bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comprehensive Review of Control Challenges and Methods in End-Effector Upper-Limb Rehabilitation Robots
Authors: Mahfouz Dalia M. , Shehata Omar M. , Morgan Elsayed I. , Arrichiello Filippo
Published: December 2024

In the last decades, there has been an increasing number of human patients who suffer from upper-limb disorders limiting their motor abilities. One of the possible solutions that gained extensive research interest is the development of robot-aided rehabilitation training setups, including either end-effector or exoskeleton robots, which showed various advantages compared to traditional manual rehabilitation therapy. One of the main challenges of these systems is to control the robot’s motion to track a desirable rehabilitation training trajectory while being affected by either voluntary or involuntary human forces depending on the patient’s recovery state. Several previous studies have been targeting exoskeleton robotic systems focusing on their structure, clinical features, and control methods, with limited review on end-effector-based robotic rehabilitation systems. In this regard, an overview of the most common end-effector robotic devices used for upper-limb rehabilitation is provided in this paper, describing their mechanical structure, features, clinical application, commercialization, advantages, and shortcomings. Additionally, a comprehensive review on possible control methods applied to end-effector rehabilitation exploitation is presented. These control methods are categorized as conventional, robust, intelligent, and most importantly, adaptive controllers implemented to serve for diverse rehabilitation control modes, addressing their development, implementation, findings, and possible drawbacks.
IMERG V07B and V06B: A Comparative Study of Precipitation Estimates Across South America with a Detailed Evaluation of Brazilian Rainfall Patterns
Authors: Rozante José Roberto , Rozante Gabriela
Published: December 2024

Satellite-based precipitation products (SPPs) are essential for climate monitoring, especially in regions with sparse observational data. This study compares the performance of the latest version (V07B) and its predecessor (V06B) of the Integrated Multi-satellitE Retrievals for GPM (IMERG) across South America and the adjacent oceans. It focuses on evaluating their accuracy under different precipitation regimes in Brazil using 22 years of IMERG Final data (2000–2021), aggregated into seasonal totals (summer, autumn, winter, and spring). The observations used for the evaluation were organized into 0.1° × 0.1° grid points to match IMERG’s spatial resolution. The analysis was restricted to grid points containing at least one rain gauge, and in cases where multiple gauges were present within a grid point the average value was used. The evaluation metrics included the Root Mean Square Error (RMSE) and categorical indices. The results reveal that while both versions effectively capture major precipitation systems such as the mesoscale convective system (MCS), South Atlantic Convergence Zone (SACZ), and Intertropical Convergence Zone (ITCZ), significant discrepancies emerge in high-rainfall areas, particularly over oceans and tropical zones. Over the continent, however, these discrepancies are reduced due to the correction of observations in the final version of IMERG. A comprehensive analysis of the RMSE across Brazil, both as a whole and within the five analyzed regions, without differentiating precipitation classes, demonstrates that version V07B effectively reduces errors compared to version V06B. The analysis of statistical indices across Brazil’s five regions highlights distinct performance patterns between IMERG versions V06B and V07B, driven by regional and seasonal precipitation characteristics. V07B demonstrates a superior performance, particularly in regions with intense rainfall (R1, R2, and R5), showing a reduced RMSE and improved categorical indices. These advancements are linked to V07B’s reduced overestimation in cold-top cloud regions, although both versions consistently overestimate at rain/no-rain thresholds and for light rainfall. However, in regions prone to underestimation, such as the interior of the Northeastern region (R3) during winter, and the northeastern coast (R4) during winter and spring, V07B exacerbates these issues, highlighting challenges in accurately estimating precipitation from warm-top cloud systems. This study concludes that while V07B exhibits notable advancements, further enhancements are needed to improve accuracy in underperforming regions, specifically those influenced by warm-cloud precipitation systems.
Seasonal Analysis of Planetary Boundary Layer and Turbulence in Warsaw, Poland Through Lidar and LES Simulations
Authors: Carneiro Rayonil G. , Karasewicz Maciej , Borges Camilla K. , Janicka Lucja , Wang Dongxiang , Fisch Gilberto , Stachlewska Iwona S.
Published: December 2024

