bluuebunny/crossref_metadata_2025 huggingface dataset:
Suicidal ideation and associated factors among people living with HIV/AIDS in Ethiopia: a systematic review and meta-analysis
Authors: Gebrekidan Amanuel Yosef , Lombebo Afework Alemu , Efa Amelework Gonfa , Azeze Gedion Asnake , Kassie Gizachew Ambaw , Haile Kirubel Eshetu , Asgedom Yordanos Sisay , Woldegeorgis Beshada Zerfu , Dejenie Tadesse Asmamaw
Published: September 2024

Background

Suicide is one of the main causes of mortality in the world, accounting for more fatalities than homicide, war, human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), breast cancer, and malaria. Significantly, the biggest risk factors for suicide in the general population are having already attempted suicide and suicidal ideation. Despite the availability of studies on suicidal ideation among people living with HIV/AIDS (PLWHA) in Ethiopia, the results are inconsistent. Thus, a systematic review and meta-analysis was conducted to estimate the pooled prevalence of suicidal ideation among people living with HIV/AIDS.

Methods

A systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. To find papers published in the English language before 20 May 2023, the electronic databases of Medline, Science Direct, Excerpta Medica Database, Cochrane Library, African Journals Online (AJOL), and Google Scholar were searched. The DerSimonian and Laird method for random effects models was used to estimate the pooled prevalence of suicidal ideation with a 95% confidence interval in STATA V.14.0 statistical software. To test for heterogeneity between studies and publication bias, respectively, forest plots and funnel plots were used. Additionally, leave-one-out sensitivity was conducted.

Results

A total of nine studies with 3,411 study participants were included in this systematic review and meta-analysis. The pooled prevalence of suicidal ideation among PLWHA was 20.55% (95% CI 14.76, 26.33). Being female (Odds ratio (OR) = 4.27, 95% CI = 2.29, 7.97), living alone (OR = 5.02, 95% CI = 2.15, 11.64), poor social support (OR = 3.80, 95% CI = 2.56, 5.65), perceived stigma (OR = 3.50, 95% CI = 1.55, 7.87), depression (OR = 5.08, 95% CI = 2.55, 11.48), undisclosed HIV status (OR = 4.8, 95% CI = 2.10, 10.93), and World Health Organization HIV clinical stages of III or IV (OR = 4.40, 95% CI = 2.95, 6.58) were significantly associated with suicidal ideation.

Conclusion

Suicidal ideation among PLWHA is high in Ethiopia. Therefore, emphasis should be given to psychiatric assessment and interventions with a special focus on individuals having the associated factors.

Systematic Review Registration

PROSPERO (CRD42023429613).
Community engagement in mobile and hard-to-reach populations: a community-based intervention for malaria elimination in a tri-national region of the Guiana Shield
Authors: Jimeno-Maroto Irene , Galindo Muriel Suzanne , Miller Jane Bordalo , Lambert Yann , Carboni Carlotta , Bardon Teddy , Plessis Lorraine , Vreden Stephen , Suarez-Mutis Martha , Douine Maylis , Sanna Alice
Published: September 2024

Several countries of the Guiana Shield are aiming at the control and elimination of malaria in areas where Artisanal and Small-scale Gold Mining (ASGM) activities predominate, raising questions about how to strengthen community engagement to improve the effectiveness of health programs. The Curema project focuses its intervention on the mobile and hard-to-reach ASGM population, complementing the efforts of national programs in the Guiana Shield. The Curema intervention combines targeted drug administration for suspected Plasmodium vivax asymptomatic carriers, the Malakit distribution, and health education activities. The primary goals of this manuscript are to outline a pathway to foster community participation in the Curema project aimed at eliminating malaria. Thus, it presents a vision of the challenges that the AGSM community poses in terms of community participation for an asymptomatic problem; and highlights the community-based model and the Information, Education and Communication (IEC) components as foundations for participation. In addition, it also presents culturally sensitive IEC strategies designed through iterative and collaborative consultative processes and other bottom-up outreach activities. The community engagement approach facilitates adaptability and responsiveness in a complex, evolving context increasing the effectiveness of interventions.
Detection of SARS-CoV-2 on the environmental surfaces and its implications for pandemic preparedness
Authors: Jamil Kazi , Abdulrazack Nasreem , Fakhraldeen Saja , Kamal Heba , Al-Mutairi Anwar , Al-Feili Batool , Ahmed Imtiaz , Kumar Vinod
Published: September 2024

