bluuebunny/crossref_metadata_2025 huggingface dataset:
Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data
Authors: Guo Xianyu , Yin Junjun , Li Kun , Yang Jian
Published: September 2024

Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging geometry, weather conditions, and system parameters. Therefore, labeled samples in one image could not be suitable to represent the same target in other images. The domain distribution shift of different images reduces the reusability of the labeled samples. Thus, exploring cross-domain interpretation methods is of great potential for SAR images to improve the reuse rate of existing labels from historical images. In this study, an unsupervised cross-domain classification method is proposed that utilizes the Gini coefficient to rank the robust and stable polarimetric features in both the source and target domains (GRFST) such that an unsupervised domain adaptation (UDA) can be achieved. This method selects the optimal features from both the source and target domains to alleviate the domain distribution shift. Both fully polarimetric (FP) and compact polarimetric (CP) SAR features are explored for crop-domain terrain type classification. Specifically, the CP mode refers to the hybrid dual-pol mode with an arbitrary transmitting ellipse wave. This is the first attempt in the open literature to investigate the representing abilities of different CP modes for cross-domain terrain classification. Experiments are conducted from four aspects to demonstrate the performance of CP modes for cross-data, cross-scene, and cross-crop type classification. Results show that the GRFST-UDA method yields a classification accuracy of 2% to 12% higher than the traditional UDA methods. The degree of scene similarity has a certain impact on the accuracy of cross-domain crop classification. It was also found that when both the FP and circular CP SAR data are used, stable, promising results can be achieved.
Producer Welfare Benefits of Rating Area Yield Crop Insurance
Authors: Falsafian Azadeh , Ghahremanzadeh Mohammad , Aref Eshghi Taravat , Rasooli Sharabiani Vali , Szymanek Mariusz , Dziwulska-Hunek Agata
Published: September 2024

Index-based insurance is an innovative concept for evaluating agricultural risks and payouts, which uses an index instead of traditional on-site loss assessment. Area yield insurance, as an index-based approach, is an effective strategy to mitigate moral hazard and adverse selection issues. This study aims to develop area yield insurance as a new insurance plan in Iran for two major crops: wheat and barley. It utilized kernel and joint kernel distributions to price the insurance and assessed producer welfare benefits by comparing the certainty equivalence (CE) of farmers’ utility with and without the policy. Data were collected from East Azerbaijan Province, including county-level yield data for irrigated and rainfed wheat and barley from 1975 to 2019 and 446 individual-level yield data from 2015 to 2019. A two-stage method was used to model yield risk: the first stage fits a trend model, while the second estimates the yield distributions with the detrended data. The results showed a significant difference in premiums calculated by the two distributions, with joint kernel distribution offering the best empirical fit and reasonable premiums. The findings indicate that area yield crop insurance provides positive welfare benefits and should serve as a viable alternative or complement to existing yield insurance plans. The successful implementation of this policy in various countries suggests it can be a suitable risk management program for developing countries like Iran.
Research on a Novel Citrus Reticulata ‘Chachi’ Orientation Adjustment Mechanism (COAM) and Machine Vision Guidance Control
Authors: Yan Guo-Qi , Liang Jia-Yu , Chen Qiu-Shuo , Liang De-Yu , Zhou Xi-En , Mo Jia-Si
Published: September 2024

The initial processing of Citrus Reticulata ‘Chachi’ involves peeling as a crucial step. Currently, there is some semi-automatic peeling equipment available. However, due to the requirement of adjusting the orientation of Citrus Reticulata ‘Chachi’ to ensure the stem (or navel) is facing upwards before peeling and because the peeling process must retain the stem as a marker for fresh fruit picking, the loading of Citrus Reticulata ‘Chachi’ for peeling still solely relies on manual operation, resulting in low efficiency and poor standardization. With the rapid growth of the pericarp of the Citrus Reticulata ‘Chachi’ industry, semi-automatic processing equipment is no longer able to meet production demands. The loading issue before peeling Citrus Reticulata ‘Chachi’ is a complex hand–eye coordination problem. In response to this issue, this paper proposes a novel Citrus Reticulata ‘Chachi’ orientation adjustment mechanism (COAM). This mechanism utilizes frictional force to adjust the orientation of Citrus Reticulata ‘Chachi’. First, the conceptual design and kinematic modelling analysis of the mechanism were conducted. Next, the omnidirectional friction-driven wheels were optimized in design. Subsequently, a prototype was manufactured and assembled to conduct validation tests on its open-loop motion performance. Finally, a visual feedback-guided algorithm was introduced to complement the kinematic model, enabling the automatic and rapid adjustment of Citrus Reticulata ‘Chachi’ orientation. The experimental results indicate that the COAM designed in this study can effectively and rapidly adjust the orientation of Citrus Reticulata ‘Chachi’ fruits of different sizes and shapes. It demonstrates strong adaptability, and under visual feedback guidance, the orientation adjustment error is less than 10% of the fruit’s diameter. This meets the requirements for automated production in the initial processing of Citrus Reticulata ‘Chachi’. The research presented in this paper also provides new insights for the orientation adjustment and loading of similar spherical fruits.
Soil Organic Matter and Bulk Density: Driving Factors in the Vegetation-Mediated Restoration of Coastal Saline Lands in North China
Authors: Li Weiliu , Li Jingsong , Wu Yujie , Guo Kai , Feng Xiaohui , Liu Xiaojing
Published: September 2024

