bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative Inversion of Martian Hydrous Minerals Based on LSTM-1DCNN Model
Authors: Liu Xinbao , Jin Ming , Liu Xiangnan , Yang Zhiming , Hou Zengqian , Ding Xiaozhong
Published: January 2025

Hydrous minerals are significant indicators of the ancient aqueous environment on Mars, and orbital hyperspectral data are one of the most effective tools for obtaining information about the distribution of hydrous minerals on the Martian surface. However, prolonged weathering, erosion, and other external forces result in complex mixing effects, often weakening the spectral absorption features of individual minerals. This study proposes a quantitative inversion method for Martian hydrous minerals by integrating a radiative transfer model with a deep learning network. Based on the physics of the Hapke radiative transfer model, the single-scattering albedo spectra of mineral end members were obtained. Additionally, the Linear Spectral Mixture Model was employed to generate a large number of fully constrained mineral mixture samples, providing theoretical support for experimental data. An LSTM-1DCNN model was trained to establish a data-driven quantitative inversion framework. CRISM data were applied to the Eberswalde Crater region to retrieve the abundances of 21 hydrous minerals, including tremolite, opal, and serpentine. The average abundance of hydrous minerals was calculated to be 0.018, with a total area proportion of approximately 8%. Additionally, by analyzing the distribution areas of hydrous silicates, hydrous sulfates, and hydrous hydroxides, the water activity history of the region was inferred. The results align with findings from related studies and mineral spectral index results. By incorporating deep learning into traditional mixing models, this study identifies the distribution of various low-abundance hydrous minerals, enhancing the accuracy of Martian hydrous mineral inversion. It is expected to provide valuable references for the selection of landing sites for Tianwen-3 and support the smooth implementation of China’s Mars exploration mission.
Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River
Authors: Wang Manqi , Zhou Caili , Shi Jiaqi , Lin Fei , Li Yucheng , Hu Yimin , Zhang Xuesheng
Published: January 2025

The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (R2) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers.
CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images
Authors: Hu Jianming , Wei Yangyu , Chen Wenbin , Zhi Xiyang , Zhang Wei
Published: January 2025

Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively.
High-Resolution Estimation of Daily PM2.5 Levels in the Contiguous US Using Bi-LSTM with Attention
Authors: Wang Zhongying , Crooks James L. , Regan Elizabeth Anne , Karimzadeh Morteza
Published: January 2025

Estimating surface-level PM2.5 concentrations at any given location is crucial for public health monitoring and cohort studies. Existing models and datasets for this purpose have limited precision, especially on high-concentration days. Additionally, due to the lack of open-source code, generating estimates for other areas and time periods remains cumbersome. We developed a novel deep learning-based model that improves the surface-level PM2.5 concentration estimates by capitalizing on the temporal dynamics of air quality. Specifically, we improve the estimation precision by developing a Long Short-Term Memory (LSTM) network with Attention and integrating multiple data sources, including in situ measurements, remotely sensed data, and wildfire smoke density observations, which improve the model’s ability to capture high-concentration events. We rigorously evaluate the model against existing products, demonstrating a 2.2% improvement in overall RMSE, and a 9.8% reduction in RMSE on high-concentration days, highlighting the superior performance of our approach, particularly on high-concentration days. Using the model, we have produced a comprehensive dataset of PM2.5 estimates from 2005 to 2021 for the contiguous United States and are releasing an open-source framework to ensure reproducibility and facilitate further adaptation in air quality studies.
Enhanced Inversion of Sound Speed Profile Based on a Physics-Inspired Self-Organizing Map
Authors: Xu Guojun , Qu Ke , Li Zhanglong , Zhang Zixuan , Xu Pan , Gao Dongbao , Dai Xudong
Published: January 2025

The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid.
Underwater Sonar Image Classification with Image Disentanglement Reconstruction and Zero-Shot Learning
Authors: Peng Ye , Li Houpu , Zhang Wenwen , Zhu Junhui , Liu Lei , Zhai Guojun
Published: January 2025