Even though death due to COVID-19 is no longer a public health emergency, less virulent but highly transmissible forms of SARS-CoV-2 continue to spread in many countries leading to outbreaks and rise in hospitalizations in the affected regions. Lessons learned during the pandemic must be put into action to protect the world's population from another catastrophe like COVID-19. Novel approaches that were developed for tracking the spread of SARS-CoV-2 included analysis of wastewater, air samples, and various environmental surfaces. We conducted a study in Kuwait during the peak of COVID-19 pandemic to examine if SARS-CoV-2 could be detected in swabs taken from frequently touched environmental surfaces. We selected 12 Cooperative Society Stores—two from each governorate of Kuwait—for collection of surface samples. The Cooperative Society Stores are widely distributed across the whole country and cater to daily household needs including groceries and other essential items. These stores operated even during the “lockdown” imposed at the height of the pandemic. We collected swabs from high-touch surfaces including the handles of the shopping carts and freezers, the elevators, the keypads of the point-of-service terminals of cash counters, and the automated teller machines. All the surfaces tested showed a variable presence of SARS-CoV-2 by reverse transcriptase quantitative PCR, showing the validity of the proof-of-concept study. Monitoring of the presence of SARS-CoV-2 by surface sampling thus offers a cheap but effective means of environmental surveillance for coronaviruses. We therefore strongly recommend the addition of surface environmental sampling as a strategy for pandemic preparedness everywhere.
Virtual Reality in planning, design, and management of urban green and blue infrastructure
Authors: Schewenius Maria , Wallhagen Marita
Published: September 2024

There is a need for new tools in urban planning, design, and management (urban PDM) of green-blue infrastructure (GBI) to better support resilience and sustainable development. Virtual Reality (VR) emerges as a potential tool in this context. GBI provides a diversity of ecosystem services that increase the capacity of urban environments to absorb change while continuing to develop (adaptive capacity), a key factor in resilience and sustainability. However, there is a lack of tools capable of effectively capturing and communicating the designs, functions, and uses of GBI. Despite this need, research on the contributions of VR as a tool in urban PDM of GBI remains limited. To address this gap, an exploratory experiment using inductive qualitative data analysis was conducted. Participants of different ages and with different expertise viewed 360-degree video clips from urban areas through VR, completed a questionnaire, and participated in interviews. The results showed that the 360-degree video VR experience enhanced participants’ understanding of the viewed sites. Specifically, participants paid attention to details, captured the context, responded by generating emotional engagement, identified site development potential, and described an immersive experience. These results align with key principles for the sustainable management of ecosystem services, such as participation, learning, diversity, place-making, and stewardship; and adaptive capacity aspects such as processes and results grounded in complexity-embracing creativity. Our analysis shows that 360-degree video VR can increase the understanding of complexities, connections, and nuances in the urban environment. We conclude that 360-degree video VR could serve as a valuable complement to traditional tools in urban PDM, enriching decision-making with information that can support adaptive capacity, resilience, and sustainability.
Case report: Localized coloproctitis caused by novel Basidiobolus arizonensis in a dog
Authors: Black Annalise , Wiertek Marcellina , Ferguson Sylvia , Wycislo Kathryn , Rayhel Laura , Reid Heather , Wiederhold Nathan , Cañete-Gibas Connie
Published: September 2024

A 6-year-old male neutered boxer mix canine was presented for a one-month history of dyschezia, hematochezia, and constipation. Colonoscopy and endoscopic biopsies revealed non-specific lymphoplasmacytic, eosinophilic colitis. Despite pursuing various therapies over a 3.5-month clinical course (including hypoallergenic diet, antibiotics, prokinetics, laxatives, and anti-inflammatory glucocorticoids), the patient’s condition did not improve. Two and a half months after initial presentation, the patient developed circumferential proctitis with multiple draining tracts and obstipation. Humane euthanasia and postmortem examination were elected. Gross and histological findings revealed locally extensive pyogranulomatous coloproctitis with many intralesional PAS-positive, GMS-negative 30–40 μm in diameter, hyaline, pauciseptate, irregularly branching fungal hyphae, hyphal bodies or chlamydospores and 25–45 μm in diameter thick-walled zygospores. Fungal culture of fluid from the draining tracts was performed, and DNA sequence analysis of the ITS and partial LSU of the nuclear ribosomal RNA genes were used to identify and confirm a novel species, Basidiobolus arizonensis. Basidiobolus spp. are saprobes in the order Basidiobolales and most commonly cause granulomatous infections of the skin, respiratory tract, and gastrointestinal tract in veterinary species and humans. To the authors’ knowledge, this is the first report of novel Basidiobolus arizonensis causing localized coloproctitis in a dog.
Recognition of Rice Species Based on Gas Chromatography-Ion Mobility Spectrometry and Deep Learning
Authors: Zhao Zhongyuan , Lian Feiyu , Jiang Yuying
Published: September 2024