Coastal saline soils are an important soil resource that, when restored, can enhance arable land and preserve the natural ecology. With the aim of improving the use of coastal saline soils, we conducted a spot survey at Bohai coastal saline land to investigate the differences in soil properties between different vegetation types. The soil physical and chemical properties of various vegetation types, including Aeluropus sinensis, Imperata cylindrica, Tamarix chinensis, Lycium chinense, Hibiscus moscheutos, Helianthus annuus, Gossypium hirsutum, and Zea mays, were examined at two depth layers: 0–20 cm and 20–40 cm, and in two seasons, spring and autumn. The soil properties were compared with bare land as a control. The results indicated that the electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density of soils with vegetation cover were lower than those with bare land. On the other hand, soil pH, organic matter content, mean weight diameter, and saturated hydraulic conductivity were higher. The redundancy analysis results revealed a substantial positive correlation between soil pH, saturated hydraulic conductivity, water content, mean weight diameter, and organic matter content, as well as a significant positive correlation between soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density. Soil pH, saturated hydraulic conductivity, water content, mean weight diameter, organic matter content, and soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density were negatively correlated. The results of the structural equation model and variance decomposition showed that soil organic matter and bulk density were the key factors affecting the degree of soil salinization, and compared with their independent effects, their combined effect on soil salinization was greater. This study’s conclusions can provide a point of reference for further research on the mechanisms of soil improvement and desalinization in coastal saline land.
PM10 Organic Aerosol Fingerprints by Using Liquid Chromatography Orbitrap Mass Spectrometry: Urban vs. Suburban in an Eastern Mediterranean Medium-Sized Coastal City
Authors: Stergiou Evangelos , Chatziioannou Anastasia Chrysovalantou , Pergantis Spiros A. , Kanakidou Maria
Published: September 2024

This study compares the PM10 (particulate matter of diameter smaller than 10 μm) organic aerosol composition between urban and suburban stations in Heraklion, Crete, during winter 2024 in order to highlight the impact of local anthropogenic activities on urban atmospheric particulate matter pollution. Using an HPLC-ESI-MS Orbitrap analyzer (High Performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry) in full MS scan mode at a resolution of 140,000, 48 daily aerosol filter extracts were analyzed in both positive and negative modes, resulting in the detection of 2809 and 3823 features, respectively. Features with at least five times higher intensity in the urban environment compared to the suburban, and p < 0.05, were deemed significant. A correlation with black carbon (r > 0.6) was observed for 71% of significant urban features in positive mode. These features showed a predominance of low O:C ratios (<0.2) and the majority were classified as intermediate volatility organic compounds (IVOCs), indicating fresh primary emissions. A clear urban–suburban distinction was shown by PCA of positive mode features, unlike the negative mode features. Regarding the total intensity of the features, urban samples were on average 55% higher than suburban samples in positive mode and 39% higher in negative mode. This study reveals the molecular profile of locally emitted combustion related organics observed in positive mode in an urban environment.
Factors Associated with Medication Noncompliance in Dogs in New Zealand
Authors: Odom Thomas F. , Riley Christopher B. , Benschop Jackie , Hill Kate E.
Published: September 2024

Client compliance with prescribed medication instructions to treat their pets is a concern. This study describes factors associated with the noncompliance of dog owners with veterinary recommendations for medication, as well as client-reported barriers and aids to administering medications. A cross-sectional survey of dog owners’ compliance with veterinary medication recommendations was performed from 9 January 2019 to 18 July 2020. A convenience sample of owners who prescribed medication for their dogs during or following elective veterinary examination was surveyed regarding medication administration experience and compliance. Owners were followed up to determine if the course of medication had been completed. Compliance data were analyzed descriptively. Logistic regression was performed with compliance as the outcome. Medication noncompliance was recorded for 47% (71/151) of owners. Increasing dog age was associated with better owner compliance (p < 0.05). Pet owners who used “nothing” as an aid to medicating were less likely to be noncompliant (p < 0.05). Forty-seven percent (71/151) of owners reported that “nobody” showed them how to administer the medication. One-third of dog owners (47/151) reported challenges in medicating their pets. The most common reason cited by clients reporting challenges was a resistant pet. Demonstration of medication administration techniques and discussion about available aids to medicating a pet may improve client compliance.
Whole-Genome Sequencing Reveals Temporal Trends in Antibiotic Resistance Genes in Escherichia coli Causing Pediatric Urinary Tract Infections in Central Vietnam
Authors: Le Huyen Thanh Thi , Hoang Trang Thu , Nguyen Ngoc Anh Thi , Nguyen Sang Ngoc , Nguyen Ung Dinh , Hoang Cuong Xuan , Vo Nam S. , Le Duc Quang , Nguyen Son Hoang , Cao Minh Duc , Ho Tho Huu
Published: September 2024