Sonar is a valuable tool for ocean exploration since it can obtain a wealth of data. With the development of intelligent technology, deep learning has brought new vitality to underwater sonar image classification. However, due to the difficulty and high cost of acquiring underwater sonar images, we have to consider the extreme case when there are no available sonar data of a specific category, and how to improve the prediction ability of intelligent classification models for unseen sonar data. In this work, we design an underwater sonar image classification method based on Image Disentanglement Reconstruction and Zero-Shot Learning (IDR-ZSL). Initially, an image disentanglement reconstruction (IDR) network is proposed for generating pseudo-sonar samples. The IDR consists of two encoders, a decoder, and three discriminators. The first encoder is responsible for extracting the structure vectors of the optical images and the texture vectors of the sonar images; the decoder is in charge of combining the above vectors to generate the pseudo-sonar images; and the second encoder is in charge of disentangling the pseudo-sonar images. Furthermore, three discriminators are incorporated to determine the realness and texture quality of the reconstructed image and feedback to the decoder. Subsequently, the underwater sonar image classification model performs zero-shot learning based on the generated pseudo-sonar images. Experimental results show that IDR-ZSL can generate high-quality pseudo-sonar images, and improve the prediction accuracy of the zero-shot classifier on unseen classes of sonar images.
Sliding-Window Dissimilarity Cross-Attention for Near-Real-Time Building Change Detection
Authors: Lu Wen , Nguyen Minh
Published: January 2025

A near-real-time change detection network can consistently identify unauthorized construction activities over a wide area, empowering authorities to enforce regulations efficiently. Furthermore, it can promptly assess building damage, enabling expedited rescue efforts. The extensive adoption of deep learning in change detection has prompted a predominant emphasis on enhancing detection performance, primarily through the expansion of the depth and width of networks, overlooking considerations regarding inference time and computational cost. To accurately represent the spatio-temporal semantic correlations between pre-change and post-change images, we create an innovative transformer attention mechanism named Sliding-Window Dissimilarity Cross-Attention (SWDCA), which detects spatio-temporal semantic discrepancies by explicitly modeling the dissimilarity of bi-temporal tokens, departing from the mono-temporal similarity attention typically used in conventional transformers. In order to fulfill the near-real-time requirement, SWDCA employs a sliding-window scheme to limit the range of the cross-attention mechanism within a predetermined window/dilated window size. This approach not only excludes distant and irrelevant information but also reduces computational cost. Furthermore, we develop a lightweight Siamese backbone for extracting building and environmental features. Subsequently, we integrate an SWDCA module into this backbone, forming an efficient change detection network. Quantitative evaluations and visual analyses of thorough experiments verify that our method achieves top-tier accuracy on two building change detection datasets of remote sensing imagery, while also achieving a real-time inference speed of 33.2 FPS on a mobile GPU.
Vibration Control of Flexible Launch Vehicles Using Fiber Bragg Grating Sensor Arrays
Authors: van der Veek Bartel , Gutierrez Hector , Wise Brian , Kirk Daniel , van Barschot Leon
Published: January 2025

The effects of mechanical vibrations on control system stability could be significant in control systems designed on the assumption of rigid-body dynamics, such as launch vehicles. Vibrational loads can also cause damage to launch vehicles due to fatigue or excitation of structural resonances. This paper investigates a method to control structural vibrations in real time using a finite number of strain measurements from a fiber Bragg grating (FBG) sensor array. A scaled test article representative of the structural dynamics associated with an actual launch vehicle was designed and built. The main modal frequencies of the test specimen are extracted from finite element analysis. A model of the test article is developed, including frequency response, thruster dynamics, and sensor conversion matrices. A model-based robust controller is presented to minimize vibrations in the test article by using FBG measurements to calculate the required thrust in two cold gas actuators. Controller performance is validated both in simulation and on experiments with the proposed test article. The proposed controller achieves a 94% reduction in peak–peak vibration in the first mode, and 80% reduction in peak–peak vibration in the second mode, compared to the open loop response under continuously excited base motion.
Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants
Authors: Ledmaoui Younes , El Maghraoui Adila , El Aroussi Mohamed , Saadane Rachid
Published: January 2025

This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures. The analysis also emphasizes how challenging it is to adopt predictive maintenance in the renewable energy industry. Achieving a balance between model complexity and accuracy, dealing with system unpredictability, and adjusting to shifting environmental conditions are among the challenges. It also highlights the Internet of Things (IoT), machine learning (ML), and deep learning (DL), which are all incorporated into solar panel predictive maintenance. By enabling real-time monitoring, data analysis, and anomaly identification, these developments improve the accuracy and effectiveness of maintenance procedures.
Study on Long-Term Temperature Variation Characteristics of Concrete Bridge Tower Cracks Based on Deep Learning
Authors: Lv Zhaoxu , Ding Youliang , Zhang Yan
Published: January 2025

