bluuebunny/crossref_metadata_2025 huggingface dataset:
Motion Tape Strain During Trunk Muscle Engagement in Young, Healthy Participants
Authors: Spiegel Spencer , Wyckoff Elijah , Barolo Jay , Lee Audrey , Farcas Emilia , Godino Job , Patrick Kevin , Loh Kenneth J. , Gombatto Sara P.
Published: October 2024

Background: Motion Tape (MT) is a low-profile, disposable, self-adhesive wearable sensor that measures skin strain. Preliminary studies have validated MT for measuring lower back movement. However, further analysis is needed to determine if MT can be used to measure lower back muscle engagement. The purpose of this study was to measure differences in MT strain between conditions in which the lower back muscles were relaxed versus maximally activated. Methods: Ten participants without low back pain were tested. A matrix of six MTs was placed on the lower back, and strain data were captured under a series of conditions. The first condition was a baseline trial, in which participants lay prone and the muscles of the lower back were relaxed. The subsequent trials were maximum voluntary isometric contractions (MVICs), in which participants did not move, but resisted the examiner force in extension or rotational directions to maximally engage their lower back muscles. The mean MT strain was calculated for each condition. A repeated measures ANOVA was conducted to analyze the effects of conditions (baseline, extension, right rotation, and left rotation) and MT position (1–6) on the MT strain. Post hoc analyses were conducted for significant effects from the overall analysis. Results: The results of the ANOVA revealed a significant main effect of condition (p < 0.001) and a significant interaction effect of sensor and condition (p = 0.01). There were significant differences in MT strain between the baseline condition and the extension and rotation MVIC conditions, respectively, for sensors 4, 5, and 6 (p = 0.01–0.04). The largest differences in MT strain were observed between baseline and rotation conditions for sensors 4, 5, and 6. Conclusions: MT can capture maximal lower back muscle engagement while the trunk remains in a stationary position. Lower sensors are better able to capture muscle engagement than upper sensors. Furthermore, MT captured muscle engagement during rotation conditions better than during extension.
The Photometric Testing of High-Resolution Digital Cameras from Smartphones—A Pilot Study
Authors: Zalewski Sławomir , Skarżyński Krzysztof
Published: October 2024

Luminance is the fundamental photometric quantity representing the technical meaning of brightness. It is usually measured from a distance using a matrix sensor, which is the basis of the professional instrument. However, specific technical requirements must be fulfilled to achieve accurate results. This paper considers whether modern high-resolution smartphone cameras are suitable for luminance measurements. Three cameras from high-end smartphones were evaluated on a dedicated laboratory stand. The sensors’ output characteristics showed relatively good linearity of the individual R, G, and B channels. Unfortunately, the spectral sensitivities were unfavorable, as the minimum error achieved was about 17%. This device is classified outside the generally accepted quality scale for photometric instruments. The presented investigation confirmed that none of the high-resolution smartphone cameras tested was suitable for use as a universal luminance camera. However, one of the test devices can be developmental if restrictively calibrated and used only in a specialistic laboratory stand. Using a smartphone (or only its camera) for luminance measurements requires proper advanced calibration. It is possible, but it limits us to only dedicated applications. The pilot study presented in this paper will help create a suitable test stand for spectacle vision systems, e.g., virtual reality equipment.
PE-MCAT: Leveraging Image Sensor Fusion and Adaptive Thresholds for Semi-Supervised 3D Object Detection
Authors: Li Bohao , Song Shaojing , Ai Luxia
Published: October 2024

