bluuebunny/crossref_metadata_2025 huggingface dataset:
Polypropylene Crystallinity Reduction through the Synergistic Effects of Cellulose and Silica Formed via Sol–Gel Synthesis
Authors: Shambilova Gulbarshin K. , Iskakov Rinat M. , Bukanova Aigul S. , Kairliyeva Fazilat B. , Kalauova Altynay S. , Kuzin Mikhail S. , Novikov Egor M. , Gerasimenko Pavel S. , Makarov Igor S. , Skvortsov Ivan Yu.
Published: October 2024

This study focuses on the development of environmentally sustainable polypropylene (PP)-based composites with the potential for biodegradability by incorporating cellulose and the oligomeric siloxane ES-40. Targeting industrial applications such as fused deposition modeling (FDM) 3D printing, ES-40 was employed as a precursor for the in situ formation of silica particles via hydrolytic polycondensation (HPC). Two HPC approaches were investigated: a preliminary reaction in a mixture of cellulose, ethanol, and water, and a direct reaction within the molten PP matrix. The composites were thoroughly characterized using rotational rheometry, optical microscopy, differential scanning calorimetry, and dynamic mechanical analysis. Both methods resulted in composites with markedly reduced crystallinity and shrinkage compared to neat PP, with the lowest shrinkage observed in blends prepared directly in the extruder. The inclusion of cellulose not only enhances the environmental profile of these composites but also paves the way for the development of PP materials with improved biodegradability, highlighting the potential of this technique for fabricating more amorphous composites from crystalline or semi-crystalline polymers for enhancing the quality and dimensional stability of FDM-printed materials.
From Palace Lady to Tara: Exploring the Transformation of the Female Role in Buddhist Belief through the Sinicization of Buddhism
Authors: Wang Yun , Zhang Shaojiao
Published: October 2024

This paper argues that the historical process of the Sinicization of Buddhism and the evolution of the value of female roles in Buddhist beliefs have advanced hand in hand; the more Buddhism becomes Sinicized, the higher the status of women in Buddhist beliefs becomes. To some extent, the Sinicization of Buddhism can be considered a feminization process. Female roles in Buddhist beliefs are often passive objects and marginal positions that are uncertain and continuously presupposed by others. This article starts from a cultural, philosophical perspective, aiming to examine the internal logic of the evolution of female roles in Buddhist beliefs. In terms of individual experience, the highlighting of the identity as a “subject” of sexual desire makes women a necessary but insufficient prerequisite “option” for guiding male sexual desire—in Indian Buddhist beliefs, women’s enlightenment involves both reflecting on sexual desire itself and negating their own gender disadvantage. As Buddhism spread worldwide, Western Buddhist traditions laid the foundation of Buddhist belief on “sacred images”, while various local cultural genes infiltrated the imagination of “Western Buddhist nations”. The Buddha’s gender orientation achieved a transcendence of both male and female or a bilateral blend, leaning more towards female. Ultimately, from the perspective of family identity, in a Chinese Buddhist belief world organized by the patriarchal system, the unique role of women in the family—“mother”—pushes Buddhist belief back to the scene of the emotional world.
A Novel Pre-Processing Approach and Benchmarking Analysis for Faster, Robust, and Improved Small Object Detection Methods
Authors: Al-Hababi Mohammed Ali Mohammed , Habib Ahsan , Thabit Fursan , Liu Ying
Published: October 2024

Detecting tiny objects in aerial imagery presents a major challenge regarding their limited resolution and size. Existing research predominantly focuses on evaluating average precision (AP) across various detection methods, often neglecting computational efficiency. Furthermore, state-of-the-art techniques can be complex and difficult to understand. This paper introduces a comprehensive benchmarking analysis specifically tailored for enhancing small object detection within the DOTA dataset, focusing on one-stage detection methods. We propose a novel data-processing approach to enhance the overall AP for all classes in the DOTA-v1.5 dataset using the YOLOv8 framework. Our approach utilizes the YOLOv8’s darknet architecture, a proven effective backbone for object detection tasks. To optimize performance, we introduce innovative pre-processing techniques, including data formatting, noise handling, and normalization, in order to improve the representation of small objects and improve their detectability. Extensive experiments on the DOTA-v1.5 dataset demonstrate the superiority of our proposed approach in terms of overall class mean average precision (mAP), achieving 66.7%. Additionally, our method establishes a new benchmark regarding computational efficiency and speed. This advancement not only enhances the performance of small object detection but also sets a foundation for future research and applications in aerial imagery analysis, paving the way for more efficient and effective detection techniques.
Remotely Sensed Estimation of Daily Near-Surface Air Temperature: A Comparison of Metop and MODIS
Authors: Zhang Zhenwei , Li Peisong , Zheng Xiaodi , Zhang Hongwei
Published: October 2024