(1) Background: Pediatric urinary tract infections (UTIs) pose significant challenges due to drug-resistant Escherichia coli (E. coli) strains. This study utilizes whole-genome sequencing to analyze temporal trends in antibiotic resistance genes (ARGs) in clinical E. coli isolates from pediatric UTI cases in central Vietnam. (2) Methods: We conducted whole-genome sequencing on 71 E. coli isolates collected from pediatric UTI patients between 2018 and 2020. ARGs were identified, and their prevalence over time was analyzed. Statistical tests were used to correlate ARG presence with antibiotic resistance. (3) Results: Of the 47 E. coli isolates with complete data, 40 distinct ARGs were identified, with a median of 10 resistance genes per isolate. A significant increase in the total number of ARGs per isolate was observed over time, from an average of 8.88 before June 2019 to 11.63 after. Notably, the prevalence of the aadA2 gene (aminoglycoside resistance) rose from 0% to 26.7%, and that of the blaNDM-5 gene (beta-lactam and carbapenem resistance) increased from 0% to 23.3%. Key correlations include blaEC with cephalosporin resistance, blaNDM-5 with carbapenem resistance, and sul2 with sulfamethoxazole/trimethoprim resistance. (4) Conclusions: Whole-genome sequencing reveals complex and evolving antibiotic resistance patterns in pediatric E. coli UTIs in central Vietnam, with a marked increase in ARG prevalence over time. Continuous surveillance and targeted treatments are essential to address these trends. Understanding genetic foundations is crucial for effective intervention strategies.
An Institutional Febrile Neutropenia Protocol Improved the Antibacterial Treatment and Encouraged the Development of a Computerized Clinical Decision Support System
Authors: Taş Zahit , Metan Gökhan , Telli Dizman Gülçin , Yavuz Eren , Dizdar Ömer , Büyükaşık Yahya , Uzun Ömrüm , Akova Murat
Published: September 2024

We investigated the influence of a local guideline on the quality of febrile neutropenia (FN) management and the applicability of a computerized decision support system (CDSS) using real-life data. The study included 227 FN patients between April 2016 and January 2019. The primary outcome measure was the achievement of a 20% increase in the rate of appropriate empirical treatment of FN in bacteremic patients. The compatibility of the CDSS (the development of which was completed in November 2021) with local protocols was tested using standard patient scenarios and empirical antibiotic recommendations for bacteremic FN patients. In total, 91 patients were evaluated before (P1: between April 2016 and May 2017) and 136 after (P2: between May 2017 and January 2019) the guideline’s release (May 2017). The demographic characteristics were similar. Appropriate empirical antibacterial treatment was achieved in 58.3% of P1 and 88.1% of P2 patients (p = 0.006). The need for escalation of antibacterial treatment was significantly lower in P2 (49.5% vs. 35.3%; p = 0.03). In P2, the performance of the CDSS and consulting physicians was similar (CDSS 88.8% vs. physician 88.83%; p = 1) regarding appropriate empirical antibacterial treatment. The introduction of the local guideline improved the appropriateness of initial empirical treatment and reduced escalation rates in FN patients. The high rate of compliance of the CDSS with the local guideline-based decisions in P2 highlights the usefulness of the CDSS for these patients.
Innovative Strategies in Drug Repurposing to Tackle Intracellular Bacterial Pathogens
Authors: Lorente-Torres Blanca , Llano-Verdeja Jesús , Castañera Pablo , Ferrero Helena Á. , Fernández-Martínez Sergio , Javadimarand Farzaneh , Mateos Luis M. , Letek Michal , Mourenza Álvaro
Published: September 2024

Intracellular bacterial pathogens pose significant public health challenges due to their ability to evade immune defenses and conventional antibiotics. Drug repurposing has recently been explored as a strategy to discover new therapeutic uses for established drugs to combat these infections. Utilizing high-throughput screening, bioinformatics, and systems biology, several existing drugs have been identified with potential efficacy against intracellular bacteria. For instance, neuroleptic agents like thioridazine and antipsychotic drugs such as chlorpromazine have shown effectiveness against Staphylococcus aureus and Listeria monocytogenes. Furthermore, anticancer drugs including tamoxifen and imatinib have been repurposed to induce autophagy and inhibit bacterial growth within host cells. Statins and anti-inflammatory drugs have also demonstrated the ability to enhance host immune responses against Mycobacterium tuberculosis. The review highlights the complex mechanisms these pathogens use to resist conventional treatments, showcases successful examples of drug repurposing, and discusses the methodologies used to identify and validate these drugs. Overall, drug repurposing offers a promising approach for developing new treatments for bacterial infections, addressing the urgent need for effective antimicrobial therapies.
Analysis of the Model Used to Predict Continuous Deformations of the Land Surface in Areas Subject to Discontinuous Deformations—A Case Study
Authors: Paszek Justyna
Published: September 2024