Existing 3D object detection frameworks in sensor-based applications heavily rely on large-scale annotated data to achieve optimal performance. However, obtaining such annotations from sensor data—like LiDAR or image sensors—is both time-consuming and costly. Semi-supervised learning offers an efficient solution to this challenge and holds significant potential for sensor-driven artificial intelligence (AI) applications. While it reduces the need for labeled data, semi-supervised learning still depends on a small amount of labeled samples for training. In the initial stages, relying on such limited samples can adversely affect the effective training of student–teacher networks. In this paper, we propose PE-MCAT, a semi-supervised 3D object detection method that generates high-precision pseudo-labels. First, to address the challenges of insufficient local feature capture and poor robustness in point cloud data, we introduce a point enrichment module. This module incorporates information from image sensors and combines multiple feature fusion methods of local and self-features to directly enhance the quality of point clouds and pseudo-labels, compensating for the limitations posed by using only a few labeled samples. Second, we explore the relationship between the teacher network and the pseudo-labels it generates. We propose a multi-class adaptive threshold strategy to initially filter and create a high-quality pseudo-label set. Furthermore, a joint variable threshold strategy is introduced to refine this set further, enhancing the selection of superior pseudo-labels.Extensive experiments demonstrate that PE-MCAT consistently outperforms recent state-of-the-art methods across different datasets. Specifically, on the KITTI dataset and using only 2% of labeled samples, our method improved the mean Average Precision (mAP) by 0.7% for cars, 3.7% for pedestrians, and 3.0% for cyclists.
A Low-Computational-Complexity Digital Predistortion Model for Wideband Power Amplifier
Authors: Lu Xu , Zhou Qiang , Zhu Lei , Wei Zhihu , Wu Yaqi , Liu Zunyan , Chen Zhang
Published: October 2024

This paper proposes a Composition Piecewise Memory Polynomial (CPMP) digital predistortion model based on a Vector Switched (VS) behavioral model to address the challenges of severe nonlinearity and strong memory effects in wideband power amplifiers (PAs). To tackle this issue, two thresholds are calculated and used to segment the envelope values of the input signal according to the nonlinear distortion characteristics of the PA. In this approach, a Generalized Memory Polynomial (GMP) model is employed for the lower segment, a Memory Polynomial (MP) model is employed for the middle segment, and a higher-order GMP model is employed for the upper segment. By sharing the fundamental MP among the proposed segmented models and leveraging a design methodology that configures different cross terms, memory depths, and polynomial orders for each segment, this model achieves superior linearization performance while simultaneously reducing the computational complexity associated with model extraction. The experimental results demonstrate that the adjacent channel power ratio (ACPR) of the predistorted PA output signal using the proposed model improves from −36 dBc to −54 dBc, matching the performance of the GMP model. Furthermore, this performance is 0.5 dBc better than the Piecewise Dynamic Deviation Reduction (PDDR) and Decomposed Vector Rotation (DVR) models. Notably, the complexity of the proposed parameter extraction process is 28.8% of the DVR model, 21.79% of the GMP model, and 12.83% of the PDDR model.
A Narrative Review of Haptic Technologies and Their Value for Training, Rehabilitation, and the Education of Persons with Special Needs
Authors: Irigoyen Eloy , Larrea Mikel , Graña Manuel
Published: October 2024

Haptic technologies are increasingly valuable for human–computer interaction in its many flavors, including, of course, virtual reality systems, which are becoming very useful tools for education, training, and rehabilitation in many areas of medicine, engineering, and daily life. There is a broad spectrum of technologies and approaches that provide haptic stimuli, ranging from the well-known force feedback to subtile pseudo-haptics and visual haptics. Correspondingly, there is a broad spectrum of applications and system designs that include haptic technologies as a relevant component and interaction feature. Paramount is their use in training of medical procedures, but they appear in a plethora of systems deploying virtual reality applications. This narrative review covers the panorama of haptic devices and approaches and the most salient areas of application. Special emphasis is given to education of persons with special needs, aiming to foster the development of innovative systems and methods addressing the enhancement of the quality of life of this segment of the population.
Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots
Authors: Liu Sisi , Zhao Zhan , Wei Jun , Zhou Qianqian
Published: October 2024

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.
Improvement of the Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks Using Mean Field Games
Authors: Ntabeni Unalido , Basutli Bokamoso , Alves Hirley , Chuma Joseph
Published: October 2024

The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach.
Method for Extracting Optical Element Information Using Optical Coherence Tomography
Authors: Nie Jiucheng , Wang Yukun , Wang Dacheng , Ding Yue , Zhou Chengchen , Wang Jincheng , Zhang Shuangshuang , Song Junwei , Cai Mengxue , Wang Junlin , Cui Zhongxu , Hou Yuhan , Chen Si , Liu Linbo , Wang Xiaokun
Published: October 2024