The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2–1.4 K, 1.5–2.0 K, and 1.8–1.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26–0.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03–1.82 K, 1.3–2.49 K, and 1.26–2.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments.
Enhanced Precipitation Nowcasting via Temporal Correlation Attention Mechanism and Innovative Jump Connection Strategy
Authors: Yu Wenbin , Fu Daoyong , Zhang Chengjun , Chen Yadang , Liu Alex X. , An Jingjing
Published: October 2024

This study advances the precision and efficiency of precipitation nowcasting, particularly under extreme weather conditions. Traditional forecasting methods struggle with precision, spatial feature generalization, and recognizing long-range spatial correlations, challenges that intensify during extreme weather events. The Enhanced Temporal Correlation Jump Prediction Network (ETCJ-PredNet) introduces a novel attention mechanism that optimally leverages spatiotemporal data correlations. This model scrutinizes and encodes information from previous frames, enhancing predictions of high-intensity radar echoes. Additionally, ETCJ-PredNet addresses the issue of gradient vanishing through an innovative jump connection strategy. Comparative experiments on the Moving Modified National Institute of Standards and Technology (Moving-MNIST) and Hong Kong Observatory Dataset Number 7 (HKO-7) validate that ETCJ-PredNet outperforms existing models, particularly under extreme precipitation conditions. Detailed evaluations using Critical Success Index (CSI), Heidke Skill Score (HSS), Probability of Detection (POD), and False Alarm Ratio (FAR) across various rainfall intensities further underscore its superior predictive capabilities, especially as rainfall intensity exceeds 30 dbz,40 dbz, and 50 dbz. These results confirm ETCJ-PredNet’s robustness and utility in real-time extreme weather forecasting.
MPG-Net: A Semantic Segmentation Model for Extracting Aquaculture Ponds in Coastal Areas from Sentinel-2 MSI and Planet SuperDove Images
Authors: Chen Yuyang , Zhang Li , Chen Bowei , Zuo Jian , Hu Yingwen
Published: October 2024

Achieving precise and swift monitoring of aquaculture ponds in coastal regions is essential for the scientific planning of spatial layouts in aquaculture zones and the advancement of ecological sustainability in coastal areas. However, because the distribution of many land types in coastal areas and the complex spectral features of remote sensing images are prone to the phenomenon of ‘same spectrum heterogeneous objects’, the current deep learning model is susceptible to background noise interference in the face of complex backgrounds, resulting in poor model generalization ability. Moreover, with the image features of aquaculture ponds of different scales, the model has limited multi-scale feature extraction ability, making it difficult to extract effective edge features. To address these issues, this work suggests a novel semantic segmentation model for aquaculture ponds called MPG-Net, which is based on an enhanced version of the U-Net model and primarily comprises two structures: MS and PGC. The MS structure integrates the Inception module and the Dilated residual module in order to enhance the model’s ability to extract the features of aquaculture ponds and effectively solve the problem of gradient disappearance in the training of the model; the PGC structure integrates the Global Context module and the Polarized Self-Attention in order to enhance the model’s ability to understand the contextual semantic information and reduce the interference of redundant information. Using Sentinel-2 and Planet images as data sources, the effectiveness of the refined method is confirmed through ablation experiments conducted on the two structures. The experimental comparison using the FCN8S, SegNet, U-Net, and DeepLabV3 classical semantic segmentation models shows that the MPG-Net model outperforms the other four models in all four precision evaluation indicators; the average values of precision, recall, IoU, and F1-Score of the two image datasets with different resolutions are 94.95%, 92.95%, 88.57%, and 93.94%, respectively. These values prove that the MPG-Net model has better robustness and generalization ability, which can reduce the interference of irrelevant information, effectively improve the extraction precision of individual aquaculture ponds, and significantly reduce the edge adhesion of aquaculture ponds in the extraction results, thereby offering new technical support for the automatic extraction of aquaculture ponds in coastal areas.
Evaluating the Multidimensional Stability of Regional Ecosystems Using the LandTrendr Algorithm
Authors: Li Lijuan , Du Jiaqiang , Wu Jin , Sheng Zhilu , Zhu Xiaoqian , Song Zebang , Zhai Guangqing , Chong Fangfang
Published: October 2024

