bluuebunny/crossref_metadata_2025 huggingface dataset:
Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
Authors: Zhao Jiayi , Wang Yongliang , Liao Guisheng , Liu Xiaoning , Li Kun , Yu Chunyu , Zhai Yang , Xing Hang , Zhang Xuepan
Published: November 2023

Space-borne synthetic aperture radar (SAR), as an all-weather observation sensor, is an important means in modern information electronic warfare. Since SAR is a broadband active radar system, radio frequency interference (RFI) in the same frequency band will affect the normal observation of the SAR system. Untangling the above problem, this research explores a quick and accurate method for detecting and segmenting RFI-contaminated images. The purpose of the current method is to quickly detect the existence of RFI and to locate it in massive SAR data. Based on deep learning, the method shown in this paper realizes the existence of RFI by determining the presence or absence of interference in the image domain and then performs pixel-level image segmentation on Sentinel-1 RFI-affected quick-look images to locate RFI. Considering the need to quickly detect RFI in massive SAR data, an improved network based on MobileNet is proposed, which replaces some inverted residual blocks in the network with ghost blocks, reducing the number of network parameters and the inference time to 6.1 ms per image. Further, this paper also proposes an improved network called the Smart Interference Segmentation Network (SISNet), which is based on U2Net and replaces the convolution of the VGG blocks in U2Net with a residual convolution and introduces attention mechanisms and a modified RFB module to improve the segmentation mIoU to 87.46% on average. Experiment results and statistical analysis based on the MID dataset and PAIS dataset show that the proposed methods can achieve quicker detection than other CNNs while ensuring a certain accuracy and can significantly improve segmentation performance under the same conditions compared to the original U2Net and other semantic segmentation networks.
Multi-Source Satellite and WRF-Chem Analyses of Atmospheric Pollution from Fires in Peninsular Southeast Asia
Authors: Liang Ailin , Gu Jingyuan , Xiang Chengzhi
Published: November 2023

Atmospheric pollutant gases emitted from straw burning and forest fires can lead to air quality and human health problems. This work explored the evolutionary trends of atmospheric CO2 and other pollutant gases in five countries of Peninsular Southeast Asia (PSEA) over a long time series using various satellite remote sensing data. The research results indicate that a considerable number of fires occur in the region every spring, which negatively affects air quality. The concentration of CO2 increased every year, indicating a correlation coefficient of 0.57 with the number of fire points. The concentration of CO and NO2, respectively, showed a correlation coefficient of 0.87 and 0.95 with the number of fire points as well. Additionally, the AOD reflects the relationship between fire points and air quality. The study also used the meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) to simulate the fire season in March 2016. In this sensitivity study, we examined the impact of air pollutant gases on air quality in PSEA under a hypothetical scenario with and without fire emissions. The simulation results were also compared with satellite observations, which showed that the WRF-Chem model and the FINN (Fire INventory from NCAR) inventory could effectively simulate the spatial distribution and spatial–temporal variability characteristics of CO concentration in the fire, but the simulation result of NO2 was not satisfactory. This study suggests that spring wildfires affect not only air quality, but also short-term weather in the region.
AMFF-Net: An Effective 3D Object Detector Based on Attention and Multi-Scale Feature Fusion
Authors: Li Guangping , Mo Zuanfang , Ling Bingo Wing-Kuen
Published: November 2023

With the advent of autonomous vehicle applications, the importance of LiDAR point cloud 3D object detection cannot be overstated. Recent studies have demonstrated that methods for aggregating features from voxels can accurately and efficiently detect objects in large, complex 3D detection scenes. Nevertheless, most of these methods do not filter background points well and have inferior detection performance for small objects. To ameliorate this issue, this paper proposes an Attention-based and Multiscale Feature Fusion Network (AMFF-Net), which utilizes a Dual-Attention Voxel Feature Extractor (DA-VFE) and a Multi-scale Feature Fusion (MFF) Module to improve the precision and efficiency of 3D object detection. The DA-VFE considers pointwise and channelwise attention and integrates them into the Voxel Feature Extractor (VFE) to enhance key point cloud information in voxels and refine more-representative voxel features. The MFF Module consists of self-calibrated convolutions, a residual structure, and a coordinate attention mechanism, which acts as a 2D Backbone to expand the receptive domain and capture more contextual information, thus better capturing small object locations, enhancing the feature-extraction capability of the network and reducing the computational overhead. We performed evaluations of the proposed model on the nuScenes dataset with a large number of driving scenarios. The experimental results showed that the AMFF-Net achieved 62.8% in the mAP, which significantly boosted the performance of small object detection compared to the baseline network and significantly reduced the computational overhead, while the inference speed remained essentially the same. AMFF-Net also achieved advanced performance on the KITTI dataset.
Coherent DOA Estimation Algorithm with Co-Prime Arrays for Low SNR Signals
Authors: Zhang Fan , Cao Hui , Wang Kehao
Published: November 2023