Monitoring existing cracks is a critical component of structural health monitoring in bridges, as temperature fluctuations significantly influence crack development. The study of the Huai’an Bridge indicated that concrete cracks predominantly occur near the central tower, primarily due to temperature variations between the inner and outer surfaces. This research aims to develop a deep learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict crack depth based on the thermal variations experienced by the main tower. The efficacy of the LSTM network will be rigorously evaluated, employing multiple temperature input datasets to account for spatial dimensional variations in the data. This methodology is anticipated to enhance the model’s accuracy in predicting crack widths. By leveraging the deep learning regression model, precise temperature thresholds for crack formation can be established, facilitating early detection of anomalies in the crack widths of the main tower and providing effective technical solutions for monitoring crack status.
Regeneration Filter: Enhancing Mosaic Algorithm for Near Salt & Pepper Noise Reduction
Authors: Ivković Ratko M. , Milošević Ivana M. , Milivojević Zoran N.
Published: January 2025

This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution. To ensure the results are measurable and comparable with other methods, the filter’s performance was evaluated using standard image quality assessment metrics. Experimental evaluations across various image databases confirm that our filter consistently provides high-quality results. The code is implemented in the R programming language, and both data and code used for the experiments are available in a public repository, allowing for replication and verification of the findings.
An Efficient Cluster-Based Mutual Authentication and Key Update Protocol for Secure Internet of Vehicles in 5G Sensor Networks
Authors: Su Xinzhong , Xu Youyun
Published: January 2025

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks. The ECAUP meets the unique mobility and security demands of IoV by enabling fine-grained access control and dynamic key updates for RSUs through a factorial tree structure, ensuring both forward and backward secrecy. Additionally, physical unclonable functions (PUFs) are utilized to provide end-to-end authentication and physical layer security, further enhancing the system’s resilience against sophisticated cyber-attacks. The security of the ECAUP is formally verified using BAN Logic and ProVerif, and a comparative analysis demonstrates its superiority in terms of overhead efficiency (more than 50%) and security features over existing protocols. This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments.
A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection
Authors: Trigka Maria , Dritsas Elias
Published: January 2025

Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations. Additionally, the survey delves into various metrics for assessing model effectiveness, including precision, recall, and intersection over union (IoU), while addressing ongoing challenges in the field, such as managing occlusions, varying object scales, and improving real-time processing capabilities. Furthermore, we critically examine recent breakthroughs, including advanced architectures like Transformers, and discuss challenges and future research directions aimed at overcoming existing barriers. By synthesizing current advancements, this survey provides valuable insights for enhancing the robustness, accuracy, and efficiency of object detection systems across diverse and challenging applications.
Geotechnical Assessment of Foundation Stability for Preserving the Agrippa Monument at the Acropolis of Athens
Authors: Marinos Vassilis , Prountzopoulos Georgios , Papouli Dimitra , Michalopoulou Dionisia , Eleftheriou Vassiliki
Published: January 2025

This study focuses on the geotechnical evaluation of the foundation conditions of the Agrippa Monument at the Acropolis of Athens, aiming to propose interventions to improve stability and reduce associated risks. The assessment reveals highly uneven foundation conditions beneath the monument. A thorough collection of bibliographic references and geotechnical surveys was conducted, classifying geomaterials into engineering-geological units and evaluating critical parameters for geotechnical design. Geotechnical models were developed and 3D finite element analyses were performed. The qualitative evaluation of the foundation under static conditions indicates no immediate risk of failure, as no accelerated movement has been observed and the monument’s tilt remains well below critical values. Time-dependent settlements are not expected from any clay layers in the artificial fills. However, further soil compaction could occur due to seismic events, water action (causing erosion or voids), or changes in the monument’s weight or tilt under static conditions. The study also proposes instrumental monitoring, foundation soil improvement, and water management strategies to enhance the monument’s stability and mitigate potential risks.
In Vitro Cytotoxicity Assessment of Betulinic Acid Organic Salts on Triple-Negative Breast Cancer Cells
Authors: Apostolova Sonia , Georgieva Irina , Ossowicz-Rupniewska Paula , Klebeko Joanna , Todinova Svetla , Tzoneva Rumiana , Guncheva Maya
Published: January 2025