Stability is a key characteristic for understanding ecosystem processes and evolution. However, research on the stability of complex ecosystems often faces limitations, such as reliance on single parameters and insufficient representation of continuous changes. This study developed a multidimensional stability assessment system for regional ecosystems based on disturbances. Focusing on the lower reaches of the Yellow River Basin (LR-YRB), we integrated the remote sensing ecological index (RSEI) with texture structural parameters, and applied the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to analyze the continuous changes in disturbances and recovery from 1986 to 2021, facilitating the quantification and evaluation of resistance, resilience, and temporal stability. The results showed that 72.27% of the pixels experienced 1–9 disturbances, indicating the region’s sensitivity to external factors. The maximum disturbances primarily lasted 2–3 years, with resistance and resilience displaying inverse spatial patterns. Over the 35-year period, 61.01% of the pixels exhibited moderate temporal stability. Approximately 59.83% of the pixels recovered or improved upon returning to pre-disturbance conditions after maximum disturbances, suggesting a strong recovery capability. The correlation among stability dimensions was low and influenced by disturbance intensity, underscoring the necessity for a multidimensional assessment of regional ecosystem stability based on satellite remote sensing.
On the Suitability of Different Satellite Land Surface Temperature Products to Study Surface Urban Heat Islands
Authors: Hurduc Alexandra , Ermida Sofia L. , DaCamara Carlos C.
Published: October 2024

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval.
A Novel Wearable Sensor for Measuring Respiration Continuously and in Real Time
Authors: Ali Amjad , Wei Yang , Elsaboni Yomna , Tyson Jack , Akerman Harry , Jackson Alexander I. R. , Lane Rod , Spencer Daniel , White Neil M.
Published: October 2024

In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting.
Effects of Fatigue on Ankle Flexor Activity and Ground Reaction Forces in Elite Table Tennis Players
Authors: Lu Yunfei , Wang Jun , Ren Yuanshi , Ren Jie
Published: October 2024

Fatigue specifically affects the force production capacity of the working muscle, leading to a decline in athletes’ performance. This study investigated the impact of fatigue on ankle flexor muscle activity and ground reaction forces (GRFs) in elite table tennis players, with a focus on the implications for performance and injury risk. Twelve elite male table tennis athletes participated in this study, undergoing a fatigue protocol that simulated intense gameplay conditions. Muscle activity of the soleus (SOL) and gastrocnemius lateralis (GL) muscles, heel height, and GRFs were measured using a combination of wireless electromyography (EMG), motion capture, and force plate systems. Results showed a significant decrease in muscle activity in both legs post-fatigue, with a more pronounced decline in the right leg. This decrease in muscle activity negatively affected ankle joint flexibility, limiting heel lift-off. Interestingly, the maximal anteroposterior GRF generated by the left leg increased in the post-fatigue phase, suggesting the use of compensatory strategies to maintain balance and performance. These findings underscore the importance of managing fatigue, addressing muscle imbalances, and improving ankle flexibility and strength to optimize performance and reduce the risk of injuries.
Path Tracing-Inspired Modeling of Non-Line-of-Sight SPAD Data
Authors: Scholes Stirling , Leach Jonathan
Published: October 2024

Non-Line of Sight (NLOS) imaging has gained attention for its ability to detect and reconstruct objects beyond the direct line of sight, using scattered light, with applications in surveillance and autonomous navigation. This paper presents a versatile framework for modeling the temporal distribution of photon detections in direct Time of Flight (dToF) Lidar NLOS systems. Our approach accurately accounts for key factors such as material reflectivity, object distance, and occlusion by utilizing a proof-of-principle simulation realized with the Unreal Engine. By generating likelihood distributions for photon detections over time, we propose a mechanism for the simulation of NLOS imaging data, facilitating the optimization of NLOS systems and the development of novel reconstruction algorithms. The framework allows for the analysis of individual components of photon return distributions, yielding results consistent with prior experimental data and providing insights into the effects of extended surfaces and multi-path scattering. We introduce an optimized secondary scattering approach that captures critical multi-path information with reduced computational cost. This work provides a robust tool for the design and improvement of dToF SPAD Lidar-based NLOS imaging systems.
A Modular Smart Ocean Observatory for Development of Sensors, Underwater Communication and Surveillance of Environmental Parameters
Authors: Bergh Øivind , Danre Jean-Baptiste , Stensland Kjetil , Lima Keila , Nguyen Ngoc-Thanh , Heldal Rogardt , Kristensen Lars-Michael , Oyetoyan Tosin Daniel , Graves Inger , Sætre Camilla , Skålvik Astrid Marie , Tomasi Beatrice , Henriksen Bård , Holstad Marie Bueie , Walree Paul van , Altamiranda Edmary , Bjerke Erik , Husøy Thor Storm , Henne Ingvar , Wehde Henning , Stiansen Jan Erik
Published: October 2024