To address the challenge of relying on complex biochemical methods for identifying rice species, a prediction model that combines gas chromatography-ion mobility spectroscopy (GC-IMS) with a convolutional neural network (CNN) was developed. The model utilizes the GC-IMS fingerprint data of each rice variety sample, and an improved CNN structure is employed to increase the recognition accuracy. First, an improved generative adversarial network based on the diffusion model (DGAN) is used for data enhancement to expand the dataset size. Then, on the basis of a residual network called ResNet50, a transfer learning method is introduced to improve the training effect of the model under the condition of a small sample. In addition, a new attention mechanism called Triplet is introduced to further highlight useful features and improve the feature extraction performance of the model. Finally, to reduce the number of model parameters and improve the efficiency of the model, a method called knowledge distillation is used to compress the model. The results of our experiments revealed that the recognition accuracy for identifying the 10 rice varieties was close to 96%; hence, the proposed model significantly outperformed traditional models such as principal component analysis and support vector machine. Furthermore, compared to the traditional CNN, our model reduced the number of parameters and number of computations by 53% and 55%, respectively, without compromising classification accuracy. The study also suggests that the combination of GC-IMS and our proposed deep learning method had better discrimination abilities for rice varieties than traditional chromatography and other spectral analysis methods and that it effectively identified rice varieties.
A Method for Cropland Layer Extraction in Complex Scenes Integrating Edge Features and Semantic Segmentation
Authors: Lu Yihang , Li Lin , Dong Wen , Zheng Yizhen , Zhang Xin , Zhang Jinzhong , Wu Tao , Liu Meiling
Published: September 2024

Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments.
An In Vitro Study on the Antioxidant Properties of Cistus incanus Extracts
Authors: Olszowy-Tomczyk Małgorzata , Wianowska Dorota
Published: September 2024

This paper concerns the evaluation of the antioxidant activity (AA) of extracts obtained from cistus herbs grown in Albania and Turkey. The extracts were prepared in a Soxhlet apparatus, as well as by the maceration and infusion methods, similar to the home method of preparing herbal teas. AA was determined using the DPPH (2,2′-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), FRAP (ferric reducing antioxidant power) and β-carotene methods. It was proven that the AA of extracts depends not only on the extraction technique and AA assessment method but also on the place of plant cultivation. The smallest activity was determined using the β-carotene bleaching method, while the highest values were obtained using the FRAP method. On the other hand, the ABTS method showed a greater activity of the Albanian herb prepared using the Soxhlet technique. In addition, the antioxidant properties of extracts were compared with those of rutin standard solutions, showing that this characteristic component of cistus is not the only one that determines AA of extracts. As a result, the rutin content is not an indicator of the antioxidant properties of extracts. The other polyphenolic compounds, although occurring at lower concentration levels compared to rutin, modify the resultant AA of extracts. These studies confirmed the biological activity of cistus as a valuable source of polyphenolic compounds in the human diet.
Enhancing Literature Review Efficiency: A Case Study on Using Fine-Tuned BERT for Classifying Focused Ultrasound-Related Articles
Authors: Panagides Reanna K. , Fu Sean H. , Jung Skye H. , Singh Abhishek , Eluvathingal Muttikkal Rose T. , Broad R. Michael , Meakem Timothy D. , Hamilton Rick A.
Published: September 2024

Over the past decade, focused ultrasound (FUS) has emerged as a promising therapeutic modality for various medical conditions. However, the exponential growth in the published literature on FUS therapies has made the literature review process increasingly time-consuming, inefficient, and error-prone. Machine learning approaches offer a promising solution to address these challenges. Therefore, the purpose of our study is to (1) explore and compare machine learning techniques for the text classification of scientific abstracts, and (2) integrate these machine learning techniques into the conventional literature review process. A classified dataset of 3588 scientific abstracts related and unrelated to FUS therapies sourced from the PubMed database was used to train various traditional machine learning and deep learning models. The fine-tuned Bio-ClinicalBERT (Bidirectional Encoder Representations from Transformers) model, which we named FusBERT, had comparatively optimal performance metrics with an accuracy of 0.91, a precision of 0.85, a recall of 0.99, and an F1 of 0.91. FusBERT was then successfully integrated into the literature review process. Ultimately, the integration of this model into the literature review pipeline will reduce the number of irrelevant manuscripts that the clinical team must screen, facilitating efficient access to emerging findings in the field.
Quercus infectoria Gall Ethanolic Extract Accelerates Wound Healing through Attenuating Inflammation and Oxidative Injuries in Skin Fibroblasts
Authors: Wunnoo Suttiwan , Sermwittayawong Decha , Praparatana Rachanida , Voravuthikunchai Supayang Piyawan , Jakkawanpitak Chanawee
Published: September 2024