The conversion of betulinic acid (BA) to organic salts is a strategic approach to modulate its physicochemical properties and biological activity. In our previous study, we demonstrated the enhanced cytotoxicity of certain amino acid ethyl ester betulinates ([AAOEt][BA]) compared to BA against hormone-dependent breast cancer cells (MCF-7). In this study, we extended our investigation to evaluate the cytotoxic response and thermodynamic properties of hormone-independent breast cancer cells (MDA-MB-231) following 72 h of treatment with the same series of betulinates. Our data reveal a lower cytotoxic response in MDA-MB-231 cells, indicated by higher half-maximal inhibitory concentration (IC50) values, which ranged between 31 and 109 μM. Differential scanning calorimetry analysis supported these findings, showing negligible changes in the thermodynamic parameters of the treated MDA-MB-231 cells. However, consistent with our previous observations, [LysOEt][BA]2, exhibited the highest cytotoxicity and induced the most pronounced morphological alterations in the cancer cells. Overall, our results suggest that MDA-MB-231 cells are less sensitive to [AAOEt][BA] compared to MCF-7 cells, likely due to their distinct phenotypic and genotypic profiles and differences in oncogenic signalling pathways. Nonetheless, the fact that [LysOEt][BA]2 enhances the cytotoxic activity of BA even in hormone-independent breast cancer cells underscores its therapeutic potential and warrants further investigation, particularly in the context of adjuvant breast cancer therapy.
Investigation on the Fracture Mechanism of Self-Healing Mortar Specimens Using Acoustic Emission (AE) Analysis and a Lattice Discrete Element Method (LDEM)
Authors: Lacidogna Giuseppe , Montanari Pedro Marin , Tanzi Boris Nahuel Rojo , Iturrioz Ignácio , Tulliani Jean-Marc , Anglani Giovanni , Antonaci Paola
Published: January 2025

This paper discusses the combined application of the Acoustic Emission (AE) technique and a Lattice Discrete Element Method (LDEM) to study the damage process in cement mortar specimens subjected to three-point bending tests. The experimental work was carried out in a previous study by the research team. The specimens contain macro-capsules filled with a polyurethane resin that promotes a self-healing mechanism upon crack formation. The numerical model here developed provides an estimate of the fracture energy of the resin, which is difficult to obtain by relying purely on experimental results. Furthermore, this study includes the analysis of both fracture energy and Acoustic Emission energy based on experimental tests using the same specimens. Through the comparison of experimental and numerical results, a correlation between the toughness of the specimens and AE activity is established. The findings indicate that, for the self-healing specimens, there is a decrease in fracture energy as the emitted Acoustic Emission (AE) energy increases.
Laws of Large Numbers for Uncertain Random Variables in the Framework of U-S Chance Theory
Authors: Fu Xiaoting , Hu Feng , Meng Xue , Tian Yu , Yang Deguo
Published: January 2025

The paper introduces U-S chance spaces, a new framework based on uncertainty theory and sub-linear expectation theory, to depict human uncertainty and sub-linear features, simultaneously. These spaces can be used to analyze the characteristics of uncertain random variables and study investments and other related issues in incomplete financial markets. Within the framework, sub-linear expectation theory describes the randomness in financial behaviors, while uncertainty theory describes the uncertainty associated with government macro-control or experts’ opinions. The main achievement of this paper is the derivation of the Kolmogorov law of large numbers for uncertain random variables under U-S chance spaces. Examples are provided, and the theorems can be applied to uncertain random variables that are functions of random variables with symmetric or asymmetric distributions and uncertain variables with symmetric or asymmetric distributions. In some cases, when both random and uncertain variables are symmetric, the limit in the law exhibits the form that is characterized by symmetrical uncertain variables.
Exploring the Spatial Agglomeration Characteristics and Determinants of Strategic Emerging Industries: Evidence from 12,979 Industrial Enterprises in China
Authors: Zhao Xiaofeng , Wang Yanyan , Li Ying , Lin Sheng-Hau , Shi Haixia
Published: January 2025

Strategic emerging industries (SEIs) have the potential to be a nation’s leading industries in the post-industrialization era. Exploring the spatial distribution of SEIs and the impetuses of their location choice plays a key role in formulating policies conducive to regional industrial and economic development. However, most studies on relevant topics neglected the impact of institutional environment and local innovation on the formation of spatial patterns of SEIs. By investigating 12,979 industrial enterprises in China, this research applied spatial autocorrelation and spatial regression analysis to explore the spatial characteristics of SEIs and identify the variables affecting the location selection of SEIs that result in these spatial patterns. The findings indicated significant spatial differences in the spatial distributions and agglomeration patterns of SEIs. Institutional environment, local innovation, and regional economy have significant impacts on the location choice of SEIs. The interactive effects of local innovation and institutional environment on the spatial agglomeration of SEIs revealed that a higher degree of decentralization and stronger local innovation capability would promote a stronger agglomeration of SEIs. Regions with strong (weak) marketization and weak (strong) institutions of higher education would promote SEIs agglomeration. Based on the findings, policy options were suggested to facilitate SEIs planning and differentiated pathways of industrial transformation.
Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network
Authors: Dunai Larisa , Verdú Isabel Seguí , Turcanu Dinu , Bostan Viorel
Published: January 2025

Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy.
Influence of Nutrient Desorbed from Sediments and Density Variations Driven by Organic Matter on Flow Patterns in Closed Water Bodies
Authors: Koue Jinichi
Published: January 2025

In enclosed water bodies, water quality deterioration has emerged as a critical environmental issue. Eutrophication contributes to phenomena such as red tides and blue tides, raising concerns about foul odors and adverse impacts on surrounding aquatic ecosystems. Despite efforts to reduce nutrient loading through water quality management measures, reports of stagnant or a worsening water quality persist. One key factor is the accumulation of nutrients in deep layers. Nutrient-rich fluids form density currents along the lakebed, transporting nutrients and organic matter to deeper regions. This study investigates the hydrodynamic properties of a nutrient release from the lakebed in Lake Biwa using hydrodynamic and ecosystem models. The results reveal that a nutrient release triggers plume formation along sloping lakebed surfaces, facilitating the transport of nutrients and dissolved organic matter. Additionally, water circulation driven by density currents and nutrient concentrations along the slopes generate compensatory flows, leading to dynamic variability in Lake Biwa’s hydrodynamics.
Perspectives of Utilizing Greywater in Agricultural Irrigation with a Special Reference to Vegetated Wall Agrosystems
Authors: Sulaiman Muhammad Omair , Mackey Hamish R. , Al-Ghouti Mohammad A. , Saththasivam Jayaprakash , Abu-Dieyeh Mohammed H.
Published: January 2025

With increasing urbanization, greywater, generated from domestic activities like bathing and washing, is a marginal water resource that can be valorized to meet irrigation demands and overcome water scarcity. This review paper delves into the characteristics and variability in greywater quality, highlighting the benefits of greywater reuse, such as water conservation, nutrient supply, and potential cost savings, as well as challenges like pathogen contamination and salinity buildup. Various treatment methods, including physical, chemical, and biological processes, are discussed concerning their effective use for irrigation. This paper explores the integration of greywater irrigation with vegetated walls, an innovative urban greening solution that offers numerous environmental and social benefits. The types of vegetated walls, their irrigation requirements, and studies involving greywater application in these systems are discussed. By synthesizing the current knowledge, this review article provides a comprehensive understanding of enhancing urban sustainability through the valorization of an otherwise wasted resource.
Crises and Disruption on Bali Tourism: Natural Disaster, Civil Unrest and Terrorism
Authors: Shantika Budi , Bestari Ni Made Prasiwi
Published: January 2025

This study investigates the significant impacts of various crises on the tourism industry in Bali, focusing on natural disasters, civil unrest, and terrorism. As a globally renowned destination, Bali's tourism sector is crucial to its economy, yet it faces substantial challenges that threaten its sustainability and cultural integrity. Through a systematic narrative review and bibliometric analysis, this research synthesizes existing literature to identify key crises that have historically influenced tourist arrivals and perceptions. Notably, events such as earthquakes, volcanic eruptions, and political instability have led to dramatic declines in visitor numbers and have instilled lasting psychological effects on potential travelers. For instance, the 2018 Lombok earthquake resulted in a 30% drop in tourist arrivals, highlighting the vulnerability of the tourism infrastructure. The findings underscore the urgent need for Bali to diversify its economic base, adopt sustainable tourism practices, and develop comprehensive crisis management strategies to enhance resilience against future disruptions. Additionally, the study reveals that while economic challenges are pronounced, cultural resilience has emerged, with local traditions adapting to maintain engagement through virtual platforms during crises. Recommendations for future research include exploring the evolving dynamics of tourism in Bali in the context of global challenges such as climate change and shifting tourist behaviors. By addressing these issues holistically, Bali can better safeguard its natural and cultural resources while continuing to thrive as a premier tourist destination.
Keywords:  Bali, tourism, natural disasters, civil unrest, crisis management, sustainability.
Toward an Affirmational Theory of Congregational Thriving
Authors: Goodwin Jordan M , Raffety Erin
Published: January 2025

