bluuebunny/crossref_metadata_2025 huggingface dataset:
Photocatalytic Composites Based on Biochar for Antibiotic and Dye Removal in Water Treatment
Authors: Bratovčić Amra , Tomašić Vesna
Published: December 2024

Many semiconductor photocatalysts are characterized by high photostability and non-toxicity but suffer from the limited excitation in the UV part of the spectrum and the fast recombination of the photogenerated electron–hole pairs. To improve the above properties, biochar-supported composite photocatalysts have recently attracted much attention. Compared with the pure photocatalyst, the biochar-enriched catalyst has superior specific surface area and high porosity, catalytic efficiency, stability, and recoverability. Biochar can be obtained from various carbon-rich plant or animal wastes by different thermochemical processes such as pyrolysis, hydrothermal carbonization, torrefaction, and gasification. The main features of biochar are its low price, non-toxicity, and the large number of surface functional groups. This paper systematically presents the latest research results on the method of preparation of various composites in terms of the choice of photoactive species and the source of biomass, their physico-chemical properties, the mechanism of the photocatalytic activity, and degradation efficiency in the treatment of organic contaminants (dyes and antibiotics) in an aquatic environment. Particular emphasis is placed on understanding the role of biochar in improving the photocatalytic activity of photoactive species.
Conical Annular Nozzle Pressure Prediction and Applications to 3D Food-Printing for Dysphagia Diets
Authors: Wang Yibo , Yan Ming , Yang Kun , Wang Shourui , Ao Chenyang , Su Xin , Ren Changzai
Published: December 2024

In order to solve the dietary problems of patients with dysphagia, a mathematical model for predicting extrusion pressure is established. The predictive model parameters are determined with the aid of the finite element method, and a 3D printing nozzle capable of printing nutrient-rich sandwich food is designed according to the predictive model. Pumpkin puree and minced pork are verified according to IDDSI standards. Finally, the accuracy of the predictive model and the printing effect of the design nozzle are verified by extrusion and printing experiments, respectively. The results show that four groups of simulation experiments reveal that the extrusion pressure increases by 15.6%, 13.5%, 12.7% and 12.4%, respectively, with a 1 cm increase in nozzle length. When the nozzle length is in the range of 1–5 cm, the extrusion pressure increases with the increase of the volume flow rate in the extrusion cylinder. The extrusion speed has little correlation with the length of the nozzle outlet, but for every 1 cm3/s increase in the inlet volume flow rate, the extrusion speed increases by about 1.5%. The finite element simulation experiment determines that the parameters of the prediction model are σ0 = 0.6, α = 1.1, m = 0.21, τ0 = 0, β = 0.52 and n = 0.2; the error between the predictive value and the experimental value is 15%, and the printed sandwich food has smooth lines, good molding and complies with IDDSI standards.
The Race Against Time for the Enhancement of African National Strategic Plans in the Neuroblastoma Research Heterogeneity
Authors: Motshudi Mmei Cheryl , Naidoo Clarissa Marcelle , Mkolo Nqobile Monate
Published: December 2024

The valuation of neuroblastoma research heterogeneity at African country level is unspecified. Therefore, the study assesses the heterogeneity of neuroblastoma research in 54 African countries and develops recommendations for national cancer-control plans. Metadata of peer-reviewed scientific publications allied to African neuroblastoma research were retrieved from the Web of Science™ Core Collection Database for bibliometric analysis. Comprehensive science mapping analysis and statistical analyses were performed with bibliometric online platform2 and GraphPad Prism v. 10.2.3. This study revealed that African countries focused the neuroblastoma research publications mainly on the sustainable development goal of good health and well-being. The dominating research area in Africa is oncology followed by pharmacology. Only 26 of 54 African countries were accountable for total neuroblastoma research in Africa, with South Africa and Egypt contributing 61% of the whole continent’s neuroblastoma research. Egypt, South Africa, Tunisia, Morocco, and Nigeria are the five most active African countries, and they are funded by different funding agencies internationally and domestically. The collected analysed data of this study draws special attention to heterogeneity and enduring upward correlating trajectory of Africa’s neuroblastoma publication numbers, their citations, acquired funds, and countries’ cooperation. Furthermore, this heterogeneity finding flags the necessity of developing a comprehensive strategic plan and implementation to cultivate neuroblastoma research as a fundamental part of each African country’s national cancer control plans.
Robust Nonlinear Model Predictive Control for the Trajectory Tracking of Skid-Steer Mobile Manipulators with Wheel–Ground Interactions
Authors: Aro Katherine , Guevara Leonardo , Torres-Torriti Miguel , Torres Felipe , Prado Alvaro
Published: December 2024