Quercus infectoria Olivier (Fagaceae) nutgall, a traditional Asian medicine, is renowned for its efficacy in treating wounds and skin disorders. Although the gall extract has shown promising results in accelerating wound healing in diabetic animal models, its mechanisms, particularly the effects on redox balance, remain poorly understood. This study aims to investigate the effects and mechanisms of Q. infectoria gall ethanolic extract (QIG) on wound healing in fibroblasts, with a specific emphasis on its modulation of oxidative stress. Hydrogen peroxide (H2O2)-treated L929 cells were used as an in vitro model of oxidation-damaged fibroblasts. QIG exhibited potent antioxidant activity with 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), and ferric reducing antioxidant power (FRAP) assay values of 305.43 ± 7.48, 508.94 ± 15.12, and 442.08 ± 9.41 µM Trolox equivalents (TE)/µg, respectively. Elevated H2O2 levels significantly reduced L929 cell viability, with a 50% lethal concentration of 1.03 mM. QIG mitigated H2O2-induced cytotoxicity in a dose-dependent manner, showing protective effects in pre-, post-, and co-treatment scenarios. QIG significantly reduced H2O2-induced intracellular reactive oxygen species production and inflammation-related gene expression (p < 0.05). Additionally, at 25 µg/mL, QIG remarkably improved wound closure in H2O2-treated L929 cells by approximately 9.4 times compared with the H2O2 treatment alone (p < 0.05). These findings suggest QIG has potential therapeutic applications in wound healing, mediated through the regulation of oxidative stress and inflammatory response.
Supervised Machine Learning to Predict Drilling Temperature of Bone
Authors: Islam Md Ashequl , Kamarrudin Nur Saifullah Bin , Ijaz Muhammad Farzik , Daud Ruslizam , Basaruddin Khairul Salleh , Abdullah Abdulnasser Nabil , Takemura Hiroshi
Published: September 2024

Surgeons face a significant challenge due to the heat generated during drilling, as excessive temperatures at the bone–tool interface can lead to irreversible damage to the regenerative soft tissue and result in thermal osteonecrosis. While previous studies have explored the use of machine learning to predict the temperature rise during bone drilling, this in vitro study introduces a comprehensive approach by combining the Response Surface Methodology (RSM) with advanced machine learning techniques. The main objective lies in the comprehensive evaluation and comparison of support vector machine (SVM) and random forest (RF) models specifically for the optimization of the bone drilling parameters to prevent thermal bone necrosis. A total of 27 experiments were conducted using a multi-level factorial method, with analysis performed via the Minitab software version 19.1. Performance metrics such as the mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used to assess model accuracy. The RF model emerged as the most effective, with R2 values of 94.2% for testing and 97.3% for training data, significantly outperforming other models in predicting temperature fluctuations. This study demonstrates the superior predictive capabilities of the RF model and offers a robust framework for the optimization of surgical procedures to mitigate the risk of thermal damage.
Research on Safety Performance Evaluation and Improvement Path of Prefabricated Building Construction Based on DEMATEL and NK
Authors: Xiong Zhihua , Lin Yuting , Wang Qiankun , Yang Wanjun , Shen Chuxiong , Zhang Jiaji , Zhu Ke
Published: September 2024

To address the common issues of lacking indicator system identification, causal relationship quantification, and path simulation analysis in the current research on safety performance in prefabricated construction, a method for improving safety performance in prefabricated construction based on the decision-making trial and evaluation laboratory (DEMATEL) and NK model is proposed. Firstly, through theoretical analysis and literature review, the indicator system for safety performance in prefabricated construction is identified using the grounded theory. Secondly, expert research and quantitative analysis are combined to analyze the causal relationship of the indicators using the DEMATEL method. Then, the DEMATEL method is integrated with the NK model to carry out a key indicator adaptability modeling analysis and three-dimensional simulation. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and method. The results show that X6 (construction and implementation of safety management system) had the highest impact on the other indicators, and X14 (quality and safety status of prefabricated components) was most influenced by other indicators. X6 (construction and implementation of safety management system), X1 (personnel safety awareness and attitude), X14 (quality and safety status of prefabricated components), and X12 (construction site working environment) were identified as key performance indicators. “X6 (construction and implementation of safety management system) → X1 (personnel safety awareness and attitude) → X14 (quality and safety status of prefabricated components) → X12 (construction site working environment)” was considered the optimal path to improve construction safety performance.
Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework
Authors: Gomes Fernanda O. , Pellungrini Roberto , Monreale Anna , Renso Chiara , Martina Jean E.
Published: September 2024

With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets.
A-Star (A*) with Map Processing for the Global Path Planning of Autonomous Underwater and Surface Vehicles Operating in Large Areas
Authors: Kot Rafał , Szymak Piotr , Piskur Paweł , Naus Krzysztof
Published: September 2024

The global path planning system is one of the basic systems ensuring the autonomous operation of unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) in a complex aquatic environment. The A* path planning algorithm is one of the most well-known algorithms used to obtain an almost optimal path, avoiding obstacles even in a complex environment containing objects with specific shapes and non-uniform arrangements. The main disadvantage of this algorithm is the computational cost of path calculation. This article presents a new approach based on the image processing of the map before determining the path using A*. The results of numerical research based on a large-sized map expressing the port area confirm the proposed method’s effectiveness, which reduces the calculation time by over 500 times with a slight increase in the path length compared to the basic version of the A* algorithm. Based on the obtained results, the proposed approach also increases the path’s safety by designating narrow and risky areas as closed to vehicle movement. For this reason, the method seems suitable for use in global path planning for autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) operating in large areas.
Effect of Atmospheric Stability on Meandering and Wake Characteristics in Wind Turbine Fluid Dynamics
Authors: Løvøy Alvestad Bendik Peter , Fevang-Gunn Leon , Panjwani Balram , Bracchi Tania Kalogiannidis
Published: September 2024

