bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of an Automotive-Relevant Recycling Process for Paper Fiber-Reinforced Polypropylene Composites
Authors: Grubb Cecile A. , Mokhtarnejad Mahshid , Greene John , Misasi John , Keffer David J. , Kardos Marton , Mainka Hendrik , Harper David P.
Published: December 2024

The automotive industry is under growing pressure from regulatory agencies to improve the recyclability of its plastic components. Simultaneously, manufacturers are adopting natural fiber composites in vehicles to reduce their carbon footprint and decrease reliance on petroleum-based materials. This presents a challenge at vehicle end-of-life, however, as natural fiber-reinforced polymers are substantially more difficult to recycle than their unreinforced counterparts. This study investigated the development of a mechanical recycling process for paper fiber-reinforced polypropylene composites, focusing on the impact of injection molding parameters—specifically, injection temperature and rate—on the thermal, mechanical, and water uptake properties of the composites. The results showed that processing temperature had a greater influence on composite performance than injection rate, with some limited interaction effects between the two. Higher processing intensity damaged the paper fibers, increasing the number of nucleation sites and resulting in greater polypropylene crystallinity. These structural changes reduced tensile properties at higher intensities, while flexural properties improved. Objective function analysis was applied to identify optimal processing conditions, balancing these competing trends. Overall, the findings demonstrate that paper fiber-reinforced polypropylene composites can be recycled into automotive-relevant injection molding compounds using conventional plastic manufacturing techniques, though careful tuning of processing parameters is essential to achieve optimal performance.
Evaluating MULTIOBS Chlorophyll-a with Ground-Truth Observations in the Eastern Mediterranean Sea
Authors: Livanou Eleni , Sauzède Raphaëlle , Psarra Stella , Mandalakis Manolis , Dall’Olmo Giorgio , Brewin Robert J. W. , Raitsos Dionysios E.
Published: December 2024

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m−3). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity.
Evaluating the Signal Contribution of the DTU21MSS on Coastal Mean Dynamic Topography and Geostrophic Current Modeling: A Case Study in the African–European Region
Authors: Shi Hongkai , He Xiufeng , Andersen Ole Baltazar
Published: December 2024

With the accumulation of synthetic aperture radar (SAR) altimetry data and advancements in retracking algorithms, the improved along-track spatial resolution and signal-to-noise ratio have significantly enhanced the availability and precision of sea surface height (SSH) measurements, particularly in challenging environments such as coastal areas, ocean currents, and polar regions. These improvements have refined the accuracy and reliability of mean sea surface (MSS) models, which in turn have enhanced the precision of mean dynamic topography (MDT) and geostrophic current models. However, in-depth research is required to quantify the specific contributions of SAR altimetry to these critical regions and their impacts on the MSS, MDT, and geostrophic currents. Given that DTU21MSS (Technical University of Denmark MSS 2021) incorporates a substantial amount of SAR altimetry data, this study utilized independent Sentinel-3A altimetric observations to evaluate the signal improvements of DTU21MSS compared with DTU15MSS, with a focus on its performance in polar, coastal, and current regions. In addition, a least-squares-based approach was employed to assess the impact of the improved MSS model on the deduced MDT and geostrophic current signals. The numerical results revealed that DTU21MSS achieved an accuracy improvement of ~8% within 20 km offshore compared with DTU15MSS. In the polar regions within 100 km offshore, DTU21MSS exhibited a maximum signal enhancement of ~0.1 m, with overall improvements of 10–20%. The DTU21MSS-derived MDT solution demonstrates better consistency with validation data, reducing the standard deviation of misfits from 0.058 m to 0.054 m. Signal enhancements of maximumly 0.1 m were observed in the polar regions and the Mediterranean/Red Sea. Furthermore, improvements in the MSS and its error information could directly enhance the deduced MDT models, highlighting its foundational role in precise oceanographic modeling.
Multi-Source Remote Sensing Images Semantic Segmentation Based on Differential Feature Attention Fusion
Authors: Zhang Di , Yue Peicheng , Yan Yuhang , Niu Qianqian , Zhao Jiaqi , Ma Huifang
Published: December 2024