This paper presents a robust control strategy for trajectory-tracking control of Skid-Steer Mobile Manipulators (SSMMs) using a Robust Nonlinear Model Predictive Control (R-NMPC) approach that minimises trajectory-tracking errors while overcoming model uncertainties and terra-mechanical disturbances. The proposed strategy is aimed at counteracting the effects of disturbances caused by the slip phenomena through the wheel–terrain contact and bidirectional interactions propagated by mechanical coupling between the SSMM base and arm. These interactions are modelled using a coupled nonlinear dynamic framework that integrates bounded uncertainties for the mobile base and arm joints. The model is developed based on principles of full-body energy balance and link torques. Then, a centralized control architecture integrates a nominal NMPC (disturbance-free) and ancillary controller based on Active Disturbance-Rejection Control (ADRC) to strengthen control robustness, operating the full system dynamics as a single robotic body. While the NMPC strategy is responsible for the trajectory-tracking control task, the ADRC leverages an Extended State Observer (ESO) to quantify the impact of external disturbances. Then, the ADRC is devoted to compensating for external disturbances and uncertainties stemming from the model mismatch between the nominal representation and the actual system response. Simulation and field experiments conducted on an assembled Pioneer 3P-AT base and Katana 6M180 robotic arm under terrain constraints demonstrate the effectiveness of the proposed method. Compared to non-robust controllers, the R-NMPC approach significantly reduced trajectory-tracking errors by 79.5% for mobile bases and 42.3% for robot arms. These results highlight the potential to enhance robust performance and resource efficiency in complex navigation conditions.
Urban Morphology and Surface Urban Heat Island Relationship During Heat Waves: A Study of Milan and Lecce (Italy)
Authors: Esposito Antonio , Pappaccogli Gianluca , Donateo Antonio , Salizzoni Pietro , Maffeis Giuseppe , Semeraro Teodoro , Santiago Jose Luis , Buccolieri Riccardo
Published: December 2024

The urban heat island (UHI) effect, marked by higher temperatures in urban areas compared to rural ones, is a key indicator of human-driven environmental changes. This study aims to identify the key morphological parameters that primarily contribute to the development of surface urban heat island intensity (SUHII) and investigates the relationship between SUHII and urban morphology using land surface temperature (LST) data from the Sentinel-3 satellite. The research focuses on Milan and Lecce, analyzing how urban geometry affects SUHII. Factors such as building height, aspect ratio, sky visibility, and surface cover are examined using approximately 1000 satellite images from 2022 and 2023. The study highlights seasonal and diurnal variations in SUHII, with particular emphasis on HW periods. Through multicollinearity and multiple regression analyses, the study identifies the main morphological drivers influencing SUHII in the two cities, specifically the Impervious Surface Fraction (ISF) and Mean Building Height (HM). Milan consistently exhibits higher SUHII, particularly during HWs, while Lecce experiences a negative SUHII, especially during the summer, due to lower urban density, more vegetation, and the low soil moisture around the urban area. Both cities show positive SUHII values at night, which are slightly elevated during HWs. The heat wave analysis reveals the areas most susceptible to overheating, typically characterized by high urban density, with ISF and HM values in some cases above the 90th percentile (0.8 and 13.0 m, respectively) compared to the overall distribution, particularly for Milan. The research emphasizes the importance of urban morphology in influencing SUHII, suggesting that detailed morphological analysis is crucial for developing climate adaptation and urban planning strategies to reduce urban overheating and improve urban resilience to climate change.
Assessing the Consistency of Five Remote Sensing-Based Land Cover Products for Monitoring Cropland Changes in China
Authors: Deng Fuliang , Peng Xinqin , Cai Jiale , Li Lanhui , Li Fangzhou , Liang Chen , Liu Wei , Yuan Ying , Sun Mei
Published: December 2024

The accuracy assessment of cropland products is a critical prerequisite for agricultural planning and food security evaluations. Current accuracy assessments of remote sensing-based cropland products focused on the consistency of spatial patterns for specific years, yet the reliability of these cropland products in time-series analysis remains unclear. Using cropland area data from the second and third national land surveys of China (referred to as NLSCD) as a benchmark, we evaluate the area-based and spatial-based consistency of cropland changes in five 30 m time-series land cover products covering 2010 and 2020, including the annual cropland dataset of China (CACD), the annual China Land Cover Dataset (CLCD), China’s Land-use/cover dataset (CLUD), the Global Land-Cover product with Fine Classification System (GLC_FCS30), and GlobeLand30. We also employed the GeoDetector model to explore the relationships between the consistency in cropland change and the environmental factors (e.g., cropland fragmentation, topographic features, frequency of cloud cover, and management practices). The area-based consistency analysis showed that all five cropland products indicate a declining trend in cropland areas in China over the past decade, while the amount of cropland loss ranges from 5.59% to 57.85% of that reported by the NLSCD. At the prefecture-level city scale, the correlation coefficients between the cropland area changes detected by five cropland products and the NLSCD are low, with GlobeLand30 having the highest coefficient at 0.67. The proportion of prefecture-level cities where the change direction of cropland area in each cropland product is inconsistent with the NLSCD ranges from 13.27% to 39.23%, with CLCD showing the highest proportion and CLUD the lowest. At the pixel scale, the spatial-based consistency analysis reveals that 79.51% of cropland expansion pixels and 77.79% of cropland loss pixels are completely inconsistent across five cropland products, with the southern part of China exhibiting greater inconsistency compared to Northwest China. Besides, the frequency of cloud cover and management practices (e.g., irrigation) are the primary environmental factors influencing consistency in cropland expansion and loss, respectively. These results suggest low consistency in cropland change across five cropland products, emphasizing the need to address these inconsistencies when generating time-series cropland datasets via remote sensing.
The Impact of Urbanization-Induced Land Use Change on Land Surface Temperature
Authors: Halefom Afera , He Yan , Nemoto Tatsuya , Feng Lei , Li Runkui , Raghavan Venkatesh , Jing Guifei , Song Xianfeng , Duan Zheng
Published: December 2024