This study investigates the impact of atmospheric stability on wind turbine flow dynamics, focusing on wake deflection and meandering. Using the high-fidelity large-eddy simulation coupled with the Actuator Line model, we explore three stability conditions for the Vestas V80 turbine, both with and without yaw. The results indicate that wake meandering occurs predominantly along the deflected wake axis. Despite varying wake deficits and meandering behaviors, neutral and stable conditions exhibit similar wake deflection trajectories during yawed turbine operations. Spectral analysis of meandering reveals comparable cutoff and peak frequencies between neutral and stable cases, with a consistent Strouhal number (St=0.16). The unstable condition shows significant deviations, albeit with associated uncertainties. Overall, increased stability decreases both oscillation amplitude and frequency, highlighting the complex interplay between atmospheric stability and wind turbine wake dynamics.
Intensity Prediction Equations Based on the Environmental Seismic Intensity (ESI-07) Scale: Application to Normal Fault Earthquakes
Authors: Pizza Marco , Ferrario Francesca , Michetti Alessandro M. , Velázquez-Bucio M. Magdalena , Lacan Pierre , Porfido Sabina
Published: September 2024

Earthquake environmental effects may significantly contribute to the damage caused by seismic events; similar to ground motion, the environmental effects are globally stronger in the vicinity and decrease moving away from the epicenter or seismogenic source. To date, a single intensity prediction equation (IPE) has been proposed in the Italian Apennines for intensity scale dealings with environmental effects: the Environmental Seismic Intensity (ESI-07). Here, we evaluate the sensitivity of the IPE with respect to input data and methodological choices and we propose IPEs with global validity for crustal normal faults. We show the strong influence of input data on the obtained attenuation investigating the 1980 Irpinia–Basilicata (Southern Italy) earthquake. We exploit a dataset of 26 earthquakes to build an IPE considering the epicentral distance. We also propose an IPE considering the distance from the fault rupture, which is derived from a dataset of 10 earthquakes. The proposed equations are valid for normal faults up to 40 km from the epicenter/fault and may flank other models predicting ground motion or damage to the built environment. Our work thus contributes to the use of the ESI-07 scale for hazard purposes.
Towards Green and Low-Carbon Transformation via Optimized Polygeneration System: A Case Study of the Iron and Steel Industry
Authors: Xu Mao , Li Shufang , Wang Yihan , Liu Qunyi
Published: September 2024

Polygeneration systems have significant potential for energy conservation and emission reduction and can effectively promote green and low-carbon development in energy-intensive industries, such as the iron and steel industry. However, its application faces the difficulty in technology selection under multiple objectives simultaneously, which is to determine the technology portfolio to achieve the synergy of energy conservation goals and air pollutant emission reduction goals, as well as ensure the economic benefits of the enterprises. This study investigated a case polygeneration system where the iron and steel plant are the core with four polygeneration paths and twenty polygeneration technologies. A multi-objective optimization model is developed to select the optimal technology combination of each polygeneration path under energy conservation, emission reduction, and cost control objectives, which is solved by the non-dominated sorting genetic algorithm-II (NSGA-II). The optimal results can reach significant energy conservation and emission reduction effects while obtaining economic benefits. However, synergistic and conflicting relationships among the objectives exist in both scales of iron and steel plants. The final decision scheme can achieve the mitigations equivalent to 15.9–27.1% and 16.3–42.6% of the energy consumption and air pollutant emissions of the steel enterprises with annual production of 3 Mt/a and 9 Mt/a, respectively. There are thirteen and twelve technologies that are selected as the final decision scheme in the polygeneration system in these two case enterprises. These findings demonstrate the significant roles the polygeneration system plays and provide critical insights and methodology in the technical selection of the polygeneration system.
Effect of Storage Time on the Structural Integrity of Silts and Organic Soils: An Analysis of Moisture Content, Unconfined Compressive Strength, and Elasticity
Authors: Calderón-Carrasco Alei , Galarza-Poveda Bethsabe , Damián-Chalán Andreina , Albuja-Sánchez Jorge
Published: September 2024

The impact of storage duration on the geotechnical properties of soils is a recurring issue in the field of geotechnical engineering. Due to the lack of previous research addressing this topic, an experimental study was conducted to evaluate the variation of these properties over time. Undisturbed samples of silty and organic soil from Quito, Ecuador, were obtained. These samples were subjected to unconfined compressive strength (UCS) and moisture content (MC) tests at various intervals (1, 3, 7, 14, 21, 28, and 56 days). Results revealed a significant correlation between MC, UCS, and modulus of elasticity (ME). A progressive increase in UCS and ME was observed as MC decreased, with peak values observed to occur between 20 and 30 days. These findings suggest that matric suction plays a predominant role in increasing cohesion and, consequently, UCS. Therefore, it is concluded that the time elapsed between sample extraction and testing is a critical factor influencing the preservation of MC and, hence, the accurate assessment of the soil’s mechanical properties.
Levitating Control System of Maglev Ruler Based on Active Disturbance Rejection Controller
Authors: Sun Jiyuan , Tian Gengyun , Li Pin , Tian Chunlin , Zhou Zhenxiong
Published: September 2024