Multi-source remote sensing image semantic segmentation can provide more detailed feature attribute information, making it an important research field for remote sensing intelligent interpretation. However, due to the complexity of remote sensing scenes and the feature redundancy caused by multi-source fusion, multi-source remote sensing semantic segmentation still faces some challenges. In this paper, we propose a multi-source remote sensing semantic segmentation method based on differential feature attention fusion (DFAFNet) to alleviate the problems of difficult multi-source discriminant feature extraction and the poor quality of decoder feature reconstruction. Specifically, we achieve effective fusion of multi-source remote sensing features through a differential feature fusion module and unsupervised adversarial loss. Additionally, we improve decoded feature reconstruction without introducing additional parameters by employing an attention-guided upsampling strategy. Experimental results show that our method achieved 2.8% and 2.0% mean intersection over union (mIoU) score improvements compared with the competitive baseline algorithm on the available US3D and ISPRS Potsdam datasets, respectively.
Real-Time Orbit Determination of Micro–Nano Satellite Using Robust Adaptive Filtering
Authors: Chen Jing , Jin Xiaojun , Hou Cong , Zhu Likai , Xu Zhaobin , Jin Zhonghe
Published: December 2024

Low-performing GPS receivers, often used in challenging scenarios such as attitude maneuver and attitude rotation, are frequently encountered for micro–nano satellites. To address these challenges, this paper proposes a modified robust adaptive hierarchical filtering algorithm (named IARKF). This algorithm leverages robust adaptive filtering to dynamically adjust the distribution of innovation vectors and employs a fading memory weighted method to estimate measurement noise in real time, thereby enhancing the filter’s adaptability to dynamic environments. A segmented adaptive filtering strategy is introduced, allowing for flexible parameter adjustment in different dynamic scenarios. A micro–nano satellite equipped with a miniaturized dual-frequency GPS receiver is employed to demonstrate precise orbit determination capabilities. On-orbit GPS data from the satellite, collected in two specific scenarios—slow rotation and Earth-pointing stabilization—are analyzed to evaluate the proposed algorithm’s ability to cope with weak GPS signals and satellite attitude instability as well as to assess the achievable orbit determination accuracy. The results show that, compared to traditional Extended Kalman Filters (EKF) and other improved filtering algorithms, the IARKF performs better in reducing post-fit residuals and improving orbit prediction accuracy, demonstrating its superior robustness. The three-axes orbit determination internal consistency precision can reach the millimeter level. This work explores a feasible approach for achieving high-performance orbit determination in micro–nano satellites.
Designing a Multivariate Belt Conveyor Idler Stall Detection and Identification System with Scalability Analysis
Authors: Shin Kyeong Su , Nam Younho , Suh Young-Joo
Published: December 2024

Belt conveyor idlers are freely rotating idlers supporting the belt of a conveyor, and can induce severe frictional damage to the belt as they fail. Therefore, fast and accurate detection of idler faults is crucial for the effective maintenance of belt conveyor systems. In this article, we implement and evaluate the performance of an idler stall detection system based on a multivariate deep learning model using accelerometers and microphone sensor data. Emphasis is place on the scalability of the system, as large belt conveyor installations can span multiple kilometers, potentially requiring hundreds or even thousands of sensor units to monitor. The accuracy of the proposed system are analyzed and reported, along with its network bandwidth and energy requirements. The results suggest that while implementing accurate large-scale idler stall detection is feasible, careful consideration must be paid to observing the available network bandwidth and energy budget in order to avoid prolonged downtimes.
NAILS: Normalized Artificial Intelligence Labeling Sensor for Self-Care Health
Authors: Tenze Livio , Canessa Enrique
Published: December 2024

Visual examination of nails can reflect human health status. Diseases such as nutritive imbalances and skin diseases can be identified by looking at the colors around the plate part of the nails. We present the AI-based NAILS method to detect fingernails through segmentation and labeling. The NAILS leverages a pre-trained Convolutional Neural Network model to segment and label fingernail regions from fingernail images, normalizing RGB values to monitor tiny color changes via a GUI and the use of an HD webcam in real time. The use of normalized RGB values combined with AI-based segmentation for real-time health monitoring is novel and innovative. The NAILS algorithm could be used to self-extract and archive primary signs of diseases in humans, especially in rural areas or when other testing may be not available.
Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
Authors: Perez-Portero Adrian , Querol Jorge , Camps Adriano
Published: December 2024

Interferometric radiometers operating at L-band, such as ESA’s SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors’ fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FPGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS.
Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring
Authors: Jenkins Marcus , Franklin Kirsty A. , Nicoll Malcolm A. C. , Cole Nik C. , Ruhomaun Kevin , Tatayah Vikash , Mackiewicz Michal
Published: December 2024

Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data. In this paper, we show that the performance of an object detector in a single frame of a time-lapse sequence can be improved by including spatio-temporal features from the prior frames. We propose a method that leverages temporal information by integrating two additional spatial feature channels which capture stationary and non-stationary elements of the scene and consequently improve scene understanding and reduce the number of stationary false positives. The proposed technique achieves a significant improvement of 24% in mean average precision (mAP@0.05:0.95) over the baseline (temporal feature-free, single frame) object detector on a large dataset of breeding tropical seabirds. We envisage our method will be widely applicable to other wildlife monitoring applications that use time-lapse imaging.
A Noise Reduction Method for Signal Reconstruction and Error Compensation of a Maglev Gyroscope Under Persistent External Interference
Authors: Liu Di , Shi Zhen , Yang Ziyi , Zou Chenxi
Published: December 2024

To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal. Furthermore, an empirical mode decomposition algorithm was employed to extract the trend term of the reconstructed signal, which was then used to compensate for the errors in the reconstructed signal, achieving significant noise reduction. On-site experiments were conducted on the high-precision GNSS baseline of the Xianyang Yuan Tunnel in the second phase of the project to divert water from the Han River to the Wei River, where this method was applied to process and analyze multiple sets of rotor signals. The experimental results show that this method effectively suppresses continuous external environmental interference, reducing the average standard deviation of the compensated signals by 46.10% and the average measurement error of the north azimuth by 45.63%. Its noise reduction performance surpasses that of the other four algorithms.
Understanding the Interaction of Röntgen Radiation Employed in Computed Tomography/Cone Beam Computed Tomography Investigations of the Oral Cavity by Means of Surface-Enhanced Raman Spectroscopy Analysis of Saliva
Authors: Borșa Rareș-Mario , Toma Valentin , Nășcuțiu Melania-Teodora , Onaciu Anca , Colceriu-Șimon Ioana-Maria , Băciuț Grigore , Bran Simion , Dinu Cristian-Mihail , Onișor Florin , Armencea Gabriel , Culic Carina , Hedeșiu Mihaela-Carmen , Știufiuc Rareș-Ionuț , Băciuț Mihaela-Felicia
Published: December 2024

The use of Raman spectroscopy, particularly surface-enhanced Raman spectroscopy (SERS), offers a powerful tool for analyzing biochemical changes in biofluids. This study aims to assess the modifications occurring in saliva collected from patients before and after exposure to cone beam computed tomography (CBCT) and computed tomography (CT) imaging. SERS analysis revealed significantly amplified spectra in post-imaging samples compared to pre-imaging samples, with pronounced intensification of thiocyanate and opiorphin bands, which, together with proteins, dominated the spectra. The changes were more pronounced in the case of CT as compared to CBCT, probably due to the use of a high radiation dose in the case of the first-mentioned technique. These findings underscore the impact of CBCT and CT on salivary composition, highlighting the relevance of SERS as a sensitive method for detecting subtle molecular changes in biofluids post-radiation exposure. This study’s results emphasize the importance of monitoring biochemical markers in patients undergoing diagnostic imaging to better understand the systemic effects of ionizing radiation.
Doubly Structured Data Synthesis for Time-Series Energy-Use Data
Authors: Kim Jiwoo , Lee Changhoon , Jeon Jehoon , Choi Jungwoong , Kim Joseph H. T.
Published: December 2024

As the demand for efficient energy management increases, the need for extensive, high-quality energy data becomes critical. However, privacy concerns and insufficient data volume pose significant challenges. To address these issues, data synthesis techniques are employed to augment and replace real data. This paper introduces Doubly Structured Data Synthesis (DS2), a novel method to tackle privacy concerns in time-series energy-use data. DS2 synthesizes rate changes to maintain longitudinal information and uses calibration techniques to preserve the cross-sectional mean structure at each time point. Numerical analyses reveal that DS2 surpasses existing methods, such as Conditional Tabular GAN (CTGAN) and Transformer-based Time-Series Generative Adversarial Network (TTS-GAN), in capturing both time-series and cross-sectional characteristics. We evaluated our proposed method using metrics for data similarity, utility, and privacy. The results indicate that DS2 effectively retains the underlying characteristics of real datasets while ensuring adequate privacy protection. DS2 is a valuable tool for sharing and utilizing energy data, significantly enhancing energy demand prediction and management.
ElasticPay: Instant Peer-to-Peer Offline Extended Digital Payment System
Authors: Reddy Annapureddy Venkata Sai Kumar , Banda Gourinath
Published: December 2024

