bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing the Enzymatic Hydrolysis of Bioflocculated Microalgae for Bioethanol Production
Authors: Simon Viviane , Freitag João Felipe , da Silva Júlia Lorenzato , Colla Luciane Maria
Published: January 2025

Spirulina platensis is a promising microalga, but biomass harvesting remains a challenge. Fungal bioflocculation offers a potential solution, facilitating the production of valuable bioproducts like bioethanol. Effective cell disruption methods, including physical-chemical and enzymatic treatments, can enhance biomass utilization. However, commercial enzymes are not optimized for microalgae, necessitating research on ideal operational conditions. This study evaluated physical and enzymatic processes to hydrolyze bioflocculated microalgae for bioethanol production. The microalga was harvested using a fungal bioflocculant produced via submerged fermentation. Biomass hydrolysis involved physical methods (autoclaving, ultrasound + autoclaving, ultrasound + gelatinization, and gelatinization) combined with enzymes (amylase, amyloglucosidase, cellulase, and xylanase), optimized for pH, temperature, and enzyme load. Hydrolysates were then used for bioethanol production. Results showed a microalgae harvest efficiency of 99.7% with a 1:8 fungus-to-microalgae ratio. Enzyme optimization identified ideal conditions (e.g., pH 4.5; 60 °C for amylase/amyloglucosidase, 70 °C for cellulase, and 50 °C for xylanase). Combined enzymatic treatments achieved approximately 70% hydrolysis efficiency, yielding 19.06 g/L glucose and 7.29 g/L ethanol (~79% conversion). Ethanol productivity was ~0.6 g per 1 g bioflocculated biomass L−1·hr. These findings highlight the potential of enzymatic hydrolysis for complex biomasses, although further studies are needed to refine enzyme applications for better biomass utilization.
Enhancing Mass Transfer Coefficient Prediction from Field Emission Scanning Electron Microscope Images Through Convolutional Neural Networks and Data Augmentation Techniques
Authors: Marcato Agnese , Boccardo Gianluca , Pisano Roberto
Published: January 2025

With the growing demand for drug products requiring lyophilization, it is essential to either expand aseptic drying capacity or improve the efficiency of existing capacity through process intensification, ensuring that resources are utilized to their full potential. In this regard, mathematical models are highly recommended to assist professionals in process optimization. To effectively utilise these models, it is also essential to develop robust techniques for determining key parameters, including the product resistance to vapour flow. Traditional experimental methods for evaluating this coefficient are time-intensive and/or require the insertion of probes into the product, which is not feasible at a manufacturing scale. This study addresses these challenges by introducing a novel deep learning framework designed to predict the mass transfer coefficient directly from Field Emission Scanning Electron Microscope images. This approach significantly streamlines the evaluation process, leveraging the high-resolution capabilities of Field Emission Scanning Electron Microscope for detailed analysis. In this work, we focus on advanced Field Emission Scanning Electron Microscope image processing, choice of strategic convolutional neural network configuration, and thorough model performance evaluation to predict the mass transfer coefficient. Given the frequent scarcity of datasets in this field, we have employed data augmentation techniques to enhance the robustness of our model. The results demonstrate good predictive accuracy (error on the interpolation test data lower than 5%), highlighting the potential of this framework to facilitate the assessment of mass transfer coefficients in freeze-dried products.
The New Moral Absolutism in Catholic Moral Teaching: A Critique Based on Veritatis Splendor
Authors: Mike Károly
Published: January 2025

This paper examines a recent shift in Catholic moral teaching, characterized by the emergence of a ‘new moral absolutism’, in which certain acts traditionally subject to prudential judgment—such as the death penalty, ecological harm, and restrictive migration policies—are increasingly portrayed as universally and gravely wrong in our age. Simultaneously, traditional moral absolutes, especially in sexual and life ethics, have experienced cautious relativization. Drawing on the framework of Veritatis Splendor (1993), the paper critiques the approach of this new moral absolutism, arguing that it undermines the proper role of individual conscience and situational discernment while failing to provide coherent guidance on complex moral dilemmas. It links its emergence to proportionalist ethics: when traditional moral absolutes are relativized, new types of wrongs take their place. The paper proposes a return to the principles of Veritatis Splendor, advocating for a nuanced approach that preserves the constant and limited set of absolute negative norms and encourages the formation and use of conscience for all other matters.
Interreligious Dialogue, Conflict Resolution and Peacebuilding: A Review
Authors: Driessen Michael Daniel
Published: January 2025