The Direction of Arrival (DOA) estimation of coherent signals in co-prime arrays has become a popular research topic. However, traditional spatial smoothing and subspace algorithms fail to perform well under low signal-to-noise ratio (SNR) and small snapshots. To address this issue, we have introduced an Enhanced Spatial Smoothing (ESS) algorithm that utilizes a space-time correlation matrix for de-noising and decoherence. Finally, an Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm is used for DOA estimation. In comparison to other decoherence methods, when the SNR is −8 dB and the number of snapshots is 150, the mean square error (MSE) of the proposed algorithm approaches the Cramér–Rao bound (CRB), the probability of resolution (PoR) can reach over 88%, and, when the angular resolution is greater than 4°, the estimation accuracy can reach over 90%.
Preparation and Gas-Sensing Properties of Two-Dimensional Molybdenum Disulfide/One-Dimensional Copper Phthalocyanine Heterojunction
Authors: Chen Guoqing , Xu Xiaojie , Wang Hao , Shaymurat Talgar
Published: November 2023

Although 2D MoS2 alone shows excellent gas-sensing performance, it is prone to stacking when used as the sensitive layer, resulting in insufficient contact with the target gas and lower sensitivity. To solve this, a 2D-MoS2/1D-CuPc heterojunction was prepared with different weight ratios of MoS2 nanosheets to CuPc micro-nanowires, and its room-temperature gas-sensing properties were studied. The response of the 2D-MoS2/1D-CuPc heterojunction to a target gas was related to the weight ratio of MoS2 to CuPc. When the weight ratio of MoS2 to CuPc was 20:7 (7-CM), the gas sensitivity of MoS2/CuPc composites was the best. Compared with the pure MoS2 sensor, the responses of 7-CM to 1000 ppm formaldehyde (CH2O), acetone (C3H6O), ethanol (C2H6O), and 98% RH increased by 122.7, 734.6, 1639.8, and 440.5, respectively. The response of the heterojunction toward C2H6O was twice that of C3H6O and 13 times that of CH2O. In addition, the response time of all sensors was less than 60 s, and the recovery time was less than 10 s. These results provide an experimental reference for the development of high-performance MoS2-based gas sensors.
Enhancing QoS of Telecom Networks through Server Load Management in Software-Defined Networking (SDN)
Authors: Mehmood Khawaja Tahir , Atiq Shahid , Hussain Muhammad Majid
Published: November 2023

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased.
A Deep Anomaly Detection System for IoT-Based Smart Buildings
Authors: Cicero Simona , Guarascio Massimo , Guerrieri Antonio , Mungari Simone
Published: November 2023

In recent years, technological advancements in sensor, communication, and data storage technologies have led to the increasingly widespread use of smart devices in different types of buildings, such as residential homes, offices, and industrial installations. The main benefit of using these devices is the possibility of enhancing different crucial aspects of life within these buildings, including energy efficiency, safety, health, and occupant comfort. In particular, the fast progress in the field of the Internet of Things has yielded exponential growth in the number of connected smart devices and, consequently, increased the volume of data generated and exchanged. However, traditional Cloud-Computing platforms have exhibited limitations in their capacity to handle and process the continuous data exchange, leading to the rise of new computing paradigms, such as Edge Computing and Fog Computing. In this new complex scenario, advanced Artificial Intelligence and Machine Learning can play a key role in analyzing the generated data and predicting unexpected or anomalous events, allowing for quickly setting up effective responses against these unexpected events. To the best of our knowledge, current literature lacks Deep-Learning-based approaches specifically devised for guaranteeing safety in IoT-Based Smart Buildings. For this reason, we adopt an unsupervised neural architecture for detecting anomalies, such as faults, fires, theft attempts, and more, in such contexts. In more detail, in our proposal, data from a sensor network are processed by a Sparse U-Net neural model. The proposed approach is lightweight, making it suitable for deployment on the edge nodes of the network, and it does not require a pre-labeled training dataset. Experimental results conducted on a real-world case study demonstrate the effectiveness of the developed solution.
The Impact of Early Robotics Education on Students’ Understanding of Coding, Robotics Design, and Interest in Computing Careers
Authors: Ragusa Gisele , Leung Lilian
Published: November 2023