The widespread reliance on paper-based currency poses significant drawbacks, such as counterfeiting, lack of transparency, and environmental impacts. While Central Bank Digital Currencies (CBDCs) address many of these issues, their dependence on continuous internet connectivity limits their usability in scenarios with poor or no network access. To overcome such limitations, this paper introduces ElasticPay, a novel Peer-to-Peer (P2P) Offline Digital Payment System that leverages advanced hardware security measures realised through Trusted Platform Modules (TPMs), Trusted Execution Environments (TEEs), and Secure Elements (SEs). ElasticPay ensures transaction privacy, unforgeability, and immediate settlement while preventing double spending. Our approach integrates robust recovery mechanisms and provides a scalable solution for diverse environments. Extensive experimentation validates the system’s reliability and practicality, highlighting its potential to advance secure and inclusive CBDC ecosystems. We demonstrate the proposed solution implementation on the iPhone mobilephone because it has an inbuilt Secure Enclave, which is an integrated implementation of the necessary TPM, TEE, and SE functionalities.
Development of a Grape Cut Point Detection System Using Multi-Cameras for a Grape-Harvesting Robot
Authors: Yang Liangliang , Noguchi Tomoki , Hoshino Yohei
Published: December 2024

Harvesting grapes requires a large amount of manual labor. To reduce the labor force for the harvesting job, in this study, we developed a robot harvester for the vine grapes. In this paper, we proposed an algorithm that using multi-cameras, as well as artificial intelligence (AI) object detection methods, to detect the thin stem and decide the cut point. The camera system was constructed by two cameras that include multi-lenses. One camera is mounted at the base of the robot and named the “base camera”; the other camera is mounted at the robot hand and named the “hand camera” to recognize grapes and estimate the stem position. At the first step, the grapes are detected by using a You Only Look Once (YOLO) method, while the stems of the grapes are detected at the second step using a pixel-level semantic segmentation method. Field experiments were conducted at an outdoor grapes field. The experiment results show that the proposed algorithm and the camera system can successfully detect out the cut point, and the correct detection rate is around 98% and 93% in the indoor and outdoor conditions, respectively. The detection system was integrated to a grape-harvesting robot in the experiment, and the experiment results show the system can successfully harvest the grapes in the outdoor conditions.
Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare
Authors: Michielon Andrea , Litta Paolo , Bonelli Francesca , Don Gregorio , Farisè Stefano , Giannuzzi Diana , Milanesi Marco , Pietrucci Daniele , Vezzoli Angelica , Cecchinato Alessio , Chillemi Giovanni , Gallo Luigi , Mele Marcello , Furlanello Cesare
Published: December 2024

We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based vision adapted from industrial applications and human behavioral analysis, the framework includes modules for markerless animal identification and health status assessment (e.g., locomotion score and body condition score). Methods for behavioral analysis are also included to evaluate how nutritional and rearing conditions impact behaviors. These models are initially trained on public datasets and then fine-tuned on original data. We demonstrate the approach through two use cases: a health monitoring system for dairy cattle and a piglet behavior analysis system. The results indicate that scalable deep learning and edge computing solutions can support precision livestock farming by automating welfare assessments and enabling timely, data-driven interventions.
In Medio Stat Virtus: Moderate Cognitive Flexibility as a Key to Affective Flexibility Responses in Long-Term HRV
Authors: Borghesi Francesca , Simoncini Gloria , Chirico Alice , Cipresso Pietro
Published: December 2024

This study examines the relationship between cognitive and affective flexibility, two critical aspects of adaptability. Cognitive flexibility involves switching between activities as rules change, assessed through task-switching or neuropsychological tests and questionnaires. Affective flexibility, meanwhile, refers to shifting between emotional and non-emotional tasks or states. Although similar conceptually, prior research shows inconsistent findings on their link, especially regarding physiological and self-reported measures. Affective flexibility was operationalized as changes in heart rate variability (HRV) in response to transitioning affective stimuli, while cognitive flexibility was assessed using self-report questionnaires that captured individuals’ perceived adaptability. The findings revealed that individuals with extremely high or low cognitive flexibility displayed similar HRV patterns in response to emotional stimuli, while those with medium levels of cognitive flexibility showed distinct responses. The Extreme group exhibited higher baseline autonomic activation that decreased after exposure, whereas the Average group’s moderate baseline activation increased post-stimuli. This interaction was mainly seen in long-term HRV indexes, while short-term indexes showed a uniform response across groups, suggesting that differences in flexibility were probably not discernible via state features but rather as traits and long-term attributes. The findings suggest that cognitive and affective flexibility exist on a continuum; extremely high and low cognitive flexibility is linked to intense affective responses, while moderate cognitive flexibility corresponds to balanced physiological regulation, supporting the notion that “in medio stat virtus” (virtue lies in moderation).
Geohazard Identification in Underground Mines: A Mobile App
Authors: Lopez Pedro , Risso Nathalie , Anani Angelina , Momayez Moe
Published: December 2024