Over the last twenty years, policymakers and practitioners have supported the growth of multiple interreligious initiatives aimed at peacebuilding and conflict resolution, and scholars have sought to understand and evaluate their efficiency. This article introduces and reviews the emerging scholarship which has developed to understand and analyze these efforts. It begins by drawing from recent empirical research to outline how scholars understand religion as being linked to conflicts and naming a number of initiatives that have been constructed as responses to them. The article then considers how interreligious dialogue could be understood to serve as a conduit for conflict mediation and peacebuilding. Finally, the paper discusses several persistent criticisms that have been raised about interreligious dialogue as a model of conflict resolution, especially in light of the ongoing wars in Ukraine and Gaza, and ends with some reflections about contemporary strategies for interreligious peacebuilding in light of them.
Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition
Authors: Ayaz Fahad , Alhumaily Basim , Hussain Sajjad , Imran Muhammad Ali , Arshad Kamran , Assaleh Khaled , Zoha Ahmed
Published: January 2025

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner–Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing.
Mapping of Industrial IoT to IEC 62443 Standards
Authors: Cindrić Ivan , Jurčević Marko , Hadjina Tamara
Published: January 2025

The increasing adoption of the Industrial Internet of Things (IIoT) has led to significant improvements in operational efficiency but has also brought new challenges for cybersecurity. To address these challenges, a number of standards have been introduced over the years. One of the best-known series of standards for this purpose is ISA/IEC 62443. This paper examines the applicability of the ISA/IEC 62443 series of standards, traditionally used for securing industrial automation and control systems, to the IIoT environment. For each requirement described in the ISA/IEC 62443 standards, relevant research on that subject is reviewed and presented in a table-like manner. Based on this table, areas for future research are identified, including system hardening, asset inventory, safety instrumented system isolation, risk assessment methodologies, change management systems, data storage security, and incident response procedures. The focus on future improvement is performed for the area of system hardening, for which research and guidelines already exist but not for the specific area of IIoT environments.
Graph-Based Topological Embedding and Deep Reinforcement Learning for Autonomous Voltage Control in Power System
Authors: Wei Hongtao , Chang Siyu , Zhang Jiaming
Published: January 2025

With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids.
Gait-to-Gait Emotional Human–Robot Interaction Utilizing Trajectories-Aware and Skeleton-Graph-Aware Spatial–Temporal Transformer
Authors: Li Chenghao , Seng Kah Phooi , Ang Li-Minn
Published: January 2025

The emotional response of robotics is crucial for promoting the socially intelligent level of human–robot interaction (HRI). The development of machine learning has extensively stimulated research on emotional recognition for robots. Our research focuses on emotional gaits, a type of simple modality that stores a series of joint coordinates and is easy for humanoid robots to execute. However, a limited amount of research investigates emotional HRI systems based on gaits, indicating an existing gap in human emotion gait recognition and robotic emotional gait response. To address this challenge, we propose a Gait-to-Gait Emotional HRI system, emphasizing the development of an innovative emotion classification model. In our system, the humanoid robot NAO can recognize emotions from human gaits through our Trajectories-Aware and Skeleton-Graph-Aware Spatial–Temporal Transformer (TS-ST) and respond with pre-set emotional gaits that reflect the same emotion as the human presented. Our TS-ST outperforms the current state-of-the-art human-gait emotion recognition model applied to robots on the Emotion-Gait dataset.
Design of Selective Detector for Distributed Targets Through Stochastic Characteristic of the Fictitious Signal
Authors: Xiong Gaoqing , Cao Hui , Liu Weijian , Zhang Jialiang , Wang Kehao , Yan Kai
Published: January 2025

We investigate the problem of detecting the distributed targets buried in the Gaussian noise whose covariance matrix is unknown when signal mismatch occurs. The idea is to add a fictitious signal under the null hypothesis of the origin detection problem so that when signal mismatch occurs, the fictitious signal captures the mismatched signals, thus making the null hypothesis more plausible. More precisely, the fictitious signal is modeled as a Gaussian component with a covariance matrix of a stochastic factor multiplied by a rank-one matrix. The generalized likelihood ratio test (GLRT) is employed to address the modification detection problem. We present an exhaustive derivation of the detector and prove that it possesses the constant false alarm rate (CFAR) property. The performance analysis demonstrates the effectiveness of the proposed detector. When the SNR is 23 dB, as generalized cosine squared decreases from 1 to 0.83, the detection probability of the proposed GLRT-SL drops to 0.65, exhibiting the fastest decline compared to the G-ABORT-HE, which falls to 0.98, and the GW-ABORT-HE, which decreases to 0.85.
Visual Localization Domain for Accurate V-SLAM from Stereo Cameras
Authors: Di Salvo Eleonora , Bellucci Sara , Celidonio Valeria , Rossini Ilaria , Colonnese Stefania , Cattai Tiziana
Published: January 2025