Although churches inclusive for lesbian, gay, bi-sexual, transgender, and questioning (LGBTQ) individuals have been studied, little is known about how churches practice radical affirmation and advocacy of LGBTQ persons. This article employs a case study approach to identify markers of affirmational advocacy supplement existing standards of congregational thriving in an LGBTQ-affirming congregation in Chicago, Urban Village Church (UVC). This two-year ethnographic study demonstrates how for UVC, congregational thriving depends on five additional  themes: 1) LGBTQ affirmational theological stance, 2) “Disagreement and discussion as healthy”, 3) Representational and queer leadership, 4) Deep community care for LGBTQ and other marginalized group, and 5) Receptivity to the wider LGBTQ community’s needs. Implications for micro and macro practice are discussed for both social workers and religious institutions.
Avaliação de reação e aprendizagem dos cursos de educação continuada em enfermagem no ensino à distância do Hospital do Servidor Público Municipal de São Paulo, diante a pandemia
Authors: Pereira Amanda Barbosa Monteiro Vasques , Moura Cassia Sampaio
Published: January 2025

Considerando, sobretudo, a crise epidemiológica mundial, que trouxe novas realidades nos processos de trabalho da saúde, dentre eles o distanciamento social e as restrições no atendimento assistencial, o Hospital do Servidor Público Municipal de São Paulo realizou no período de janeiro a dezembro do ano de 2021 cursos de ensino a distância (EAD), abordando temas fundamentais para a qualidade das ações desempenhadas na saúde. Esse estudo analisou o impacto dos cursos de curta duração no módulo EAD, realizados pela enfermagem. Adotando como método uma abordagem quantitativa e qualitativa, transversal, de natureza exploratória e descritiva. A pesquisa contou com a participação de auxiliares, técnicos de enfermagem e enfermeiros que atuam diretamente na assistência hospitalar. Na coleta de dados foi utilizada a análise da avaliação de reação, transpondo a satisfação e a percepção dos profissionais da equipe de enfermagem quanto o EAD; e a análise de aprendizado em cada curso, considerando o conhecimento prévio e pós realização do tema abordado. A análise da avaliação de reação indicou que 97% dos participantes consideram o EAD eficaz. A análise de aprendizado demonstrou a média de 17% de melhora no desempenho dos conteúdos abordados no EAD. O trabalho demonstra a modalidade EAD é satisfatória, possibilitando à equipe de enfermagem a assimilação dos conhecimentos necessários de acordo com o tema abordado.
Análise da hanseníase em Itaberaba, Bahia: desafios e estratégias de saúde pública
Authors: Serra Natalicio de Araujo , Pinto Sheila , Santos Camila Pedreira dos , Silva Janderson Barbosa , Nascimento Mateus Sapucaia Oliveira do , Machado Yargo Alexandre de Farias , Oliveira Cristiely Alves , Marques Lílian Simeão , Barbosa Francisca Cláudia Pinheiro , Soares Fabiana Santos
Published: January 2025

Introdução: A hanseníase, causada pelo Mycobacterium leprae, é uma doença infectocontagiosa de alta infectividade, mas baixa patogenicidade, ainda prevalente no Brasil. Associada a fatores socioeconômicos e ao estigma social, permanece um desafio de saúde pública, especialmente em municípios como Itaberaba, Bahia. Objetivo: Analisar a situação epidemiológica da hanseníase em Itaberaba, com enfoque nos desafios e estratégias de saúde pública para controle e prevenção. Metodologia: Trata-se de um estudo descritivo baseado em revisão de dados epidemiológicos extraídos do Painel de Monitoramento de Hanseníase no Brasil do Sinan/SVS/MS, 2024 e do sistema ESUS/VS-ES, com ênfase nos anos de 2012 a 2024. Foram utilizadas fontes oficiais, como o Ministério da Saúde e literatura científica pertinente. Resultados:Itaberaba apresenta taxas de incidência superiores à média regional, com 33 casos ativos registrados em 2024, equivalente a 50,8 casos por 100 mil habitantes. Fatores como pobreza, densidade domiciliar e condições de moradia precárias contribuem para a persistência da doença. Além disso, a pandemia de COVID-19 afetou negativamente a notificação e o controle da hanseníase. Conclusão: A hanseníase em Itaberaba reflete a necessidade de estratégias integradas, incluindo campanhas de conscientização, busca ativa de casos, capacitação de profissionais de saúde e melhoria das condições socioeconômicas locais, visando à redução da incidência e à promoção da qualidade de vida da população afetada.
Effects of Cinnamomum cassia and Thymus ciliatus Essential Oils on Bisphenol A-induced Liver Toxicity in Wistar Rats
Authors: Kourat Dallal , Adli Djallal Eddine Houari , Brahmi Mostapha , Bouzouira Bakhta , Kahloula Khaled , Slimani Miloud
Published: January 2025