Mining is a critical industry that provides essential minerals and resources for modern society. Despite its benefits, the industry is also recognized as one of the most dangerous occupations, with geotechnical hazards being a primary concern. This study introduces the hazard recognition in underground mines application (HUMApp), a mobile application developed to enhance safety within underground mines by efficiently identifying geotechnical hazards, specifically focusing on roof falls. Employing a convolutional neural network (CNN), HUMApp achieved an accuracy of 90%, with recall and F1-score also reaching 90%, reflecting its high reliability in hazard detection. The application’s effectiveness was validated against expert assessments, showing significant agreement in identifying critical hazards. This validation highlights HUMApp’s potential to enhance proactive risk management in underground mining. This paper details each step of HUMApp’s development, from dataset preparation and model training to performance evaluation and application design, showcasing a scalable solution adaptable to various mining environments.
Volatile Organic Compounds in Honey: Tandem Mass Spectrometry as Tool to Quantitate Priority VOCs
Authors: Tzanetou Evangelia N. , Vousaxaki Efstathia , Machera Kyriaki , van der Steen Jozef , Kasiotis Konstantinos M.
Published: December 2024

A headspace gas chromatographic tandem mass spectrometric (HS-GC-MS/MS) method was developed and fully validated, aiming for the simultaneous determination of 25 volatile organic compounds (VOCs, some of them previously unreported in honey bee studies) in 52 selected honey samples from Greece. The HS conditions were optimized, and method validation criteria were extensively investigated. The existence impact of the matrix effect was assessed, and matrix-matched calibration curves were developed for quantification purposes. The limits of quantification of the 25 analytes ranged from 0.2 ng g−1 to 0.6 ng g−1. Isoprene was the most commonly detected VOC, followed by octane and styrene. Other detected VOCs include benzene, n-hexane, trimethylbenzenes, xylenes, toluene, and p-dichlorobenzene. Concentrations fluctuated from 0.5 ng g−1 for isoprene and toluene, as well as 1,2,4-trimethylbenzene, to 22.6 ng g−1 for isoprene. Despite VOCs not being at the forefront of honey’s potential contamination, their prevalence in honey can provide significant data for human health risk assessment, considering their undisputable widespread consumption and the documented potential toxicity of VOCs in humans. In this sense, risk assessment for adults and children, as depicted in the hazard quotient and index and carcinogenic risk determination, did not disclose any potential threat after consumption of the investigated honey samples.
Current Feasibility of Urologic Telesurgery in Low/Middle Income Countries
Authors: Bart Alex S. , Albala Jack F. , Albala David M.
Published: December 2024

It is estimated that nearly five billion people do not have access to surgical care. Approximately 94% of individuals in low- and middle-income countries (LMICs) lack access to surgery in comparison to 14.9% in high-income countries (HICs). There are several urologic conditions requiring surgical intervention that are not treated because of the limited number of expert urologists in LMICs. Telesurgery is a concept that connects patients and surgeons in different locations through the use of a robotic surgery system. In this review, we explain the origins of telesurgery as well as the benefits and obstacles to its global implementation. Telesurgery can reduce travel times and the dangers associated with traveling for surgical care in LMICs. Additionally, telesurgery allows patients in LMICs to gain access to expert urologists while also providing effective training to upcoming surgeons. However, LMICs require substantial investment to improve digital infrastructure that will support urologic telesurgery. There will also be ethical, legal, and policy considerations that will need to be resolved for safe and equitable urologic telesurgery to occur. There have been multiple successful applications of urologic telesurgery, suggesting that the technology for this to become routine is already available. The time for international collaboration must begin now to reduce global disparities in access to urologic surgery.
Leveraging AI and Data Visualization for Enhanced Policy-Making: Aligning Research Initiatives with Sustainable Development Goals
Authors: Lino Ferreira da Silva Barros Maicon Herverton , Medeiros Neto Leonides , Santos Guto Leoni , Leal Roberto Cesar da Silva , Leal da Silva Raysa Carla , Lynn Theo , Dourado Raphael Augusto , Endo Patricia Takako
Published: December 2024

