bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas
Authors: Mbouombouo Mboungam Abdel Hamid , Zhi Yongfeng , Fonzeu Monguen Cedric Karel
Published: October 2024

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target’s two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.
ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System
Authors: Baniya Babu Kaji
Published: October 2024

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.
Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights
Authors: Braunfelds Janis , Jakovels Gints , Murans Ints , Litvinenko Anna , Senkans Ugis , Rumba Rudolfs , Onzuls Andis , Valters Guntis , Lidere Elina , Plone Evija
Published: October 2024

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator’s “LMT” JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4–LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: −100 dBm for RSRP, −16 dB for RSRQ, −5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided.
Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence
Authors: Abbas Sagheer , Qaisar Adnan , Farooq Muhammad Sajid , Saleem Muhammad , Ahmad Munir , Khan Muhammad Adnan
Published: October 2024

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.
Enhanced Infant Movement Analysis Using Transformer-Based Fusion of Diverse Video Features for Neurodevelopmental Monitoring
Authors: Turner Alexander , Sharkey Don
Published: October 2024

Neurodevelopment is a highly intricate process, and early detection of abnormalities is critical for optimizing outcomes through timely intervention. Accurate and cost-effective diagnostic methods for neurological disorders, particularly in infants, remain a significant challenge due to the heterogeneity of data and the variability in neurodevelopmental conditions. This study recruited twelve parent–infant pairs, with infants aged 3 to 12 months. Approximately 25 min of 2D video footage was captured, documenting natural play interactions between the infants and toys. We developed a novel, open-source method to classify and analyse infant movement patterns using deep learning techniques, specifically employing a transformer-based fusion model that integrates multiple video features within a unified deep neural network. This approach significantly outperforms traditional methods reliant on individual video features, achieving an accuracy of over 90%. Furthermore, a sensitivity analysis revealed that the pose estimation contributed far less to the model’s output than the pre-trained transformer and convolutional neural network (CNN) components, providing key insights into the relative importance of different feature sets. By providing a more robust, accurate and low-cost analysis of movement patterns, our work aims to enhance the early detection and potential prediction of neurodevelopmental delays, whilst providing insight into the functioning of the transformer-based fusion models of diverse video features.
Joint Beamforming Design and User Clustering Algorithm in NOMA-Assisted ISAC Systems
Authors: Yang Qingqing , Tang Runpeng , Peng Yi
Published: October 2024

To enhance the performance of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems in multi-user distributed scenarios, an improved Gaussian Mixture Model (GMM)-based user clustering algorithm is proposed. This algorithm is tailored for ISAC systems, significantly improving bandwidth reuse gains and reducing serial interference. First, using the Sum of Squared Errors (SSE), the algorithm reduces sensitivity to the initial cluster center locations, improving clustering accuracy. Then, direction weight factors are introduced based on the base station position and a penalty function involving users’ Euclidean distances and sensing power. Modifications to the EM algorithm in calculating posterior probabilities and updating the covariance matrix help align user clusters with the characteristics of NOMAISAC systems. This improves users’ interference resistance, lowers decoding difficulty, and optimizes the system’s sensing capabilities. Finally, a fractional programming (FP) approach addresses the non-convex joint beamforming design problem, enhancing power and channel gains and achieving co-optimizing sensing and communication signals. The simulation results show that, under the improved GMM user clustering algorithm and FP optimization, the NOMA-ISAC system improves user spectral efficiency by 4.3% and base station beam intensity by 5.4% compared to traditional ISAC systems.
Advancements in Flexible and Stretchable Electronics for Resistive Hydrogen Sensing: A Comprehensive Review
Authors: Park Kwonpil , Kim Minsoo P.
Published: October 2024