The aim of our study was to examine the impact of Bisphenol A (BPA) exposure on the liver of young Wistar rats and to evaluate the effectiveness of treatments using essential oils from Cinnamomum cassia (CEO) and Thymus ciliatus (TEO) in mitigating the damage caused by this chemical toxin. The essential oils were extracted via hydrodistillation, resulting in yields of 2.3% for CEO and 2.26% for TEO. Acute toxicity from BPA was induced by administering an oral dose of 50 mg/kg over 60 days. In rats exposed to BPA, there were significant increases in hepatic markers such as AST and ALT and ALP in the blood, alongside a decrease in albumin levels. Additionally, the liver showed signs of tissue damage. Treatment with CEO and TEO significantly alleviated the biochemical alterations, as well as the histological and cellular damage observed in the liver tissue.
Internalisasi Nilai Humanitas Melalui Pemeriksaan Dan Pengobatan Kesehatan Gratis
Authors: Darmin Darmin , Aman Ma’arij  , M. Noris  , Soliha Nu’tiha  , M. Yunus
Published: January 2025

Pengecekan kesehatan gratis merupakan proses pembentukan dalam memberikan kesadaran kepada masyarakat tentang pentingnya menjaga kesehatan. Kesehatan merupakan faktor penting dalam kehidupan manusia. Tujuan dari kegiatan ini yaitu untuk meningkatkan kesadaran kesehatan masyarakat di Desa Lido. Melalui kegiatan ini, diharapkan mampu memberikan pemahaman yang lebih baik tentang pentingnya menjaga kesehatan secara holistic. Metode yang digunakan dalam pengabdian ini adalah diskusi dengan tenaga kesehatan dari PKU Muhammadiyah Bima dan PKM Ngali terkait dengan apa yang dibutuhkan dalam kegiatan pengecekan kesehatan gratis. Kegiatan pengabdian masyarakat dilakukan pada tanggal 29 Juli 2024. Pelaksanaan kegiatan ini dilakukan 1 hari mulai dari jam 15.30 sampai jam 20.00 jumlah warga yang hadir dan melakukan pemeriksaan kesehatan sebanyak 152 orang. Kesimpulan dari kegiatan ini diharapkan masyarakat Desa Lido dapat mengadopsi gaya hidup sehat dan mengurangi resiko terjadinya penyakit yang dapat mengganggu kualitas hidup mereka.
A Sustainable Regulations to Preserve Commoning in Urban Agriculture: The 'Kultum' (Kuliah Tumbuhan) Series at Selarasa - Jagakarsa Food Lab
Authors: Al Islami Muhammad Al Anhar
Published: January 2025

Penelitian ini bertujuan untuk menganalisis upaya keberlanjutan dan inisiatif pendidikan yang diadopsi oleh Selarasa - Jagakarsa Food Lab, sebuah inisiatif pertanian urban di pinggiran kota Jakarta, dalam konteks praktik commoning. Fokus utama penelitian ini adalah pada keberlanjutan praktik commoning dan bagaimana inisiatif pendidikan berkontribusi pada pengetahuan dan keterlibatan komunitas. Penelitian ini akan menyelidiki berbagai upaya keberlanjutan yang telah diterapkan oleh Selarasa - Jagakarsa Food Lab, termasuk praktik pengelolaan sumber daya dan inovasi teknologi yang digunakan. Evaluasi dampak keberlanjutan ini akan melibatkan analisis ekologis, ekonomis, dan sosial untuk mengukur kontribusi praktik commoning terhadap keberlanjutan di tingkat lokal melalui series Kultum (Kuliah Tumbuhan). Penelitian ini juga akan mendokumentasikan inisiatif pendidikan yang dilakukan oleh Selarasa - Jagakarsa Food Lab untuk mentransfer pengetahuan tentang pertanian urban kepada anggota komunitas. Ini mencakup pelatihan, workshop, dan kampanye edukasi yang bertujuan meningkatkan pemahaman dan keterlibatan komunitas dalam praktik commoning. Analisis efektivitas inisiatif pendidikan akan mempertimbangkan peningkatan pemahaman dan keterlibatan anggota komunitas serta dampaknya pada pemberdayaan masyarakat lokal. Tantangan dan peluang dalam kedua aspek keberlanjutan dan pendidikan akan diidentifikasi untuk merancang rekomendasi yang dapat meningkatkan praktik commoning di Selarasa - Jagakarsa Food Lab. Hasil penelitian ini diharapkan dapat memberikan kontribusi pada pemahaman lebih lanjut tentang praktik commoning dalam konteks pertanian urban dan memberikan wawasan kepada pemangku kepentingan, organisasi non-pemerintah, dan kebijakan publik (public policies) mengenai strategi pengembangan dan penguatan praktik commoning yang berkelanjutan di masyarakat perkotaan.
Edukasi Pencegahan Diare Pada Balita Di Desa Bungko Kota Kotamobagu
Authors: Hamzah Strahmawati , B Hamzah , Gobel Anisa , Gaib Istiara
Published: January 2025