Early robotics education has been sparsely researched, especially for children in elementary education. This research pertains to an early education study that introduced robotics design and programming to children in early education with the purpose of increasing their robotics design knowledge, improving their coding skills, and inspiring their aspirations for future careers. It represents a seven-year study of students ages seven through ten years in a large urban school district. The study engaged a pre–post program comparison of the robotics and coding intervention that focused on children’s improved understanding of robotics in addition to their career aspirations. The study resulted in increases in the participating students’ understanding of robotics design as well as improved coding skills in robotics contexts. Furthermore, the study also led to increases in the students’ career aspirations toward computing fields.
KeepRunning: A MoCap-Based Rapid Test to Prevent Musculoskeletal Running Injuries
Authors: Rodríguez Javier , Marín Javier , Royo Ana C. , Padrón Luis , Pérez-Soto Manuel , Marín José J.
Published: November 2023

The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a runner’s anthropometric body characteristics, dexterity and skill. Therefore, this study aims to develop a motion capture-based running analysis test on a treadmill called KeepRunning to obtain running patterns rapidly, which will aid coaches and clinicians in assessing changes in running technique considering changes in the study variables. Therefore, a review and proposal of the most representative events and variables of analysis in running was conducted to develop the KeepRunning test. Likewise, the minimal detectable change (MDC) in these variables was obtained using test–retest reliability to demonstrate the reproducibility and viability of the test, as well as the use of MDC as a threshold for future assessments. The test–retest consisted of 32 healthy volunteer athletes with a running training routine of at least 15 km per week repeating the test twice. In each test, clusters of markers were placed on the runners’ body segments using elastic bands and the volunteers’ movements were captured while running on a treadmill. In this study, reproducibility was defined by the intraclass correlation coefficient (ICC) and MDC, obtaining a mean value of ICC = 0.94 ± 0.05 for all variables and MDC = 2.73 ± 1.16° for the angular kinematic variables. The results obtained in the test–retest reveal that the reproducibility of the test was similar or better than that found in the literature. KeepRunning is a running analysis test that provides data from the involved body segments rapidly and easily interpretable. This data allows clinicians and coaches to objectively provide indications for runners to improve their running technique and avoid possible injury. The proposed test can be used in the future with inertial motion capture and other wearable technologies.
Diffuse Correlation Spectroscopy: A Review of Recent Advances in Parallelisation and Depth Discrimination Techniques
Authors: James Edward , Munro Peter R. T.
Published: November 2023

Diffuse correlation spectroscopy is a non-invasive optical modality used to measure cerebral blood flow in real time, and it has important potential applications in clinical monitoring and neuroscience. As such, many research groups have recently been investigating methods to improve the signal-to-noise ratio, imaging depth, and spatial resolution of diffuse correlation spectroscopy. Such methods have included multispeckle, long wavelength, interferometric, depth discrimination, time-of-flight resolution, and acousto-optic detection strategies. In this review, we exhaustively appraise this plethora of recent advances, which can be used to assess limitations and guide innovation for future implementations of diffuse correlation spectroscopy that will harness technological improvements in the years to come.
A Privacy and Energy-Aware Federated Framework for Human Activity Recognition
Authors: Khan Ahsan Raza , Manzoor Habib Ullah , Ayaz Fahad , Imran Muhammad Ali , Zoha Ahmed
Published: November 2023

Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. The hybrid spiking-LSTM (S-LSTM) model synergistically combines the event-driven efficiency of SNNs and the sequence modelling capability of LSTMs. The model is trained using surrogate gradient learning and backpropagation through time, enabling fully supervised end-to-end learning. Extensive evaluations of two public datasets demonstrate that the proposed approach outperforms LSTM, CNN, and S-CNN models in accuracy and energy efficiency. For instance, the proposed S-LSTM achieved an accuracy of 97.36% and 89.69% for indoor and outdoor scenarios, respectively. Furthermore, the results also showed a significant improvement in energy efficiency of 32.30%, compared to simple LSTM. Additionally, we highlight the significance of personalisation in HAR, where fine-tuning with local data enhances model accuracy by up to 9% for individual users.
Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures
Authors: Liu Ruonan , Xin Yizhong
Published: November 2023

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset.
Assessment of Elongation of the Mesocotyl-Coleoptile and Biomass in Parents and Crosses of Corn Seedlings of the High Valleys of Mexico
Authors: Villalobos González Antonio , Benítez Riquelme Ignacio , Castillo González Fernando , Mendoza Castillo Ma. , Espinosa Calderón Alejandro
Published: November 2023