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen’s role as a clean energy carrier and its explosive nature at high concentrations. This review paper provides a comprehensive overview of the recent advancements in flexible and stretchable electronics tailored for resistive hydrogen sensing applications. It begins by introducing the fundamental principles underlying the operation of flexible and stretchable resistive sensors, highlighting the innovative materials and fabrication techniques that enable their exceptional mechanical resilience and adaptability. Following this, the paper delves into the specific strategies employed in the integration of these resistive sensors into hydrogen detection systems, discussing the merits and limitations of various sensor designs, from nanoscale transducers to fully integrated wearable devices. Special attention is paid to the sensitivity, selectivity, and operational stability of these resistive sensors, as well as their performance under real-world conditions. Furthermore, the review explores the challenges and opportunities in this rapidly evolving field, including the scalability of manufacturing processes, the integration of resistive sensor networks, and the development of standards for safety and performance. Finally, the review concludes with a forward-looking perspective on the potential impacts of flexible and stretchable resistive electronics in hydrogen energy systems and safety applications, underscoring the need for interdisciplinary collaboration to realize the full potential of this innovative technology.
An Ultra-Broadband Conductor-Backed Coplanar Waveguide with Sine Edges
Authors: Xie Tingting , Gong Pengwei , Cheng Xiaohe , Xiu Tao , Yao Yuan
Published: October 2024

In this paper, a conductor-backed coplanar waveguide with sine edges (CBCPW-SE), consisting of a conductor-backed coplanar with periodic sine edges supported by a dielectric substrate, is proposed as a promising new transmission line (TL) at millimeter-wave and terahertz frequencies. This configuration offers a distinct advantage by maintaining a constant input impedance of 50 Ω across a broad frequency spectrum, eliminating the necessity for any additional impedance matching transitions, thereby enhancing the overall efficiency and simplicity of the transmission system. To verify the design, the CBCPW-SE was fabricated and measured. The measurement results demonstrate that, from 10 MHz to 100 GHz, the insertion loss is less than 0.1 dB/mm and the reflection coefficient is better than −10 dB. The measured S21 (dB) for a 50 mm-long CBCPW-SE section is less than −5 dB from 10 MHz to 100 GHz. The measured group delay per unit length of the trace is 3.4–5 ps, ranging from 10 MHz to 100 GHz. Moreover, the measured group velocity dispersion (GVD) approaches zero, signifying the minimal temporal spreading of the signal components, a crucial aspect for maintaining signal integrity and facilitating high-speed data transmission.
Phonocardiogram (PCG) Murmur Detection Based on the Mean Teacher Method
Authors: Luo Yi , Fu Zuoming , Ding Yantian , Chen Xiaojian , Ding Kai
Published: October 2024

Cardiovascular diseases (CVDs) are among the primary causes of mortality globally, highlighting the critical need for early detection to mitigate their impact. Phonocardiograms (PCGs), which record heart sounds, are essential for the non-invasive assessment of cardiac function, enabling the early identification of abnormalities such as murmurs. Particularly in underprivileged regions with high birth rates, the absence of early diagnosis poses a significant public health challenge. In pediatric populations, the analysis of PCG signals is invaluable for detecting abnormal sound waves indicative of congenital and acquired heart diseases, such as septal defects and defective cardiac valves. In the PhysioNet 2022 challenge, the murmur score is a weighted accuracy metric that reflects detection accuracy based on clinical significance. In our research, we proposed a mean teacher method tailored for murmur detection, making full use of the Phyionet2022 and Phyionet2016 PCG datasets, achieving the SOTA (State of Art) performance with a murmur score of 0.82 and an AUC score of 0.90, providing an accessible and high accuracy non-invasive early stage CVD assessment tool, especially for low and middle-income countries (LMICs).
A Low-Measurement-Cost-Based Multi-Strategy Hyperspectral Image Classification Scheme
Authors: Bai Yu , Liu Dongmin , Zhang Lili , Wu Haoqi
Published: October 2024

The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model’s dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU).
Simultaneous Recognition and Detection of Adenosine Phosphates by Machine Learning Analysis for Surface-Enhanced Raman Scattering Spectral Data
Authors: Nishitsuji Ryosuke , Nakashima Tomoharu , Hisamoto Hideaki , Endo Tatsuro
Published: October 2024