We analyzed the planetary boundary layer (PBL) characteristics in Warsaw, Poland for a day of summer, autumn, winter, and spring of 2021 by integrating and comparing measured and simulated data. Using remote sensing lidar sensor data, the PBLH was calculated using wavelet covariance transform (WCT) and the gradient method (GM). Also, simulations of turbulent fluxes were performed utilizing the large eddy simulation (LES) from the Parallel Large Eddy Simulation Model (PALM) to better understand how turbulence and convection behave across different seasons in Warsaw. The PBLH diurnal cycles showed pronounced changes in their vertical structure as a function of the season: the winter heights were shallow (~0.7 km), while summer heights were deeper (~1.7 km). The spring and autumn presented transient characteristics of PBLH around 1.0 km. This study is crucial for enhancing urban air quality and climate modeling. The PBLH simulations from PALM showed agreement with the measured data, with an underestimation of approximately 10% in both methods. Through PALM, it was possible to observe that summer exhibited increased convection, enhanced mixing efficiency, and a deeper boundary layer compared to other seasons throughout the daily cycle. Winter has a lower sensible heat flux and little convection throughout the day. Spring and autumn showed intermediate characteristics. In this way, the effectiveness of the applicability of the PALM model to obtain flows within the PBL and their heights is highlighted, because correlations ranged from strong to very strong (r ≥ 0.70).
Target Detection and Characterization of Multi-Platform Remote Sensing Data
Authors: Chhapariya Koushikey , Ientilucci Emmett , Buddhiraju Krishna Mohan , Kumar Anil
Published: December 2024

Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration.
Viability of Substituting Handheld Metal Detectors with an Airborne Metal Detection System for Landmine and Unexploded Ordnance Detection
Authors: Lekhak Sagar , Ientilucci Emmett J. , Brinkley Anthony Wayne
Published: December 2024

Commonly found landmines, such as the TM-62M, MON-100, and PDM-1, in the recent Russia–Ukraine war confirm the continued use of metals in munitions. Traditional demining techniques, primarily relying on handheld metal detectors and Ground Penetrating Radar (GPR) systems, remain state of the art for subsurface detection. However, manual demining with handheld metal detectors can be slow and pose significant risks to operators. Drone-based metal detection techniques offer promising solutions for rapid and effective landmine detection, but their reliability and accuracy remain a concern, as even a single missed detection can be life-threatening. This study evaluates the potential of an airborne metal detection system as an alternative to traditional handheld detectors. A comparative analysis of three distinct metal detectors for landmine detection is presented: the EM61Lite, a sensitive airborne metal detection system (tested in a pseudo-drone-based scenario); the CTX 3030, a traditional handheld all-metal detector; and the ML 3S, a traditional handheld ferrous-only detector. The comparison focuses on the number of metallic targets each detector identifies in a controlled test field containing inert landmines and UXOs. Our findings highlight the strengths and limitations of airborne metal detection systems like the EM61Lite and emphasize the need for advanced processing techniques to facilitate their practical deployment. We demonstrate how our experimental normalization technique effectively identifies additional anomalies in airborne metal detector data, providing insights for improved detection methodologies.
Large Offsets in the Impacts Between Enhanced Atmospheric and Soil Water Constraints and CO2 Fertilization on Dryland Ecosystems
Authors: Tian Feng , Wang Lei , Yuan Ye , Chen Jin
Published: December 2024

Greening dryland ecosystems greatly benefits from significant CO2 fertilization. This greening trend across global drylands, however, has also been severely constrained by enhancing atmospheric and soil water (SW) deficits. Thus far, the relative offsets in the contributions between the atmospheric vapor pressure deficit (VPD), SW at varying depths, and CO2 fertilization to vegetation dynamics, as well as the differences in the impacts of decreasing SW at different soil depths on dryland ecosystems over long periods, remain poorly recorded. Here, this study comprehensively explored the relative offsets in the contributions to vegetation dynamics between high VPD, low SW, and rising CO2 concentration across global drylands during 1982–2018 using process-based models and satellite-observed Leaf Area Index (LAI), Gross Primary Productivity (GPP), and solar-induced chlorophyll fluorescence (SIF). Results revealed that decreasing-SW-induced reductions of LAI in dryland ecosystems were larger than those caused by rising VPD. Furthermore, dryland vegetation was more severely constrained by decreasing SW on the subsurface (7–28 cm) among various soil layers. Notable offsets were found in the contributions between enhanced water constraints and CO2 fertilization, with the former offsetting approximately 38.49% of the beneficial effects of the latter on vegetation changes in global drylands. Process-based models supported the satellite-observed finding that increasing water constraints failed to overwhelmingly offset significant CO2 fertilization on dryland ecosystems. This work emphasizes the differences in the impact of SW at different soil depths on vegetation dynamics across global drylands as well as highlights the far-reaching importance of significant CO2 fertilization to greening dryland ecosystems despite increasing atmospheric and SW constraints.
SymSwin: Multi-Scale-Aware Super-Resolution of Remote Sensing Images Based on Swin Transformers
Authors: Jiao Dian , Su Nan , Yan Yiming , Liang Ying , Feng Shou , Zhao Chunhui , He Guangjun
Published: December 2024