Scientists, research institutions, funding agencies, and policy-makers have all emphasized the need to monitor and prioritize research investments and outputs to support the achievement of the United Nations Sustainable Development Goals (SDGs). Unfortunately, many current and historic research publications, proposals, and grants were not categorized against the SDGs at the time of submission. Manual post hoc classification is time-consuming and prone to human biases. Even when classified, few tools are available to decision makers for supporting resource allocation. This paper aims to develop a deep learning classifier for categorizing research abstracts by the SDGs and a decision support system for research funding policy-makers. First, we fine-tune a Bidirectional Encoder Representations from Transformers (BERT) model using a dataset of 15,488 research abstracts from authors at leading Brazilian universities, which were preprocessed and balanced for training and testing. Second, we present a PowerBI dashboard that visualizes classifications for supporting informed resource allocation for sustainability-focused research. The model achieved an F1-score, precision, and recall exceeding 70% for certain classes and successfully classified existing projects, thereby enabling better tracking of Agenda 2030 progress. Although the model is capable of classifying any text, it is specifically optimized for Brazilian research due to the nature of its fine-tuning data.
Leadership Behaviors and Leader Effectiveness: The Mediating Role of Cultural Intelligence
Authors: Yüksel Sakınç Ayça , Ergün Ercan
Published: December 2024

The aim of this study is to examine the mediating role of cultural intelligence in the relationship between leadership behaviors and leadership effectiveness. A quantitative approach was adopted to test the hypotheses, and data were collected through surveys from 410 employees working in four- and five-star hotels in Istanbul. The analysis of the obtained data was conducted using Structural Equation Modeling (SEM), regression analysis, and the PROCESS macro. The results of the analysis revealed that cultural intelligence has a mediating effect on the relationships between task-oriented leadership behaviors and leadership effectiveness, relationship-oriented leadership behaviors and leadership effectiveness, and change-oriented leadership behaviors and leadership effectiveness. In the globalized world, cultural diversity in businesses is increasing, which is especially pronounced in the hospitality industry. The fact that guests come from different cultural backgrounds further highlights the importance of leaders’ cultural awareness and cultural intelligence. As a result, both academics and managers attach great importance to this issue. However, there are still significant gaps in understanding the relationship between cultural intelligence and leadership. In this context, the study aims to contribute to the literature on leadership and cultural intelligence.
The Impact of the Production and Consumption of Renewable Energy on Economic Growth—The Case of Poland
Authors: Bąk Iwona , Barej-Kaczmarek Emilia , Oesterreich Maciej , Szczecińska Beata , Wawrzyniak Katarzyna , Sulikowski Piotr
Published: December 2024

In the last decade, rapid technological development and a simultaneous increase in social awareness related to environmental protection have been determinants of the research and development of new techniques for generating energy from renewable sources. The international situation after 24 February 2022 in turn caused an increase in demand for energy that can be generated locally. This trend has not bypassed Poland either, where dynamic development of the market related to generating energy from renewable sources has been observed for many years. The aim of this article is to identify regularities in the degree of impact of the production and consumption of renewable energy on economic growth in Poland in 2005–2022. In this study, we used power econometric models. The results obtained by the authors show that energy consumption including renewable energy affects economic growth. In Poland, in the period under review, the increase in the consumption and production of energy from renewable sources, both on a national scale and on the scale of separate regions (city agglomerations, towns and suburbs, rural areas), contributed to the increase in measures characterizing economic development.
How Can Digital–Real Economy Integration Affect Urban Green Innovation? Evidence from China
Authors: Huang Tao , Xia Haili
Published: December 2024

As the integration of the digital economy into the real economy accelerates and the goals of green and low-carbon development become increasingly clear, promoting green innovation (GI) through digital–real economy integration (DRI) is of great practical significance for achieving green, high-quality urban development. This study, based on a sample of 284 prefecture-level cities and above in China, used the entropy method to measure the development levels of the digital economy and real economy in each city and employed a coupling coordination model to calculate the degree of DRI. By constructing a bidirectional fixed effects model, a mediation effect model, a threshold regression model, and a spatial Durbin model, this study explored the impact of DRI on GI in cities. The research found that DRI has a non-linear promoting effect on GI. When a city’s DRI level surpasses the second threshold, each 1% increase in DRI leads to a 1.439% rise in GI. This effect also shows heterogeneity based on city location and resource endowment. In this process, transaction costs and the upgrading of the industry structure serve as mediating factors, with each 1% increase in DRI reducing transaction costs by 0.163% and enhancing industrial upgrading with a coefficient of 0.176. Additionally, DRI in one city can significantly enhance the level of GI in neighboring cities through spatial spillover effects. For instance, under the geographic distance weighting matrix, the indirect effect of DRI reaches 4.693, and similar significant spillover effects are observed under the economic distance and economic geography weighting matrices.
Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset
Authors: Aman Nakib , Islam Md. Rabiul , Ahamed Md. Faysal , Ahsan Mominul
Published: December 2024