Adenosine phosphates (adenosine 5′-monophosphate (AMP), adenosine 5′-diphosphate (ADP), and adenosine 5′-triphosphate (ATP)) play important roles in energy storage and signal transduction in the human body. Thus, a measurement method that simultaneously recognizes and detects adenosine phosphates is necessary to gain insight into complex energy-relevant biological processes. Surface-enhanced Raman scattering (SERS) is a powerful technique for this purpose. However, the similarities in size, charge, and structure of adenosine phosphates (APs) make their simultaneous recognition and detection difficult. Although approaches that combine SERS and machine learning have been studied, they require massive quantities of training data. In this study, limited AP spectral data were obtained using fabricated gold nanostructures for SERS measurements. The training data were created by feature selection and data augmentation after preprocessing the small amount of acquired spectral data. The performances of several machine learning models trained on these generated training data were compared. Multilayer perceptron model successfully detected the presence of AMP, ADP, and ATP with an accuracy of 0.914. Consequently, this study establishes a new measurement system that enables the highly accurate recognition and detection of adenosine phosphates from limited SERS spectral data.
Noncircular Distributed Source DOA Estimation with Nested Arrays via Reduced-Dimension MUSIC
Authors: Chen Kaiyuan , Chen Weiyang , Li Jiaqi
Published: October 2024

This paper focuses on the direction-of-arrival (DOA) estimation for noncircular coherently distributed (CD) sources with nested arrays. Usually, for point sources, sparse arrays have the potential to improve the estimation performance of algorithms by obtaining more degrees of freedom. However, algorithms have to be reconsidered for CD sources with sparse arrays and many problems arise. One thorny problem is the disappearance of displacement invariance of the virtual array manifold constructed by the virtualization technique. To deal with this issue, a nested array processing method for CD sources transmitting noncircular signals is proposed in this paper. Firstly, we construct the virtual sum-and-difference co-array by leveraging the noncircular quality of signals with a nested array. Then, an approximation is made to degrade CD sources into point sources. In this way, spatial smoothing techniques can be applied to restore the rank. Finally, in order to reduce the complexity, we modify the reduced-dimension MUSIC to estimate DOAs through a one-dimensional peak-searching procedure. The simulation results prove the superiority of our algorithm against other competitors.
Inertial Measurement Unit-Based Frozen Shoulder Identification from Daily Shoulder Tasks Using Machine Learning Approaches
Authors: Liu Chien-Pin , Lu Ting-Yang , Wang Hsuan-Chih , Chang Chih-Ya , Hsieh Chia-Yeh , Chan Chia-Tai
Published: October 2024

Frozen shoulder (FS) is a common shoulder condition accompanied by shoulder pain and a loss of shoulder range of motion (ROM). The typical clinical assessment tools such as questionnaires and ROM measurement are susceptible to subjectivity and individual bias. To provide an objective evaluation for clinical assessment, this study proposes an inertial measurement unit (IMU)-based identification system to automatically identify shoulder tasks whether performed by healthy subjects or FS patients. Two groups of features (time-domain statistical features and kinematic features), seven machine learning (ML) techniques, and two deep learning (DL) models are applied in the proposed identification system. For the experiments, 24 FS patients and 20 healthy subjects were recruited to perform five daily shoulder tasks with two IMUs attached to the arm and the wrist. The results demonstrate that the proposed system using deep learning presented the best identification performance using all features. The convolutional neural network achieved the best identification accuracy of 88.26%, and the multilayer perceptron obtained the best F1 score of 89.23%. Further analysis revealed that the identification performance based on wrist features had a higher accuracy compared to that based on arm features. The system’s performance using time-domain statistical features has better discriminability in terms of identifying FS compared to using kinematic features. We demonstrate that the implementation of the IMU-based identification system using ML is feasible for FS assessment in clinical practice.
Performance of a Radio-Frequency Two-Photon Atomic Magnetometer in Different Magnetic Induction Measurement Geometries
Authors: Rushton Lucas Martin , Ellis Laura Mae , Zipfel Jake David , Bevington Patrick , Chalupczak Witold
Published: October 2024

Measurements monitoring the inductive coupling between oscillating radio-frequency magnetic fields and objects of interest create versatile platforms for non-destructive testing. The benefits of ultra-low-frequency measurements, i.e., below 3 kHz, are sometimes outweighed by the fundamental and technical difficulties related to operating pick-up coils or other field sensors in this frequency range. Inductive measurements with the detection based on a two-photon interaction in rf atomic magnetometers address some of these issues as the sensor gains an uplift in its operational frequency. The developments reported here integrate the fundamental and applied aspects of the two-photon process in magnetic induction measurements. In this paper, all the spectral components of the two-photon process are identified, which result from the non-linear interactions between the rf fields and atoms. For the first time, a method for the retrieval of the two-photon phase information, which is critical for inductive measurements, is also demonstrated. Furthermore, a self-compensation configuration is introduced, whereby high-contrast measurements of defects can be obtained due to its insensitivity to the primary field, including using simplified instrumentation for this configuration by producing two rf fields with a single rf coil.
Comparing Human Performance on Target Localization in Near Infrared and Long Wave Infrared for Cluttered Environments
Authors: Zhang Li , Martino Mark , Furxhi Orges , Jacobs Eddie L. , Driggers Ronald G. , Renshaw C. Kyle
Published: October 2024