Despite the successful applications of the remote sensing image in agriculture, meteorology, and geography, its relatively low spatial resolution is hindering the further applications. Super-resolution technology is introduced to conquer such a dilemma. It is a challenging task due to the variations in object size and textures in remote sensing images. To address that problem, we present SymSwin, a super-resolution model based on the Swin transformer aimed to capture a multi-scale context. The symmetric multi-scale window (SyMW) mechanism is proposed and integrated in the backbone, which is capable of perceiving features with various sizes. First, the SyMW mechanism is proposed to capture discriminative contextual features from multi-scale presentations using corresponding attentive window size. Subsequently, a cross-receptive field-adaptive attention (CRAA) module is introduced to model the relations among multi-scale contexts and to realize adaptive fusion. Furthermore, RS data exhibit poor spatial resolution, leading to insufficient visual information when merely spatial supervision is applied. Therefore, a U-shape wavelet transform (UWT) loss is proposed to facilitate the training process from the frequency domain. Extensive experiments demonstrate that our method achieves superior performance in both quantitative metrics and visual quality compared with existing algorithms.
Research on Key Influencing Factors of Ecological Environment Quality in Barcelona Metropolitan Region Based on Remote Sensing
Authors: Zhang Xu , Ramos Blanca Arellano , Cladera Josep Roca
Published: December 2024

With the rapid development of urbanization, the ecological environment is being degraded. Taking the Barcelona Metropolitan Region as an example, this paper developed an ecological environment quality-assessment system suitable for different times and regions, based on remote sensing, to evaluate the quality of the ecological environment from 2006 to 2018. We also built various ordinary least squares models to analyze multiple variables affecting the ecological environment. Finally, the characteristic triangular spatial structure was used to explain the interaction between the two key variables. The results showed that the ecological quality was unevenly distributed. The largest green space contributed the most benefits but was decreasing and becoming fragmented. NDVI (normalized difference vegetation index) was the most significant natural variable related to the distribution of green space. Precipitation was the most closely related climate factor to NDVI. There was a complex two-way interaction mechanism between the two, and its boundary value was getting higher and higher. In conclusion, the environmental quality of the BMR needs improvement. The characteristic triangle can effectively explain the interaction mechanism between precipitation and NDVI. This study deeply analyzes how various factors affect environmental quality from both the global and internal perspectives and provides a scientific basis for urban ecological management and sustainable development.
Deep FS: A Deep Learning Approach for Surface Solar Radiation
Authors: Kihtir Fatih , Oztoprak Kasim
Published: December 2024

Contemporary environmental challenges are increasingly significant. The primary cause is the drastic changes in climates. The prediction of solar radiation is a crucial aspect of solar energy applications and meteorological forecasting. The amount of solar radiation reaching Earth’s surface (Global Horizontal Irradiance, GHI) varies with atmospheric conditions, geographical location, and temporal factors. This paper presents a novel methodology for estimating surface sun exposure using advanced deep learning techniques. The proposed method is tested and validated using the data obtained from NASA’s Goddard Earth Sciences Data and Information Services Centre (GES DISC) named the SORCE (Solar Radiation and Climate Experiment) dataset. For analyzing and predicting accurate data, features are extracted using a deep learning method, Deep-FS. The method extracted and provided the selected features that are most appropriate for predicting the surface exposure. Time series analysis was conducted using Convolutional Neural Networks (CNNs), with results demonstrating superior performance compared to traditional methodologies across standard performance metrics. The proposed Deep-FS model is validated and compared with the traditional approaches and models through the standard performance metrics. The experimental results concluded that the proposed model outperforms the traditional models.
Smart Driving Technology for Non-Invasive Detection of Age-Related Cognitive Decline
Authors: Serhan Peter , Victor Shaun , Osorio Perez Oscar , Abi Karam Kevin , Elghoul Anthony , Ransdell Madison , Al-Hindawi Firas , Geda Yonas , Chahal Geetika , Eagan Danielle , Wu Teresa , Tsow Francis , Forzani Erica
Published: December 2024