This article analyses and evaluates the model used by the Jastrzebie Coal Company in Poland to forecast the values of continuous deformations of land surfaces caused by underground mining. Particular attention is paid to the values of terrain inclinations, which cause significant deflections of buildings located in mining areas affected by exploitation. The inclination forecasts were made using Bialek’s model, with the values of its parameters determined in situ and after completion of the exploitation of a longwall. The obtained results were compared with the values of the terrain inclinations calculated from the results of geodetic measurements carried out at observation points located near the buildings and discontinuous linear deformations (i.e., the ground steps). The conducted research shows that the correlations between the absolute values of the practical inclinations and the values of the theoretical inclinations were rather weak, as the values of their correlation coefficients did not exceed 0.24. The tested model underestimated 45.5% of the values of the inclinations observed in situ by an average of −53.5%. The model values of the inclinations for the remaining observed inclinations (54.5%) were overestimated by an average of 461.9%. The largest anomalies were obtained in the case of inclination values from points located near the ground steps. The effectiveness of predicting inclination values with a commonly used model is strongly reduced when discontinuous linear deformations appear. Nonetheless, it reflects well the mining area categories.
Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
Authors: Yin Deyang , Zhao Lei , Zhai Kai , Zheng Jianfeng
Published: September 2024

Accurate wind power prediction helps to fully utilize wind energy and improve the stability of the power grid. However, existing studies mostly analyze key wind power-related features equally without distinguishing the importance of different features. In addition, single models have limitations in fully extracting input feature information and capturing the time-dependent relationships of feature sequences, posing significant challenges to wind power prediction. To solve these problems, this paper presents a wind power forecasting approach that combines feature weighting and a combination model. Firstly, we use the attention mechanism to learn the weights of different input features, highlighting the more important features. Secondly, a Multi-Convolutional Neural Network (MCNN) with different convolutional kernels is employed to extract feature information comprehensively. Next, the extracted feature information is input into a Stacked BiLSTM (SBiLSTM) network to capture the temporal dependencies of the feature sequence. Finally, the prediction results are obtained. This article conducted four comparative experiments using measured data from wind farms. The experimental results demonstrate that the model has significant advantages; compared to the CNN-BiLSTM model, the mean absolute error, mean squared error, and root mean squared error of multi-step prediction at different prediction time resolutions are reduced by 35.59%, 59.84%, and 36.77% on average, respectively, and the coefficient of determination is increased by 1.35% on average.
PRE-YOLO: A Lightweight Model for Detecting Helmet-Wearing of Electric Vehicle Riders on Complex Traffic Roads
Authors: Yang Xiang , Wang Zhen , Dong Minggang
Published: September 2024

Electric vehicle accidents on the road occur frequently, and head injuries are often the cause of serious casualties. However, most electric vehicle riders seldom wear helmets. Therefore, combining target detection algorithms with road cameras to intelligently monitor helmet-wearing has extremely important research significance. Therefore, a helmet-wearing detection algorithm based on the improved YOLOv8n model, PRE-YOLO, is proposed. First, we add small target detection layers and prune large target detection layers. The sophisticated algorithm considerably boosts the effectiveness of data manipulation while significantly reducing model parameters and size. Secondly, we introduce a convolutional module that integrates receptive field attention convolution and CA mechanisms into the backbone network, enhancing feature extraction capabilities by enhancing attention weights within both channel and spatial aspects. Lastly, we incorporate an EMA mechanism into the C2f module, which strengthens feature perception and captures more characteristic information while maintaining the same model parameter size. The experimental outcomes indicate that in comparison to the original model, the proposed PRE-YOLO model in this paper has improved by 1.3%, 1.7%, 2.2%, and 2.6% in terms of precision P, recall R, mAP@0.5, and mAP@0.5:0.95, respectively. At the same time, the number of model parameters has been reduced by 33.3%, and the model size has been reduced by 1.8 MB. Generalization experiments are conducted on the TWHD and EBHD datasets to further verify the versatility of the model. The research findings provide solutions for further improving the accuracy and efficiency of helmet-wearing detection on complex traffic roads, offering references for enhancing safety and intelligence in traffic.
LDMNet: Enhancing the Segmentation Capabilities of Unmanned Surface Vehicles in Complex Waterway Scenarios
Authors: Dai Tongyang , Xiang Huiyu , Leng Chongjie , Huang Song , He Guanghui , Han Shishuo
Published: September 2024