In the context of rapid advancements in AI, the accuracies and speeds among various AI models and methods are often compared. However, a basic question is rarely asked: is AI better than humans, and if so, under what conditions? This paper investigates human ability to detect distant landmark targets under cluttered surroundings such as buildings, trees, and clouds in NIR and LWIR images, aiming to facilitate AI object detection performance analysis. Our investigation employs perception tests and a human performance model to analyze object detection capabilities. The results reveal distinctive differences in NIR and LWIR detectability, showing that although LWIR performs less effectively at range, it offers superior robustness across various environmental conditions. Our findings suggest that AI could be particularly advantageous for object detection in LWIR as it outperform humans in terms of detection accuracy at a long range.
Horizontal Heat Flux Spread in an Inner Corner of Buildings
Authors: Šejnová Pitelková Daniela , Hejtmánek Petr , Mózer Vladimír
Published: October 2024

This study investigates fire separation distances as essential means of passive fire protection in building design. The focus is on the inner corner configuration of building exterior walls, which represents the worst-case scenario for façade fire spread outside of a building. The inner-corner configuration appears to increase the intensity of the radiative heat flux due to reflection and reradiation of heat. Comprehensive approaches for determining fire separation distances around the various façade geometries can be found, but none of them is focused on detailed descriptions of the unprotected area in an inner corner. A medium-scale scenario was chosen and was experimentally validated with a radiant panel for a better understanding of heat flux spread. This paper compares the experiment with analytical and numerical models. The analytical model is based on the Stefan–Boltzmann law and the calculated configuration factor as per Eurocode 1. The numerical model combines radiative and convective components of the heat flux because convection is non-negligible near the heat source. Experimental data confirm the prediction based on the numerical and analytical model and show agreement. The final increase in heat flux due to the corner configuration investigated at the medium scale reaches up to 29%.
Treatment and Recycling of Tungsten Beneficiation Wastewater: A Review
Authors: Zhu Wenxia , Kang Jianhua , Zhang Danxian , Sun Wei , Gao Zhiyong , Han Haisheng , Liu Runqing
Published: October 2024

The large amount of wastewater containing various pollutants generated during the tungsten beneficiation process has become a bottleneck for the sustainable development of tungsten mining enterprises. Typical pollutants mainly include suspended solids (SSs), silicate ions, metal ions, and residual organic reagents. The direct discharge of untreated tungsten beneficiation wastewater can cause serious harm to the ecological environment, while recycling can significantly affect flotation indicators. In this paper, the sources and characteristics of typical pollutants were analyzed, and various purification techniques were outlined, including coagulation, adsorption, chemical precipitation, oxidation, and biological treatment methods. Among these techniques, coagulation is particularly effective for the removal of SSs, while adsorption and chemical precipitation are recommended for the removal of soluble ions. For residual organic reagents, oxidation methods have demonstrated high treatment efficiencies. The mainstream methods for wastewater recycling were summarized, including centralized recycling, as well as internal recycling at certain stages. For tungsten beneficiation such a complex process, where the quality of wastewater varies greatly between different stages, it is suitable to recycle the wastewater after appropriate treatment at a specific stage. Furthermore, this study provided a perspective on the future directions of tungsten beneficiation wastewater treatment, serving as a reference for related research and industrial practices.
Childhood Adversity and Life Satisfaction in Adults with Alternative Care Experience in 12 Low- and Middle-Income Nations: The Mediating Roles of Individual and Relational Protective Factors and Resilience
Authors: Wilke Nicole Gilbertson , Roberts Megan , Newsom Lindsey , Mitchell Tony , Howard Amanda Hiles
Published: October 2024