The elongation of the mesocotyl and the coleoptile and other seedling traits were analyzed from 16 hybrids of two seed sizes, five varieties and a control. Sowing was conducted in sand beds during the S-F 2020 cycle, where nine genotypes were identified that differed in the elongation of the mesocotyl: long (H-48, HS-2 and Promesa); medium (H-44-H-52 and H-70); and short (H-49 AE, H-40 and H-32). A total of 36 possible crosses were obtained between these nine parents, which were established in the S-S 2021 cycle, and on sand beds. Results show that seed size affected (p< 0.05) the speed and percentage of emergence, the elongation of mesocotyl–coleoptile, the biomass and the heterosis in parents and their crosses. The H-48 hybrid presented greater speed and percentage of emergence and elongation of the mesocotyl and the coleoptile with both seed sizes. The highest dry weight of mesocotyl, coleoptile, roots, and leaves was found in the hybrids Promesa and H-48. The crosses between parents with contrasting mesocotyl presented superior elongation and dry weight (p ≤ 0.05) compared to their parents, with the long × long (1 × 2, 1 × 3 and 2 × 3) crosses standing out for all the traits measured. A strong positive association was obtained (p ≤ 0.01) between the elongation of the mesocotyl–coleoptile, the percentage of emergence, and the production of total dry matter in parents and their crosses.
Experimental Investigations of the Detachment of Different Particle Structures from a Magnetizable Fiber in the Gas Phase
Authors: Szabadi-Fuchs Julia , Meyer Jörg , Dittler Achim
Published: November 2023

A current subject of research is the application of magnetic effects for the detachment of accumulated particles of fibrous collectors in gas particle separation. Initial studies have already shown the magnetically induced detachment behavior of a compact particle structure after a single deflection from a single fiber. In this study, the detachment behavior of particle structures with different morphologies from a single fiber is investigated as a function of the particle loading stage on the fiber, the external magnetic flux density, the inflow velocity and the number of regenerations of the fiber for a certain parameter range. Diffusive and more compact particle structures with non-magnetic properties are deposited on the magnetizable single fiber. By applying an external magnetic field, the fiber is magnetized and experiences a torsional moment. The deposited particle structures on the fiber are detached by the acceleration forces. The detachment of the particle structures is observed using a high-speed camera and the image sequences are analyzed. By determining the projection area before and after the fiber deflection, a degree of regeneration is calculated. With magnetic-induced regeneration, high degrees of regeneration close to 100% can be achieved. Repetitive fiber deflections improve the detachment of the particle structures. The magnetic-induced regeneration is suitable for applications where flow reversal is not possible and can be performed either online or offline. Due to the gentle regeneration, fewer emissions are produced on the clean gas side than, for example, with jet pulse cleaning. It makes it easier to achieve emission limits and simplifies product recovery.
Measures of Violence within the United Kingdom Household Longitudinal Survey and the Crime Survey for England and Wales: An Empirical Assessment
Authors: Blom Niels , Gash Vanessa
Published: November 2023

Criminology has been hampered by a lack of longitudinal data to examine the consequences of victimisation. However, recently, ‘Understanding Society’, the United Kingdom Household Panel Survey (UKHLS), began fielding a small battery of questions relating to violence experience. Here, we examined the strengths and weaknesses of these UKHLS measures with similar indices from the Crime Survey for England and Wales (CSEW), a widely used and regarded but cross-sectional survey. We empirically assessed the extent to which the UKHLS variables are comparable with those in the CSEW to determine the viability of the UKHLS for the longitudinal study of (fear of) violence and its consequences. Overall, we regarded the UKHLS to provide an important resource for future panel research on the consequences of victimisation. We found the indicators measuring physical assault to be similar in both sets of data, but also noted differences in prevalence and/or different distributions by socioeconomic group for the indices relating to being threatened and of feeling unsafe. Nonetheless, we maintain their utility for researchers in this field, allowing researchers to uncover new inequalities in violence exposure.
The Logistic Burr XII Distribution: Properties and Applications to Income Data
Authors: Guerra Renata Rojas , Peña-Ramírez Fernando A. , Cordeiro Gauss M.
Published: November 2023

We define and study the four-parameter logistic Burr XII distribution. It is obtained by inserting the three-parameter Burr XII distribution as the baseline in the logistic-X family and may be a useful alternative method to model income distribution and could be applied to other areas. We illustrate that the new distribution can have decreasing and upside-down-bathtub hazard functions and that its density function is an infinite linear combination of Burr XII densities. Some mathematical properties of the proposed model are determined, such as the quantile function, ordinary and incomplete moments, and generating function. We also obtain the maximum likelihood estimators of the model parameters and perform a Monte Carlo simulation study. Further, we present a parametric regression model based on the introduced distribution as an alternative to the location-scale regression model. The potentiality of the new distribution is illustrated by means of two applications to income data sets.
Background Data in the Context of Pinus sylvestris, L. Glued Laminated Timber Manufacturing in Spain
Authors: Garnica Teresa , Montilla Soledad , Otero Sheila , Tenorio José Antonio , Conde Marta
Published: November 2023

