulation in LO2 cells by reducing the aggregation of intracellular lipid droplets and lowering cell TG and TC levels. In addition, it can alleviate OA-induced LO2 cell damage and oxidative stress, reduce cellular ALT and AST secretion, lower cellular MDA levels, and increase GSH-Px viability. Regulation of lipid metabolism in LO2 cells involves inhibiting the cellular lipid synthesis pathway and activating the expression of proteins related to the triglyceride catabolic and fatty acid oxidation pathways. PA contributes to regulating lipid accumulation in LO2 cells. This study provides new insights into alleviating liver fat accumulation and a theoretical basis for exploring the mechanism of protein regulation of liver cell lipid metabolism.
Understanding Why Metabolic-Dysfunction-Associated Steatohepatitis Lags Behind Hepatitis C in Therapeutic Development and Treatment Advances
Authors: Ferrari Caesar , Ashraf Bilal , Saeed Zainab , Tadros Micheal
Published: October 2024

Therapeutic development for metabolic-dysfunction-associated steatohepatitis (MASH) trails behind the success seen in hepatitis C virus (HCV) management. HCV, characterized by a viral etiology, benefits from direct-acting antivirals (DAAs) targeting viral proteins, achieving cure rates exceeding 90%. In contrast, MASH involves complex metabolic, genetic, and environmental factors, presenting challenges for drug development. Non-invasive diagnostics like ultrasound, FibroScan, and serum biomarkers, while increasingly used, lack the diagnostic accuracy of liver biopsy, the current gold standard. This review evaluates therapies for MASH, including resmetirom (Rezdiffra) and combinations like pioglitazone and vitamin E, which show potential but offer modest improvements due to MASH’s heterogeneity. The limited efficacy of these treatments highlights the need for multi-targeted strategies addressing metabolic and fibrotic components. Drawing parallels to HCV’s success, this review emphasizes advancing diagnostics and therapies for MASH. Developing effective, patient-specific therapies is crucial to closing the gap between MASH and better-managed liver diseases, optimizing care for this growing health challenge.
Experimental Investigation of the Combustion Characteristics and Thermal Hazards of Methylsilyl-Modified Silica Aerogels
Authors: Wu Xiaoxu , Shen Kai , Hu Min , Zhou Fang , Chen Zikang , Liu Qiong , Li Zijun , Li Zhi
Published: October 2024

The thermal safety of hydrophobic silica aerogels (SAs) is essential to thermal insulation applications. Herein, trimethylchlorosilane (TMCS), dimethyldichlorosilane (DMDCS), and methyltrichlorosilane (MTCS) were employed as surface modifiers to prepare three different methylsilyl-modified SAs (i.e., TSA, DSA, and MSA) and their combustion characteristics and thermal hazards were experimentally studied in detail. The cone calorimeter test found that the three SAs have similar combustion processes and the variations in ignition time and fire spread rate with the heat flux obey simple logarithmic and linear relationships, respectively. It further found that TSA has the most methylsilyl groups on silica skeletons and thus has the largest heat release, followed by DSA and MSA in turn, implying that TSA has the greatest fire hazard among the three SAs. These results further demonstrate that the type and quantity of methylsilyl groups on the skeletons of SAs significantly affect the thermal hazard of methylsilyl-modified SAs. In addition, the combustion mechanism of the methylsilyl-modified SAs is discussed. In total, this work experimentally studies the combustion characteristics of methylsilyl-modified SAs and compares their thermal hazards, clarifying the potential fire risk of methylsilyl-modified SAs in practical thermal insulation applications.
Development of a Novel Stress and Immune Gene Panel for the Australasian Snapper (Chrysophrys auratus)
Authors: Bentley-Hewitt Kerry L. , Flammensbeck Christina K. , Crowhurst Ross N. , Hedderley Duncan I. , Wellenreuther Maren
Published: October 2024