Adults with care experience have elevated rates of adverse childhood experiences (ACEs) and a greater likelihood of poor outcomes. Some research has examined how protective factors and resilience impact outcomes among care leavers, but few studies include those from low- and middle-income countries (LMICs). Further, little work has examined the interactive impact of protective factors and resilience in this population. We investigated (a) the prevalence of ACEs among care leavers from LMICs, the mediating roles of (b) resilience and (c) individual and relational protective factors on the relationship between ACEs and life satisfaction, and (d) whether mediation was occurring both in parallel and sequentially. Five hundred forty-two care leavers from twelve LMICs completed an online survey. Participants completed the Brief Resilience Scale, ACEs Questionnaire, and open-ended questions regarding care experiences. Open-ended questions were coded for protective factors. There was a high prevalence of ACEs among care leavers. The relationship between ACEs and life satisfaction was partially mediated by resilience. Individual and relational protective factors partially mediated the relationship between ACEs and life satisfaction. Finally, resilience and protective factors partially mediated the relationship between ACEs and life satisfaction in both parallel and sequential mediation among care leavers from LMICs. Protective factors operate interactively to influence resilience, increasing life satisfaction among care leavers from LMICs.
No Effect of Intermittent Palm or Sole Cooling on Acute Training Volume during Resistance Exercise in Physically Active Adults: A Summary of Protocols
Authors: Kenville Rouven , Clauß Martina , Arup Aleksander , Ragert Patrick , Maudrich Tom
Published: October 2024

Intermittent palm (PC) and sole cooling (SC) are proposed ergogenic methods for enhancing exercise performance during high-intensity and fatiguing conditions. However, findings in the literature regarding its positive effect remain inconclusive. This study aimed at investigating the effects of intermittent PC and SC compared to no cooling (NC) on acute training volume during resistance exercise, particularly focusing on the total number of repetitions (TR) performed. Three separate randomized crossover protocols, incorporating commonly practiced resistance exercises (Protocol 1: pullups; Protocol 2: pushups; Protocol 3: leg extensions), were conducted, enrolling healthy, physically active adults (overall sample: n = 41 (12 female), age: 23.9 ± 4.0 years (mean ± SD), height: 174.4 ± 9.5 cm, body mass: 69.3 ± 12.4 kg). During Protocol 3, tympanic temperature (TT), rate of perceived exertion (RPE), and electromyography (EMG) of quadriceps muscles were additionally assessed for SC. PC resulted in less TR compared to NC in Protocol 1 (p < 0.001). Protocol 2 and 3 did not reveal significant ergogenic benefits of PC or SC compared to NC (p > 0.05). Furthermore, SC had no effect on TT, RPE, or EMG amplitudes (all p > 0.05). The inconsistent findings suggest that intermittent PC and SC might have limited effectiveness in enhancing training volume during resistance exercise in physically active adults. Future research should examine various resistance training protocols under controlled conditions, and incorporate comprehensive physiological measurements to elucidate the potential benefits and mechanisms of intermittent cooling in resistance exercise contexts.
An Input–Output Analysis for the Economic Potential of a New Convergence Industry: A Focus on the Autonomous Vehicle Sector in South Korea
Authors: Kim Kyunam
Published: October 2024

While it is crucial to grasp the economic potential in emerging convergence industries in order to develop effective policies and strategies for digital transformation, evaluating the potential is still challenging. This study develops a novel input–output model suitable for analyzing the economic potentials of subsectors in a new convergence industry. Furthermore, this research quantitatively assessed the economic potential of three sectors—components, vehicles, and services—within the autonomous vehicle industry in South Korea using the method newly proposed by this study and contrasted the results with those obtained from a traditional approach. It is found that the experimental approach introduced in this study can capture both the inter-sectoral relationships within the emerging convergence industry and its fundamental characteristics related to other sectors. As an empirical result, the autonomous vehicle service sector is found to drive the highest level of increase in production and labor for other industries. The vehicle sector has relatively high forward and backward-linkage effects, functioning as both supply and demand across the value chain of the autonomous vehicle industry. It also shows that the components sector has a relatively higher ripple effect on industries related to the infrastructure of autonomous vehicles, such as maintenance and energy fields. To rapidly establish an autonomous vehicle ecosystem and enhance its economic impact at the national level, it is essential to develop a “Soft Power” corresponding service sector. Consequently, the implementation of supportive and market-driven policies in this area is imperative.
Smart Product-Service System for Parking Furniture—Sale of Storage Space in Parking Places
Authors: Salwin Mariusz , Chmielewski Tomasz
Published: October 2024