Alzheimer’s disease (AD) and Alzheimer’s Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline and is traditionally monitored through infrequent clinical tests. In this research, the Smart Driving System, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases, is presented. The system comprises a multi-modal biosensing array (MMS) and AI algorithms, including driving performance and driver’s biometrics, offering insights into a driver’s cognitive function. This publication is the first work reported towards the ultimate goal of developing the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies.
Design and Study of Pulsed Eddy Current Sensor for Detecting Surface Defects in Small-Diameter Bars
Authors: Han Lei , Jiang Yi , Yuan Ming
Published: December 2024

The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection technologies. This article presents the construction of a non-coaxial differential eddy current sensor (Tx-Rx sensor) and examines the detection of surface defects in a small diameter bar. A COMSOL 3D model is developed to examine the variations in eddy current distribution and defect signal characteristics between the plate and rod components. The position of the excitation coil on the bar and the eddy current disruption around the defect are examined. Additionally, a Tx-Rx sensor has been developed and enhanced concerning coil dimensions, coil separation, and elevation height. An experimental system is established to detect bar structures with surface defects of varying depths, and a model correlating differential signal attenuation with defect depth is proposed, achieving a quantitative relative error of less than 5%, thereby offering a reference for the quantitative detection of bar surface defects.
Effectiveness of a Telerehabilitation-Based Exercise Program in Patients with Chronic Neck Pain—A Randomized Clinical Trial
Authors: Guerra-Arencibia Laura , Santana-Déniz Cristina , Pecos-Martín Daniel , Fernández-Carnero Samuel , de Miguel-Hernando Nerea , Achalandabaso-Ochoa Alexander , Rodríguez-Almagro Daniel
Published: December 2024

Background: Non-specific chronic neck pain is a prevalent musculoskeletal disorder with a significant impact on individuals’ quality of life. The lack of consensus on effective therapeutic management complicates the establishment of standardized treatment protocols. Home exercise programs have yielded positive results. This study aimed to assess the effectiveness of a telerehabilitation program distributed through videoconferencing for patients with non-specific chronic neck pain compared to a home-based exercise program. Methods: A randomized controlled trial was conducted involving 36 participants who were divided into two groups: the experimental group (n = 18) received manual therapy combined with telerehabilitation, while the home-based group (n = 18) received the same manual therapy treatment along with recommendations for home exercises. Key outcome measures, including neck-related disability, kynesiophobia, anxiety and depression, pain intensity, pressure pain threshold, quality of life, and adherence to self-treatment, were evaluated at baseline and post-treatment. Results: No statistically significant differences were observed between groups. However, both groups demonstrated improvements in all study variables except for the mental component of quality of life immediately post-treatment. Conclusions: After eight weeks of manual therapy and exercise, both the telerehabilitation and home-based exercise programs resulted in significant improvements in disability, pain, and kynesiophobia, indicating that telerehabilitation is as effective as home-based exercise.
Implementation and Experimental Application of Industrial IoT Architecture Using Automation and IoT Hardware/Software
Authors: Calderón David , Folgado Francisco Javier , González Isaías , Calderón Antonio José
Published: December 2024

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation. Aiming at overcoming these limitations, this paper presents the experimental application of an IIoT architecture divided into four functional layers, namely, Sensing, Network, Middleware and Application layers. Automation and IoT hardware and software are used to implement and apply the architecture. Special attention is put on the software Grafana, chosen in the top layer to deploy graphical user interfaces that are remotely accessible via web. A pilot microgrid integrating photovoltaic energy and hydrogen served as scenario to test and prove the suitability of the architecture in four application cases.
Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach
Authors: Lai Zhimao , Guo Yang , Hu Yongjian , Su Wenkang , Feng Renhai
Published: December 2024

Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems. Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition. However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets featuring light makeup faces. If these instances are incorrectly flagged as fraudulent by face anti-spoofing systems, it could lead to user inconvenience. In response, we develop a face anti-spoofing database that includes light makeup faces and establishes a criterion for determining light makeup to select appropriate data. Building on this foundation, we assess multiple established face anti-spoofing algorithms using the newly created database. Our findings reveal that the majority of these algorithms experience a decrease in performance when faced with light makeup faces. Consequently, this paper introduces a general face anti-spoofing algorithm specifically designed for light makeup faces, which includes a makeup augmentation module, a batch channel normalization module, a backbone network updated via the Exponential Moving Average (EMA) method, an asymmetric virtual triplet loss module, and a nearest neighbor supervised contrastive module. The experimental outcomes confirm that the proposed algorithm exhibits superior detection capabilities when handling light makeup faces.
Optimal Position and Orientation of an Ossicular Accelerometer for Human Auditory Prostheses
Authors: Burovikhin Dmitrii , Kitsopoulos Panagiota , Lauxmann Michael , Grosh Karl
Published: December 2024