This study examines the measurement of film thickness, curvature, and defects on the surface or inside of an optical element using a highly accurate and efficient method. This is essential to ensure their quality and performance. Existing methods are unable to simultaneously extract the three types of information: thickness, curvature, and defects. Spectral-domain optical coherence tomography (SD-OCT), a non-invasive imaging technique with imaging depths down to the millimeter scale, provides the possibility of detecting the optical element components’ parameters. In this paper, we propose an error correction model for compensating delay differences in A-scan, field curvature, and aberration to improve the accuracy of system fitting measurements using SD-OCT. During data processing, we use the histogram-equalized gray stretching (IAH-GS) method to deal with strong reflections in the thin film layers inside the optics using individual A-scan averages. In addition, we propose a window threshold cutoff algorithm to accurately identify defects and boundaries in OCT images. Finally, the system is capable of rapidly detecting the thickness and curvature of film layers in optical elements with a maximum measurement depth of 4.508 mm, a diameter of 15 × 15 mm, a resolution of 5.69 microns, and a sampling rate of 70 kHz. Measurements were performed on different standard optical elements to verify the accuracy and reliability of the proposed method. To the best of our knowledge, this is the first time that thickness, curvature, and defects of an optical film have been measured simultaneously, with a thickness measurement accuracy of 1.924 µm, and with a difference between the calibrated and nominal curvature measurements consistently within 1%. We believe that this research will greatly advance the use of OCT technology in the testing of optical thin films, thereby improving productivity and product quality.
Photoacoustic Resonators for Non-Invasive Blood Glucose Detection Through Photoacoustic Spectroscopy: A Systematic Review
Authors: Kaysir Md Rejvi , Zaman Thasin Mohammad , Rassel Shazzad , Wang Jishen , Ban Dayan
Published: October 2024

Diabetes mellitus is a prevalent disease with a rapidly increasing incidence projected worldwide, affecting both industrialized and developing regions. Effective diabetes management requires precise therapeutic strategies, primarily through self-monitoring of blood glucose levels to achieve tight glycemic control, thereby mitigating the risk of severe complications. In recent years, there have been significant advancements in non-invasive techniques for measuring blood glucose using photoacoustic spectroscopy (PAS), as it shows great promise for the detection of glucose using the infrared region (e.g., MIR and NIR) of light. A critical aspect of this method is the detection of the photoacoustic signal generated from blood glucose, which needs to be amplified through a photoacoustic resonator (PAR). In this work, an overview of various types of PARs used for non-invasive glucose sensing is reviewed, highlighting their operating principle, design requirements, limitations, and potential improvements needed to enhance the analysis of photoacoustic signals. The motivation behind this review is to identify and discuss main parameters crucial to the efficient design of PARs used in non-invasive glucose detection, which will be helpful for furthering the basic understanding of this technology and achieving the highly sensitive PAR required for non-invasive glucose monitoring.
Development of a Sexological Ontology
Authors: Radomski Dariusz S. , Oscik Zuzanna , Dmoch-Gajzlerska Ewa , Szczotka Anna
Published: October 2024

This study aimed to show what role biomedical engineering can play in sexual health. A new concept of sexological ontology, an essential tool for building evidence-based models of sexual health, is proposed. This ontology should be based on properly validated mathematical models of sexual reactions identified using reliable measurements of physiological signals. This paper presents a review of the recommended measurement methods. Moreover, a general human sexual reaction model based on dynamic systems built at different levels of time × space × detail is presented, and the actual used modeling approaches are reviewed, referring to the introduced model. Lastly, examples of devices and computer programs designed for sexual therapy are described, indicating the need for legal regulation of their manufacturing, similar to that for other medical devices.
Waste Material Classification Based on a Wavelength-Sensitive Ge-on-Si Photodetector
Authors: Manakkakudy Kumaran Anju , De Iacovo Andrea , Ballabio Andrea , Frigerio Jacopo , Isella Giovanni , Colace Lorenzo
Published: October 2024