Growing competition, changing customer needs, and growing environmental protection requirements mean companies are forced to change their approach to business. Traditional product sales are being replaced by systemic solutions focused on meeting specific customer requirements while reducing negative impacts on the environment. One such solution is the Product-Service System (PSS). This allows manufacturers to offer their products’ functionalities and features through related services. By extending the life of products, promoting the reuse of materials, and reducing the amount of waste, the implementation of PSS strongly supports sustainable development. The paper focuses on a new product group—garage boxes (GB). It discusses a new PSS business model that responds to the needs of people living in blocks of flats with no tenant storage lockers or rooms in the basement. The new business model sells the function (storing various possessions) and eliminates problems faced by tenants due to the lack of sufficient storage space. It provides customers with high-quality GB for as long as they need them. Customers can pick and choose equipment with additional services depending on their needs. The idea of the model is the outcome of a nationwide study carried out in Poland on a group of 500 residents of blocks of flats and consultations with manufacturers, homeowner associations, wholesale and retail traders, and the financial sector. The study provided us with information and data that provided a comprehensive picture of the problem of the absence of storage lockers or rooms for residents and the needs connected with GB. The results of the conducted research indicate that the developed business model responds to the diverse requirements of residents and supports sustainable solutions. It is an alternative to the lack of a storage unit assigned to each apartment. The business model developed in the paper is highly innovative and comprehensive. This makes it an attractive solution for residents of apartment blocks, and its implementation can significantly reduce the environmental impact.
Sustainable Soil Management and Crop Production Research
Authors: Roussis Ioannis , Kakabouki Ioanna
Published: October 2024

Soil provides crop plants with the essential nutrients, water, and root support they require to grow and thrive [...]
Prospects for Long-Distance Cascaded Liquid—Gaseous Hydrogen Delivery: An Economic and Environmental Assessment
Authors: Yu Yaoyao , Yu Lixia , Chen Xiaoyuan , Zhang Zhiying , Qing Ke , Shen Boyang
Published: October 2024

As an important energy source to achieve carbon neutrality, green hydrogen has always faced the problems of high use cost and unsatisfactory environmental benefits due to its remote production areas. Therefore, a liquid-gaseous cascade green hydrogen delivery scheme is proposed in this article. In this scheme, green hydrogen is liquefied into high-density and low-pressure liquid hydrogen to enable the transport of large quantities of green hydrogen over long distances. After long-distance transport, the liquid hydrogen is stored and then gasified at transfer stations and converted into high-pressure hydrogen for distribution to the nearby hydrogen facilities in cities. In addition, this study conducted a detailed model evaluation of the scheme around the actual case of hydrogen energy demand in Chengdu City in China and compared it with conventional hydrogen delivery methods. The results show that the unit hydrogen cost of the liquid-gaseous cascade green hydrogen delivery scheme is only 51.58 CNY/kgH2, and the dynamic payback periods of long- and short-distance transportation stages are 13.61 years and 7.02 years, respectively. In terms of carbon emissions, this scheme only generates indirect carbon emissions of 2.98 kgCO2/kgH2 without using utility electricity. In sum, both the economic and carbon emission analyses demonstrate the advantages of the liquid-gaseous cascade green hydrogen delivery scheme. With further reductions in electricity prices and liquefication costs, this scheme has the potential to provide an economically/environmentally superior solution for future large-scale green hydrogen applications.
Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge
Authors: Zhang Jiangshi , Li Yongtun , Wu Jingru , Ren Xiaofeng , Wang Yaona , Jia Hongfu , Xie Mengyu
Published: October 2024

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.
Exploring Diversification Strategies among Italian Farms
Authors: Cardillo Concetta , Bartoli Luca , De Rosa Marcello , Francescone Martina , Masi Margherita , Sahir Hanae , Vecchio Yari
Published: October 2024

