bluuebunny/crossref_metadata_2025 huggingface dataset:
A Critical Review of the Propagation Models Employed in LoRa Systems
Authors: Azevedo Joaquim Amândio , Mendonça Fábio
Published: June 2024

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model’s outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models.
Compact Numerical Aperture 0.5 Fiber Optic Spectrometer Design Using Active Image Plane Tilt
Authors: Yue Pinliang , Yang Mingyu , Jiao Qingbin , Xu Liang , Wang Xiaoxu , Zhang Mingle , Tan Xin
Published: June 2024

The numerical aperture of the spectrometer is crucial for weak signal detection. The transmission lens-based configuration has more optimization variations, and the grating can work approximately in the Littrow condition; thus, it is easier to acquire high numerical aperture (NA). However, designing a large aperture focusing lens remains challenging, and thus, ultra-high NA spectrometers are still difficult to acquire. In this paper, we propose a method of setting image plane tilt ahead directly when designing the large aperture focusing lens to simplify the high NA spectrometer design. By analyzing the accurate demands of the focusing lens, it can be concluded that a focusing lens with image plane tilt has much weaker demand for achromatism, and other monochromatic aberration can also be reduced, which is helpful to increase the NA. An NA0.5 fiber optic spectrometer design is given to demonstrate the proposed method. The design results show that the NA can achieve 0.5 using four lenses of two materials, and the MTF is higher than 0.5 when the spectral dispersion length is 12.5 mm and the pixel size is 25 μm, and thus, the spectral resolution can achieve 6.5 nm when the spectral sampling ratio is 2:1. The proposed method can provide reference for applications when appropriate materials are limited and high sensitivity is necessary.
Advances in Nanomaterials and Colorimetric Detection of Arsenic in Water: Review and Future Perspectives
Authors: Bhat Abhijnan , Tian Furong , Singh Baljit
Published: June 2024

Arsenic, existing in various chemical forms such as arsenate (As(V)) and arsenite (As(III)), demands serious attention in water and environmental contexts due to its significant health risks. It is classified as “carcinogenic to humans” by the International Agency for Research on Cancer (IARC) and is listed by the World Health Organization (WHO) as one of the top 10 chemicals posing major public health concerns. This widespread contamination results in millions of people globally being exposed to dangerous levels of arsenic, making it a top priority for the WHO. Chronic arsenic toxicity, known as arsenicosis, presents with specific skin lesions like pigmentation and keratosis, along with systemic manifestations including chronic lung diseases, liver issues, vascular problems, hypertension, diabetes mellitus, and cancer, often leading to fatal outcomes. Therefore, it is crucial to explore novel, cost-effective, and reliable methods with rapid response and improved sensitivities (detection limits). Most of the traditional detection techniques often face limitations in terms of complexity, cost, and the need for sophisticated equipment requiring skilled analysts and procedures, which thereby impedes their practical use, particularly in resource-constrained settings. Colorimetric methods leverage colour changes which are observable and quantifiable using simple instrumentation or even visual inspection. This review explores the colorimetric techniques designed to detect arsenite and arsenate in water. It covers recent developments in colorimetric techniques, and advancements in the role of nanomaterials in colorimetric arsenic detection, followed by discussion on current challenges and future prospects. The review emphasizes efforts to improve sensitivity, selectivity, cost, and portability, as well as the role of advanced materials/nanomaterials to boost the performance of colorimetric assays/sensors towards combatting this pervasive global health concern.
Solar Tracking Control Algorithm Based on Artificial Intelligence Applied to Large-Scale Bifacial Photovoltaic Power Plants
Authors: Santos de Araújo José Vinícius , de Lucena Micael Praxedes , da Silva Netto Ademar Virgolino , Gomes Flávio da Silva Vitorino , Oliveira Kleber Carneiro de , de Souza Neto José Mauricio Ramos , Cavalcante Sidneia Lira , Morales Luis Roberto Valer , Villanueva Juan Moises Mauricio , Macedo Euler Cássio Tavares de
Published: June 2024

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun’s path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm.
A Pix2Pix Architecture for Complete Offline Handwritten Text Normalization
Authors: Barreiro-Garrido Alvaro , Ruiz-Parrado Victoria , Moreno A. Belen , Velez Jose F.
Published: June 2024

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture.
Dynamic Validation of Calibration Accuracy and Structural Robustness of a Multi-Sensor Mobile Robot
Authors: Liu Yang , Cui Ximin , Fan Shenghong , Wang Qiang , Liu Yuhan , Sun Yanbiao , Wang Guo
Published: June 2024