Waste material classification is critical for efficient recycling and waste management. This study proposes a novel, low-cost material classification system based on a single, voltage-tunable Ge-on-Si photodetector operating across the visible and short-wave infrared (SWIR) spectral regions. Thanks to its tunability, the sensor is able to extract spectral information, and the system effectively distinguishes between seven different materials, including plastics, aluminum, glass, and paper. The system operates with a broadband illuminator, and material identification is obtained through the processing of the photocurrent signal at different bias voltages with classification algorithms. Here, we demonstrate the basic system functionality and near real-time classification of different waste materials.
An Adaptive Energy Orchestrator for Cyberphysical Systems Using Multiagent Reinforcement Learning
Authors: Robles-Enciso Alberto , Robles-Enciso Ricardo , Skarmeta Gómez Antonio F.
Published: October 2024

Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial intelligence has the potential to make this easier. The smart building concept and intelligent energy management are key points to increase the use of renewable sources of energy as opposed to fossil fuels. In addition, cyber-physical systems (CPSs) provide an abstraction of the management of services that allows the integration of both virtual and physical systems in a seamless control architecture. In this paper, we propose to use multiagent reinforcement learning (MARL) to model the CPS services control plane in a smart house, with the purpose of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Furthermore, our proposal dynamically adapts its behaviour in real time according to current and historic energy production, thus being able to handle occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally, several simulations with different configurations are evaluated to verify the performance. The simulation results show that the reinforcement learning solution outperformed the priority-based and the heuristic-based solutions in both power consumption and adaptability in all configurations.
Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures
Authors: Lavazza Luigi , Locoro Angela , Meli Roberto
Published: October 2024

Functional size measures are widely used for estimating software development effort. After the introduction of Function Points, a few “simplified” measures have been proposed, aiming to make measurement simpler and applicable when fully detailed software specifications are not yet available. However, some practitioners believe that, when considering “complex” projects, traditional Function Point measures support more accurate estimates than simpler functional size measures, which do not account for greater-than-average complexity. In this paper, we aim to produce evidence that confirms or disproves such a belief via an empirical study that separately analyzes projects that involved developments from scratch and extensions and modifications of existing software. Our analysis shows that there is no evidence that traditional Function Points are generally better at estimating more complex projects than simpler measures, although some differences appear in specific conditions. Another result of this study is that functional size metrics—both traditional and simplified—do not seem to effectively account for software complexity, as estimation accuracy decreases with increasing complexity, regardless of the functional size metric used. To improve effort estimation, researchers should look for a way of measuring software complexity that can be used in effort models together with (traditional or simplified) functional size measures.
Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention
Authors: Han Ping , He Tingting , Feng Can , Wang Yihan
Published: October 2024

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.
Food Self-Sufficiency in the Honey Market in Poland
Authors: Pawłowska-Tyszko Joanna , Jarka Sławomir , Olech Igor
Published: October 2024

Looking from the perspective of the importance of beekeeping production for agriculture, and its impact on production sustainability, biodiversity and food security, research on food self-sufficiency in the honey market is important. The aim of this article was to assess food self-sufficiency in the honey market in Poland in terms of the sustainability of production. The research covered the years 2002–2023. The research material consisted of secondary sources of information from the FAOSTAT 2024 database, reports of the Institute of Horticulture, Department of Beekeeping in Puławy, and market reports of IERiGŻ-PIB. The research used dynamic indicators, Pearson’s correlation coefficient, self-sufficiency ratios (SSR) and intra-industry trade (IIT) indicators. The analysis showed that Poland is not food self-sufficient in honey production. Environmental issues and related food security will be important for a change in the model of beekeeping in Poland, as this sector plays an important role in maintaining sustainability and biodiversity; hence, the assessment of food self-sufficiency in honey production should be treated broadly, including the benefits for agriculture.
Experimental and Numerical Studies of Heat Transfer Through a Double-Glazed Window with Electric Heating of the Glass Surface
Authors: Koshlak Hanna , Basok Borys , Pavlenko Anatoliy , Goncharuk Svitlana , Davydenko Borys , Piotrowski Jerzy
Published: October 2024