Background: Snapper (Chrysophrys auratus) is a commercially, recreationally and culturally important teleost species in New Zealand and has been selected as a potential new species for aquaculture. Selective breeding to enhance stress tolerance, survival and growth are major breeding targets, yet research into snapper immune and stress responses has been limited. Methods: We explored a set of candidate genes in the fin, head kidney and liver tissues of 50 individuals by exposing 20 fish to increasing temperature (up to 31 °C) and 20 fish to decreasing temperature (down to 7 °C) for up to 37 h. Of these, we analysed 10 temperature-sensitive and 10 temperature-tolerant fish, along with 10 fish kept at 18 °C (acclimation temperature) as a control group. Results: Expression analyses of candidate stress genes in the three tissue types via NanoString Technologies, Inc., Seattle, WA, USA. showed that 20 out of 25 genes significantly changed in each experiment, demonstrating the significant impact of temperature on stress and immune responses. We further document that 10 key gene biomarkers can be used to predict genotypes that are tolerant to extreme temperatures. Conclusions: Taken together, our novel NanoString method can be used to monitor stress in snapper rapidly, and applications of this tool in this and potentially closely related teleost species can provide insights into stress resilience of wild stocks and inform the selection of grow-out locations for aquaculture.
ΜicroRNA (miRNA) Variants in Male Infertility: Insights from Whole-Genome Sequencing
Authors: Kyrgiafini Maria-Anna , Vasilev Veselin Veselinov , Chatziparasidou Alexia , Mamuris Zissis
Published: October 2024

Background/Objectives: Male infertility is a complex condition with various underlying genetic factors. microRNAs (miRNAs) play a crucial role in gene regulation, and their disruption can significantly impact fertility. This study aimed to identify variants within miRNA genes and elucidate their impact on male infertility. Methods: Whole genome sequencing was performed on blood samples from men with asthenozoospermia, oligozoospermia, and teratozoospermia, compared to normozoospermic controls. The analysis revealed a significant number of unique variants in each infertile group. We subsequently focused on variants in miRNA regions, followed by an in silico analysis to investigate the role of the identified variants and miRNAs in male infertility. Results: Focused analysis on miRNA genes identified 19 exclusive variants in teratozoospermic men, 24 in asthenozoospermic, and 27 in oligozoospermic, all mapping to pre-miRNAs or mature miRNAs. Functional analyses using Gene Ontology (GO) and KEGG pathways highlighted key biological processes and pathways disrupted by these variants and miRNA–mRNA interactions, including transcription regulation, signaling, and cancer-related pathways. Furthermore, six variants (rs17797090, rs1844035, rs7210937, rs451887, rs12233076, and rs6787734) were common across the infertile groups, suggesting their importance in male infertility or their potential as biomarkers. Common variants were also validated in another clinically relevant group of men. Some miRNAs with identified variants, such as hsa-miR-449b and hsa-miR-296, have been previously implicated in male infertility and exhibit differential expression between fertile and infertile men, according to the literature, too. Conclusion: These results provide new insights into the genetic basis of male infertility and open avenues for future research and therapeutic interventions.
OHDLF: A Method for Selecting Orthologous Genes for Phylogenetic Construction and Its Application in the Genus Camellia
Authors: Cai Junhao , Lu Cui , Cui Yuwei , Wang Zhentao , Zhang Qunjie
Published: October 2024

Accurate phylogenetic tree construction for species without reference genomes often relies on de novo transcriptome assembly to identify single-copy orthologous genes. However, challenges such as whole-genome duplication (WGD), heterozygosity, gene duplication, and loss can hinder the selection of these genes, leading to limited data for constructing reliable species trees. To address these issues, we developed a new analytical pipeline, OHDLF (Orthologous Haploid Duplication and Loss Filter), which filters orthologous genes from transcript data and adapts parameter settings based on genomic characteristics for further phylogenetic tree construction. In this study, we applied OHDLF to the genus Camellia and evaluated its effectiveness in constructing phylogenetic trees. The results highlighted the pipeline’s ability to handle challenges like high heterozygosity and recent gene duplications by selectively retaining genes with a missing rate and merging duplicates with high similarity. This approach ensured the preservation of informative sites and produced a highly supported consensus tree for Camellia. Additionally, we evaluate the accuracy of the OHDLF phylogenetic trees for different species, demonstrating that the OHDLF pipeline provides a flexible and effective method for selecting orthologous genes and constructing accurate phylogenetic trees, adapting to the genomic characteristics of various plant groups.
A Solution to Reduce the Impact of Patients’ No-Show Behavior on Hospital Operating Costs: Artificial Intelligence-Based Appointment System
Authors: Toker Kerem , Ataş Kadir , Mayadağlı Alpaslan , Görmezoğlu Zeynep , Tuncay Ibrahim , Kazancıoğlu Rümeyza
Published: October 2024