Semantic segmentation-based Complex Waterway Scene Understanding has shown great promise in the environmental perception of Unmanned Surface Vehicles. Existing methods struggle with estimating the edges of obstacles under conditions of blurred water surfaces. To address this, we propose the Lightweight Dual-branch Mamba Network (LDMNet), which includes a CNN-based Deep Dual-branch Network for extracting image features and a Mamba-based fusion module for aggregating and integrating global information. Specifically, we improve the Deep Dual-branch Network structure by incorporating multiple Atrous branches for local fusion; we design a Convolution-based Recombine Attention Module, which serves as the gate activation condition for Mamba-2 to enhance feature interaction and global information fusion from both spatial and channel dimensions. Moreover, to tackle the directional sensitivity of image serialization and the impact of the State Space Model’s forgetting strategy on non-causal data modeling, we introduce a Hilbert curve scanning mechanism to achieve multi-scale feature serialization. By stacking feature sequences, we alleviate the local bias of Mamba-2 towards image sequence data. LDMNet integrates the Deep Dual-branch Network, Recombine Attention, and Mamba-2 blocks, effectively capturing the long-range dependencies and multi-scale global context information of Complex Waterway Scene images. The experimental results on four benchmarks show that the proposed LDMNet significantly improves obstacle edge segmentation performance and outperforms existing methods across various performance metrics.
Low Stress Level and Low Stress Amplitude Fatigue Loading Simulation of Concrete Components Containing Cold Joints under Fatigue Loading
Authors: Fu He-Lin , Deng Huang-Shi , Wu Yi-Min , Zhao Yi-Bo , Xie Cheng-Da
Published: September 2024

Concrete linings containing cold joint defects may crack or detach under the aerodynamic fatigue loading generated by high-speed train operation, which posing a serious threat to the normal operation of high-speed trains. However, there is currently no simulation method specifically for fatigue damage of concrete linings containing cold joints. Based on the Roe-Siegmund cycle cohesive force model, a cohesive force fatigue damage elements were developed. A large dataset was constructed through numerical simulation software to build a BP neural network for back-calculated parameter of cohesive force fatigue damage elements. By combining experimental data, fatigue damage parameters corresponding to different pouring interval cold joints were back-calculated. These back-calculated parameters were then incorporated into the numerical model to compare simulation results with experimental results to validate the applicability of cohesive force fatigue damage elements and back propagation neural networks (BP neural network). The research results show that the difference between the fatigue life and fracture process calculated by numerical simulation and experimental data is small, verifying the applicability of the method proposed in this paper. The pouring interval directly affects the initial strength of the cold joint interface and the starting conditions of fatigue damage. The possibility of fatigue damage and fracture of concrete components containing cold joints increases with the increase of pouring interval, while the variability of fatigue life decreases with the increase of pouring interval. Interface strength and thickness are the main factors affecting the possibility of fatigue damage occurrence and the variability of fatigue life. The research results can be used to analyze the damage and cracking status of concrete linings containing cold joints under aerodynamic fatigue loading.
CFD Research for Air Bearing with Gradient-Depth Recesses
Authors: Wen Zhongpu , Chi Yuchen , Gu Hui , Qu Huajie , Shi Zhaoyao
Published: September 2024

Ultra-precision measurement and manufacturing need high-precision machines, just as a photolithography machine needs air bearings. In gas lubrication, the use of compound restrictors with recesses has been widely proven to be an effective method to improve stiffness, which directly affects the accuracy of the machine. However, determination of the structural parameters of recesses is lacking in theoretical models. This paper has established a mechanical property model for a small-scale guideway, which can respond to the variation in force caused by micron-level changes in the recesses’ depth. To meet the requirements of high positioning accuracy and movement accuracy, this paper puts forward a high-stiffness guideway without an air tube. In order to improve rotational stiffness and determine the structural parameters of recesses, this paper found a guideway with the optimal gradient depth of recesses. Both AFVM (adaptive finite volume method) research and experimental results show that the gradient depth of recesses could significantly improve the rotational stiffness of guideways without air tubes.
Influence of Additives on Grinding Performance of Digital Light Processing-Printed Phenol Bond Grinding Wheels
Authors: Habel Ammar , Barmouz Mohsen , Steinhäuser Felix , Azarhoushang Bahman
Published: September 2024

Resin bond grinding wheels are the most common grinding tools in the industry. Until now, all research on the additive manufacturing of resin bond grinding wheels has focused on commercially available acrylate resin. However, using a phenol-based bond to print resin-bond grinding wheels has been challenging for researchers and industries. In this study, a photo-curable phenol resin bond grinding wheel was introduced for the first time, offering advantages such as lower cost, high thermal resistance, and good mechanical properties. To enhance the grinding performance of the printed wheels, various additives, such as copper, glass fiber, and carbon fiber, were incorporated into the composition. Different on-machine and out-of-machine measurements, such as force, tool wear, dimensional accuracy, and optical microscopy measurements, were conducted to investigate the grinding performance of the printed wheels. The results demonstrate that printed grinding wheels have strong potential in grinding applications, which was more prominent for the bond reinforced by glass fibers, providing improved mechanical properties (up to 50%), wear resistance (up to 75%), and higher dimensional accuracy (up to 11%).
Mutation-Based Multivariate Time-Series Anomaly Generation on Latent Space with an Attention-Based Variational Recurrent Neural Network for Robust Anomaly Detection in an Industrial Control System
Authors: Jeon Seungho , Koo Kijong , Moon Daesung , Seo Jung Taek
Published: September 2024