Rapid urbanization can change local climate by increasing land surface temperature (LST), particularly in metropolitan regions. This study uses two decades of remote sensing data to investigate how urbanization-induced changes in land use/land cover (LULC) affect LST in the Beijing Region, China. By focusing on the key issue of LST and its contributing variables through buffer zones, we determined how variables influence LST across buffer zones—core, transit, and suburban areas. This approach is crucial for identifying and prioritizing key variables in each zone, enabling targeted, zone-specific measures that can more effectively mitigate LST rise. The main driving variables for the Beijing Region were determined, and the spatial-temporal relationship between LST and driving variables was investigated using a geographically weighted regression (GWR) model. The results demonstrate that the Beijing Region’s LST climbed from 2002 to 2022, with increases of 0.904, 0.768, and 0.248 °C in core, transit, and suburban areas, respectively. The study found that human-induced variables contributed significantly to the increase in LST across core and transit areas. Meanwhile, natural variables in suburban areas predominated and contributed to stabilizing local climates and cooling. Over two decades and in all buffer zones, GWR models slightly outperformed ordinary least squares (OLS) models, suggesting that the LST is highly influenced by its local geographical location, incorporating natural and human-induced variables. The results of this study have substantial implications for designing methods to mitigate LST across the three buffer zones in the Beijing Region.
Vegetation Changes in the Arctic: A Review of Earth Observation Applications
Authors: Wenzl Martina , Baumhoer Celia A. , Dietz Andreas J. , Kuenzer Claudia
Published: December 2024

The Arctic, characterised by severe climatic conditions and sparse vegetation, is experiencing rapid warming, with temperatures increasing by up to four times the global rate since 1979. Extensive impacts from these changes have far-reaching consequences for the global climate and energy balance. Satellite remote sensing is a valuable tool for monitoring Arctic vegetation dynamics, particularly in regions with limited ground observations. To investigate the ongoing impact of climate change on Arctic and sub-Arctic vegetation dynamics, a review of 162 studies published between 2000 and November 2024 was conducted. This review analyses the research objectives, spatial distribution of study areas, methods, and the temporal and spatial resolution of utilised satellite data. The key findings reveal circumpolar tendencies, including Arctic greening, lichen decline, shrub increase, and positive primary productivity trends. These changes impact the carbon balance in the tundra and affect specialised fauna and local communities. A large majority of studies conducted their analysis based on multispectral data, primarily using AVHRR, MODIS, and Landsat sensors. Although the warming of the Arctic is linked to greening trends, increased productivity, and shrub expansion, the diverse and localised ecological shifts are influenced by a multitude of complex factors. Furthermore, these changes can be challenging to observe due to difficult cloud cover and illumination conditions when acquiring optical satellite data. Additionally, the difficulty in validating these changes is compounded by the scarcity of in situ data. The fusion of satellite data with different spatial–temporal characteristics and sensor types, combined with methodological advancements, may help mitigate data gaps. This may be particularly crucial when assessing the Arctic’s potential role as a future carbon source or sink.
Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management
Authors: Konara Bhashitha , Krishnapillai Manokararajah , Galagedara Lakshman
Published: December 2024

Crop nitrogen (N) management in agricultural fields is crucial in preventing various environmental and socio-economic issues arising from excess N use. However, precise crop N management (PNM) is hindered by its intensive data requirements, high cost, and time requirements. Digital image processing (DIP) offers a promising approach to overcoming these challenges, and numerous studies have explored its application in N management. This review aims to analyze research trends in applying DIP for N management over the past 5 years, summarize the most recent studies, and identify challenges and opportunities. Web of Science, Scopus, IEEE Xplore, and Engineering Village were referred to for literature searches. A total of 95 articles remained after the screening and selection process. Interest in integrating machine learning and deep learning algorithms with DIP has increased, with the frequently used algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boost, and Convolutional Neural Networks—achieving higher prediction accuracy levels. In addition, image data using more variables as model inputs, including agriculture sensors and meteorological data, have increased prediction accuracy. Nonetheless, several challenges associated with DIP, including obtaining high-quality datasets, complex image processing steps, costly infrastructure, and a user-unfriendly technical environment, still need to be addressed.
Desertification Monitoring Using Machine Learning Techniques with Multiple Indicators Derived from Sentinel-2 in Turkmenistan
Authors: Berdyyev Arslan , Al-Masnay Yousef A. , Juliev Mukhiddin , Abuduwaili Jilili
Published: December 2024