In this study, a method for determining the optimal location and orientation of an implantable piezoelectric accelerometer on the short process of the incus is presented. The accelerometer is intended to be used as a replacement for an external microphone to enable totally implantable auditory prostheses. The optimal orientation of the sensor and the best attachment point are determined based on two criteria—maximum pressure sensitivity sum and minimum loudness level sum. The best location is determined to be near the incudomalleolar joint. We find that the angular orientation of the sensor is critical and provide guidelines on that orientation. The method described in this paper can be used to further optimize the design and performance of the accelerometer.
Adaptive Memory-Augmented Unfolding Network for Compressed Sensing
Authors: Feng Mingkun , Ning Dongcan , Yang Shengying
Published: December 2024

Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS). Concretely, without loss of interpretability, we integrate an adaptive content-aware strategy into the gradient descent step of the proximal gradient descent (PGD) algorithm, driving it to adaptively capture the adequate features. In addition, we extended AMAUN-CS based on the memory storage mechanism of the human brain and propose AMAUN-CS+ to develop the dependency of deep information across cascading stages. The experimental results show that the AMAUN-CS model surpasses other advanced methods on various public benchmark datasets while having lower complexity in training.
Seeds vs. Seedlings: The Long-Term Success of Seeds Versus Seedlings in the Restoration of the Federally Endangered Baptisia arachnifera (Fabaceae) During Experimental Planting
Authors: Pascarella John B.
Published: December 2024

The use of seeds versus seedlings may yield different restoration trajectories depending on seed germination (=seedling emergence) success in the of restoration plantings versus seedling establishment. Effective restoration and sustainable population dynamics will require both for long-term persistence. This study compared seed germination in both greenhouse and natural conditions, examining the growth, survival, and reproduction of seedlings from seeds and transplants of a federally endangered perennial, Baptisia arachnifera W.H. Duncan (Fabaceae), in Georgia, USA. Under greenhouse conditions, seed germination is rapid and yields 68% germination across a variety of conditions. In field germination trials, seedling emergence is moderate (20%), but survival, growth, and reproduction are low. Over an 18-yr period (2004–2022) at a restoration planting site in Lowndes County, Georgia, USA, survival, growth, and reproduction were much higher for plants established from seedlings versus plants established from seeds. While dormant plants and seeds are not harmed by winter burns, seedlings derived from both seeds and seedlings are negatively impacted by growing season burns. Restoration efforts in this species should focus on germinating seeds in greenhouses and the effective establishment and management of seedlings, not using seeds directly for restoration efforts.
The Removal of Organic Pollutants and Ammonia Nitrogen from High-Salt Wastewater by the Electro-Chlorination Process and Its Mechanism
Authors: Zhou Yujun , Hou Tangrui , Zhou Bo
Published: December 2024

Electro-chlorination (E-Cl) is an emerging and promising electrochemical advanced oxidation technology for wastewater treatment with the advantages of high efficiency, deep mineralization, a green process, and easy operation. It was found that the mechanism of pollutant removal by electro-chlorination mainly involves an indirect oxidation process, in which pollutant removal is mainly driven by the intermediate active species, especially RCS and chlorine radicals, with a strong oxidization ability produced at the anodes. In this work, we summarized the principles and pathways of the removal/degradation of pollutants (organic pollutants and ammonia nitrogen) by E-Cl and the major affecting factors including the applied current density, voltage, electrolyte concentration, initial pH value, etc. In the E-Cl system, the DSA and BDD electrodes were the most widely used electrode materials. The flow-through electrode reactor was considered to be the most promising reactor since it had a high porosity and large pore size, which could effectively improve the mass transfer efficiency and electron transfer efficiency of the reaction. Of the many detection methods for chlorine radicals and RCS, electron paramagnetic resonance (EPR) and spectrophotometry with N, N-diethyl-1,4-phenylenediamine sulfate (DPD) as the chromogenic agent were the two most widely used methods. Overall, the E-Cl process had excellent performance and prospects in treating salt-containing wastewater.
Generative Artificial Intelligence and Regulations: Can We Plan a Resilient Journey Toward the Safe Application of Generative Artificial Intelligence?
Authors: Bodini Matteo
Published: December 2024