The autonomous displacement and displacement measurement functions of the maglev ruler are performed by the mover core. The magnetic levitation ruler can serve as a viable alternative to the linear measurement system of a coordinate measuring machine. The stability of the four magnetic fields in air gaps and the levitation position of the maglev ruler is one of the key factors for the stability of the thrust force on the power core, and it is also one of the key factors for ensuring the precision of the maglev ruler. There is cross-coupling between the two ends of the mover core of the maglev ruler, resulting in a strongly coupled, nonlinear, multi-input and multi-output system for the levitating system of the maglev ruler. This paper establishes a mathematical model for the levitating system of the maglev ruler and designs a levitating control system for the maglev ruler based on an active disturbance rejection control algorithm to achieve decoupling and disturbance suppression. Through simulation analysis and experimental testing of the levitating system with starting and disturbance, it is proved that the levitating control system of the maglev ruler has good dynamic characteristics, static characteristics, and robustness.
Evaluating the Plastic Anisotropic Effect on the Forming Limit Curve of 2024-T3 Aluminum Alloy Sheets Using Marciniak Tests and Digital Image Correlation
Authors: Iquilio Roberto , Fehrmann Kurt , Núñez Sepúlveda Sergio , Tesser Enzo , Valín Meyli , Valín José Luis
Published: September 2024

This study thoroughly investigates the influence of anisotropy on the formability of 2024-T3 aluminum alloy sheets using advanced techniques such as digital image correlation (DIC) and Marciniak tests. A key finding is the relatively small variation in anisotropy values across different strain paths and orientations, contrasting with more significant variations reported in other studies. Tests were conducted on nine samples with various geometries to induce specific strain paths, including uniaxial, plane, and balanced biaxial strains, oriented in different directions relative to the rolling direction. The study also provides a detailed analysis of microstructural and mechanical characteristics, such as precipitate distribution and anisotropy behavior, which are crucial for understanding the relationship between microstructure and material formability. The results show that while anisotropy impacts deformation capacity, the differences in formability among the directions were minimal, with slightly greater formability observed in the diagonal direction. These findings are compared with forming limit curves (FLCs), offering an integrated view of how relatively uniform anisotropic properties influence formability. These insights are essential for optimizing the processing and application of 2024-T3 alloy in industrial contexts, emphasizing the importance of understanding anisotropy in the design of metal components.
Predictive Modeling of UHPC Compressive Strength: Integration of Support Vector Regression and Arithmetic Optimization Algorithm
Authors: Wang Liuyan , Liu Lin , Dai Dong , Liu Bo , Cheng Zhenya
Published: September 2024

Based on an in-depth analysis of the factors influencing the compressive strength of ultra-high-performance concrete (UHPC), this study examined the impact of both single factorsand combined factors on UHPC performance using experimental data. The correlation analysis indicates that cement content, water content, steel fiber, and fly ash significantly affect the strength of UHPC, whereas silica fume, superplasticizers, and slag powder have a relatively smaller influence. This analysis provides a scientific basis for model development. Furthermore, the support vector regression (SVR) model was optimized using the arithmetic optimization algorithm (AOA). The superior performance and computational efficiency of the AOA–SVR model in predicting UHPC compressive strength were validated. Compared to SVR, support vector machine (SVM), and other single models, the AOA–SVR model achieves the highest R2 value and the lowest error rates. The results demonstrate that the optimized AOA–SVR model possesses excellent generalization ability and can more accurately predict the compressive strength of UHPC.
Evaluation Methodology for Circular and Resilient Information Systems
Authors: Lounis Stavros , Koukopoulos Anastasios , Farmakis Timoleon , Aryblia Maria
Published: September 2024