Anomaly detection involves identifying data that deviates from normal patterns. Two primary strategies are used: one-class classification and binary classification. In Industrial Control Systems (ICS), where anomalies can cause significant damage, timely and accurate detection is essential, often requiring analysis of time-series data. One-class classification is commonly used but tends to have a high false alarm rate. To address this, binary classification is explored, which can better differentiate between normal and anomalous data, though it struggles with class imbalance in ICS datasets. This paper proposes a mutation-based technique for generating ICS time-series anomalies. The method maps ICS time-series data into a latent space using a variational recurrent autoencoder, applies mutation operations, and reconstructs the time-series, introducing plausible anomalies that reflect multivariate correlations. Evaluations of ICS datasets show that these synthetic anomalies are visually and statistically credible. Training a binary classifier on data augmented with these anomalies effectively mitigates the class imbalance problem.
Changes in Soil Moisture Improved by High-Performance Ester Materials under Dry–Wet Cycles
Authors: Lai Haoqiang , Zhou Cuiying , Liu Zhen
Published: September 2024

The mechanism for improving soil with high-performance ester material is not yet clear, especially changes in soil moisture under dry–wet cycling conditions. Soil moisture is closely related to its ecological performance, which also leads to a lack of scientific bases for the application range, climate conditions, and long-term performance evolution evaluation of high-performance ester materials in ecological restoration. Our research revealed an optimal ratio for the amount of high-performance ester materials that can improve soil moisture and ecological performance under multiple dry–wet cycling conditions. Dry–wet cycling experiments and planting tests were conducted to study the soil moisture improvement mechanism and the changes caused by high-performance ester materials. Changes in the soil’s moisture, EC, and ecological performance were explored. The results indicate that ester materials can effectively improve soil moisture and EC. Even after multiple dry–wet cycles, ester materials can still play an effective role in improving soil moisture and ecological performance. An optimal ratio for the amount of high-performance ester materials was also found. This research reveals the improvement mechanism for ester materials on soil moisture and ecological performance under dry–wet cycling conditions. Our results provide new data and technical guidance for the improvement of soil moisture and ecological restoration by high-performance ester materials.
Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial
Authors: Żmuda Przemysław , Khaidakov Barbara , Krasowska Maria , Czapska Katarzyna , Dobkowski Michał , Guzowski Julian , Kowalczyk Paulina , Lemke Krzysztof , Folwarski Marcin , Foryś Aleksander , Domian Ewa , Postuła Marek
Published: September 2024

The purpose of this study was to evaluate the properties and pharmacokinetics of liposomal vitamin C in powder form obtained by a method devoid of organic solvents. The powder and liposome morphology were analyzed using scanning electron microscopy (SEM) and cryogenic transmission electron microscopy (cryo-TEM), respectively. Additionally, the carrier particle size, size distribution (STEP-Technology®; L.U.M. GmbH, Berlin, Germany), and zeta potential value were determined. The pharmacokinetic parameters of liposomal and non-liposomal vitamin C (AUC, Cmax, C10h, and C24h) were compared in a randomized, single-dose, double-blind, cross-over trial (ClinicalTrials.gov ID: NCT05843617) involving healthy adult volunteers (n = 10, 1000 mg dose). The process of spray drying used to transform liquid suspensions of the liposomes into powder form did not adversely affect the quality of the carrier particles obtained. Compared to non-encapsulated vitamin C, oral administration of the liposomal formulation resulted in significantly better absorption of ascorbic acid into the bloodstream, which equated to a higher bioavailability of the liposomal product (30% increase in AUC, p < 0.05). The duration of elevated vitamin C blood levels was also longer (C24h increase of 30%, p < 0.05). Although the results obtained are promising and suggest higher bioavailability for the liposomal form of vitamin C, the limited sample size necessitates further research with a larger cohort to confirm these findings.
Use of New and Light Materials in Automotive Engineering for Towing System
Authors: Petrici Andrei Victor , Scutaru Maria Luminita , Munteanu Violeta Mihaela , Vlase Sorin
Published: September 2024