The rapid advancements of Generative Artificial Intelligence (GenAI) technologies, such as the well-known OpenAI ChatGPT and Microsoft Copilot, have sparked significant societal, economic, and regulatory challenges. Indeed, while the latter technologies promise unprecedented productivity gains, they also raise several concerns, such as job loss and displacement, deepfakes, and intellectual property violations. The present article aims to explore the present regulatory landscape of GenAI across the major global players, highlighting the divergent approaches adopted by the United States, United Kingdom, China, and the European Union. By drawing parallels with other complex global issues such as climate change and nuclear proliferation, this paper argues that the available traditional regulatory frameworks may be insufficient to address the unique challenges posed by GenAI. As a result, this article introduces a resilience-focused regulatory approach that emphasizes aspects such as adaptability, swift incident response, and recovery mechanisms to mitigate potential harm. By analyzing the existing regulations and suggesting potential future directions, the present article aims to contribute to the ongoing discourse on how to effectively govern GenAI technologies in a rapidly evolving regulatory landscape.
Disentangling the Role of Composition Factors in Fertility Responses to Unemployment
Authors: García-Pereiro Thaís , Misuraca Roberta , Pace Roberta , Patimo Raffaella
Published: December 2024

Using a macro-panel data set from 20 Italian regions, this study explores the relationship between unemployment and fertility in Italy between 2006 and 2018. It contributes to recent literature on this subject by considering gender-specific unemployment measures and examining the influence of two important compositional factors on fertility responses, namely the presence of foreign women and daily childcare coverage, and the influence of both of these on the link between unemployment and fertility. The study reveals a procyclical relationship between unemployment rates, used as a proxy of variations in business cycles, and fertility. Positive influences of foreign women’s contribution and daily childcare coverage on fertility are uncovered, suggesting a potential mitigation effect on declining fertility trends during the period under observation.
Examining Career Calling Through Career Exploration: The Influence of Decision Self-Efficacy and Openness
Authors: Su Xiaomin , Ahmad Anuar , Alias Jamsari
Published: December 2024

In today’s rapidly changing social and employment environment, young university students face unprecedented career development challenges. They must not only acquire strong professional knowledge and skills but also navigate the complex job market. Career calling, as a profound professional value, provides individuals with a clear sense of purpose, laying a solid foundation for their career development. Therefore, cultivating career calling has gained increasing attention in higher education, which is essential for developing future social workers who are both responsible and purposeful. This study aims to reveal the impact of individuals’ career exploration on career calling and its underlying mechanisms, as well as the influence of personality differences during the exploration process. The study was conducted in two universities in Guangdong Province, China, using online questionnaires that yielded 421 valid responses. The results confirmed that career exploration positively influenced career calling, with career decision-making self-efficacy mediating this relationship. Additionally, openness to experience moderates the connection between career exploration and career calling. These findings offer valuable insights for educators and career counselors, helping them develop effective strategies and interventions to support university students in finding and realizing their career calling.
Educational Intervention on Awareness of Health-Damaging Behaviors in Educators
Authors: Focaroli Valentina , Chiaro Marina , Battaglia Maria Vittoria , Guidetti Laura , Velardi Andrea
Published: December 2024

Doping prevention transcends elite sports, highlighting a broader societal challenge where performance enhancement is driven by pressures to increase strength, beauty, and status. This issue extends to adolescents and non-competitive sports participants, where self-optimization pressures are increasingly normalized. Research underscores the need for tailored educational interventions that go beyond punitive measures, fostering ethical decision-making and personal responsibility. The recent literature emphasizes that addressing psychological protective factors, such as self-efficacy and emotional regulation, can effectively mitigate substance use risks. The project “Sport Informa” involved the educational community and adolescents between the ages of 16 and 19, with the goal of providing knowledge about the phenomenon of doping and acquiring tools useful for enhancing self-esteem and self-management skills, in order to prevent the risk of doping by promoting a balanced psychophysical development in young people. A 16 h online training program was delivered to high school teachers. The training provided knowledge about doping, its psychological and social implications, and practical tools for promoting protective factors in students. A longitudinal design was employed, with data collected before and after the intervention using validated psychometric tools, including the Self-Efficacy Scale, the Self-Compassion Scale, and the Emotional Regulation Questionnaire. Results showed significant improvements in teachers’ psychological dimensions, particularly in self-efficacy and emotional regulation, which they subsequently applied in their interactions with students. Finally, a survey was conducted with young participants who took part in the project activities to assess their self-evaluation of key factors relevant to designing future educational interventions and events aimed at preventing doping use.
Analyzing Parental Involvement in Youth Basketball
Authors: Lopes Maria V. , Ihle Andreas , Gouveia Élvio Rúbio , Marques Adilson , França Cíntia
Published: December 2024