Background: Patient no-show behavior is a critical factor complicating hospital resource optimization and causing waste. The inefficiency caused by patients’ no-shows and the resulting increased operating costs negatively affect the hospitals’ financial structure and service quality. For this reason, health managers must make accurate predictions about whether patients will attend an appointment and plan the appointment system within the framework of these predictions. This research aims to optimize the hospital appointment system by making accurate predictions regarding the no-show behavior of the patients, based on recorded data. Methods: An artificial intelligence-based appointment system has been developed according to patients’ demographics and past behavior patterns. The forecast results and realized performance results were compared. The artificial intelligence we have developed continuously improves appointment assignments by learning from past and current data. Results: According to the findings, the artificial intelligence-based appointment system increased the rate of patients attending appointments by 10% per month. Likewise, the hospital capacity utilization rate increased by 6%. Conclusions: Findings from the study confirmed that no-show risks could be managed in the appointment process through artificial intelligence. This artificial intelligence-based design for appointment systems significantly decreases hospital costs and improves service quality performance.
Treatment Strategies Used in Treating Myelofibrosis: State of the Art
Authors: Martino Massimo , Pitea Martina , Sgarlata Annalisa , Delfino Ilaria Maria , Cogliandro Francesca , Scopelliti Anna , Marafioti Violetta , Polimeni Simona , Porto Gaetana , Policastro Giorgia , Utano Giovanna , Pellicano Maria , Leanza Giovanni , Alati Caterina
Published: October 2024

Background: Current drug therapy for myelofibrosis does not alter the natural course of the disease or prolong survival, and allogeneic stem cell transplantation is the only curative treatment modality. For over a decade, the Janus kinase (JAK) inhibitor ruxolitinib has been the standard of care. More recently, newer-generation JAK inhibitors have joined the ranks of accepted treatment options. Objectives: The primary goal of treatment is to reduce spleen size and minimize disease-related symptoms. Prognostic scoring systems are used to designate patients as being at lower or higher risk. For transplant-eligible patients, transplant is offered to those with a bridge of a JAK inhibitor; patients who are not eligible for transplant are usually offered long-term therapy with a JAK inhibitor. Limited disease-modifying activity, dose-limiting cytopenias, and other adverse effects have contributed to discontinuation of JAK inhibitor treatment. Conclusions: Novel JAK inhibitors and combination approaches are currently being explored to overcome these shortcomings. Further research will be essential to establish optimal therapeutic approaches in first-line and subsequent treatments.
Reconnecting River-City: A Visibility and Accessibility Assessment of the Ping River’s View Characters in Chiang Mai City
Authors: Sukwai Janjira , Srinurak Nattasit
Published: October 2024

A river’s landmarks have advantages in attracting people to engage with natural/historical settings. However, uncontrolled development can have negative effects on the visual and obstruct people’s access. In this context, researchers have highlighted obstacles to these landmarks and suggested features of the relationship between the visibility and accessibility to these landmarks, aiming to preserve heritage sites. This project evaluated riverscape landmarks to develop a strategic urban design that uses scientific and spatial toolkits to maintain the historic landmark on the riverside in Chiang Mai city. The methodology utilized for GIS included visibility and accessibility assessments as variables for analyzing interconnections and classification utilizing the statistical method of hierarchical clustering analysis (HCA). The findings indicate differences in the accessibility of angular step coverage (ASC) and walking distance coverage (WDC), which have an impact on landmark designation. The city-level landmarks have the highest ASC, while various religious landmarks display the highest visibility. These factors have a unique impact on the HCA results, which are categorized into six groups that are consistent with the historical content and purpose of the landmarks. The clustered groups represent the strategic design of the riverscape, which necessitates policies to enhance the potential for preserving these icons as part of the historic landscape.
Effect of Salinity on the Growth and Biochemical Profile of Hedypnois cretica and Plantago coronopus Plants in Relation to the Cropping System and Growth Environment
Authors: Vidalis Nikolina , Pentotis Efstathios , Thanos Nikolaos , Alexopoulos Alexios , Tsouvaltzis Pavlos , Petropoulos Spyridon A. , Karapanos Ioannis
Published: October 2024