Trajectory estimation from stereo image sequences remains a fundamental challenge in Visual Simultaneous Localization and Mapping (V-SLAM). To address this, we propose a novel approach that focuses on the identification and matching of keypoints within a transformed domain that emphasizes visually significant features. Specifically, we propose to perform V-SLAM in a VIsual Localization Domain (VILD), i.e., a domain where visually relevant feature are suitably represented for analysis and tracking. This transformed domain adheres to information-theoretic principles, enabling a maximum likelihood estimation of rotation, translation, and scaling parameters by minimizing the distance between the coefficients of the observed image and those of a reference template. The transformed coefficients are obtained from the output of specialized Circular Harmonic Function (CHF) filters of varying orders. Leveraging this property, we employ a first-order approximation of the image-series representation, directly computing the first-order coefficients through the application of first-order CHF filters. The proposed VILD provides a theoretically grounded and visually relevant representation of the image. We utilize VILD for point matching and tracking across the stereo video sequence. The experimental results on real-world video datasets demonstrate that integrating visually-driven filtering significantly improves trajectory estimation accuracy compared to traditional tracking performed in the spatial domain.
Strain Decay Monitoring and Analytical Prediction of RC Columns Using Brillouin Optical Technology and Time-Dependent Deterioration Factor
Authors: Pasityothin Ittipon , Thansirichaisree Phromphat , Buatik Apichat , Imjai Thanongsak , Sridhar Radhika , Garcia Reyes , Noguchi Takafumi
Published: January 2025

This study presents a novel approach to the design and assessment of slender reinforced concrete (RC) columns by integrating Brillouin Optical Time Domain Analysis (BOTDA) for real-time, distributed strain monitoring and introducing a “time-dependent deterioration factor” strain decay (ηdecay). Experimental tests on 200 mm × 200 mm RC columns with lengths of 1800 mm and slenderness ratios of 29.4, reinforced with four 12 mm bars, captured strain variations up to 400 microstrain under an axial load of 1200 kN, demonstrate BOTDA’s sensitivity and precision. Unlike conventional strain gauges, BOTDA provided a continuous strain profile along the column height, accurately capturing strain decay with a resolution exceeding 95%, enabling the detection of localized strain reductions often missed by traditional methods. The integration of ηdecay into ACI 318 and Eurocode 2 models conservatively improved predictions, particularly for specimens tested with long-term testing (720 days), with experimental-to-predicted (E/P) ratios of 1.42 and 1.29, respectively, compared to higher discrepancies in the original codes. The ηdecay factor accounts for strain reduction along the column height caused by time-dependent effects such as creep, shrinkage, and material degradation, significantly improving the accuracy of axial load capacity predictions. Finite element analysis (FEA) validated these improvements, showing good agreement with experimental data up to the yield load. Post-yield, the modified equations effectively addressed underestimations caused by microcracking, highlighting the necessity of ηdecay for reliable long-term performance predictions. This research combines advanced BOTDA technology with an innovative ηdecay framework, addressing long-term structural deterioration and refining design codes. It establishes a robust foundation for integrating time-dependent effects into predictive models, enhancing the resilience, safety, and sustainability of RC structures under real-world conditions.
Multi-Channel Power Scheduling Based on Intrusion Detection System Under DDoS Attack: A Starkberg Game Approach
Authors: Yi Youwen , Peng Lianghong
Published: January 2025

This study aims to explore the optimal power allocation problem under Distributed Denial of Service (DDoS) attack in wireless communication networks. The Starkberg Equilibrium (SE) framework is employed to analyze the strategic interactions between defenders and attacker under conditions of incomplete information. Considering the energy constraints of both sensors and attacker, this paper also proposes an Intrusion Detection System (IDS) based on remote estimation to achieve an optimal defense strategy, with Packet Reception Rate (PPR) serving as a criterion for intrusion detection. Targeting leaders and followers, the optimal power allocation solution is derived with Signal-to-Interference-Noise Ratio (SINR) and transmission cost as the objective functions. By combining the Adaptive Penalty Function (APF) method with the Differential Evolution (DE) algorithm, the study effectively addresses related non-linear and non-convex optimization problems. Finally, the effectiveness of the proposed method is verified through case studies.
Seismic Risk Classification of Building Clusters Using MST Clustering and UAV Remote Sensing
Authors: Wang Xianteng , Li Xue , Liu Zhumei , Wu Zihao , Xie Yike , Han Zijie
Published: January 2025