The multifunctionality model is receiving more and more attention from policymakers as a result of recent initiatives to build more resilient and sustainable food systems as well as the potential for increased farm revenue. This paper explores the role of multifunctional farming in the Italian agriculture viewed through the lens of an entrepreneurial strategy grounded on-farm diversification. Farm diversification strategies, which broaden the farm’s traditional boundaries to include additional activities at the farm level, help the evolution towards multifunctionality. A policy-driven transition towards multifunctional farming has been noticed in Italy during the past few decades, which has prompted a strategic reconfiguration of the farm’s business models. Drawing on the identified activity of portfolio diversification, this study provides an overview of the analyzed 49,429 Italian farms, by articulating diversification strategies into four entrepreneurial activities, which are related to on/off-farm/farm-related or farm-diverse diversification strategies. This article has attempted to verify the presence of farm types that responded to portfolio diversification management strategies through the use of a cluster analysis on data from the general census of Italian agriculture. Supporting new patterns in the adoption of business models focused on multifunctionality should be considered in European rural development policies.
Effect of Plow Pan on the Redistribution Dynamics of Water and Nutrient Transport in Soils
Authors: Zhang Meng’en , Dai Shuting , Gul Saima , He Lizhi , Chen Hanbo , Liu Dan
Published: October 2024

Plow pans are an essential part of the agricultural soil structure. By adjusting the soil bulk density and plow pan height, the water and nutrient transport are dynamically redistributed. Plow pans play a crucial role in promoting crop growth, increasing yields, and supporting sustainable land management. In this study, a column experiment was conducted to investigate the effects of plow pan height (10 cm and 15 cm) and bulk density (1.2, 1.4, and 1.6 g cm⁻3) on soil nutrient and water leaching under high-volume (HV) and low-volume (LV) fertilizer applications. The results reveal that the leachate volume decreased by 61.9% at a plow pan height of 10 cm and by 96.2% at a plow pan height of 15 cm when the bulk density was increased from 1.2 to 1.4 g cm⁻3 under HV conditions. There was no leachate when the plow pan bulk density was 1.6 g cm−3. The reserved concentration of alkali-hydrolyzable N in the plow pan soils was the highest when the plow pan had a bulk density of 1.4 g cm−3 and a height of 15 cm. However, when the plow pan height was 15 cm, the available P content in the plow pan soils decreased by 27.0% and 21.0% at bulk densities of 1.4 g cm⁻3 and 1.6 g cm⁻3, respectively, when compared with 1.2 g cm⁻3. Furthermore, the available P concentrations in the plow pan and subsoil layers decreased with an increase in the plow pan height. The available K concentrations in the topsoil decreased by 26.8% and 24.0% when the plow pan bulk density was increased from 1.2 to 1.4 g cm−3 at heights of 10 and 15 cm, respectively. Thus, the optimal plow pan height and bulk density are closely related to the types of soil nutrients. However, it is clear that excessively high bulk densities (e.g., 1.6 g cm−3) negatively impact soil properties. For different nutrient requirements, a bulk density of 1.2 or 1.4 g cm−3 can be chosen, with each providing suitable options based on the specific nutrient needs. This research offers practical insights into changes in nutrient adsorption and fixation in agricultural production associated with alterations in plow pan bulk density.
Sustainable Personalized E-Learning through Integrated Cross-Course Learning Path Planning
Authors: Xiao Qin , Zhang Yong-Wei , Xin Xiao-Qi , Cai Li-Wen
Published: October 2024

This study addresses the growing need for sustainable and personalized learning solutions in online education by optimizing cross-course learning paths. With the increasing volume of e-learning resources, students often struggle to select appropriate courses and learning paths that align with their individual abilities and goals. A novel cross-course learning path planning model is proposed, which integrates resources from multiple courses using modified matching functions based on Item Response Theory (IRT) and a knowledge graph. This model effectively matches learner attributes, such as abilities, learning styles, and goals, with material attributes like difficulty, types, and prerequisites. An innovative variable-length continuous representation (VLCR) and an improved differential evolution algorithm are employed to optimize the multi-attribute matching (MAM) model, enhancing learning personalization. Results from numerical experiments indicate that cross-course learning paths significantly enhance learning outcomes for a wide range of learners, with over 45% benefiting from improved matches compared to single-course paths. Additionally, 70% of learners experienced similar or better results with cross-course learning. This approach not only promotes efficient learning but also supports sustainable educational practices, preparing educators and learners to meet the challenges of a rapidly changing world.