Towing systems are an important component in the automobile industry, having to meet specific quality and resistance conditions. In most cases, the towbar is made of steel. Decreases in vehicle weight and manufacturing price mean that, in this field, research is also being conducted in order to replace the materials that make up the towbar and replace the steel with composite materials or aluminum alloys. In this paper, research was performed on towing systems built from other materials, and the obtained results were compared with those of steel systems. Theoretical calculations and experimental results made it possible to obtain a database and recommendations for the use of new and composite materials. The experimental tests validated the theoretical results obtained. Five towing systems made of different materials were studied. The results of the research are emphasized through recommendations regarding the manufacturing of towing systems.
Can Stylized Products Generated by AI Better Attract User Attention? Using Eye-Tracking Technology for Research
Authors: Tang Yunjing , Chen Chen
Published: September 2024

The emergence of AIGC has significantly improved design efficiency, enriched creativity, and promoted innovation in the design industry. However, whether the content generated from its own database meets the preferences of target users still needs to be determined through further testing. This study investigates the appeal of AI-generated stylized products to users, utilizing 12 images as stimuli in conjunction with eye-tracking technology. The stimulus is composed of top-selling gender-based stylized Bluetooth earphones from the Taobao shopping platform and the gender-based stylized earphones generated by the AIGC software GPT4.0, categorized into three experimental groups. An eye-tracking experiment was conducted in which 44 participants (22 males and 22 females, mean age = 21.75, SD = 2.45, range 18–27 years) observed three stimuli groups. The eye movements of the participants were measured while viewing product images. The results indicated that variations in stimuli category and gender caused differences in fixation durations and counts. When presenting a mix of the two types of earphones, the AIGC-generated earphones and earphones from the Taobao shopping platform, the two gender groups both showed a significant effect in fixation duration with F (2, 284) = 3.942, p = 0.020 < 0.05, and η = 0.164 for the female group and F (2, 302) = 8.824, p < 0.001, and η = 0.235 for the male group. They all had a longer fixation duration for the AI-generated earphones. When presenting exclusively the two types of AI-generated gender-based stylized earphones, there was also a significant effect in fixation duration with F (2, 579) = 4.866, p = 0.008 < 0.05, and η = 0.129. The earphones generated for females had a longer fixation duration. Analyzing this dataset from a gender perspective, there was no significant effect when the male participants observed the earphones, with F (2, 304) = 1.312 and p = 0.271, but there was a significant difference in fixation duration when the female participants observed the earphones (F (2, 272) = 4.666, p = 0.010 < 0.05, and η = 0.182). The female participants had a longer fixation duration towards the earphones that the AI generated for females.
Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
Authors: Xie Xuebin , Zhang Huaxi
Published: September 2024

The increase in mining depth necessitates higher strength requirements for hard rock pillars, making mine pillar stability analysis crucial for pillar design and underground safety operations. To enhance the accuracy of predicting the stability state of mine pillars, a prediction model based on the subtraction-average-based optimizer (SABO) for hyperparameter optimization of the least-squares support vector machine (LSSVM) is proposed. First, by analyzing the redundancy of features in the mine pillar dataset and conducting feature selection, five parameter combinations were constructed to examine their effects on the performance of different models. Second, the SABO-LSSVM prediction model was compared vertically with classic models and horizontally with other optimized models to ensure comprehensive and objective evaluation. Finally, two data sampling methods and a combined sampling method were used to correct the bias of the optimized model for different categories of mine pillars. The results demonstrated that the SABO-LSSVM model exhibited good accuracy and comprehensive performance, thereby providing valuable insights for mine pillar stability prediction.
Dynamic Response and Rock Damage of Different Shapes of Cavities under Blasting Loads
Authors: Cui Xuejiao , Zhao Mingsheng , Li Qiyue
Published: September 2024

In order to investigate the dynamic response and rock mass damage characteristics of cavities with different shapes under blasting loads, this paper, through a combination of model tests and numerical simulations, studies the stress distribution, strain, failure modes, and blasting fragment size distribution of cavities with different shapes subjected to blasting loads. The results show that under the action of blasting loads, the presence of cavities with different shapes significantly affects the blasting effects and rock mass damage. Spherical cavities exhibit excellent blast resistance, whereas rectangular and triangular cavities are prone to stress concentration at their tips, which in turn promotes rock mass damage and failure. Subsequent analysis of the blasting fragment sizes reveals that rectangular and triangular cavities yield more favorable blasting results than spherical cavities. The research findings provide important theoretical foundations and practical guidance for the design and construction of underground engineering blasting, contributing to enhancing engineering safety and promoting the sustainable development of the underground engineering industry.
The Biological Impact of Residual Aluminum Particles on Sand-Blasted Dental Implant Surfaces: A Systematic Review of Animal Studies
Authors: Panda Sourav , Tumedei Margherita , Panda Sital , Goker Funda , Depalma Cristina Maria , Pande Tejas , Del Fabbro Massimo
Published: September 2024