The rapid growth of marine industries has emphasized the focus on environmental impacts for all industries, as well as the influence of key environmental parameters on, for instance, offshore wind or aquaculture performance, animal welfare and structural integrity of different constructions. Development of automatized sensors together with efficient communication and information systems will enhance surveillance and monitoring of environmental processes and impact. We have developed a modular Smart Ocean observatory, in this case connected to a large-scale marine aquaculture research facility. The first sensor rigs have been operational since May 2022, transmitting environmental data in near real-time. Key components are Acoustic Doppler Current Profilers (ADCPs) for measuring directional wave and current parameters, and CTDs for redundant measurement of depth, temperature, conductivity and oxygen. Communication is through 4G network or cable. However, a key purpose of the observatory is also to facilitate experiments with acoustic wireless underwater communication, which are ongoing. The aim is to expand the system(s) with demersal independent sensor nodes communicating through an “Internet of Underwater Things (IoUT)”, covering larger areas in the coastal zone, as well as open waters, of benefit to all ocean industries. The observatory also hosts experiments for sensor development, biofouling control and strategies for sensor self-validation and diagnostics. The close interactions between the experiments and the infrastructure development allow a holistic approach towards environmental monitoring across sectors and industries, plus to reduce the carbon footprint of ocean observation. This work is intended to lay a basis for sophisticated use of smart sensors with communication systems in long-term autonomous operation in remote as well as nearshore locations.
Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults
Authors: Robbins Rebecca , Weaver Matthew D. , Sullivan Jason P. , Quan Stuart F. , Gilmore Katherine , Shaw Samantha , Benz Abigail , Qadri Salim , Barger Laura K. , Czeisler Charles A. , Duffy Jeanne F.
Published: October 2024

Sleep tracking by consumers is becoming increasingly prevalent; yet, few studies have evaluated the accuracy of such devices. We sought to evaluate the accuracy of three devices (Oura Ring Gen3, Fitbit Sense 2, and Apple Watch Series 8) compared to the gold standard sleep assessment (polysomnography (PSG)). Thirty-five participants (aged 20–50 years) without a sleep disorder were enrolled in a single-night inpatient study, during which they wore the Oura Ring, Fitbit, and Apple Watch, and were monitored with PSG. For detecting sleep vs. wake, the sensitivity was ≥95% for all devices. For discriminating between sleep stages, the sensitivity ranged from 50 to 86%, as follows: Oura ring sensitivity 76.0–79.5% and precision 77.0–79.5%; Fitbit sensitivity 61.7–78.0% and precision 72.8–73.2%; and Apple sensitivity 50.5–86.1% and precision 72.7–87.8%. The Oura ring was not different from PSG in terms of wake, light sleep, deep sleep, or REM sleep estimation. The Fitbit overestimated light (18 min; p < 0.001) sleep and underestimated deep (15 min; p < 0.001) sleep. The Apple underestimated the duration of wake (7 min; p < 0.01) and deep (43 min; p < 0.001) sleep and overestimated light (45 min; p < 0.001) sleep. In adults with healthy sleep, all the devices were similar to PSG in the estimation of sleep duration, with the devices also showing moderate to substantial agreement with PSG-derived sleep stages.
Research on Low-Voltage Arc Fault Based on CNN–Transformer Parallel Neural Network with Threshold-Moving Optimization
Authors: Ning Xin , Ding Tianli , Zhu Hongwei
Published: October 2024

Low-voltage arc fault detection can effectively prevent fires, electric shocks, and other accidents, reducing potential risks to human life and property. The research on arc fault circuit interrupters (AFCIs) is of great significance for both safety in production scenarios and daily living disaster prevention. Considering the diverse characteristics of loads between the normal operational state and the arc fault condition, a parallel neural network structure is proposed for arc fault recognition, which is based on a convolutional neural network (CNN) and a Transformer. The network uses convolutional layers and Transformer encoders to process the low-frequency current and high-frequency components, respectively. Then, it uses Softmax classification to perform supervised learning on the concatenated features. The method combines the advantages of both networks and effectively reduces the required depth and computational complexity. The experimental results show that the accuracy of this method can reach 99.74%, and with the threshold-moving method, the erroneous judgment rate can be lower. These results indicate that the parallel neural network can definitely detect arc faults and also improve recognition efficiency due to its lean structure.
Supply Strategies and Business Model Options for Online Retailers of Agricultural Products
Authors: Li Chenxing , Shi Xianliang
Published: October 2024