This paper presents experimental and theoretical studies of heat transfer through single- and double-glazed windows with electrical heating of the internal surfaces. Heating is achieved by applying a voltage to the low emissivity coating of the inner glass. A thermophysical model has been developed to simulate the heat transfer through these units, allowing us to determine their thermal characteristics. Experimental data are used to validate the numerical model. The resulting heat flux and temperature distributions on the external and internal surfaces of electrically heated double-glazed units are analysed. According to the results of experimental and numerical studies, it was found that the adopted electric heating scheme allows 83–85% of the heat to enter the room and 15–17% is removed to the outside. This makes it possible to increase the radiation component of the heat flow from the window to the room and improve the thermal comfort in the room. In general, this article shows that existing industrial windows with low-emissivity glass surface coating can be upgraded with simple and inexpensive modernisation, without compromising the main function of the window—efficient transmission of visible light—and create an additional (backup) heating device that can work effectively together with the existing heating system in the event of a sudden cold snap at low temperatures (below −20 °C), to prevent condensation of water vapour in the windows, and to prevent condensation on the surface of the window facade wall. Formally, a back-up (emergency) heating system is created in the room, which contributes to the energy sustainability of the building and therefore to energy security in general.
Oman’s Green Horizon: Steering Towards Sustainability Through Decarbonization and Energy Transition
Authors: Abdel-Gadir Sufian Eltayeb Mohamed , Mohammed Mwahib Gasmelsied Ahmed
Published: October 2024

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2024, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. The analysis utilizes stepwise regression to systematically identify the most significant predictors, ensuring a parsimonious model. Robust least squares (ROLSs) are employed to account for potential outliers and heteroscedasticity in the data, providing more reliable estimates. Fully Modified Least Squares (FMOLSs) is applied to address issues of endogeneity and serial correlation, offering robust long-term coefficient estimates. Canonical cointegrating regression (CCR) further refines these estimates by handling non-stationary variables and ensuring consistency in the presence of cointegration. Cointegration tests, including the Johansen and Engle–Granger methods, confirm long-term equilibrium relationships among the variables; this study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, the Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle–Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman’s efforts towards sustainable development and decarbonization.
Village Organization and Sustainable Growth of Farmers’ Income: An Empirical Study Based on Dynamic Survey Data of the Labor Force in China
Authors: Wang Qinghao , Zhang Chentao , Gong Mingyue , Zhu Beiqi
Published: October 2024

Common prosperity is the core goal of rural revitalization, and the sustainable growth of farmers’ income is an inevitable requirement. This study uses CLDS2014-2018 village- and family-level questionnaire data to reveal how the degree of village organization affects the sustainable growth of farmers’ income, thus providing a reference for local government decision-making to promote the increase in farmers’ income and narrow the income gap at the village level. The results show that the degree of village organization can significantly affect farmers’ income, and the analysis results of the degree of formal organization are consistent with this. Specifically, for every 1% increase in the degree of village organization, the per capita household income will increase by 0.324%, among which, the wage income will increase by 0.226%, the operational income will increase by 5%, the property income will increase by 0.008%, and the transfer income will increase by 0.042%. The estimation results obtained by using instrumental variables are consistent, and the results are robust. At the same time, improving the degree of village organization can also significantly increase the proportion of farmers’ non-agricultural income and narrow the income gap among farmers. On the impact of heterogeneity, it was found that the geographical distance between villages and county and township governments, as well as the political identity and education level of farmers, will affect the role of the degree of organization. The results of the mechanism analysis show that improving the degree of village organization is mainly achieved by promoting the circulation of agricultural land, socializing the service of agricultural machinery, and providing industrial and commercial capital to the countryside to increase farmers’ income. The research revealed that the modernization of rural governance should be accelerated and the degree of village organization should be improved, especially the degree of formal organization, so as to build benign trust and a cooperative relationship between village committees and villagers and implement policies and measures that improve the income of farmers according to local conditions. At the same time, this will strengthen the publicity and education of party members to improve the proportion of rural party members and the education level of farmers.
Exploration of the Path of Industrial Structure Adjustment Forced by the Rigid Constraint of Water Ecology in the Yangtze River Economic Belt
Authors: Ma Jun , Ji Jiale
Published: October 2024