This research offers a fresh understanding of desertification in Turkmenistan by utilizing satellite remote sensing data and machine learning techniques. With 80% of its area covered by desert, Turkmenistan has particular difficulties as a result of the harsh effects of desertification, which are made worse by climate change and irresponsible land use. Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). These indicators include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), Enhanced Vegetation Index (EVI), and land surface temperature (LST). Based on Sentinel-2 satellite data processed by the Google Earth Engine (GEE) platform, the findings show that the country’s northern, central, and eastern regions are undergoing severe desertification. Moreover, RF and XGBoost performed better than the straightforward models like NB and KNN in terms of accuracy (96% and 96.33%), sensitivity (both 100%), and kappa (0.901 and 0.9095). By concentrating on Turkmenistan, this study fills a significant gap and provides a framework for tracking desertification in similar regions around the world.
LHSDNet: A Lightweight and High-Accuracy SAR Ship Object Detection Algorithm
Authors: Dai Dahai , Wu Hao , Wang Yue , Ji Penghui
Published: December 2024

At present, the majority of deep learning-based ship object detection algorithms concentrate predominantly on enhancing recognition accuracy, often overlooking the complexity of the algorithm. These complex algorithms demand significant computational resources, making them unsuitable for deployment on resource-constrained edge devices, such as airborne and spaceborne platforms, thereby limiting their practicality. With the purpose of alleviating this problem, a lightweight and high-accuracy synthetic aperture radar (SAR) ship image detection network (LHSDNet) is proposed. Initially, GhostHGNetV2 was utilized as the feature extraction network, and the calculation amount of the network was reduced by GhostConv. Next, a lightweight feature fusion network was designed to combine shallow and deep features through lightweight convolutions, effectively preserving more information while minimizing computational requirements. Lastly, the feature extraction module was integrated through parameter sharing, and the detection head was lightweight to save computing resources further. The results from our experiments demonstrate that the proposed LHSDNet model increases mAP50 by 0.7% in comparison to the baseline model. Additionally, it illustrates a pronounced decrease in parameter count, computational demand, and model file size by 48.33%, 51.85%, and 41.26%, respectively, when contrasted with the baseline model. LHSDNet achieves a balance between precision and computing resources, rendering it more appropriate for edge device implementation.
Angle Expansion Estimation and Correction Based on the Lindeberg–Feller Central Limit Theorem Under Multi-Pulse Integration
Authors: Cai Jiong , Wang Rui , Yang Handong
Published: December 2024

The radar monopulse angle measurement can obtain a target’s angle information within a single pulse, meaning that factors such as target motion and amplitude fluctuations, which vary over time, do not affect the angle measurement accuracy. However, in practical applications, when a target’s signal-to-noise ratio (SNR) is low, the single pulse signal is severely affected by noise, leading to a significant deterioration in angle measurement accuracy. Therefore, it is usually necessary to coherently integrate multiple pulses before estimating the angle. This paper constructs an angle expansion model for a multi-pulse angle measurement under coherent integration. The analysis reveals that even under noise-free conditions, after coherently integrating multiple pulses, the coupling of target amplitude fluctuations and motion state can still cause significant errors in the angle measurement. Subsequently, this paper conducts a detailed analysis of the impact of the amplitude fluctuations and target maneuvers on the random angle measurement error. It also derives approximate probability density functions of angle measurement errors under various fluctuation and motion scenarios based on the Lindeberg–Feller central limit theorem. In addition, based on the angle expansion model and the random error distribution, this paper proposes an angle correction algorithm based on multi-pulse integration and long-term estimation. Numerical experiments and radar data in the field verify the impact of target characteristics on the angle measurement under multi-pulse integration and the effectiveness of the angle correction algorithm.
Local Dynamic Stability of Trunk During Gait Can Detect Dynamic Imbalance in Subjects with Episodic Migraine
Authors: Castiglia Stefano Filippo , Sebastianelli Gabriele , Abagnale Chiara , Casillo Francesco , Trabassi Dante , Di Lorenzo Cherubino , Ziccardi Lucia , Parisi Vincenzo , Di Renzo Antonio , De Icco Roberto , Tassorelli Cristina , Serrao Mariano , Coppola Gianluca
Published: December 2024