Online retail of agricultural products is an emerging form of online shopping that has enormous value for researching sustainable agricultural product logistics and the sustainability of e-commerce. By reviewing these practices in China, this paper summarizes three models of online retail of agricultural products: community group buying, prepositioned warehousing, and a mixed model in which the former two are carried out simultaneously. This paper considers the uncertainty of demand and applies the newsboy model to obtain the expected profit function of the three models. The paper proves that the objective functions of the optimization models are all convex functions of the supply capacity. The optimal supply strategy and the expression for each business model are then derived. Next, the intervals for enterprises to choose the profit-optimal business model are given and visually demonstrated through graphs. These findings lead to managerial insights: in economically underdeveloped regions, it is appropriate for enterprises to conduct community group buying businesses; in economically developed regions, it is appropriate for enterprises to conduct prepositioned warehouse businesses; and in regions with average economic development, it is appropriate for enterprises to conduct both businesses. Finally, this paper verifies the optimal supply strategy for the online retail model for agricultural products through numerical experiments and sensitivity analyses for different cost parameters.
Changes in Soil C, N, and P Concentrations and Stocks after Caatinga Natural Regeneration of Degraded Pasture Areas in the Brazilian Semiarid Region
Authors: Silva Luiz Filipe dos Santos , Pessoa Luiz Guilherme Medeiros , Silva Emanuelle Maria da , Freire Maria Betânia Galvão dos Santos , de Souza Eduardo Soares , Oresca Denizard , Silva José Orlando Nunes da , Júnior Genival Barros , Bezerra Alan Cézar , Santos Eduardo Silva dos
Published: October 2024

The conversion of caatinga vegetation areas into pastures in the Brazilian semiarid region has depleted soil fertility due to degradation. The natural regeneration of the caatinga has been the alternative adopted to restore soil fertility. However, the real effectiveness of this practice in recovering organic carbon (SOC) and nutrient stocks is unknown. This work evaluated the effect of natural regeneration of the caatinga vegetation on the restoration of levels and stocks of SOC, N, and P as a strategy to maintain the sustainability of the environment. We collected soil samples every 5 cm, up to 20 cm deep, in areas of degraded pasture, caatinga in regeneration (10 years), and regenerated caatinga (35 years) to evaluate the levels and stocks of SOC, N, and P. The depth significantly affects the levels and stocks of SOC, N, and P. Stocks and levels of SOC, N, and P were higher in regenerated caatinga > regenerating caatinga > degraded pasture. The regenerated caatinga increased SOC, N, and P stocks by 115%, 110%, and 117%, respectively, compared to the degraded pasture. Although 35 years of regeneration of the caatinga has improved soil fertility, the levels and stocks of SOC, N, and P remain low.
Balancing Nets and Lives: A Socio-Ecological Analysis of Sustainable Fisheries on the Indian Coast of the Gulf of Mannar
Authors: Saha Deepanjana , Kumar Devarajulu Suresh , Krishnan Pandian , Mukherjee Rajdeep , Vidhyavathi A. , Maheswari M. , Vijayabhama M.
Published: October 2024

The Gulf of Mannar, a UNESCO World Biosphere Reserve, faces severe overfishing and habitat degradation threats. In this study, we investigate the pivotal role of ecosystem services in sustaining local livelihoods and overall well-being. By conducting a comprehensive survey of 480 respondents across two districts, we gathered extensive data on demographics, livelihoods, fishing practices, reliance on ecosystem services, and community management participation. The analysis reveals a critical dependence on the Gulf’s resources (income, food security, traditions) with gender disparities (men fish, women in pre-/post-harvest). Still, fishing is only allowed in the 10 km buffer zone (not the core zone). The findings emphasize the promise of community-based strategies, such as Marine Protected Areas and reviving co-management committees, for achieving sustainable fisheries management. However, we also identify gaps, including the need for more nuanced well-being indicators and improved models for community management participation. To address these challenges, we advocate for sustainable fishing practices, tackling social inequities, especially gender disparities in resource access and decision-making, and investing in fishing communities’ healthcare, education, and social safety nets. Promoting alternative livelihoods can alleviate pressure on fish stocks, and empowering local communities through capacity building and community-based management initiatives is crucial for ensuring the long-term sustainability of the Gulf of Mannar ecosystem and the well-being of its dependent communities. This multifaceted approach holds significant promise for balancing ecological health with human prosperity.
Research on Integration of Safety Policy System in Petrochemical Enterprises Based on Risk Hierarchical Control and Hidden Danger Investigation
Authors: Yu Kai , Liu Pingping , Zhou Lujie , Feng Rui
Published: October 2024