Penyakit diare merupakan salah satu penyakit infeksi saluran pencernaan yang menjadi masalah kesehatan di dunia termasuk Indonesia. Menurut WHO dan UNICEF, terjadi sekitar 2 milyar kasus diare dan 1,9 juta anak balita meninggal karena diare di seluruh dunia setiap tahun. Dari semua kematian tersebut, 78% terjadi di negara berkembang. Pada tahun 2023 jumlah kasus diare di Sulawesi Utara sebanyak 19.435 dengan jumlah penemuan kasus diare di Kota Kotamobagu sebanyak 1.482. Studi pendahuluan yang dilakukan di Desa Bungko dari 20 terdapat 62% ibu belum mengetahui cara melakukan pananggulangan pertama ketika ada anak mengalami diare. Kegiatan pengabdian ini meliputi tiga tahapan yaitu tahap perencanaan dengan melakukan koordinasi pelaksana, studi pendahuluan dan perizinan. Tahap pelaksanaan dengan melakukan edukasi kepada peserta tentang diare dengan tujuan untuk meningkatkan pengetahuan ibu tentang pencegahan dan penanggulangan diare. Tahap evaluasi dilakukan dengan memberikan umpan balik kepada peserta untuk mengetahui sejauh mana tingkat pemahaman ibu. Kegiatan edukasi pencegahan diare di Desa Bungko terlaksana dengan baik dan sesuai dengan perencanaan. Terjadi peningkatan pengetahuan peserta setelah diberikan edukasi tentang pencegahan diare dan meningkatnya pemahaman ibu untuk melakukan penanggulangan diare yang tepat.
Lokakarya dan Pendampingan Program Guru Penggerak Indonesia Maju Angkatan 7 Kabupaten Bima
Authors: Tayeb Irfan
Published: January 2025

Masalah yang dihadapi kompetensi guru, dan kreatif dalam menerapkan metode pembelajaran. Tujuan program guru penggerak mendorong guru memiliki kompetensi yang memadai berupa kualitas pengajaran, kreatif guru dalam memanfaatkan ekosistem pengajaran. Metode Kegiatan program guru penggerak angkatan 7 dilaksanakan pada Januari-Juli 2023, tempat SMPN 2 Bolo Kabupaten Bima, jumlah peserta 61 orang yang mengikuti lokakarya. Kegiatan pendampingan berjumlah 4 orang calon guru penggerak (CGP). Tahapan kegiatan lokakarya pembukaan, presensi, umpan balik, review, latihan, praktik, dan penutup. Pelaksanaan kegiatan pendampingan dengan metode diskusi, aksi nyata. Hasil. Bahwa guru penggerak menyampaikan prakarsa perubahan level diri selama di sekolah, serta memberikan dampak terhadap lingkungan sekolah. Kemampuan guru menunjukkan adanya peningkatan kemampuan dalam mengendalikan diri, teliti, dan kreatif. Guru penggerak mampu memahami masalah yang dihadapi oleh sekolah, sehingga solusinya dengan menerapkan coaching dan supervisi yang tepat.  Guru penggerak dapat mengidentifikasi kekuatan dirinya yang dapat mendukung program sekolah yang sudah direncanakan. Panen hasil belajar sebagai eksplorasi capaian guru penggerak dalam memberikan pengajaran yang kreatif disertai beberapa media pembelajaran serta produk-produk inovasi yang dihasilkan oleh guru penggerak dan peserta didik. Kesimpulan akumulasi pengetahuan guru penggerak melalui peningkatan kemampuan kegiatan lokakarya dan pendampingan individu yang efektif, sehingga guru penggerak mampu membangun komunitas lingkungan belajar, penerapan pembelajaran berdiferensiasi yang efektif, kreatif dalam pengajaran dan pemilihan media yang tepat untuk mencerdaskan peserta didik.