Background/Hypothesis: Motion sensitivity symptoms, such as dizziness or unsteadiness, are frequently reported as non-headache symptoms of migraine. Postural imbalance has been observed in subjects with vestibular migraine, chronic migraine, and aura. We aimed to assess the ability of largest Lyapunov’s exponent for a short time series (sLLE), which reflects the ability to cope with internal perturbations during gait, to detect differences in local dynamic stability between individuals with migraine without aura (MO) with an episodic pattern between attacks and healthy subjects (HS). Methods: Trunk accelerations of 47 MO and 38 HS were recorded during gait using an inertial measurement unit. The discriminative ability of sLLE was assessed through receiver-operating characteristics curves and cutoff analysis. Partial correlation analysis was conducted between the clinical and gait variables, excluding the effects of gait speed. Results: MO showed higher sLLE values, and reduced pelvic rotation, pelvic tilt, and stride length values. sLLEML and pelvic rotation showed good ability to discriminate between MO and HS and were correlated with the perceived pain, migraine disability assessment score, and each other. Conclusions: these findings may provide new insights into the postural balance control mechanism in subjects with MO and introduce the sLLEML as a potential measure of dynamic instability in MO.
Performance Assessment of a Piezoelectric Vibration Energy Harvester for Hybrid Excitation with Varying Cross Sections
Authors: Ambrożkiewicz Bartłomiej , Czyż Zbigniew , Pakrashi Vikram , Anczarski Jakub , Stączek Paweł , Koszewnik Andrzej , Wendeker Mirosław , Litak Grzegorz
Published: December 2024

This paper experimentally examines the influence of hybrid excitation on the performance of vibrational piezoelectric energy harvesting systems on a bluff body with a variable cross section along its generatrix. A combination of vibrational excitation from a shaker and airflow is considered the source from which energy is harvested. Varied excitation frequencies and airflow velocities across five different masses were considered, each defining the natural frequency of the system. The system’s performance in hybrid excitation, enhancements in energy harvesting, and challenges with these was observed, helping to determine optimal operating conditions to function effectively in ambient environments. The tests identified the conditions and ranges within which maximized harvesting responses were observed. Next, computational fluid dynamic (CFD) simulations were carried out to understand the impact of circular and square cross sections controlling the nature of the airflow and representative of the wide range of cross sections that may be utilized for such purposes. The analyses helped contextualize the opportunities and limitations of the use of such cross sections and helped in understanding if a transition from one cross section to another can lead to an assimilation of the advantages observed in using each cross section independently.
Facial Anti-Spoofing Using “Clue Maps”
Authors: Gong Liang Yu , Li Xue Jun , Chong Peter Han Joo
Published: December 2024

Spoofing attacks (or Presentation Attacks) are easily accessible to facial recognition systems, making the online financial system vulnerable. Thus, it is urgent to develop an anti-spoofing solution with superior generalization ability due to the high demand for spoofing attack detection. Although multi-modality methods such as combining depth images with RGB images and feature fusion methods could currently perform well with certain datasets, the cost of obtaining the depth information and physiological signals, especially that of the biological signal is relatively high. This paper proposes a representation learning method of an Auto-Encoder structure based on Swin Transformer and ResNet, then applies cross-entropy loss, semi-hard triplet loss, and Smooth L1 pixel-wise loss to supervise the model training. The architecture contains three parts, namely an Encoder, a Decoder, and an auxiliary classifier. The Encoder part could effectively extract the features with patches’ correlations and the Decoder aims to generate universal “Clue Maps” for further contrastive learning. Finally, the auxiliary classifier is adopted to assist the model in making the decision, which regards this result as one preliminary result. In addition, extensive experiments evaluated Attack Presentation Classification Error Rate (APCER), Bonafide Presentation Classification Error Rate (BPCER) and Average Classification Error Rate (ACER) performances on the popular spoofing databases (CelebA, OULU, and CASIA-MFSD) to compare with several existing anti-spoofing models, and our approach could outperform existing models which reach 1.2% and 1.6% ACER on intra-dataset experiment. In addition, the inter-dataset on CASIA-MFSD (training set) and Replay-attack (Testing set) reaches a new state-of-the-art performance with 23.8% Half Total Error Rate (HTER).
Posture Monitoring During Breastfeeding: Smart Underwear Integrated with an Accelerometer and Flexible Sensors
Authors: Zhou Beibei , Liang Ruixin , Zhang Jun , Li Xiaolu , Broach Zowie , Yip Joanne
Published: December 2024

The position that a woman adopts during breastfeeding is important for both infant and maternal health; however, many women experience musculoskeletal pain due to poor posture during breastfeeding, which is a known factor in low exclusive breastfeeding rates. Posture monitoring is an effective intervention, but existing wearable devices do not consider the ergonomics of nursing mothers and breastfeeding scenarios. In this study, nursing underwear was developed with posture monitoring and a real-time feedback system using accelerometers and flexible bending sensors targeting the neck and upper thoracic spine. Semi-structured interviews were conducted with 12 Chinese mothers to identify key challenges and inform the design. After designing and producing the prototype, wear trials were conducted with two participants who tested both the prototype and a commercial sample while holding a 4 kg baby doll. Video recordings and questionnaires were used to assess the underwear’s effectiveness. The results showed improvements in postural alignment and an increase in the frequency and duration of relaxation periods. Participants reported that the prototype surpassed the commercial sample in functionality, comfort, and aesthetics. These findings are significant for postpartum health and provide guidelines for future smart nursing garment development.
Adaptive Channel Division and Subchannel Allocation for Orthogonal Frequency Division Multiple Access-Based Airborne Power Line Communication Networks
Authors: Yan Ruowen , Li Qiao , Xiong Huagang
Published: December 2024