Human gait recognition (HGR) has been employed as a biometric technique for security purposes over the last decade. Various factors, including clothing, carrying items, and walking surfaces, can influence the performance of gait recognition. Additionally, identifying individuals from different viewpoints presents a significant challenge in HGR. Numerous conventional and deep learning techniques have been introduced in the literature for HGR, but traditional methods are not well suited to handling large datasets. This research explores the effectiveness of four deep learning models for gait identification in the CASIA B dataset: the convolutional neural network (CNN), multi-layer perceptron (MLP), self-organizing map (SOMs), and transfer learning with EfficientNet. The selected deep learning techniques offer robust feature extraction and the efficient handling of large datasets, making them ideal in enhancing the accuracy of gait recognition. The collection includes gait sequences from 10 individuals, with a total of 92,596 images that have been reduced to 64 × 64 pixels for uniformity. A modified model was developed by integrating sequential convolutional layers for detailed spatial feature extraction, followed by dense layers for classification, optimized through rigorous hyperparameter tuning and regularization techniques, resulting in an accuracy of 97.12% for the test set. This work enhances our understanding of deep learning methods in gait analysis, offering significant insights for choosing optimal models in security and surveillance applications.
A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
Authors: Koubàa Mohamed , Regaieg Rym , Karar Abdullah S. , Nadeem Muhammad , Bahloul Faouzi
Published: December 2024

Efficient Virtual Machine (VM) placement is a critical challenge in optimizing resource utilization in cloud data centers. This paper explores both exact and approximate methods to address this problem. We begin by presenting an exact solution based on a Multi-Objective Integer Linear Programming (MOILP) model, which provides an optimal VM Placement (VMP) strategy. Given the NP-completeness of the MOILP model when handling large-scale problems, we then propose an approximate solution using a Tabu Search (TS) algorithm. The TS algorithm is designed as a practical alternative for addressing these complex scenarios. A key innovation of our approach is the simultaneous optimization of three performance metrics: the number of accepted VMs, resource wastage, and power consumption. To the best of our knowledge, this is the first application of a TS algorithm in the context of VMP. Furthermore, these three performance metrics are jointly optimized to ensure operational efficiency (OPEF) and minimal operational expenditure (OPEX). We rigorously evaluate the performance of the TS algorithm through extensive simulation scenarios and compare its results with those of the MOILP model, enabling us to assess the quality of the approximate solution relative to the optimal one. Additionally, we benchmark our approach against existing methods in the literature to emphasize its advantages. Our findings demonstrate that the TS algorithm strikes an effective balance between efficiency and practicality, making it a robust solution for VMP in cloud environments. The TS algorithm outperforms the other algorithms considered in the simulations, achieving a gain of 2% to 32% in OPEF, with a worst-case increase of up to 6% in OPEX.
Systematic Literature Review of Epaxial Paraspinal Schwannomas: Differential Diagnosis and Treatment Approaches
Authors: Khalil Wassim , Khalil Roula , Meynard Alexandre , Perani Alexandre , Chaudruc Elodie , Duchesne Mathilde , Durand Karine , Caire François , Salle Henri
Published: December 2024