The construction sector is achieving its goal of decarbonization. Bioproducts are known to reduce the environmental footprint of the building process, but it is necessary that we determine their exact environmental value. However, assessing the environmental impact relating to buildings is challenging due to a lack of data. The objective of this study was to generate background datasets contextualized to Pinus sylvestrys, L. glulam manufacturing in Spain and apply those datasets to a cradle-to-gate life cycle assessment (LCA) to evaluate both embodied energy (EE) and carbon (EC), as well as biogenic carbon and emissions to air. The corresponding raw materials and energy flows required to apply the LCA methodology were gathered and processed from information from the Spanish forest and wood industry. The resulting background datasets include 27 vehicles and machines, which allowed the quantification of four impact category indicators: renewable primary energy (resources), non-renewable primary energy (resources), use of renewable secondary fuels and global warming potential. Biogenic carbon was also calculated. Based on those five values, the embodied energy and carbon of Pinus sylvestris, L. glulam were quantified: EE = 1401 MJ/UD and EC = −724 kgCO2-eq/UD. The generation of background datasets and environmental information is innovative and of great interest, and it is a powerful tool for prescribers and technicians.
Correction: Zhang et al. Corridors Construction and Development Strategies for Intangible Cultural Heritage: A Study about the Yangtze River Economic Belt. Sustainability 2023, 15, 13449
Authors: Zhang Tianxin , Yang Yuliang , Fan Xin , Ou Shengya
Published: November 2023

The authors would like to make the following corrections to the published paper [...]
Increasing Demand Covered by Renewable Energy in El Hierro Island
Authors: Briongos Francisco , Platero Gaona Carlos A. , Sánchez-Fernández José A.
Published: November 2023

The power system of the El Hierro island comprises a wind farm, a pumped storage hydropower plant and a diesel power plant. Its operational history shows that the renewable energy participation in the energy mix is approximately 50%. In order to increase this participation, the installation of residential photovoltaic generation and battery storage has been considered. In this paper, the operation of the whole system is analyzed by simulation. Simulations consider historical data of electric supply and demand in addition to meteorological data to obtain the wind and solar power production. The simulation tool has been tuned to successfully reproduce past system performance. From the simulations, the effect of adding new renewable generation and storage has been assessed. Also, the effect of a different operation strategy has been analyzed. From the results of these simulations, adding batteries has a negligible effect in increasing renewable energy participation. By contrast, adding solar power provides an increase in renewable energy participation. However, eliminating the requirement of 50% of synchronous generation has the largest effect on increasing renewable energy participation in the energy mix.
Overcoming Agricultural Challenges with GMOs as a Catalyst for Poverty Reduction and Sustainability in Lebanon
Authors: Roberts Richard J. , Naimy Viviane
Published: November 2023

This study assesses the potential impact of genetically modified organisms (GMOs) on poverty reduction and agricultural development in Lebanon, against a backdrop of economic crisis and agricultural uncertainties. GMO adoption is considered a viable strategy to enhance food security, spur economic growth, and alleviate poverty. Simulating various GMO adoption scenarios, specifically in the apple agriculture sector of Mount Lebanon’s Sannine–Baskinta area, this research examines their effects on poverty rates. The findings demonstrate a substantial reduction in poverty rates, from 55% to 36%, with a simulated GMO adoption rate of 70%, underscoring the transformative potential of GMOs in poverty alleviation. The study highlights the critical importance of well-informed decision-making and evidence-based policymaking to address challenges in the agriculture sector. It serves as a foundational pilot project for the introduction of genetically modified crops in Lebanese agriculture, with a focus on the Sannine–Baskinta region. The identified GMOs offer prospects for enhanced resilience to weather conditions and pests, reduced pesticide usage, elimination of chemical fertilizers, increased yield, and improved nutritional value. Future research endeavors aim to extend the project to encompass other crops and regions in Lebanon, as well as in other Arab countries.
Climate Change and New Challenges for Rural Communities: Particulate Matter Matters
Authors: Miousse Isabelle Racine , Hale Rachel B. , Alsbrook Scott , Boysen Gunnar , Broadnax Tanya , Murry Carleisha , Williams Candace , Park Chul Hyun , Richards Robert , Reedy Justin , Chalbot Marie-Cécile , Kavouras Ilias G. , Koturbash Igor
Published: November 2023