Considering the two-way influence of water ecology and industrial structure, decompose and quantify the water ecological rigidity constraints into water environment constraints and water resources constraints, construct the evaluation system based on the theoretical framework of PSR, and analyze the spatio-temporal evolution characteristics of water ecological rigidity constraints of the Yangtze River Economic Belt from 2010 to 2021 by using the entropy weighting method, the method of catastrophe progression, and the kernel density estimation. Then, putting forward the types of zoning in the Yangtze River Economic Zone through K-means algorithm analysis to explore the rigid constraints of water ecology to force the path of industrial structure adjustment. The results indicate that: (1) the water ecological rigidity constraints show a decreasing trend, and the decreasing amplitude is upstream > downstream > midstream; (2) the water ecological rigidity constraints generally show the characteristic of “gradually decreasing from east to west”, and the degree of regional differences first decreases, then increases, and then decreases again; (3) it can be divided into six types, and the zoning results gradually change from the concentration in the medium-high type and high-high type to the concentration in the low-low type and medium-low type, and the rigidity constraint of water ecology is obviously improved. In light of the findings, the Yangtze River Economic Belt is divided into five types of development regions and further discusses that the industrial restructuring in different zones has different influencing effects on the regional socio-economic development, which provides suggestions for differentiated paths of water ecological protection and industrial restructuring.
Towards Sustainable Mental Health Using the Finnish Mood Scale
Authors: Luojumäki Reko J. , Ruiz Montse C. , Adie Joshua M. , Terry Peter C.
Published: October 2024

The interest in mental health in a sports context has increased in recent years. However, there is no scientifically sound measurement tool for assessing sport-related mental health symptoms in the Finnish language. One of the most frequently used measurement tools for such symptoms in English is the Brunel Mood Scale (BRUMS). The purpose of the present study was to adapt and validate a Finnish version of the BRUMS, referred to as the Finnish Mood Scale (FIMS). The 24-item, 6-factor FIMS was administered to 445 Finnish participants concurrently with Finnish versions of the Depression, Anxiety, and Stress Scale (DASS-21) and the Psychobiosocial States Scale (PBS-S). The sample consisted of 259 females (58.2%), 177 males (39.8%), and 9 who did not disclose their gender (2%), aged 16 to 75 years (M = 35.65, SD = 13.70). Sixty-two participants (13.9%) reported being a competitive athlete, 273 (61.3%) were regular exercisers, and 109 (24.5%) did not exercise regularly. A six-factor modified CFA model indicated an adequate fit to the data (CFI = 0.944, TLI = 0.934, RMSEA = 0.060). Concurrent measures correlated with FIMS in line with theoretical predictions, supporting convergent and divergent validity. Males reported significantly more positive mood scores than females. Physically active participants reported significantly more positive mood scores than inactive individuals, endorsing the mental health benefits of physical exercise. Findings indicate that the FIMS is a suitable measurement tool to screen mood modalities in Finnish populations aged 16 years and older. FIMS may be useful for the early detection of mental ill-health, thus, promoting sustainable mental health.
Spatiotemporal Variability in Snow and Land Cover in Sefid-Rud Basin, Iran
Authors: Entezami Hersh , Mojarrad Firouz , Shahabi Himan , Ghaderpour Ebrahim
Published: October 2024