The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity between them. To enhance the classification accuracy of house structure types, this work proposes a minimum spanning tree (MST) house clustering structure type classification method based on the spatial similarity of houses. First, the method employs the geometric characteristics of residential buildings to calculate the Gestalt factor that characterizes the visual distance. Subsequently, a Delaunay triangular mesh is constructed to create a proximity map between the houses, with the MST generated using visual distance as the weighting factor. Then, the spatial proximity similarity of house clusters is obtained through pruning. Finally, a support vector machine is employed to categorize the architectural structure of the housing complex, viz., simple houses, brick–concrete houses, and frame houses. This classification is based on the geometric, textural, height, and spatial distribution characteristics of the houses. We have conducted a remote sensing classification experiment of house structure types with Zhushan County, Hubei Province as the study area. The results show that the MST clustering method improves the classification accuracy of brick–concrete houses to 95.4% and the classification accuracy of simple houses to 93.4%. Compared to the single-family-based classification method of building structure types, the classification accuracy of frame-structure buildings is improved to 87%. The Kappa coefficient increased to 0.89. This study significantly improves the classification accuracy of building structure types by introducing spatial similarity. Furthermore, it shows the potential for spatial similarity in classifying building structure types.
Trigger-Free and Low-Cross-Sensitivity Displacement Sensing System Using a Wavelength-Swept Laser and a Cascaded Balloon-like Interferometer
Authors: Zhou Jianming , Fan Jinying , Zhang Junkai , Yao Jianping , Zhang Jiejun
Published: January 2025

A wavelength-swept laser (WSL) demodulation system offers a unique time-domain analysis solution for high-sensitivity optical fiber sensors, providing a high-resolution and high-speed method compared to optical spectrum analysis. However, most traditional WSL-demodulated sensing systems require a synchronous trigger signal or an additional optical dispersion link for sensing analysis and typically use a fiber Bragg grating (FBG) as the sensing unit, which limits displacement sensitivity and increases fabrication costs. We present a novel displacement sensing system that combines a trigger-free WSL demodulation method with a cascaded balloon-like interferometer, featuring a simple structure, high sensitivity, and low temperature cross-sensitivity. The sensor is implemented by bending a short length of single-mode fiber with an optimal radius of around 4 mm to excite cladding modes, which form an interference spectral response with the core mode. Experimental findings reveal that the system achieves a high sensitivity of 397.6 pm/μm for displacement variation, corresponding to 19.88 ms/μm when demodulated using a WSL with a sweeping speed of 20 nm/s. At the same time, the temperature cross-sensitivity is as low as 5 pm/°C or 0.25 ms/°C, making it a strong candidate for displacement sensing in harsh environments with significant temperature interference.
A CMOS Optoelectronic Transceiver with Concurrent Automatic Power Control for Short-Range LiDAR Sensors
Authors: Choi Yejin , Li Juntong , Jung Dukyoo , Choi Seonhan , Park Sung-Min
Published: January 2025

This paper presents an optoelectronic transceiver (OTRx) realized in a 180 nm CMOS technology for applications of short-range LiDAR sensors, in which a modified current-mode single-ended VCSEL driver (m-CMVD) is exploited as a transmitter (Tx) and a voltage-mode fully differential transimpedance amplifier (FD-TIA) is employed as a receiver (Rx). Especially for Tx, a concurrent automatic power control (APC) circuit is incorporated to compensate for the inevitable increase in the threshold current in a VCSEL diode. For Rx, two on-chip spatially modulated P+/N- well avalanche photodiodes (APDs) are integrated with the FD-TIA to achieve circuit symmetry. Also, an extra APD is added to facilitate the APC operations in Tx, i.e., concurrently adjusting the bias current of the VCSEL diode by the action of the newly proposed APC path in Rx. Measured results of test chips demonstrate that the proposed OTRx causes the DC bias current to increase from 0.93 mA to 1.42 mA as the input current decreases from 250 µApp to 3 µApp, highlighting its suitability for short-range sensor applications utilizing a cost-effective CMOS process.
PULSE: A Fast Portable Unit for Lab-on-Site Electrochemistry
Authors: Ferreira Cláudia , Barry Fiona , Todorović Miomir , Sugrue Patrick , Teixeira Sofia Rodrigues , Galvin Paul
Published: January 2025

This study aims to develop and validate a novel fast-detection electrochemical sensing platform to enhance portable electrochemical sensor solutions. The research focuses on optimising analogue front-end circuits, developing data analysis algorithms, and validating the device through experiments to enhance measurement accuracy and detection speed, enabling on-site measurements across diverse applications. This work successfully designed a Portable Unit for Lab-on-Site Electrochemistry (PULSE) system with dimensions of (78×100×2) mm3. The device’s implementation was complemented by robust firmware that performed desired electrochemical measurements, including open circuit potentiometry (OCP), chronoamperometry (CA), and cyclic voltammetry (CV). To assess its reliability, the PULSE was benchmarked against a well-established benchtop potentiostat. The results obtained highlight the system’s rapid sensing capabilities, achieving pH detection in 2 s and performing CA in 20 s. The pH calibration curve exhibited Nernstian behaviour with an accuracy of 97.58%. A correlation analysis comparing the calibration curve datasets across all electrochemical techniques from both systems revealed high correlation coefficients (>0.99), confirming the strong agreement between the two systems.
Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
Authors: Matricardi Elisabetta , Favarelli Elia , Pucci Lorenzo , Xu Wen , Paolini Enrico , Giorgetti Andrea
Published: January 2025