This paper addresses the critical needs of the aviation industry in advancing towards More Electric Aircraft (MEA) by leveraging power line communication (PLC) technology, which merges data and power transmission to offer substantial reductions in aircraft system weight and cost. We introduce pioneering algorithms for channel division and subchannel allocation within Orthogonal Frequency Division Multiple Access (OFDMA)-based airborne PLC networks, aimed at optimizing network performance in key areas such as throughput, average delay, and fairness. The proposed channel division algorithm dynamically adjusts the count of subchannels to maximize Channel Division Gain (CDG), responding adeptly to fluctuations in network conditions and node density. Concurrently, the subchannel allocation algorithm employs a novel metric, the Subchannel Preference Score (SPS), which factors in both the signal quality and the current occupancy levels of each subchannel to determine their optimal allocation among nodes. This method ensures efficient resource utilization and maintains consistent network performance. Extensive simulations, conducted using the OMNeT++ simulator, have demonstrated that our adaptive algorithms significantly outperform existing methods, providing higher throughput, reduced delays, and improved fairness across the network. These advancements represent a significant leap in MAC protocol design for airborne PLC systems. The outcomes suggest that our algorithms offer a robust and adaptable solution, aligning with the rigorous demands of modern avionics and paving the way for the future integration of MEA technologies.
Sexual Well-Being in Old Age: Effectiveness of an Intervention Program to Promote Sexual Well-Being in Older Community-Dwelling Adults
Authors: von Humboldt Sofia , Low Gail , Leal Isabel
Published: December 2024

Objectives: While many people strive for a fulfilling sexual life as they age, the inevitable changes that come with aging can present significant challenges concerning their sexual well-being (SWB). This study aims to present a comparative analysis of the scores in the variables of sexual satisfaction, adjustment to aging (AtA), and satisfaction with life (SwL) before and after the implementation of an intervention program to promote SWB among older adults. Methods: A convenience sample of 117 older community-dwelling adults (M = 72.31; SD = 5.752) were included in this study, and an ex post facto longitudinal design was applied. A six-week intervention program aimed at improving older adults’ understanding and exchanging their experiences of SWB was performed. The following measures were utilized: (1) a sociodemographic, health and lifestyle questionnaire; (2) Adjustment to Aging Scale (AtAS); (3) Satisfaction with Life Scale (SwLS); (4) New Sexual Satisfaction Scale (NSSS-S); and (5) Mini-Mental Status Exam (MMSE). The specified assessment time points in this study were as follows: baseline and after intervention. A follow-up assessment was conducted 6 months and 12 months after the baseline measurement. Results: The results indicated an increase in the scores in sexual satisfaction, AtA, and SwL from before to after the intervention among the intervention group. Conclusions: The development of intervention programs in older communities is necessary to contribute to the enhancing older adults’ SWB and to explore the best practices for implementing tailored interventions within community settings.
Fuzzy Synthetic Evaluation of the Barriers and Sustainable Pathways for Women During the Transition from Higher Education to Empowerment in Pakistan
Authors: Rana Muhammad Qasim , Saher Noreen , Lee Angela , Shabbir Zeba
Published: December 2024

This study explores the barriers that impede women’s transition from higher education to empowerment in Pakistan. It uses Fuzzy Synthetic Evaluation (FSE). By integrating qualitative and quantitative data, this research identifies six primary barriers: sociocultural values, family-related issues, freedom and mobility, violence and harassment, economic factors, and gendered norms. Data were collected through a survey of 500 women across various regions of Pakistan, with a valid response rate of 79.8% (N = 399), providing a comprehensive analysis of the most significant obstacles to women’s empowerment. The findings reveal the intricate and pervasive nature of these challenges, with sociocultural values, family-related issues, and restrictions on freedom and mobility emerging as the most critical. These traditional norms and patriarchal structures (index 3.942) significantly hinder women’s educational and professional progress. Family-related issues (index 3.840) and restrictions on freedom and mobility (index 3.820) also play a substantial role. Violence and harassment (index 3.705) further deter women from pursuing their goals, while economic factors (index 3.695) and gendered norms (index 3.631) contribute to the persistent gender disparities. This study concludes with a call to action, emphasising the urgent need to address these barriers. To boost women’s empowerment and advance gender equality in Pakistan, comprehensive solutions comprising targeted interventions, societal change, and policy reforms are required. By prioritising these challenges, stakeholders can develop practical solutions. This research provides invaluable insights for policymakers, educators, and advocates, urging them to work towards creating an inclusive and equitable environment for women’s advancement.
A Comparison of Three Protocols for Determining Barbell Bench Press Single Repetition Maximum, Barbell Kinetics, and Subsequent Measures of Muscular Performance in Resistance-Trained Adults
Authors: Stratton Matthew T. , Massengale Austin T. , Clark Riley A. , Evenson-McMurtry Kaitlyn , Wormely Morgan
Published: December 2024