Background: Schwannomas, predominantly benign nerve sheath tumors, are typically found within the intradural extramedullary space of the spinal cord with potential extradural expansion. Other typical localizations are the upper limbs and neck area. Pure epaxial paraspinal schwannomas are very rare, often asymptomatic, and predominantly occur in the thoracic region, with only a handful of cases reported globally. The range of differential diagnoses for paraspinal lesions is extensive, emphasizing the importance of accurate diagnosis to ensure optimal therapy and avoid unnecessary treatments. Method: We conducted a systematic literature review searching for published recommendations for paraspinal lesion management in addition to examining the case of a 49-year-old male patient who presented with a history of persistent back pain. A thorough medical history and physical examination were followed by ultrasound and MRI, revealing a well-defined paravertebral mass spanning from T7 to T9. A secure ultrasound-guided biopsy was performed, leading to a preliminary diagnosis of paraspinal schwannoma. Subsequently, complete surgical resection was performed. Results: pathological reports confirmed the initial diagnosis of paraspinal schwannoma. Further investigation using FMI and RNA sequencing did not detect any specific genetic anomalies aside from an NF2 gene mutation. A follow-up MRI conducted six months later showed no signs of recurrence. Conclusions: The broad spectrum of differential diagnoses for paraspinal lesions necessitates a multidisciplinary approach to ensure accurate diagnosis and tailored treatment. This approach involves meticulous imaging interpretation followed by a secure biopsy procedure to obtain preliminary pathology results, ultimately leading to the implementation of the most suitable surgical treatment.
Exploring Novel Fungal–Bacterial Consortia for Enhanced Petroleum Hydrocarbon Degradation
Authors: Silva Monteiro João Paulo , da Silva André Felipe , Delgado Duarte Rubens Tadeu , José Giachini Admir
Published: December 2024

Bioremediation, involving the strategic use of microorganisms, has proven to be a cost-effective alternative for restoring areas impacted by persistent contaminants such as polycyclic aromatic hydrocarbons (PAHs). In this context, the aim of this study was to explore hydrocarbon-degrading microbial consortia by prospecting native species from soils contaminated with blends of diesel and biodiesel (20% biodiesel/80% diesel). After enrichment in a minimal medium containing diesel oil as the sole carbon source and based on 16S rRNA, Calmodulin and β-tubulin gene sequencing, seven fungi and 12 bacteria were identified. The drop collapse test indicated that all fungal and four bacterial strains were capable of producing biosurfactants with a surface tension reduction of ≥20%. Quantitative analysis of extracellular laccase production revealed superior enzyme activity among the bacterial strains, particularly for Stenotrophomonas maltophilia P05R11. Following antagonistic testing, four compatible consortia were formulated. The degradation analysis of PAHs and TPH (C5–C40) present in diesel oil revealed a significantly higher degradation capacity for the consortia compared to isolated strains. The best results were observed for a mixed bacterial-fungal consortium, composed of Trichoderma koningiopsis P05R2, Serratia marcescens P10R19 and Burkholderia cepacia P05R9, with a degradation spectrum of ≥91% for all eleven PAHs analyzed, removing 93.61% of total PAHs, and 93.52% of TPH (C5–C40). Furthermore, this study presents the first report of T. koningiopsis as a candidate for bioremediation of petroleum hydrocarbons.
The ‘Oma’s of the Gammas—Cancerogenesis by γ-Herpesviruses
Authors: Banerjee Anwesha , Dass Debashree , Mukherjee Soumik , Kaul Mollina , Harshithkumar R. , Bagchi Parikshit , Mukherjee Anupam
Published: December 2024

Epstein–Barr virus (EBV) and Kaposi’s sarcoma-associated herpesvirus (KSHV), which are the only members of the gamma(γ) herpesviruses, are oncogenic viruses that significantly contribute to the development of various human cancers, such as Burkitt’s lymphoma, nasopharyngeal carcinoma, Hodgkin’s lymphoma, Kaposi’s sarcoma, and primary effusion lymphoma. Oncogenesis triggered by γ-herpesviruses involves complex interactions between viral genetics, host cellular mechanisms, and immune evasion strategies. At the genetic level, crucial viral oncogenes participate in the disruption of cell signaling, leading to uncontrolled proliferation and inhibition of apoptosis. These viral proteins can modulate several cellular pathways, including the NF-κB and JAK/STAT pathways, which play essential roles in cell survival and inflammation. Epigenetic modifications further contribute to EBV- and KSHV-mediated cancerogenesis. Both EBV and KSHV manipulate host cell DNA methylation, histone modification, and chromatin remodeling, the interplay of which contribute to the elevation of oncogene expression and the silencing of the tumor suppressor genes. Immune factors also play a pivotal role in the development of cancer. The γ-herpesviruses have evolved intricate immune evasion strategies, including the manipulation of the major histocompatibility complex (MHC) and the release of cytokines, allowing infected cells to evade immune detection and destruction. In addition, a compromised immune system, such as in HIV/AIDS patients, significantly increases the risk of cancers associated with EBV and KSHV. This review aims to provide a comprehensive overview of the genetic, epigenetic, and immune mechanisms by which γ-herpesviruses drive cancerogenesis, highlighting key molecular pathways and potential therapeutic targets.