Climate change presents multiple challenges to rural communities. Here, we investigated the toxicological potential of the six types of particulate matter most common to rural Arkansas: soil, road, and agricultural dusts, pollen, traffic exhaust, and particles from biomass burning in human small airway epithelial cells (SAECs). Biomass burning and agricultural dust demonstrated the most potent toxicological responses, exhibited as significant (p < 0.05) up-regulation of HMOX1 (oxidative stress) and TNFα (inflammatory response) genes as well as epigenetic alterations (altered expression of DNA methyltransferases DNMT1, DNMT3A, and DNMT3B, enzymatic activity, and DNA methylation of alpha satellite elements) that were evident at both 24 h and 72 h of exposure. We further demonstrate evidence of aridification in the state of Arkansas and the presence of winds capable of transporting agricultural dust- and biomass burning-associated particles far beyond their origination. Partnerships in the form of citizen science projects may provide important solutions to prevent and mitigate the negative effects of the rapidly evolving climate and improve the well-being of rural communities. Furthermore, the identification of the most toxic types of particulate matter could inform local policies related to agriculture, biomass burning, and dust control.
Integrated Management of Industrial Wastewater in the Food Sector
Authors: Abdel-Fatah Mona A.
Published: November 2023

In 2019, a staggering 931 million tons of food went to waste, which is equal to about 17% of all the food available in stores. Dealing with this waste and managing wastewater from various industries will be among the world’s top challenges soon. This is because the global population is expected to grow to around 9 billion people by 2050. Food processing effluent is characterized by valuable material in considerable concentrations, including proteins and lipids with low concentrations of heavy metals and toxicants. Developing an integrated management system for food-processing wastewater should focus on recovering abundant resources, improving the economic value of the process, and mitigating the organic contaminant in the food-processing effluent. This state-of-the-art will review the wastewater management processes of the food processing industry. The latest wastewater treatment processes in different food processing sectors will be reviewed. This review will encompass various physicochemical treatment and recovery techniques, such as precipitation, membrane technology, solvent extraction, foam fractionation, adsorption, and aqueous two-phase systems. Additionally, it will delve into bio-treatment processes that leverage microorganisms and/or enzymes to utilize nutrients found in food-processing wastewater as cost-effective substrates for the production of valuable products. This includes a detailed examination of microalga biomass production within wastewater treatment systems. Finally, the review will put forward future research directions aimed at integrating the principles of the circular economy and developing comprehensive food-processing wastewater management systems.
A Review on the Advances in Socially Oriented Education
Authors: Vázquez-Villegas Patricia , Caratozzolo Patricia , Lara-Prieto Vianney , Membrillo-Hernández Jorge
Published: November 2023

An academic focus on Socially Oriented Education (SOE) has been proposed to accomplish the call of the United Nations to transform educational systems due to the dynamics of contemporary society. This work aims to map the SOE-related literature, providing an overview of the 21st-century approaches to answer the questions: (i) How is SOE defined, and what other concepts are related to it? (ii) Are there any theories involved in developing SOE? (iii) What would be the different study areas that involve SOE? SOE has been used in the academic literature to refer to educational approaches that prioritize social and ethical development, civic engagement, and social justice. Now, it should expand its reach to address pressing issues such as climate change and inequality. It should also shift toward a more student-centered approach, emphasizing student agency and empowerment. SOE should prioritize promoting intercultural competence and preparing students for a globalized world. Technology can be integrated with state-owned enterprises to create innovative learning experiences. The main function of SOE is to solve current societal problems using education as a powerful tool for transformation. There is still much to learn about the effectiveness of SOE, and future research could help advance this field.
An Investigation of the Use of Microwaves and Airborne Ultrasound in the Convective Drying of Kale: Process Efficiency and Product Characteristics
Authors: Mierzwa Dominik , Szadzińska Justyna
Published: November 2023