Background: One repetition maximum (1RM) is a vital metric for exercise professionals, but various testing protocols exist, and their impacts on the resulting 1RM, barbell kinetics, and subsequent muscular performance testing are not well understood. This study aimed to compare two previously established protocols and a novel self-led method for determining bench press 1RM, 1RM barbell kinetics, and subsequent muscular performance measures. Methods: Twenty-four resistance-trained males (n = 12, 24 ± 6.1 years) and females (n = 12, 22.5 ± 5.5 years) completed three laboratory visits in a randomized crossover fashion. During each visit, a 1RM was established using one of the three protocols followed by a single set to volitional fatigue using 80% of their 1RM. A Sex:Protocol repeated measures ANOVA was used to determine the effects of sex and differences between protocols. Results: No significant differences were observed between the protocols for any measure, except for 1RM peak power (p = 0.036). Post hoc pairwise comparisons failed to identify any differences. Males showed significantly higher 1RM, average, and peak power (ps < 0.001), while females demonstrated a greater average concentric velocity (p = 0.031) at 1RM. Conclusions: These data suggest the protocol used to establish 1RM may have minimal impact on the final 1RM, 1RM barbell kinetics, and subsequent muscular endurance in a laboratory setting.
The Role of Road Accessibility for Tourists in the Valorization of Archaeological Resources in the Dobrogea Region, Romania
Authors: Dascălu Vasile-Gabriel , Grecu Alexandra , Gruia Andreea Karina , Mihăilă Viorel , Drăghici Cristian Constantin
Published: December 2024

Given that spatial accessibility to tourist resources is crucial for tourism development, this study analyzes the role of the distance between tourists and archaeological sites in the Dobrogea region of Romania. This study highlights the impact of road distance in the valorization of the main archaeological sites in the chosen area, the results obtained providing information on the main parameters of archaeological resources in achieving tourism success. These data will be important clues in the future design of plans for the valorization of those archaeological sites not yet valorized from the tourist point of view. Spatial data modeling was performed using specific Geographic Information Systems tools, which allowed us to extract the necessary information. By corroborating the results of the geospatial analysis with the statistical ones, we were able to draw conclusions regarding the tourist behavior in the region and the decision factors of tourists in visiting the ancient or medieval ruins in Dobrogea. Our analysis shows a significant correlation between the road accessibility of archaeological sites and their tourist attractiveness, with a particular influence of their location in relation to the main tourist areas of interest in the region. The study area has a rich history that has left behind a high density of ancient fortresses, citadels, and cities, which are relatively underutilized for tourism. Heritage tourism has thus lagged behind coastal tourism, which attracts the highest number of tourists in the country. A better knowledge of the factors that favor the development of heritage tourism is needed in order to expand the region’s tourist offer. Our recommendations aim to improve the accessibility and attractiveness of these archaeological sites by investing in essential infrastructure, developing sustainable transportation policies and specific tourism facilities, and implementing a regional strategy to enhance and protect them, which will ultimately increase their contribution to local economies.
Environmental Protection Tax (EPT) and Its Influence on Firms’ Financial Health and Innovation Capabilities: Evidence from Chinese Listed Companies
Authors: Tian ErShang , Park Sung Ook
Published: December 2024

This study examines the impact of the environmental protection tax (EPT) on the liquidity and green technological innovation of listed companies in China from 2013 to 2023. Using a difference-in-differences (DID) approach, we analyze the short-term financial consequences of the EPT, focusing on liquidity ratios while also examining its effects on innovation through research and development investments. The results suggest that the EPT significantly reduces corporate liquidity, particularly in industries with high environmental compliance costs, thereby placing financial pressure on firms. Although the EPT stimulates innovation, the inherent time lag in realizing financial returns from R&D investments exacerbates short-term liquidity problems. This research offers important insights for policymakers seeking to balance sustainability goals with corporate financial health.
From Home Kitchens to School Canteens in Pandemic Italy: Bridging Family Food Habits and School Meals in the Sign of Sustainable Food Policies
Authors: Onorati Maria Giovanna , Fino Michele Antonio , Fassio Franco , Franceschini Cinzia , Corvo Paolo
Published: December 2024

The study examines the relationship between the eating habits of the families of children attending primary school and the appreciation of the meals served in school canteens during the COVID-19 pandemic in Italy. The analysis focuses on the disruptive effects of the lockdowns imposed from March 2020 onwards to contain the spread of the virus. Restrictions to mobility had an impact on family routines, changing eating habits, meal schedules, and food preparation. The extended time spent at home, combined with distance learning and the closure of school canteens, had a lasting impact on children, particularly on their attitudes toward food and their appreciation of school meals after reopening. Through qualitative and quantitative research into the eating habits of 592 families of 1092 pupils attending primary schools in different regions of Italy during the pandemic, this study highlights the influence of adult eating habits on children’s appreciation of food and allows critical insights into the sustainable school food policies implemented at the time. The findings provide a basis for advocating an alliance between schools and families to promote food education and address deficiencies in collective catering policies, which may hold strategic potential, especially during the ecological transition.
Harnessing the Power of Plants: Innovative Approaches to Pollution Prevention and Mitigation
Authors: Zaman Wajid , Ali Sajid , Akhtar Muhammad Saeed
Published: December 2024