As 6G development progresses, joint sensing and communication (JSC) is emerging as a transformative technology, promising enhanced spectrum and energy efficiency alongside innovative services. This paper delves into underexplored facets of JSC, particularly its role in vehicular technology and transportation systems. It discusses data fusion techniques that enable cooperative sensing in networked environments and underscores the critical role of resource management in balancing sensing and communication. It suggests modeling extended targets, such as vehicles, within a computationally feasible framework. Moreover, it proposes a novel integration of AI-based target recognition, allowing target-specific tracking parameters and target-based sensing resource allocation. Importantly, a case study is presented to underscore the real-world applicability of these concepts in vehicular scenarios, demonstrating how networked devices can achieve high sensing and communication performance.
Machine-Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research
Authors: Deutch Frederik , Weiss Marc Gjern , Wagner Stefan Rahr , Hansen Lars Schmidt , Larsen Frederik , Figueiredo Constanca , Moers Cyril , Keller Anna Krarup
Published: January 2025

In experimental research, animal welfare should always be of the highest priority. Currently, physical in-person observations are the standard. This is time-consuming, and results are subjective. Video-based machine learning models for monitoring experimental pigs provide a continuous and objective observation method for animal misthriving detection. The aim of this study was to develop and validate a pig tracking technology, using video-based data in a machine learning model to analyze the posture and activity level of experimental pigs living in single-pig pens. A research prototype was created using a microcomputer and a ceiling-mounted camera for live recording based on the obtained images from the experimental facility, and a combined model was created based on the Ultralytics YOLOv8n for object detection trained on the obtained images. As a second step, the Lucas–Kanade sparse optical flow technique for movement detection was applied. The resulting model successfully classified whether individual pigs were lying, standing, or walking. The validation test showed an accuracy of 90.66%, precision of 90.91%, recall of 90.66%, and correlation coefficient of 84.53% compared with observed ground truth. In conclusion, the model demonstrates how machine learning can be used to monitor experimental animals to potentially improve animal welfare.
MonoAMP: Adaptive Multi-Order Perceptual Aggregation for Monocular 3D Vehicle Detection
Authors: Hu Xiaoxi , Chen Tao , Zhang Wentao , Ji Guangyi , Jia Hongxia
Published: January 2025

Monocular 3D object detection is rapidly emerging as a key research direction in autonomous driving, owing to its resource efficiency and ease of implementation. However, existing methods face certain limitations in cross-dimensional feature attention mechanisms and multi-order contextual information modeling, which constrain their detection performance in complex scenes. Thus, we propose MonoAMP, an adaptive multi-order perceptual aggregation algorithm for monocular 3D object detection. We first introduce triplet attention to enhance the interaction of cross-dimensional feature attention. Second, we design an adaptive multi-order perceptual aggregation module. It dynamically captures multi-order contextual information and employs an adaptive aggregation strategy to enhance target perception. Finally, we propose an uncertainty-guided adaptive depth ensemble strategy, which models the uncertainty distribution in depth estimation and effectively fuses multiple depth predictions. Experiments demonstrate that MonoAMP significantly enhances performance on the KITTI dataset at the moderate difficulty level, achieving 16.80% AP3D and 24.47% APBEV. Additionally, the ablation study shows a 3.78% improvement in object detection accuracy over the baseline method. Compared to other advanced methods, MonoAMP demonstrates superior detection capabilities, especially in complex scenarios.
Social Determinants of Health and Adverse Childhood Experiences: How Are These Intertwined to Determine Childhood Overweight and Obesity in Argentina?
Authors: Guillén-Fernández Yedith B.
Published: January 2025