Digital technologies nowadays provide essential support for companies, making them a priority for businesses and a prominent area of study for researchers. In response to the increasing emphasis on sustainability and resilience, new information systems are developing to meet evolving business needs, namely circular and resilient information systems (CRISs). These systems integrate with traditional ones to optimise key performance indicators (KPIs) related to circularity and resiliency. Despite extensive methodologies for evaluating traditional information systems, systems designed for circularity and resiliency need to be assessed in parallel and in depth. Existing evaluations focus on efficiency and user satisfaction but often neglect the unique demands of circularity and resiliency. This study introduces a novel evaluation methodology for CRISs. Through a case study of an innovative system and the established literature, we address real-life needs and challenges in manufacturing. In particular, the system serves the needs of three distinct case studies: Carbon Fibre-Reinforced Polymer (CFRP) waste utilisation in drone manufacturing, recovery of magnets from Waste Electrical and Electronic Equipment (WEEE), and the repurposing of citrus processing waste into juice by-products. Our methodology is built on the 5W1H method to make our approach context-specific and aligned with each case’s unique requirements, making it also replicable for other industries. Our findings offer insights and a tool for practitioners and researchers to evaluate CRIS performance. The research highlights the importance of a two-fold evaluation approach for CRISs, evaluating both pilot-specific KPIs and the system’s technical performance. Policy implications suggest the need for regulatory frameworks and incentives to support the adoption, as well as evaluation, of CRISs and promote sustainable and resilient industrial practices.
Transfer Learning and Deep Neural Networks for Robust Intersubject Hand Movement Detection from EEG Signals
Authors: Kok Chiang Liang , Ho Chee Kit , Aung Thein Htet , Koh Yit Yan , Teo Tee Hui
Published: September 2024

In this research, five systems were developed to classify four distinct motor functions—forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV)—from EEG signals, using the WAY-EEG-GAL dataset where participants performed a sequence of hand movements. During preprocessing, band-pass filtering was applied to remove artifacts and focus on the mu and beta frequency bands. The initial system, a preliminary study model, explored the overall framework of EEG signal processing and classification, utilizing time-domain features such as variance and frequency-domain features such as alpha and beta power, with a KNN model for classification. Insights from this study informed the development of a baseline system, which innovatively combined the common spatial patterns (CSP) method with continuous wavelet transform (CWT) for feature extraction and employed a GoogLeNet classifier with transfer learning. This system classified six unique pairs of events derived from the four motor functions, achieving remarkable accuracy, with the highest being 99.73% for the GP–RV pair and the lowest 80.87% for the FW–GP pair in intersubject classification. Building on this success, three additional systems were developed for four-way classification. The final model, ML-CSP-OVR, demonstrated the highest intersubject classification accuracy of 78.08% using all combined data and 76.39% for leave-one-out intersubject classification. This proposed model, featuring a novel combination of CSP-OVR, CWT, and GoogLeNet, represents a significant advancement in the field, showcasing strong potential as a general system for motor imagery (MI) tasks that is not dependent on the subject. This work highlights the prominence of the research contribution by demonstrating the effectiveness and robustness of the proposed approach in achieving high classification accuracy across different motor functions and subjects.
Structural Equation Modeling of Musculoskeletal Pains, Work–Family Conflict, and Sleep-Related Problems on Well-Being of Food Manufacturing Workers
Authors: Kim Jun Won , Jeong Byung Yong
Published: September 2024

The objective of this study is to investigate the causal relationships between musculoskeletal pains, work–family conflict, sleep-related problems, and the well-being of food manufacturing workers using structural equation modeling. This study analyzed 523 food manufacturing workers extracted from the Sixth Korea Working Conditions Survey. We formulated six hypotheses based on literature reviews and examined the structural causal relationship between work–family conflict, musculoskeletal pains, sleep-related problems, and well-being. According to the results of structural equation modeling, work–family conflict has a significant impact on musculoskeletal pains (standardized path coefficient of 0.113). Furthermore, both musculoskeletal pains (standardized path coefficient of 0.350) and work–family conflict (standardized path coefficient of 0.212) have been found to affect sleep-related problems. It has also been established that musculoskeletal pains have a direct influence on well-being (standardized path coefficient of 0.115). The association and structural causality between musculoskeletal pain and psychological factors in food manufacturing workers can be used for customized measures to improve the well-being of food manufacturing workers. This study is also meaningful in that musculoskeletal pain and psychological factors should be managed in an integrated manner.
Mediating Effect of the Stay-at-Home Order on the Association between Mobility, Weather, and COVID-19 Infection and Mortality in Indiana and Kentucky: March to May 2020
Authors: Shakib Shaminul H. , Little Bert B. , Karimi Seyed , McKinney William Paul , Goldsby Michael , Kong Maiying
Published: September 2024