Parental involvement in youth sports is increasingly relevant in scientific research since parents have gradually developed awareness regarding youngsters’ sports experiences. This study aimed to (1) examine potential differences in athletes’ and parents’ perceptions of parental involvement practices in youth basketball, (2) verify whether these perceptions differ by sex, and (3) analyze the relationship between the different perceptions dimensions recorded. The analysis included a large sample of 423 Portuguese participants (151 youth basketball athletes and 272 parents) who completed the Parental Behaviors in Sports (PBSP) questionnaire. The PBSP comprises a version for parents and a version for athletes, composed of five dimensions: (1) sports support, (2) competition attendance, (3) technical influence, (4) performance pressure, and (5) sports expectations. No significant statistical differences regarding PBSP dimensions were observed between boys and girls in the athletes’ group. However, in the parents’ group, fathers scored significantly lower than mothers in sports expectations (p = 0.001). Differences between athletes’ and parents’ perceptions were evident in competition attendance (p = 0.023), technical influence (p ≤ 0.001), and sports expectations (p = 0.023). When analyzing the dyads of boys–fathers and girls–mothers, significant differences were found for technical influence (p = 0.035), performance pressure (p = 0.020), and sports expectations (p < 0.001) between boys and fathers. Among girls and mothers, differences were perceived exclusively for technical influence (p = 0.005). Finally, technical influence correlated significantly with performance pressure (r = 0.351, p < 0.001) and sports expectations (r = 0.367, p < 0.001). These findings demonstrate the crucial importance for sports researchers and practitioners to consider differences in perceptions based on sex and role (athlete or parent) when designing and implementing parental educational programs.
Date Palm Waste-Derived Biochar for Improving Hydrological Properties of Sandy Soil Under Saturated and Unsaturated Conditions
Authors: Alghamdi Abdulaziz G. , Alomran Abdulrasoul , Ibrahim Hesham M. , Alkhasha Arafat , Alasmary Zafer
Published: December 2024

Water conservation and effective irrigation management are vital for sustainable agriculture in arid regions. While organic soil amendments have been widely used to enhance water retention in sandy soils, research on the use of date palm waste-derived biochar remains limited. Thus, this study aimed to explore the innovative application of biochar produced from date palm waste, focusing on its effects on the hydrological properties of sandy soil. Biochars of varying particle sizes (0.5, 1, and 2 mm) and pyrolysis temperatures (300 °C, 450 °C, and 600 °C) were produced and their impacts were assessed under both saturated and unsaturated conditions on soil hydrological properties. The biochar was incorporated into soil columns at application rates of 0%, 1%, 3%, and 5% (w/w) within a 10 cm layer on top of 35 cm deep soil columns. The soil columns were placed vertically into water basins for saturation. Evaporation, infiltration, and saturated hydraulic conductivity were measured. The findings revealed that the application of 1%, 3%, and 5% biochar significantly increased soil water retention by 36.80%, 34.18%, and 29.66%, while cumulative evaporation decreased by 7.30%, 2.00%, and 1.35%, respectively, as compared to the control. Water retained at the end of the experiment was increased by 100.63%, 112.29%, and 101.68%, while unsaturated hydraulic conductivity decreased by 21.27%, 26.15%, and 26.17% after amending the soil with 1%, 3%, and 5% biochar, respectively, as compared to the control. The water retention ranged between 30.34 and 42.51%, 22.59 and 43.20%, and 22.48 and 38.81% for biochar produced at 300 °C, 450 °C, and 600 °C, respectively. Water infiltration rate and pore size was decreased with the increased pyrolysis temperature. Overall, the application rates of 3% and 5% with particle sizes of 1 and 0.5 mm and low pyrolysis temperature were most efficient for improving soil properties such as water retention, reducing unsaturated hydraulic conductivity, reducing the rate and volume of infiltration, and enhancing the micro-porosity reduction of sandy soils. In a nutshell, this study highlights the potential of date palm waste-derived biochar as an effective soil amendment, significantly enhancing water retention by up to 112.29% and reducing evaporation. By optimizing irrigation management in sandy soils, these findings contribute to more sustainable agricultural practices.
Wetland Ecosystem Service Preservation? Geochemical Changes in Systems with Mangroves and Shrimp Farms in the Northern Ecuadorean Coast
Authors: Rebolledo Monsalve Eduardo , Verduga Lita , Hurtado Ferreira Felipe
Published: December 2024