To enhance the risk management capacity of petrochemical enterprises, this paper presents a systematic and in-depth study of risk hierarchical control and hidden danger investigation technologies. Firstly, a risk hierarchical control system was developed based on text mining and Risk Breakdown Structure (RBS) theory, categorizing risk alarm levels into four tiers: no alarm, light alarm, medium alarm, and heavy alarm. Secondly, a hidden danger investigation and management system was established by integrating a three-dimensional hidden danger grading model with the Plan-Do-Check-Act (PDCA) closed-loop principle. Finally, a cooperative management technology system for risk and hidden dangers in petrochemical enterprises was constructed and validated using Shandong Luqing Petrochemical Enterprise as a case study. The results indicated that the comprehensive risk level of Shandong Luqing Petrochemical Enterprise is classified as II, with a yellow light warning signal. They demonstrated a positive correlation between the risk hierarchical control system and the hidden danger investigation and management system. The findings of this research provide valuable guidance for improving safety management in petrochemical enterprises.
Experimental Study on Performance of Cotton Small Arch Shed Recovery Machine
Authors: Liu Dejiang , Yao Keheng , Zhong Chengyi , Ma Shimin , Deng Xiaozhong , Aiwaili Sidikejiang
Published: October 2024

Most of the small-arch-shed-recycling machines in China use manual disassembly and manual recycling, with low recycling efficiency and low mechanization. Therefore, this paper designs a small recovery machine for arch sheds, greatly improving the efficiency of the recycling of arch sheds, which can realize the lifting and collection of the arch shed rod and orderly recycling of the shed film. By performing univariate experimental studies in the field, on the basis of field experiments, we carried out an experimental analysis with machine speed and different soil moisture contents as the influencing factors and took the removal rate of the shed rod, the removal rate of the shed film, and the damage rate of seedlings as the test indexes. The test results show that the optimal parameter combination is a travel speed of 1.1 m/s, at which the operation effect is the best. The results show that under the optimal operation effect, the removal rate of the shed rod was 95.72%, the removal rate of the shed film was 98.63%, the seedling injury rate was only 2.11%, and the removal rate of the shed rod was only 4.01%, which met the requirements of the recovery operation of the arch shed and means that this approach is conducive to the recycling of the arch shed materials and the realization of sustainable development. In actual operation, the parameters should be adjusted according to the actual situation in the field to meet the different recovery needs of arch sheds.
Optimizing the Operation of Grid-Interactive Efficient Buildings (GEBs) Using Machine Learning
Authors: Copiaco Czarina , Nour Mutasim
Published: October 2024

The building sector constitutes 40% of global electric energy consumption, making it vital to address for achieving the global net-zero emissions goal by 2050. This study focuses on enhancing electric load forecasting systems’ performance and interactivity by investigating the impact of weather and building usage parameters. Hourly electricity meter readings from a Texas university campus building (2012–2015) were employed, applying pre-processing techniques and machine learning algorithms such as linear regression, decision trees, and support vector machines using MATLAB R2023a. Exponential Gaussian Process Regression (GPR) showed the best performance at a one-year training data size, yielding an average normalized root mean square error (nRMSE) value of 0.52%, equivalent to a 0.3% reduction compared to leading methods. The developed system is presented through an interactive GUI and allows for prediction of external factors like PV and EV integration. Through a case study implementation, the combined system achieves 12.8% energy savings over a typical year simulated using ETAP 22 and Trimble ProDesign software version 2021.0.19. This holistic solution precisely models the electric demand management scenario of grid-interactive efficient buildings (GEBs), simultaneously enhancing reliability and flexibility to accommodate diverse applications.
Modeling the CO2 Emissions of Turkey Dependent on Various Parameters Employing ARIMAX and Deep Learning Methods
Authors: Yamacli Dilek Surekci , Tuncsiper Cagatay
Published: October 2024

CO2 emission is a big problem, not only for current living beings, but also for nature and the upcoming generations. Therefore, modeling CO2 emissions is the first step in reducing these emissions. In this work, the CO2 emissions of Turkey are modeled depending on the gross domestic product, amount of hydroelectric electricity generated, amount of energy generated from coal and the electricity obtained from natural gas power plants. The conventional autoregressive integrated moving average with exogenous variables (ARIMAX) and nonlinear deep learning methods are utilized to model CO2 emissions for the period of 1960–2020 using yearly data obtained from official sources. The modeling results of the ARIMAX and the deep learning methods are quantitatively assessed using four key figures of merit, namely the coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE) and the root mean square error (RMSE). Considering the coefficient of determination and the other performance parameters, it is observed that the deep learning model provides better performance compared with the ARIMAX model in modeling the annual CO2 emission data, especially in the pandemic period of 2019–2020. The results show that both the conventional ARIMAX and the nonlinear deep learning methods can be utilized to model CO2 emissions, therefore providing a crucial step for reducing CO2 emissions and the carbon footprint.
Cold Plasma Technology: A Sustainable Approach to Milk Preservation by Reducing Pathogens and Enhancing Oxidative Stability
Authors: Abbas Hayam M. , Altamim Ebtehal A. , Salama Mohamed , Fouad Mohamed T. , Zahran Hamdy A.
Published: October 2024