Argentina is one of the countries in the Latin American region with the highest incidence of childhood obesity. The incidence of childhood overweight and obesity among children under five years of age is approximately 15%, according to the 2022 MICS survey. Obesity is a nutritional disorder that is explained not only by biological factors but also by socioeconomic and family circumstances. The objective of this study is to investigate the factors and risks that determine childhood overweight and obesity in Argentine children under five years of age. To explain this, this article presents an integrative model of two theoretical frameworks, social determinants of health (SDH) and adverse childhood experiences (ACEs), called hereafter “IM-SDH-ACEs”, in order to understand how these determinants are related to influence childhood malnutrition. This study performs a Chi-square test to empirically corroborate that SDH and ACEs influence this type of weight disorder; thus, it is shown that socioeconomic status (SES) and age groups together with ACEs are factors associated with childhood overweight and obesity. Likewise, a multinomial logistic regression model is estimated, and the results show that the highest risks of suffering from overweight and obesity in childhood are the predictors of physical violence (PV) inflicted on the child as well as an interaction of the variables of deprivation in health care and a secondary education level of mothers or carers. It is concluded that a more inclusive social policy is required to reduce childhood obesity in Latin American societies.
Spatial Network Evolution of Water Resources Green Efficiency in Yellow River Basin Urban Agglomerations
Authors: Zhao Lihong , Zhang Yuge , Li Fuzhu , Gong Yuki Yi , Sun Hideyuki Hao , So Sing Lui , Chen Zehua
Published: January 2025

This study evaluates the transmission relationships and spatial correlation network structure of water resources green efficiency among seven urban agglomerations in the Yellow River Basin from 2008 to 2022. Using the Super-SBM model, water resources green efficiency was measured. A modified gravity model was then employed to assess the spatial linkage intensity among cities. Social network analysis was applied to explore the structural characteristics and evolution patterns of the network. Results reveal a fluctuating water efficiency trend, characterized by “rising, then falling, and finally rising”, with an average efficiency of 0.561. Significant regional disparities and considerable potential for improvement persist. The water efficiency network displays an uneven structure, with intensified spatial linkages and a “dense in the east, sparse in the west” pattern. The overall network density is moderate, characterized by more benefits than spillovers. Zhengzhou, Xi’an, and Jinan emerge as key hubs, exhibiting strong external influence and rapid communication within the network. The distinct and stable core-edge structure underscores the importance of inter-regional collaboration to enhance overall water resources green efficiency. This study provides critical insights and policy recommendations for optimizing water resource allocation and promoting sustainable regional development in the Yellow River Basin.
Tourism Entrepreneurship in Latin America: A Systematic Review of Challenges, Strategies, and Post-COVID-19 Perspectives
Authors: Fernández-Bedoya Víctor Hugo , Ruiz-Palacios Miguel Angel , Meneses-La-Riva Monica Elisa , Suyo-Vega Josefina Amanda
Published: January 2025

The COVID-19 pandemic has profoundly impacted the global tourism industry, forcing tourism entrepreneurs to adapt and innovate in order to recover. This systematic review aims to identify scientific evidence on tourism entrepreneurship experiences in Latin America during the COVID-19 pandemic. Specifically, the review seeks to uncover key challenges faced by tourism entrepreneurs, the locations and types of tourism most affected, and to draw lessons from these experiences. The authors followed the PRISMA protocol, identifying 15 research studies on tourism entrepreneurship in Latin America. The review analyzed articles from seven key databases—Scopus, Web of Science, Scielo, EBSCO, Proquest, Gale Academic Onefile, and LA Referencia—focusing on studies that examined tourism entrepreneurship within the context of COVID-19. These studies employed diverse methodologies, including case studies, surveys, and data analysis. The results show tourism entrepreneurs in Latin America faced challenges like economic crises, business closures, and unemployment. They responded with biosecurity protocols, domestic tourism, and digital tools such as online platforms and QR codes. A shift toward sustainable models like ecotourism highlighted local development and conservation. The pandemic spurred innovation and resilience, with adaptability, digital transformation, and collaboration, driving recovery. Sustainable practices and authentic experiences are key to long-term success.
Remote Sensing Techniques with the Use of Deep Learning in the Determining Dynamics of the Illegal Occupation of Rivers and Lakes: A Case Study in the Jinshui River Basin, Wuhan, China
Authors: Shen Laiyin , Huang Yuhong , Zhou Chi , Wang Lihui
Published: January 2025

The “Four Illegal Activities”, which involve occupation, extraction, and construction along shorelines, have become significant challenges in river and lake management in recent years. Due to the diverse and scattered nature of monitoring targets, coupled with the large volumes of data involved, traditional manual inspection methods are no longer sufficient to meet regulatory demands. Late fusion change detection methods in deep learning are particularly effective for monitoring river and lake occupation due to their straightforward principles and processes. However, research on this topic remains limited. To fill this gap, we selected eight popular deep learning networks—VGGNet, ResNet, MobileNet, EfficientNet, DenseNet, Inception-ResNet, SeNet, and DPN—and used the Jinshui River Basin in Wuhan as a case study to explore the application of Siamese networks to monitor river and lake occupation. Our results indicate that the Siamese network based on EfficientNet outperforms all other models. It can be reasonably concluded that the combination of the SE module and residual connections provides an effective approach for improving the performance of deep learning models in monitoring river and lake occupation. Our findings contribute to improving the efficiency of monitoring river and lake occupation, thereby enhancing the effectiveness of water resource and ecological environment protection. In addition, they aid in the development and implementation of efficient strategies for promoting sustainable development.
Prioritization of Human-Centric and Sustainable City Criteria by Proportional Spherical Fuzzy Analytic Hierarchy Process
Authors: Alkan Nursah , Kahraman Cengiz
Published: January 2025