(1) Background: The association of COVID-19 infection and mortality with mobility and weather in Indiana and Kentucky was compared for the period from 1 March to 15 May 2020. (2) Methods: The risk of COVID-19 infection and mortality was evaluated using Cox regressions with the following covariates: mobility (retail/recreation, grocery/pharmacy, and workplace), weather (precipitation, minimum and maximum temperature, ultraviolet [UV] index), and metropolitan status. (3) Results: A higher maximum temperature (adjusted hazard ratioinfection (aHRi) = 1.01; adjusted hazard ratiodeath (aHRd) = 1.001), metropolitan status (aHRi = 1.12; aHRd = 2.05), and a higher minimum temperature (aHRi = 1.01) were associated with increased risks of COVID-19 infection and/or mortality. Protection against COVID-19 infection and/or mortality was associated with retail/recreation (aHRi = 0.97; aHRd = 0.937), grocery/pharmacy (aHRi = 0.991; aHRd = 0.992), workplace (aHRi = 0.99; aHRd = 0.965), precipitation (aHRi = 0.999; aHRd = 0.9978), UV index (aHRi = 0.37; aHRd = 0.748), and a higher minimum temperature (aHRd = 0.994). COVID-19 infection (aHRi = 1.18) and mortality (aHRd = 1.59) risks were higher in Indiana compared to Kentucky. (4) Conclusions: COVID-19 infection and mortality were 18% and 59% more likely among Indiana residents compared to Kentucky residents, respectively. This may be attributed to variations in stay-at-home order compliance and enforcement between Indiana and Kentucky.
Greenhouse Gas Fluxes from Cranberry and Highbush Blueberry Plantations on Former Peat Extraction Fields Compared to Active Peat Extraction Fields and Pristine Peatlands in Latvia
Authors: Bārdule Arta , Meļņiks Raitis Normunds , Zvaigzne Zaiga Anna , Purviņa Dana , Skranda Ilona , Prysiazhniuk Oleg , Maliarenko Oksana , Lazdiņš Andis
Published: September 2024

Emissions of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4), particularly those from organic soils, need to be reduced in the context of climate change mitigation (CCM). Here, we estimated the greenhouse gas (GHG) fluxes from nutrient-poor organic soils in cranberry (Vaccinium macrocarpon) and highbush blueberry (Vaccinium corymbosum) plantations established on former peat extraction fields compared to active peat extraction fields and pristine raised bogs in Latvia. A two-year study (2016–2018) was conducted using the manual closed chamber method. In berry plantations and active peat extraction fields, annual net CO2 fluxes contributed the most to total GHG emissions, accounting for over 67%, and temperature had the most significant impact on CO2 fluxes. Conversely, annual CH4 fluxes were the primary contributor to total net GHG emissions in the pristine raised bog, which simultaneously acted as a slight CO2 sink. N2O fluxes were relatively low among all studied land use types. This study provided quantitative insights into the variation in GHG fluxes and the environmental variables influencing them, and the obtained data are valuable to estimate the impact of the establishment of berry plantations on former peat extraction fields on CCM in the hemiboreal region of Europe.
Pyrrolidinium-Based Ionic Liquids as Advanced Non-Aqueous Electrolytes for Safer Next Generation Lithium Batteries
Authors: Santiago-Alonso Antía , Sánchez-Pico José Manuel , Emeterio Raquel San , Villanueva María , Salgado Josefa , Parajó Juan José
Published: September 2024

In the current context of increasing energy demand, ionic liquids (ILs) are presented as possible candidates to replace conventional electrolytes and to develop more efficient energy storage devices. The IL 1-Methyl-1-propylpyrrolidinium bis(trifluoromethanesulfonyl)imide has been selected for this work, due to the good thermal and chemical stabilities and good electrochemical performance of the pyrrolidinium cation based ILs. Binary mixtures of this IL and lithium salt with the same anion, [TFSI], have been prepared with the aim of assessing them, as possible electrolytes for lithium batteries. These mixtures were thermally and electrochemically characterised through DSC and dielectric spectroscopy studies. The ionic conductivity decreases as the salt concentration increases, finding values ranging between 0.4 S/m and 0.1 S/m at room temperature. Additionally, a wide liquid range was found for the mixtures, which would reduce or even eliminate some of the most common problems of current electrolytes, such as their crystallisation at low temperatures and flammability. Finally, the toxicity of pure IL and the intermediate salt concentration was also evaluated in terms of the bioluminescence inhibition of the Alivibrio Fischeri bacteria, observing that, although the toxicity increases with the salt addition, both samples can be classified as practically harmless.
DBSCAN SMOTE LSTM: Effective Strategies for Distributed Denial of Service Detection in Imbalanced Network Environments
Authors: Efendi Rissal , Wahyono Teguh , Widiasari Indrastanti Ratna
Published: September 2024

In detecting Distributed Denial of Service (DDoS), deep learning faces challenges and difficulties such as high computational demands, long training times, and complex model interpretation. This research focuses on overcoming these challenges by proposing an effective strategy for detecting DDoS attacks in imbalanced network environments. This research employed DBSCAN and SMOTE to increase the class distribution of the dataset by allowing models using LSTM to learn time anomalies effectively when DDoS attacks occur. The experiments carried out revealed significant improvement in the performance of the LSTM model when integrated with DBSCAN and SMOTE. These include validation loss results of 0.048 for LSTM DBSCAN and SMOTE and 0.1943 for LSTM without DBSCAN and SMOTE, with accuracy of 99.50 and 97.50. Apart from that, there was an increase in the F1 score from 93.4% to 98.3%. This research proved that DBSCAN and SMOTE can be used as an effective strategy to improve model performance in detecting DDoS attacks on heterogeneous networks, as well as increasing model robustness and reliability.