Snow cover has a key role in balancing the Earth’s surface temperature and can help in filling rivers and reservoirs. In this study, 8-day MOD10A2 images are employed to monitor the spatiotemporal changes in snow cover in the Sefid-Rud basin and its eleven sub-basins during 2000–2019. The non-parametric Mann–Kendall (MK) test and its associated Sen’s slope estimator are utilized to estimate the trends in annual, seasonal, and monthly snow cover changes. The Sen’s slope results show a decrease in the snow cover for the basin, statistically significant toward the central and southern parts of the basin. In the winter season, a decreasing trend is observed, where its decreasing rate is higher than the annual rate. The trends in the calendar months are like the seasons, i.e., December, January, and February exhibit a decreasing trend, like the winter season. The Goltapeh-Zarinabad and Ghorveh-Dehgolan sub-basins show decreasing snow cover rates of −0.51 and −0.68 (%/year) during 2000–2019, respectively, the only two sub-basins whose gradients are statistically significant at the 95% confidence level. The Pearson correlation analysis between elevation and snow cover for each year shows that the highest and lowest correlations are 0.81 for 2007 and 0.59 for 2017. Finally, analysis of the MCD12Q1 land cover data shows that a significant portion of non-vegetated lands have turned into grasslands, mainly in the central part of the basin, where the significant gradual snow cover decline is observed. The results can guide stakeholders and policymakers in the development of a sustainable environment in the face of climate change.
Exploring the Potential of Agriculture Diversification as a Pathway to an Improved Smallholder Livelihood Dietary System in Nepal
Authors: Nepali Bikram , Jackson Tamara , Johns Craig
Published: October 2024

Agriculture, a cornerstone of Nepal’s economy, is dominated by smallholder farmers cultivating staple crops under resource and input constraints, leading to declining productivity amid rapid population growth and urban migration. This study explores the role of agricultural diversification in enhancing dietary diversity and improving smallholder livelihoods using panel data from the Nepal Household Risk and Vulnerability Survey (2016–2018). Analysis through the Simpson diversification index and food consumption scores reveals a strong positive correlation between agricultural diversification and dietary diversity. Key determinants include market access, irrigation systems (rainfed or canal-based), input availability (fertilizers and labor), and land type. Dry season crop diversification was positively associated with dietary diversity, while market access showed a significant negative impact on dietary diversity as distance increased. Upland areas exhibited higher potential for diversification, especially in the dry season, driven by improved access to inputs and irrigation. Policy recommendations highlight the need to enhance infrastructure in upland regions, improve market access, and ensure input availability, particularly fertilizers and labor, to support seasonal diversification. Such measures are essential for strengthening food security, nutritional diversity, and economic resilience among smallholder farmers in Nepal.
Research on Mechanical Properties of a 3D Concrete Printing Component-Optimized Path by Multimodal Analysis
Authors: Wang Bolin , Yang Min , Liu Shilong , Liu Xianda , Zhao Hongyu , Wang Xiangyu , Liang Yishuang , Yao Xiaofei
Published: October 2024

Three-dimensional concrete printing (3DCP) technology with solid wastes has significant potential for sustainable construction. However, the hardened mechanical properties of components manufactured using 3DCP technology are affected by weak interlayer interfaces, limiting the widespread application of 3DCP technology. To address the inherent limitations of 3DCP technology, conventional improvement strategies, such as external reinforcement and the optimization of material properties, lead to increased production costs, complex fabrication, and decreased automation. This study proposes an innovative spatial path optimization method to enhance the mechanical performance of 3D-printed, cement-based components. The novel S-path design introduces additional printed layers in the weak interlayer regions of the printed samples. This design improves the spatial distribution of fiber-reinforced filaments in continuous weak zones, thus enhancing the functional efficiency of fibers. This approach improves the mechanical performance of the printed samples, achieving compressive strengths close to those of cast samples and only a 20% reduction in average flexural strength. Compared to using a conventional printing path, the average compressive strength and flexural strength are improved by 30% and 55%, respectively, when the S-path layout is employed in 3DCP. Additionally, this method significantly reduces the anisotropy in compressive and flexural strengths to 26% and 28% of samples using conventional printing paths, respectively. Therefore, the proposed method can improve the mechanical properties and stability of the material, reducing the safety risks of printed structures.
Organic Management and Intercropping of Fruit Perennials Increase Soil Microbial Diversity and Activity in Arid Zone Orchard Cropping Systems
Authors: Janke Rhonda R. , Menezes-Blackburn Daniel , Al Hamdi Asma , Rehman Abdul
Published: October 2024