Pathogenic microorganisms and lipid oxidation are critical challenges in the dairy industry, influencing both food safety and quality. This study explores the potential of cold plasma (CP) technology as a sustainable alternative for milk preservation compared to conventional pasteurization. CP treatment utilizes ionized gas to generate reactive species, which effectively disrupt microbial cell membranes and inactivate pathogens, thereby sterilizing the milk. We assessed raw, pasteurized, and cold plasma-treated milk samples, focusing on microbial growth, lipid oxidation, and oxidative stability. Our findings indicate that CP treatment significantly reduced microbial contamination, effectively inhibiting the growth of pathogenic bacteria and delaying acidity development in milk. In contrast, pasteurized milk exhibited a notable increase in peroxide values, indicating lipid deterioration. Furthermore, the oxidative stability of cold plasma-treated milk was enhanced, with an induction period extending from approximately five to seven hours, demonstrating its superior resistance to oxidation. In conclusion, CP has emerged as a promising eco-friendly technology for prolonging the shelf life of milk by mitigating microbial growth and lipid oxidation. This method not only aligns with sustainability goals by reducing the need for chemical preservatives but also enhances the overall quality of milk products. Future research should focus on large-scale applications and the impacts of CP on other essential milk components, particularly fat-soluble vitamins, to fully understand its sustainability benefits in the dairy sector.
The Impact of Agricultural Inputs and Environmental Factors on Potato Yields and Traits
Authors: Cătuna Petrar Tatiana Mihaela , Brașovean Ioan , Racz Csaba-Pal , Mîrza Camelia Manuela , Burduhos Petru Daniel , Mălinaș Cristian , Moldovan Bianca Maria , Odagiu Antonia Cristina Maria
Published: October 2024

Potato, a component of global food security and economic stability, is cultivated extensively worldwide due to its adaptability to diverse climates and soil types. Ongoing research and technological advancements, including the use of unconventional products destined to fight the most harmful pathogens, are essential for enhancing productivity and resilience in potato farming. The current study aimed to identify the most appropriate fertilizers and phytosanitary treatments in order to attain optimal potato yields, dry matter, and starch contents, under climate-specific environments. The experiment was conducted in the northwest of Romania in 2023. The research was organized as a trifactorial experiment with the factors variety, fertilization, and phytosanitary treatment. XLSTAT (2022.2.1v.) was utilized for data processing. Mineral fertilization combined with conventional phytosanitary treatment led to the highest average yields in both potato varieties. Also, a combination of organic fertilization and treatment with 4% A. cepa extracts led to notable results, which suggests the possibility of successfully using these inputs in organic agriculture. The study shows the importance of selecting appropriate agricultural inputs to optimize potato yields, achieve specific levels of dry matter and starch content in a specific area, and promote sustainability.
Spin Wave Chiral Scattering by Skyrmion Lattice in Ferromagnetic Nanotubes
Authors: Li Na , Fan Mingming , Zeng Xiaoyan , Yan Ming
Published: October 2024

Previous studies have demonstrated that the surface curvature of cylindrical magnetic nonawires can induce fascinating dynamic magnetization properties. It was recently proposed that ferromagnetic nanotubes can be utilized as skyrmion guides, enabling the avoidance of the annihilation of skyrmions in the lateral boundaries as in flat thin-film strips. In this work, we demonstrate via micromagnetic simulation that multiple skyrmions can be stabilized in a cross-section of a ferromagnetic nanotube with interfacial Dzyaloshinskii–Moriya interaction (iDMI). When uniformly arranged, these skyrmions together can perform as a crystal lattice for spin waves (SWs) propagating in the nanotube. Our simulations show that the skyrmion lattice can contribute a chiral effect to the SW passing through, namely a circular polarization of the SW. The handedness of the polarization is found to be determined by the polarity of the skyrmions. A physical explanation of the observed effect is provided based on the exchange of angular momentum between SWs and skyrmions during the scattering process. Our results display more possibilities to exploit magnetic nanotubes as SW and skyrmion guide in the development of novel spintronic devices.
Stability and Bifurcation Analysis of a Symmetric Fractional-Order Epidemic Mathematical Model with Time Delay and Non-Monotonic Incidence Rates for Two Viral Strains
Authors: Li Zhixiang , Wu Wanqin , Tan Xuewen , Miao Qing
Published: October 2024