This study evaluated different hybrid drying modes, combining traditional convective drying with microwave radiation and airborne ultrasound for the dehydration of green leafy vegetables. The central composite design method was used to analyze the impact of microwave and ultrasonic waves on kinetics, energy consumption, and various quality parameters, like color, ascorbic acid, polyphenol, carotenoid, and chlorophyll content in Brassica oleracea, var. acephala. The results of the applied experimental design, i.e., the surface response methodology, showed that the application of microwaves and ultrasound decreased the drying time considerably and enhanced the moisture evaporation from the kale leaves, significantly improving the drying rate and energy efficiency. The drying rate increase demonstrated varying results with changes in air temperature. Specifically, ultrasound resulted in a 69–100% increase, microwaves in a 430–698% increase, and a combination of ultrasound and microwaves in a 463–950% increase at 70 and 50 °C, respectively. Specific energy consumption decreased by 42–51% for ultrasound, 80–87% for microwaves, and 82–90% for ultrasound and microwaves at 70 and 50 °C, respectively. The drying parameters were also found to be better at a higher temperature, but the increase in the drying rate caused by microwaves and ultrasound was notably lower. Moreover, the analysis of the key kinetic parameters and material qualities led to the conclusion that the synergistic action of microwave- and ultrasound-assisted convection contributes to better drying effectiveness and product quality, demonstrating greater retention of vitamin C, phenolics, and natural dyes of up to 90%.
Why Uncertainty in Deep Learning for Traffic Flow Prediction Is Needed
Authors: Kim Mingyu , Lee Donghyun
Published: November 2023

Recently, traffic flow prediction has gained popularity in the implementation of intelligent transportation systems. Most of the existing models for traffic flow prediction focus on increasing the prediction performance and providing fast predictions for real-time applications. In addition, they can reveal the integrity of a prediction when an actual value is provided. However, they cannot explain prediction uncertainty. Uncertainty has recently emerged as an important problem to be solved in deep learning. To address this issue, a Monte Carlo dropout method was proposed. This method estimates the uncertainty of a traffic prediction model. Using 5,729,640 traffic data points from Seoul, the model was designed to predict both the uncertainty and measurements. Notably, it performed better than the LSTM model. Experiments were conducted to show that the values predicted by the model and their uncertainty can be estimated together without significantly decreasing the performance of the model. In addition, a normality test was performed on the traffic flow uncertainty to confirm the normality, through which a benchmark for uncertainty was presented. Following these findings, the inclusion of uncertainty provides additional insights into our model, setting a new benchmark for traffic predictions, and enhancing the capabilities of intelligent transportation systems.
“I Feel Good, I Am a Part of the Community”: Social Responsibility Values and Prosocial Behaviors during Adolescence, and Their Effects on Well-Being
Authors: Bartolo Maria Giuseppina , Palermiti Anna Lisa , Servidio Rocco , Costabile Angela
Published: November 2023

Developing social responsibility values and a sense of community during adolescence is an important step that leads to prosocial behaviors toward others and feeling good about oneself and fellow community members. In line with the United Nations Sustainable Development Goals, sustainability is considered in a broader perspective as aimed at the development of human society, improving individual well-being and the quality of life for individuals and communities. In this sense, from a psychology of sustainability perspective, it is important to better understand the meaning of the connections between individual development and communities and the complexity of their relationships. The present study investigates the relationship between social responsibility values and well-being during adolescence, in a sample of 1925 students aged 14 to 20 years, also examining the mediating role of the sense of community and prosocial behaviors. Our mediational analysis suggests that the relation between social responsibility values and well-being is fully mediated by the sense of community and prosocial behaviors. Moreover, the sense of community has a direct effect on prosocial behaviors. This study, thus, provides new evidence and enlarges the wider sustainability science of how values and social participation enhance well-being.
Te-Doped Bi2Se3@NC Nanocomposites for High-Performance Li-Ion Battery Anodes
Authors: Zhu Yaqin , Wu Yan , Zhao Jiachang , Zeng Xiaohui , Mao Jianfeng , Chen Jiajun
Published: November 2023

Bismuth-based anodes are promising for lithium-ion batteries owing to their high theoretical capacity, garnering significant attention in recent decades. Despite its intrinsic advantages, this anode material has the shortcoming of excessive volume expansion during the charging and discharging processes, which significantly reduces the cycling stability. In this work, a nitrogen-doped carbon-coated Bi2Se3−xTex composite (Bi2Se3−xTex@NC) with excellent electrochemical properties was synthesized by the solvothermal method followed by heat treatment. Benefiting from its unique structure and Te-doping properties, the nitrogen-doped carbon-coated Bi2Se3−xTex composite maintains a discharge specific capacity of 464.0 mAh g−1 after 60 cycles at a current density of 0.1 A g−1. This research presents an efficient approach for the preparation of nitrogen-doped carbon-coated anode materials and a fresh perspective on battery electrode material design.
Study on the Optimal Allocation of Water Resources Based on the Perspective of Water Rights Trading
Authors: Wang Guangyao , Zhang Xinyue , Du Lijuan , Lei Bo , Xu Zhenghe
Published: November 2023