Innovative and sustainable environmental management strategies are urgently required to address the escalating global pollution crisis. Phytoremediation, which involves using plants to mitigate, remediate, or contain environmental contaminants, is a promising, cost-effective, and environmentally friendly alternative to conventional remediation methods. This review summarizes current research to elucidate the multifaceted roles of plants in pollution mitigation, detailing mechanisms such as phytoextraction, phytostabilization, phytodegradation, and rhizofiltration; we highlight successful case studies that demonstrate practical applications across diverse environments, such as the use of hyperaccumulator plants for heavy metal removal and genetically engineered species for organic pollutant degradation. Furthermore, this review explores recent technological advancements that have enhanced the effectiveness of phytoremediation, such as the integration of nanotechnology and genetic engineering. It also analyzes the economic and social implications of adopting plant-based pollution control strategies, emphasizing their potential for community involvement and socioeconomic benefits. Despite the promising outlook, we acknowledge the inherent challenges and limitations of phytoremediation, including public acceptance and scalability issues. Finally, we identify key opportunities for future research and innovative approaches that could expand the scope and impact of phytotechnologies in pollution mitigation. This comprehensive review underscores the potential of plants as both agents of environmental restoration and essential components of sustainable pollution management systems.
New Wine in Old Bottles: The Sustainable Application of Slow Sand Filters for the Removal of Emerging Contaminants, a Critical Literature Review
Authors: Corbett Hayley , Solan Brian , Tretsiakova-McNally Svetlana , Fernandez-Ibañez Pilar , McDermott Rodney
Published: December 2024

The current treatment of wastewater has unintended negative environmental impacts. Conventional methods frequently involve the use of harmful chemicals, generate disinfectant by-products, consume significant amounts of energy, and produce wastes requiring additional efforts for safe disposal. Water stress exacerbated by contaminants of emerging concern (CECs) and climate change, is further straining aging treatment systems. A slow sand filter (SSF), with ligno-cellulosic layers, offers a novel, promising, and economic alternative for wastewater reclamation. This review examines the key SSF characteristics, obtained from recent studies, and explores the use of sustainable materials such as ligno-cellulose, as a treatment companion. The optimal SSF design includes a bed depth of >0.6 m, particle effective size (D10) between 0.15 mm and 0.40 mm, and a uniformity coefficient (CU grain size ratio) of ≤2.0. It is established that SSF’s characteristic biolayer of microorganisms enhances contaminant removal via biodegradation. While biofilm-based removal of micropollutants is a proven mechanism, further research is needed to address CEC challenges. For example, the inclusion of sawdust in SSF filter layers can reduce energy consumption compared to conventional methods and can be recycled through thermal conversion, aligning with circular economy principles. This approach has the potential to improve wastewater treatment in emerging economies, contributing to the achievement of the UN Sustainability Goals.
An Association Rule Mining-Based Modeling Framework for Characterizing Urban Road Traffic Accidents
Authors: Du Lijing , Huang Fasheng , Lu Hua , Chen Sijing , Guo Qianwen
Published: December 2024

The World Health Organization has recognized road traffic accidents as a global crisis, particularly in urban environments. Despite extensive research endeavors, significant gaps remain in our understanding of how various factors interact to influence urban road traffic incidents. This study analyzed data from 4285 urban road traffic accidents in Hubei Province, employing a two-step clustering algorithm to classify accidents into distinct groups based on specific conditions. Subsequently, association rule mining was utilized to discern relationships between accident characteristics within each cluster. Additionally, a classification based on the association rule algorithm was implemented to develop a predictive model for analyzing factors contributing to casualties. The data were categorized into clusters based on weather and road conditions, with separate discussions conducted for each scenario. The findings indicated that urban congestion is the most critical factor contributing to accidents. Interestingly, rather than in severe weather, accidents were more prevalent during cloudy, light-rain conditions. Electric vehicles and motorcycles emerged as the most vulnerable groups. Furthermore, a notable interaction was observed between the day of the week, time of day, and weather conditions. The predictive model achieved an impressive average accuracy of 86.9%. This methodology facilitates the identification of contributing factors and mechanisms underlying urban road traffic accidents in China and holds potential for establishing accident analysis models in similar contexts. The interactive visualization of association rules further enhances the applicability of the findings. The findings of this study can provide valuable insights for traffic management authorities to understand the causes of urban road traffic accidents, assisting them in devising effective policy measures and countermeasures to reduce the occurrence of accidents and casualties.