This study investigates a symmetric fractional-order epidemic model with time delays and non-monotonic incidence rates, considering two viral strains. By confirming the existence, uniqueness, and boundedness of the system’s solutions, the research ensures the model’s well-posedness, guaranteeing its mathematical soundness and practical relevance. The study calculates and evaluates the equilibrium points and the basic reproduction numbers R01 and R02 to understand the dynamic behavior of the model under different parameter settings. Through the application of the Lyapunov method, the research examines the asymptotic global stability of the system, determining whether it will converge to a particular equilibrium state over time. Furthermore, Hopf bifurcation theory is employed to investigate potential periodic solutions and bifurcation scenarios, highlighting how the system might shift from stability to periodic oscillations under certain conditions. By utilizing the Adams-Bashforth-Moulton numerical simulation method, the theoretical results are validated, reinforcing the conclusions and demonstrating the model’s applicability in real-world contexts. It emphasizes the importance of fractional-order models in addressing epidemiological issues related to time delays (τ), individual heterogeneity (m, k), and memory effects (θ), offering greater accuracy compared with traditional integer-order models. In summary, this research provides a theoretical foundation and practical insights, enhancing the understanding and management of epidemic dynamics through fractional-order epidemic models.
Antiproliferative Effects of Naja anchietae and Naja senegalensis Venom Peptides on Glioblastoma Cell Lines
Authors: Boughanmi Yasmine , Berenguer-Daizé Caroline , Balzano Marielle , Mosrati Hend , Moulard Maxime , Mansuelle Pascal , Fourquet Patrick , Torre Franck , de Pomyers Harold , Gigmes Didier , Ouafik Lhoucine , Mabrouk Kamel
Published: October 2024

This study explores the potential of natural bioactive peptides from animal venoms as targeted anti-cancer agents with reduced toxicity. Initially, we screened a broad collection of animal venoms for their antiproliferative activity against cancer cell lines. From this collection, we selected venoms from Naja anchietae and Naja senegalensis due to their promising activity. Utilizing reverse- phase high-performance liquid chromatography (RP HPLC), mass spectrometry (MALDI-TOF MS and MALDI-TOF TOF MSMS), and Edman degradation sequencing, we isolated and characterized three peptides named CTNanc1, CTNanc2, and CTNanc3 from Naja anchietae, and three others named CTNsen1, CTNsen2, and CTNsen3 from Naja senegalensis, each with a molecular weight of around 7 kDa. These purified peptides demonstrated inhibition of U87 glioblastoma cell proliferation, but not of U251 and T98G cells, in cell viability assays. To assess the impact of these treatments on cell viability, apoptosis, and necrosis, flow cytometry assays were conducted on U87 cells at 72 h. The results showed a decrease in cell viability and an increase in dead cells, suggesting that the treatments not only promote apoptosis, but may also lead to increased necrosis or late-stage apoptosis as the exposure time increases. These findings suggest that these peptides could be developed as leads for cancer therapy.
Isolation and Antibiofilm Activity of Bacteriophages against Cutibacterium acnes from Patients with Periprosthetic Joint Infection
Authors: Chen Baixing , Chittò Marco , Tao Siyuan , Wagemans Jeroen , Lavigne Rob , Richards R. Geoff , Metsemakers Willem-Jan , Moriarty T. Fintan
Published: October 2024

Background: Infections following shoulder surgery, particularly periprosthetic joint infection (PJI), are challenging to treat. Cutibacterium acnes is the causative pathogen in 39% to 76% of these cases. This study explores the efficacy of bacteriophage therapy as an alternative to conventional antibiotics for treating such infections. Methods: Nine phages with lytic activity were isolated from the skin of humans using C. acnes ATCC 6919 as the indicator host. These phages were tested individually or in combination to assess host range and antibiofilm activity against clinical strains of C. acnes associated with PJIs. The phage cocktail was optimized for broad-spectrum activity and tested in vitro against biofilms formed on titanium discs to mimic the prosthetic environment. Results: The isolated phages displayed lytic activity against a range of C. acnes clinical isolates. The phage cocktail significantly reduced the bacterial load of C. acnes strains 183, 184, and GG2A, as compared with untreated controls (p < 0.05). Individual phages, particularly CaJIE7 and CaJIE3, also demonstrated significant reductions in bacterial load with respect to specific strains. Moreover, phages notably disrupted the biofilm structure and reduced biofilm biomass, confirming the potential of phage therapy in targeting biofilm-associated infections. Conclusions: Our preclinical findings support the potential of phage therapy as a viable adjunct to traditional antibiotics for treating C. acnes infections in orthopedic device-related infections. The ability of phages to disrupt biofilms may be particularly beneficial for managing infections associated with prosthetic implants.