Mangrove sediments serve as paleoenvironmental records of organic matter and nutrient accumulation. Ecuador, the world’s largest producer of whiteleg shrimp Penaeus vannamei in brackish waters, lost 43% of its continental mangroves between 1969 and 1999. Currently, more than 70,000 hectares of shrimp farms operate in estuaries with mangroves and within mangrove reserves. Variations in mangrove coverage and the extent of shrimp farms are described for the period 1996–2020 for two mangrove reserves, REMACAM and RVSMERM, with an observed increase in the extent of shrimp farms in both reserves. Four sediment cores obtained from mangrove drains and drains transformed into shrimp farm infrastructures were analyzed to observe the impact of this activity on the cumulative changes in total organic carbon (TOC) content and total nitrogen (TN) content, the fractionation of stable isotopes δ13C and δ15N, as well as the TOC/TN ratio. The mangrove drains in REMACAM had 3.8 times more TOC and 2.3 times more TN than drains transformed into shrimp farms in RVSMERM, with lower TOC/TN ratios. The organic matter in sediments from sectors with a higher proportion of shrimp farms mainly came from particulate organic matter of freshwater and freshwater algae compared to mangrove drains. Increases in TOC and TN content were recorded in all analyzed sectors, which could be attributed to the cumulative impact of population growth, the development of other agricultural crops, and continental deforestation, despite the fact that proportionally, the highest increases occurred in the sector with a greater influence of shrimp farms.
Assessing the Role of Machine Learning in Climate Research Publications
Authors: Niculae Andreea-Mihaela , Oprea Simona-Vasilica , Văduva Alin-Gabriel , Bâra Adela , Andreescu Anca-Ioana
Published: December 2024

Climate change is an aspect in our lives that presents urgent challenges requiring innovative approaches and collaborative efforts across diverse fields. Our research investigates the growth and thematic structure of the intersection between climate change research and machine learning (ML). Employing a mixed-methods approach, we analyzed 7521 open-access publications from the Web of Science Core Collection (2004–2024), leveraging both R and Python for data processing and advanced statistical analysis. The results reveal a striking 37.39% annual growth in publications, indicating the rapidly expanding and increasingly significant role of ML in climate research. This growth is accompanied by increased international collaborations, highlighting a global effort to address this urgent challenge. Our approach integrates bibliometrics, text mining (including word clouds, knowledge graphs with Node2Vec and K-Means, factorial analysis, thematic map, and topic modeling via Latent Dirichlet Allocation (LDA)), and visualization techniques to uncover key trends and themes. Thematic analysis using LDA revealed seven key topic areas, reflecting the multidisciplinary nature of this research field: hydrology, agriculture, biodiversity, forestry, oceanography, forecasts, and models. These findings contribute to an in-depth understanding of this rapidly evolving area and inform future research directions and resource allocation strategies by identifying both established and emerging research themes along with areas requiring further investigation.
Species-Specific Spillover Patterns Detected by Biomass Gradients in Mediterranean Marine Protected Areas
Authors: Bayle-Sempere Just Tomàs , Forcada-Almarcha Aitor , Sánchez-Jerez Pablo , Harmelin-Vivien Mireille L. , Le Diréach Laurence , Charbonnel Eric , García-Charton José Antonio , Ody Denis , Reñones Olga , Valle Carlos , Pérez-Ruzafa Ángel
Published: December 2024

The aim of this paper is to provide evidence of the species-specific export of adult fishes for some species or group of species from six well-enforced Mediterranean marine protected areas (MPAs): Cerbère-Banyuls and Carry-le-Rouet in France and Medes, Cabrera, Tabarca, and Cabo de Palos in Spain. We estimated the distance at which spillover of those individual or groups of species occur by directly assessing the existence of gradients of biomass across the MPA boundaries by means of underwater visual census, asuming that such gradients will be specifics in terms of structure (sharpness, slope, and intercept) for every species and group of species. A significant “reserve effect” was observed for biomass of some of the individual and grouped species in all MPAs. Decreasing gradients of biomass differ among taxons and are not related with the insular nature of the location. Different gradients of biomass resulted from the interaction between species characteristics and some ecological and structural drivers, and we did not find regular patterns for each taxa among MPAs, even though the same species can exhibit different gradient structure and/or spillover distances in the same MPA depending on the orientation. Habitat patch distribution and continuity seems the most important environmental factor explaining the existence and pattern of gradients at species level, interacting with fish home range and fishing pressure close to the limits of the MPAs. Managers should take in account the surrounding distribution of habitats in terms of complexity and quality in order to optimize the spillover capacity of the MPAs.