The aim of the present work is to study the impact of salinity (i.e., 2 dS/m without addition of NaCl (control); 5 dS/m (EC-5); and 10 dS/m (EC-10) with the addition of 30 and 80 mM NaCl, respectively) on yield, organoleptic quality and the content of antioxidant compounds in Plantago coronopus and Hedypnois cretica plants in relation to different cropping systems and environmental conditions during growth (i.e., pots or floating hydroponics systems in the greenhouse and pots in the field (GPs, GH and FPs, respectively)). Almost all the growth parameters of the H. cretica plants in the greenhouse were impaired by both moderate (EC-5) and high (EC-10) salinity levels, especially in the floating hydroponics system, where, compared to EC-2, a reduction was observed of 33% in EC-5 and 76% in EC-10 in leaf area and of 24% and 75%, respectively, in fresh weight. On the contrary, the growth of the P. coronopus plants was negatively affected by salinity only in the GP system. However, salinity had no effect on most growth characteristics of both species grown in the field, suggesting a strong environmental effect on the impact of salinity in the tested species. On the other hand, the content of pigments, proline and total phenolics in the P. coronopus leaves, as well as their antioxidant capacity, were not affected by salinity in most cases, whereas in H. cretica the salinity effect was significant even at moderate levels (EC-5) in relation to the cropping system and growth environment. Moreover, both salinity levels reduced the nitrate content of P. coronopus plants in all cases (up to 61% in GP plants at EC-10) and of H. cretica GH plants (up to 67% in EC-10). Finally, regardless of the salinity level, the field-grown plants of both species showed a considerably higher content of total phenolics (by 59% in H. cretica and 58% in P. coronopus) and antioxidant activity (by 63% in H. cretica and 53% in P. coronopus, FRAP values), compared to the greenhouse ones. In conclusion, our results indicate that the tested combinations of cropping systems and growth environments could be used as an eco-friendly and sustainable agronomic tool to mitigate the negative effects of salinity and to regulate the organoleptic and biochemical properties of the tested species, allowing small-scale farmers in the affected areas to cope with the ongoing climate change and the environmental pressures they currently face through the exploitation of alternative/underexploited species of high added value.
Genetic and Haplotype Diversity of Clarias gariepinus (Burchell 1822) Based on Cytochrome c Oxidase I (COI) Gene
Authors: Aminisarteshnizi Mehrnoush , Moyo Ngonidzashe A. G.
Published: October 2024

The evaluation of mitochondrial DNA and genetic analysis is helpful for economically significant species. Clarias gariepinus is a critical species in aquaculture. This study investigates the genetic diversity and population differentiation of C. gariepinus from 19 countries using 164 sequences of the mitochondrial DNA’s Cytochrome c oxidase I (COI) gene. The haplotype analysis revealed a total of 17 haplotypes, with a nucleotide diversity (π) of 0.012 and a haplotype diversity (Hd) of 0.87. The results of an AMOVA and fixation index indicated significant genetic variation and structure among the populations. Additionally, neutrality tests and mismatch distribution analysis supported the hypothesis of under-purifying selection in C. gariepinus. The findings suggested that the population did not experience expansion. In conclusion, the genetic analysis highlighted substantial variation among C. gariepinus populations from different locations, providing valuable insights for the global management of this species.
Tracking Workplace Violence over 20 Years
Authors: Magnavita Nicola , Meraglia Igor , Viti Giacomo , Gasbarri Martina
Published: October 2024

Introduction. Violence against healthcare workers (HCWs) is a widespread, underreported, and inadequately prevented problem. Only a few companies have efficient systems for assessing the extent of the phenomenon. Methods. In 2005, the health surveillance service of a public health company introduced a system that monitored violence experienced by HCWs by means of three items from the Violent Incident Form (VIF) integrated with departmental in-depth analyses using the participatory ergonomics group technique. Results. In 2005, the annual rate of physical assaults was 8.2%, that of threats was 12.0%, and the harassment rate was 19.6%. Over the past twenty years of observation (2005–2024), the percentage of workers who reported experiencing a physical attack in the previous year at their periodic medical examination has fluctuated between 5.8% and 11.1%, except for the years 2020 and 2021 when, during the COVID-19 pandemic, the rate was 3.9% and 3.2%, respectively. During the same pandemic period, the annual threat rate, which ranged from 9.4% to 20.1%, dropped to 7.7%, while the prevalence of harassment, which was between 13.5 and 19.6, fell to 7.2%. HCWs believe that (i) limiting visitor access, (ii) a better balance of the demand for services, and (iii) a better attitude towards HCWs were the causes of the reduced rate of violence during the pandemic. Conclusions. Recording the violence experienced during health surveillance is an economical, reliable, and sustainable risk assessment method.