Human-centric and sustainable cities have become one of the most popular research areas today. They are based on a holistic approach that aims to both improve people’s quality of life and ensure environmental sustainability. The weighting of numerous tangible and intangible criteria used in the performance evaluation of these cities has emerged as an important problem. The symmetrical representation of vague and imprecise data using fuzzy set theory is an absolute necessity for a successful weighting process. In this study, symmetrical representation of especially intangible criteria is carried out with proportional spherical fuzzy sets. Proportional spherical fuzzy sets provide significant convenience to the expert in determining the membership, non-membership and hesitancy degrees and ensure that the assigned values are more accurate and consistent. In the study, five main human centric and sustainable city (HCSC) criteria and 26 HCSC sub-criteria determined from the literature were weighted. In addition, criteria weights were obtained by classical spherical fuzzy analytic hierarchy process (AHP) for comparison purpose. The first four most important HCSC criteria were determined as water management and conservation, employment/unemployment rate, carbon emission reduction strategies and economic sustainability, respectively.
Salvia connivens Methanolic Extract Against Spodoptera frugiperda and Tenebrio molitor and Its Effect on Poecilia reticulata and Danio rerio
Authors: Rodríguez-Cervantes Manolo , León-Herrera Luis Ricardo , Ventura-Salcedo Salvador Alejandro , Monroy-Dosta María del Carmen , Rodríguez-deLeón Eloy , Bah Mamadou Moustapha , Campos-Guillén Juan , Amaro-Reyes Aldo , Zavala-Gómez Carlos Eduardo , Figueroa-Brito Rodolfo , Mariscal-Ureta Karla Elizabeth , Pool Héctor , Ramos-Mayorga Itzel , Ramos-López Miguel Angel
Published: January 2025

Spodoptera frugiperda (Lepidoptera: Noctuidae) and Tenebrio molitor (Coleoptera: Tenebrionidae) are two prominent pests of maize and its stored grains, respectively. Botanical pesticides have been proposed as an alternative for their management. This study evaluated the insecticidal activity of Salvia connivens (Lamiaceae) methanolic extract and rosmarinic acid against S. frugiperda and T. molitor by adding them to an artificial diet, as well as their ecotoxicological effects on Poecilia reticulata (Cyprinodontiformes: Poeciliidae) and Danio rerio (Cypriniformes: Danionidae) through acute toxicity tests. The methanolic extract showed higher mortality activity against S. frugiperda (LC50 = 874.28 ppm) than against T. molitor (LC50 = 1856.94 ppm) and was non-toxic to fish. Rosmarinic acid, the most abundant compound in the extract (80.45 mg g−1), showed higher activity against S. frugiperda (LC50 = 176.81 ppm). This compound did not cause a toxic effect on adult P. reticulata at the tested concentrations. However, in P. reticulata fingerlings and D. rerio adults, it was non-toxic, except in D. rerio embryos, where it was slightly toxic. These findings suggest that S. connivens methanolic extract has potential as a botanical product for the management of S. frugiperda and T. molitor with low ecotoxicological impact, while rosmarinic acid may be a useful compound for the management of S. frugiperda.
HPV-51 or HPV-52 Infection Could Impair Sperm Quality in Infertile Patients: A Preliminary Study on Our Experience from North-Western Italy
Authors: Omes Claudia , Rienzi Mariangela , Rossini Roberta , Piccinino Manuela , Nappi Rossella Elena
Published: January 2025

Human papillomavirus (HPV) infection is one of the most common sexually transmitted infections in all genders worldwide. Its association with male infertility is deeply investigated, although there are conflicting data on the role of the virus in the impairment of semen quality and reduced reproductive outcomes. In this study, we considered 335 semen samples of males (age: 37.63 ± 6.02 years) belonging to infertile couples who did not conceive a pregnancy after 12 months of unprotected intercourse. Residual semen samples, after routine sperm analysis, were used to amplify and type viral DNA. Positive or negative HPV semen samples were compared. In total, 42.51% (139/327) were positive for at least one HPV genotype, and in 54.68% (76/139), positivity was due to a high-risk (HR) genotype. The most prevalent was HPV-16 (16.55%) followed by HPV-52 (10.07%) and HPV-51 (7.91%). Overall, no significant differences emerged in terms of sperm concentration, sperm motility, and morphology between the two groups. However, a considerable reduction in sperm motility was found in the presence of HPV-51 or HPV-52. These data point to the importance of HPV screening in semen analysis to evaluate patients that might have a higher risk of infertility according to the type of HPV genotype.
Random Asymmetric Jets Driven by Black-Hole Hyperaccretion in Gamma-Ray Bursts
Authors: Yang Zi-Ou , Qi Yan-Qing , Liu Tong
Published: January 2025