Water rights trading plays an important role in the market mechanism to optimize the allocation of water resources. This study takes Luxian county of Sichuan province as the research area. Based on the prediction of water supply and demand, this study aims to achieve minimum water shortage and maximum economic benefits for regional water distribution, and introduces a water-saving reward and water price punishment mechanism to construct a two-layer collaborative regulation model of water rights trading for water users. The self-improved elite strategy and cogenetic algorithm (NSGA II-S) are used to solve the optimization model, and the optimal allocation of water resources and water rights trading in different towns in the planning year (2025 and 2030) under different flat and dry scenarios is studied. The results show that there would be an obvious problem in the uneven distribution of water resources between supply and demand in 2025 and 2030. The overall water shortage rates in the flat and dry scenario areas in 2025 are 13.71% and 31.99%, respectively, and the overall water shortage rates in the flat and dry scenario areas in 2030 are 11.55% and 31.94%, respectively. Water rights trading can increase the economic benefit value, with the economic benefit increasing by an average of CNY 614 million in all scenarios, an average increase of 8.68%. The research results could be helpful in alleviating the contradiction between the supply and demand of regional water resources and provide a theoretical basis for optimizing water resource allocation by means of water rights trading in the region.
Sustainability-Oriented Low-Carbon Innovation in SOEs: A Case Study of Shanghai Metro
Authors: Yu Guangyao , Zheng Qinqin , Lin Xueying , Yuan Kaiqi
Published: November 2023

State-owned enterprises (SOEs) encounter various constraints on sustainability in low-carbon development due to institutional hybridity. This study aimed to examine how SOEs develop sustainability-oriented innovation (SOI) toward low-carbon development. Drawing on a case study of Shanghai Metro, we developed a process model for sustainability-oriented low-carbon innovation (SLI) in SOEs. The model illustrated that implementing a national low-carbon strategy introduces environmental, social, and financial constraints on sustaining value pluralism for SOEs, triggering the actors to develop SLI involving sensing and idea generation, configuration, and transformation, resulting in innovative low-carbon operational processes, products/services, and business models which reconcile environmental benefits, financial returns, and social welfare. This paper enriches the emerging research on SOI and extends the existing understanding of low-carbon innovation. Beyond this, the findings also offer a new lens of SLI to the conventional research and managerial practices concerning SOEs’ hybridity and low-carbon development.
Stability of Steel Columns with Concrete-Filled Thin-Walled Rectangular Profiles
Authors: Kvocak Vincent , Kanishchev Ruslan , Platko Peter , Hodovanets Elvira , Al Ali Mohamad
Published: November 2023

This paper provides a numerical and experimental analysis of global stability of axially compressed columns made of thin-walled rectangular concrete-filled steel tubes (CFSTs), with the consideration of initial geometric imperfections. The presented work introduces the theory of stability and strength of composite structural members subjected to axial compressive force. Moreover, a numerical calculation method for the determination of column resistance under axial load is presented, taking into account the influence of second-order effects that are considered in the European standard for the design of such members. This paper also presents the method of creating 3D models using the ABAQUS software, numerical analysis, and comparison of the obtained numerical results with experimental tests. In addition to the actual boundary and load conditions, the real properties of the used materials were also taken into account during the creation of 3D models. The actual properties of the used materials were obtained experimentally. Based on the obtained results and their comparison, several new findings and proven facts about the design and assessment of axially compressed columns made of thin-walled rectangular steel tubes filled with concrete are presented in the conclusions of the paper.
Exploring Symmetry in Digital Image Forensics Using a Lightweight Deep-Learning Hybrid Model for Multiple Smoothing Operators
Authors: Agarwal Saurabh , Jung Ki-Hyun
Published: November 2023

Digital images are widely used for informal information sharing, but the rise of fake photos spreading misinformation has raised concerns. To address this challenge, image forensics is employed to verify the authenticity and trustworthiness of these images. In this paper, an efficient scheme for detecting commonly used image smoothing operators is presented while maintaining symmetry. A new lightweight deep-learning network is proposed, which is trained with three different optimizers to avoid downsizing to retain critical information. Features are extracted from the activation function of the global average pooling layer in three trained deep networks. These extracted features are then used to train a classification model with an SVM classifier, resulting in significant performance improvements. The proposed scheme is applied to identify averaging, Gaussian, and median filtering with various kernel sizes in small-size images. Experimental analysis is conducted on both uncompressed and JPEG-compressed images, showing superior performance compared to existing methods. Notably, there are substantial improvements in detection accuracy, particularly by 6.50% and 8.20% for 32 × 32 and 64 × 64 images when subjected to JPEG compression at a quality factor of 70.