(1) Background: The use of alumina particles for surface treatment of dental implants is a common practice aimed at enhancing osseointegration. However, the biological effects of residual alumina particles on implant surfaces remain a subject of debate. This systematic review evaluates the impact of residual alumina particles on the osseointegration, biocompatibility, and bacterial adhesion of dental implants based on available in vivo experimental animal studies. (2) Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Scopus to identify relevant studies. The inclusion criteria focused on experimental animal studies that assessed the biological effects of alumina-blasted dental implants. Data extraction was carried out, and quality assessments were performed using the SYRCLE risk-of-bias tool. (3) Results: Ten studies met the inclusion criteria, involving various animal models, such as rabbits, pigs, dogs, and sheep. The findings demonstrated that residual alumina particles did not negatively impact osseointegration. Some studies reported accelerated bone growth and improved osseointegration with residual alumina. Additionally, residual alumina showed potential bactericidal properties, reducing bacterial adhesion. (4) Conclusions: The available evidence from animal studies suggests that residual alumina particles do not adversely affect the osseointegration and biocompatibility of dental implants. These particles may even enhance bone growth and reduce bacterial adhesion. However, due to the scarcity of human studies and the impracticality of histological assessments in humans, further research, including long-term clinical trials, is necessary to confirm these findings.
Integrated Home Energy Management with Hybrid Backup Storage and Vehicle-to-Home Systems for Enhanced Resilience, Efficiency, and Energy Independence in Green Buildings
Authors: Pai Liu , Senjyu Tomonobu , Elkholy M. H.
Published: September 2024

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management.
Modelling and Simulation of Traditional Craft Actions
Authors: Zabulis Xenophon , Partarakis Nikolaos , Demeridou Ioannna , Bartalesi Valentina , Pratelli Nicolò , Meghini Carlo , Nikolaou Nikolaos , Fallahian Peiman
Published: September 2024

The problem of modelling and simulating traditional crafting actions is addressed, motivated by the goals of craft understanding, documentation, and training. First, the physical entities involved in crafting actions are identified, physically, and semantically characterised, including causing entities, conditions, properties, and objects, as well as the space and time in which they occur. Actions are semantically classified into a taxonomy of four classes according to their goals, which are shown to exhibit similarities in their operation principles and utilised tools. This classification is employed to simplify the create archetypal simulators, based on the Finite Element Method, by developing archetypal simulators for each class and specialising them in craft-specific actions. The approach is validated by specialising the proposed archetypes into indicative craft actions and predicting their results in simulation. The simulated actions are rendered in 3D to create visual demonstrations and can be integrated into game engines for training applications.
Study of the Effects of Plasma Pretreatment on the Microstructure of Peanuts
Authors: Wang Yingnan , Yao Qu , Li Xingjun , Yin Jun , Zhang Zhongjie , Zhou Xianqing
Published: September 2024

In this study, cold plasma treatments are employed to modify peanuts. This study systematically investigates the effects of various plasma treatment conditions, including power, duration, and gas type, on the microstructure of peanut seed coats and embryos. Observations under a scanning electron microscope (SEM) reveal that as plasma treatment power increases from 100 W to 500 W, the etching level of peanut seed coats significantly intensifies, surface roughness deepens, and concavities become more pronounced. Additionally, micro-pores on the seed coat gradually enlarge and form cracks. Specifically, when the plasma treatment is set at 200 W for 60 s, the oxygen (O2) treatment group shows interconnected cracks on the peanut seed coat surface, with lipid particles exuding and protein particles and polymers decomposing. In contrast, the helium (He) treatment group displays clear cell structures and deep grooves, with no noticeable lipid particles exuding around surface cracks. The argon (Ar) treatment group exhibits a distinct rectangular cell structure with clear boundaries, and although surface cracks form, only a few protein particles escape from the cracks. The embryo surface structure becomes looser after plasma treatment, leading to the disintegration of lipid particles, protein particles, and polymers, affecting the fusion and migration of large and small lipid bodies within the peanut’s internal structure. Increasing treatment duration intensifies the etching phenomenon, resulting in more lipid particles exuding, which indicates a positive correlation between lipid particles exuding and treatment duration. This study sheds light on the mechanisms underlying changes in peanut microstructure due to cold plasma treatment, providing scientific evidence for improving peanut quality, enhancing oil extraction efficiency, and optimizing food processing techniques.
Static Factors in Sitting Comfort: Seat Foam Properties, Temperature, and Contact Pressure
Authors: Silva P. , Ribeiro D. , Postolache O. , Seabra E. , Mendes J.
Published: September 2024

The seat characteristics have high relevance in overall comfort on any transportation means. In particular, the foam’s mechanical properties, interface pressure, and contact temperature play an important role in low- or no-vibration situations regarding static comfort. The present work presents the complete protocol for a static evaluation of different foams and seat covers to assess railway seats. Based on the evaluation of the foam’s mechanical properties and interface pressure profiles, it was concluded that higher-density foam (80 kg/m3) is the most favorable. Regarding the foam cover, a thermographic assessment demonstrated that the fabric cover that induces lower temperatures at passenger interface contact promotes higher comfort levels. It should be highlighted that experiments were conducted on real train seat cushions and environments using a thermographic camera and pressure map sensor.