The relativistic jets of gamma-ray bursts (GRBs) might be powered by a black-hole (BH) hyperaccretion system. The inherent asymmetry in these jets generates recoil forces, inducing oscillations and positional deviations of the BH from equilibrium. In this study, we explore the influence of different initial BH mass, spin, and mass accretion rate, as well as their evolutions on the dynamical properties of BH under the effect of asymmetric jets. Our results reveal that the initial mass and accretion rate significantly impact the BH’s acceleration, velocity, and displacement, while the different initial spin plays a negligible role in shaping the overall dynamical evolution. Additionally, we calculate the gravitational wave (GW) strains associated with the asymmetric jets, finding that the resulting GW signals are too weak to be detected, even for nearby GRBs. These findings provide critical insights into the dynamical response of BHs to asymmetric jets and the associated GW radiation, advancing our understanding of BH physics in GRBs.
Modeling Urban Microclimates for High-Resolution Prediction of Land Surface Temperature Using Statistical Models and Surface Characteristics
Authors: Fahad Md Golam Rabbani , Karimi Maryam , Nazari Rouzbeh , Nikoo Mohammad Reza
Published: January 2025

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R2 = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R2 = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R2 ranging from 0.79 to 0.95.
A Pentavalent HIV-1 Subtype C Vaccine Containing Computationally Selected gp120 Strains Improves the Breadth of V1V2 Region Responses
Authors: Shen Xiaoying , Korber Bette , Spreng Rachel L. , Sawant Sheetal S. , deCamp Allan , McMillan Arthur S. , Mathura Ryan , Zolla-Pazner Susan , Pinter Abraham , Parks Robert , Bowman Cindy , Sutherland Laura , Scearce Richard , Yates Nicole L. , Montefiori David C. , Haynes Barton F. , Tomaras Georgia D.
Published: January 2025

Background: HIV-1 envelope (Env) variable loops 1 and 2 (V1V2) directed non-neutralizing antibodies were a correlate of decreased transmission risk in the RV144 vaccine trial. Thus, the elicitation and breadth of antibody responses against the V1V2 of HIV-1 Env are important considerations for HIV-1 vaccine candidates. The V1V2 region’s highly variable nature and the extensive diversity of subtype C HIV-1 Envelopes (Envs) make the V1V2 response breadth a high priority for HIV-1 vaccine regimens aiming for V1V2-mediated protection in Southern Africa. Here, we determined whether the breadth of the anti-V1V2 vaccine response can be broadened by including HIV-1 Env strains computationally designed to enhance the coverage of subtype C V1V2 sequence diversity. Methods: Three subtype C Env strains were selected to maximize antibody binding coverage while complementing subtype C vaccine gp120s that were given in human clinical trials in South Africa, as well as to improve epitope accessibility. Humoral immunogenicity of a novel trivalent gp120 vaccine immunogen, a bivalent gp120 boost already in clinical trials (1086C and TV1), and a pentavalent (all five gp120s combined) were evaluated in a preclinical immunization study in guinea pigs. The pentavalent combination was further evaluated with alum versus glucopyranosyl lipid adjuvants formulated in squalene-in-water emulsion (GLA-SE) adjuvants in non-human primates. The breadth of the anti-V1V2 response was assessed using an array of cross-subtype variable loops 1&2 (V1V2) scaffold proteins and linear V2 peptides. Results: The breadth of the IgG response against V1V2 antigens of the trivalent and pentavalent groups was comparable, and both were greater than the breadth of the bivalent group. Linear epitope mapping showed that two linear epitopes in V2 were targeted by the vaccinated animals: the V2 hotspot focused at 169K that potentially correlated with decreased HIV-1 risk in RV144 and the V2.2 site (179LDV/I181) that is part of the integrin α4β7 binding site. The bivalent vaccine elicited a significantly higher magnitude of binding to the V2 hotspot compared to the trivalent vaccine whereas the trivalent vaccine elicited significantly higher binding to the V2.2 epitope compared to the bivalent vaccine, while the pentavalent recognized both regions. Conclusions: These results demonstrate that the three new computationally selected subtype C Envs successfully complemented 1086C and TV1 for broader V1V2 antibody responses, and, in concert with adjuvants that stimulate V1V2 responses, can be considered as part of a rationale immunogen design to improve V1V2 IgG coverage in future vaccine trials in South Africa.