Organic farming encourages soil management practices that can improve soil health and fertility by increasing soil organic matter inputs and system sustainability. This study evaluated the effect of three years of continuous organic farming and intercropping orchard treatments on soil microbial diversity, microbial enumeration, respiration, soil fertility and fruit yields. Organic management resulted in higher soil organic matter content, Olsen P, and water holding capacity, but did not affect soil pH, electrical conductivity (EC), K, or Na levels. Growth parameters measured on all fruit trees were not significantly different among treatments. The enumeration of bacteria was significantly higher in organic plots when compared to conventionally managed plots. Soil respiration and substrate-induced respiration were significantly higher in the organic diverse plots in comparison to both conventional systems. The genomic analysis of prokaryotes (16S rRNA) and eukaryotes/fungi (ITS) revealed a significantly higher number of taxa, Shannon H index, and Equitability index in the organic systems for both prokaryotes and eukaryotes, in comparison to conventional farming, all of which are indicators of system sustainability. The relative abundance of Operational Taxonomic Units (OTUs) previously reported as diazotrophs, denitrifiers, or involved in the sulfur cycle, as well as Arbuscular Mychorrizae Fungi (AMF)/glomeromycotan, were highest in the organically managed soils than in the conventional plots. A multivariate correlation network clustering revealed that the microbial communities within the organic and conventional soils had strong dissimilarities regarding soil microbial niches. Our work provides evidence that organic management can be used for increasing soil microbial diversity and soil health, leading to higher levels of sustainability in fruit orchard systems.
Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models
Authors: Bostan Tuba
Published: October 2024

A landslide susceptibility assessment is one of the critical steps in planning for landslide disaster prevention. Advanced machine learning methods can be used as data-driven approaches for landslide susceptibility zonation with several landslide conditioning factors. Despite there being a number of studies on landslide susceptibility assessment, the literature is limited in several contexts, such as parameter optimization, an examination of the factors in detail, and study area. This study addresses these lacks in the literature and aims to develop a landslide susceptibility map of Kentucky, US. Four machine learning methods, namely artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and stochastic gradient boosting (SGB), were used to train the dataset comprising sixteen landslide conditioning factors after pre-processing the data in terms of data encoding, data scaling, and dimension reduction. The hyperparameters of the machine learning methods were optimized using a state-of-the-art artificial bee colony (ABC) algorithm. The permutation importance and Shapley additive explanations (SHAP) methods were employed to reduce the dimension of the dataset and examine the contributions of each landslide conditioning factor to the output variable, respectively. The findings show that the ABC-SGB hybrid model achieved the highest prediction performance. The SHAP summary plot developed using the ABC-SGB model shows that intense precipitation, distance to faults, and slope were the most significant factors affecting landslide susceptibility. The SHAP analysis further underlines that increases in intense precipitation, distance to faults, and slope are associated with an increase in the probability of landslide incidents. The findings attained in this study can be used by decision makers to develop the most effective resource allocation plan for preventing landslides and minimizing related damages.
Water Permeability of Kemafil Georopes with a Geotextile Core Made of Wool Waste Based on Laboratory and Field Tests
Authors: Gruchot Andrzej , Zydroń Tymoteusz , Cholewa Mariusz , Stanisz Jacek
Published: October 2024

This paper presents the results of laboratory and field tests on the hydraulic properties of georopes produced using the Kemafil technology from sheep wool waste generated in the textile industry. The laboratory tests included the determination of the basic physical parameters and filtration properties of georopes, as well as tests of the physical properties and water permeability of the experimental training ground. As part of the field research, measurements of water infiltration through 1.0, 2.0 and 5.0 m long georopes embedded in the ground were carried out in nine monthly cycles. The conditions of water flow through the georopes were monitored on the basis of georope resistance measurements. Numerical calculations were also performed to determine the conditions of water flow through the georopes and the process of water infiltration from the georopes into the ground. The laboratory tests have shown that the water permeability of georopes is high and, based on the filtration criteria, they can act as a drainage material. The field measurements showed that the resistance of the georopes changed over time and depended on the amount of water supplied and the absorbency of the ground. The results of the numerical calculations were consistent with the results of the field measurements, at the same time indicating that some water infiltrated into the ground in the vicinity of the georopes, meaning that under the conditions that prevailed during the experiment, the georopes can act as infiltration drainage systems in the ground.