For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground–object–air cooperation mechanism, termed the “ground surface simulation field (GSSF)—mobile robot -photoelectric transmitter station (PTS)”. Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot.
Discrete Relationships between Spatiotemporal Gait Characteristics and Domain-Specific Neuropsychological Performance in Midlife
Authors: Morrison Laura , Dyer Adam H. , Dolphin Helena , Killane Isabelle , Bourke Nollaig M. , Widdowson Matthew , Woods Conor P. , Gibney James , Reilly Richard B. , Kennelly Sean P.
Published: June 2024

Midlife risk factors such as type 2 diabetes mellitus (T2DM) confer a significantly increased risk of cognitive impairment in later life with executive function, memory, and attention domains often affected first. Spatiotemporal gait characteristics are emerging as important integrative biomarkers of neurocognitive function and of later dementia risk. We examined 24 spatiotemporal gait parameters across five domains of gait previously linked to cognitive function on usual-pace, maximal-pace, and cognitive dual-task gait conditions in 102 middle-aged adults with (57.5 ± 8.0 years; 40% female) and without (57.0 ± 8.3 years; 62.1% female) T2DM. Neurocognitive function was measured using a neuropsychological assessment battery. T2DM was associated with significant changes in gait phases and rhythm domains at usual pace, and greater gait variability observed during maximal pace and dual tasks. In the overall cohort, both the gait pace and rhythm domains were associated with memory and executive function during usual pace. At maximal pace, gait pace parameters were associated with reaction time and delayed memory. During the cognitive dual task, associations between gait variability and both delayed memory/executive function were observed. Associations persisted following covariate adjustment and did not differ by T2DM status. Principal components analysis identified a consistent association of slower gait pace (step/stride length) and increased gait variability during maximal-pace walking with poorer memory and executive function performance. These data support the use of spatiotemporal gait as an integrative biomarker of neurocognitive function in otherwise healthy middle-aged individuals and reveal discrete associations between both differing gait tasks and gait domains with domain-specific neuropsychological performance. Employing both maximal-pace and dual-task paradigms may be important in cognitively unimpaired populations with risk factors for later cognitive decline—with the aim of identifying individuals who may benefit from potential preventative interventions.
Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation
Authors: Guo Runyuan , Chen Qingyuan , Liu Han , Wang Wenqing
Published: June 2024

Despite their high prediction accuracy, deep learning-based soft sensor (DLSS) models face challenges related to adversarial robustness against malicious adversarial attacks, which hinder their widespread deployment and safe application. Although adversarial training is the primary method for enhancing adversarial robustness, existing adversarial-training-based defense methods often struggle with accurately estimating transfer gradients and avoiding adversarial robust overfitting. To address these issues, we propose a novel adversarial training approach, namely domain-adaptive adversarial training (DAAT). DAAT comprises two stages: historical gradient-based adversarial attack (HGAA) and domain-adaptive training. In the first stage, HGAA incorporates historical gradient information into the iterative process of generating adversarial samples. It considers gradient similarity between iterative steps to stabilize the updating direction, resulting in improved transfer gradient estimation and stronger adversarial samples. In the second stage, a soft sensor domain-adaptive training model is developed to learn common features from adversarial and original samples through domain-adaptive training, thereby avoiding excessive leaning toward either side and enhancing the adversarial robustness of DLSS without robust overfitting. To demonstrate the effectiveness of DAAT, a DLSS model for crystal quality variables in silicon single-crystal growth manufacturing processes is used as a case study. Through DAAT, the DLSS achieves a balance between defense against adversarial samples and prediction accuracy on normal samples to some extent, offering an effective approach for enhancing the adversarial robustness of DLSS.
Sensor-Less Control of Mirror Manipulator Using Shape Memory Polyimide Composite Actuator: Experimental Work
Authors: Velusamy Vetriselvi , Kaliaperumal Dhanalakshmi , Choi Seung-Bok
Published: June 2024

Integrated thin film-based shape memory polyimide composites (SMPICs) are potentially attractive for efficient and compact design, thereby offering cost-effective applications. The objective of this article is to design and evaluate a mirror manipulator using an SMPIC as an actuator and a sensor with control. A sensor-less control strategy using the SMPIC (a self-sensing actuator) with a proportional derivative combined variable structure controller (PD-VSC) is proposed for position control of the mirror in both the vertical and angular directions. The mirror manipulator is able to move the mirror in the vertical and angular directions by 3.39 mm and 10.5 deg, respectively. A desired fast response is obtained as the performance under control. In addition, some benefits from the proposed control realization include good tracking, stable switching, no overshoot, no steady state oscillations, and robust disturbance rejection. These superior properties are experimentally validated to reflect practical feasibility.
Single-Line LiDAR Localization via Contribution Sampling and Map Update Technology
Authors: Jiang Xiaoxu , Yang David K. , Tian Zhenyu , Liu Gang , Lu Mingquan
Published: June 2024

Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments.
Pollination and Seed Production of Lavandula angustifolia Mill. (Lamiaceae)
Authors: Ingram Sam S. , Wilson Tyler M. , Wilson Joseph S. , Ziebarth Emma A. , Johnson MaKenna C. , Young Jacob G. , Carlson Richard E.
Published: June 2024

Lavandula angustifolia Mill., lavender, is an aromatic plant in the Lamiaceae family. Lavender is an important economic plant that is cultivated throughout the world. Previous studies have shown that the primary pollinators of lavender in Europe and North Africa are bee species, specifically Bombus spp. However, similar studies have not been previously performed in North America. The current study, on cultivated population lavender (grown from seed) in Utah (USA), found a diverse community of bees visiting lavender over a 4-week sequential and successive blooming period. The observed and identified bees were distinguished across 8 genera and 12 species, of which 3 were species not native to North America. The most observed bees were Apis mellifera (88.3%), Bombus fervidus (4.2%), and B. huntii (3.7%). To investigate seed production and viability, lavender plants were either caged off and their access withheld from pollinators, or selectively granted access to various pollinators, to determine (1) if lavender is capable of self-pollination, (2) if pollination increases both the seed production rates and seed viability of lavender, and (3) which bee species are the most efficient pollinators of lavender. The findings from this study demonstrate the ecological interactions between pollinators both native and non-native to North America on lavender, a plant native to the Mediterranean region.
The Violent Implications of Opposition to the Istanbul Convention
Authors: Roggeband Conny , Krizsán Andrea
Published: June 2024

This paper focuses on campaigns against the Council of Europe Convention on preventing and combating violence against women and domestic violence (Istanbul Convention). These campaigns not only obstructed ratification processes in a number of countries, but also that the openly hostile and highly gendered attacks had a direct impact on women’s rights activists and their work, seriously hindering their work, but also affecting their well-being and safety. In this paper we explore the violent implications of the campaigns against the Istanbul Convention which are part of wider anti-gender campaigns. We argue that the violence of the campaigns and the violent implications should be considered gendered political violence, which effectively marginalizes women and other targeted groups and obstructs their participation in society and politics and as such is central to current autocratization tendencies and undermining of democracy.
The Intergenerational Transmission of Pro-Environmental Behaviours: The Role of Moral Judgment in Primary School-Age Children
Authors: Giancola Marco , Pino Maria Chiara , Zacheo Cristina , Sannino Marta , D’Amico Simonetta
Published: June 2024

The environmental crisis poses a critical issue for current and future generations, driving research to investigate the key factors and psychological characteristics that motivate individuals to engage in pro-environmental behaviours (PEBs) from an early age. In this context, intergenerational transmission—which refers to how parents influence their children’s behaviour—plays a crucial role in initiating and promoting eco-friendly practices. From a children-centred perspective, the current study focused on the intergenerational transmission of PEBs, addressing the moderating role of children’s moral judgment. This latter was evaluated considering general moral judgment (i.e., moral transgressions, social-conventional transgressions, and non-harmful personal choices) and domain-specific environmental moral judgment (i.e., harmful actions with no specific victim, harmful actions to animals, and harmful actions to plants/trees). This study was carried out with 229 triads of Italian children (Mage = 8.54 years; SDage = 1.46 years; rangeage 6–11 years, 130 girls and 99 boys), fathers (Mage = 45.73 years; SDage = 5.07 years; rangeage 29–64 years), and mothers (Mage = 42.56 years; SDage = 4.67 years; rangeage 28–57 years). Results revealed that only the moral evaluations on harmful actions directed at animals (B = 0.32, SE = 0.15, t = 2.18, CI 95% = [0.030, 0.612]) and those towards plants/trees (B = 0.19, SE = 0.08, t = 2.49, CI 95% = [0.369, 0.342]) moderated the association between parents’ PEBs and children’s PEBs, boosting the intergenerational transmission of PEBs. Overall, this research yielded novel evidence on the main factors affecting the intergenerational transmission of PEBs, suggesting moral judgment as a critical mechanism in nurturing pro-environmental practices in school-age children. Implications, limitations, and future research directions are discussed.
Examining the Impact of Virtual Health Influencers on Young Adults’ Willingness to Engage in Liver Cancer Prevention: Insights from Parasocial Relationship Theory
Authors: Chung Donghwa , Wang Jiaqi , Meng Yanfang
Published: June 2024

The emergence of virtual influencers and AI doctors has significantly increased the attention of Chinese users, especially their health awareness and cancer health literacy. In our current study, guided by parasocial relationship theory, we examined the psychological antecedents that influence Chinese young adults’ willingness to engage in liver cancer prevention. Specifically, we aimed to examine the mediated mechanism of reduced unrealistic optimism within this relationship. A total of 252 respondents participated in this study, and the valid data were analyzed using hierarchical regression and mediation analysis to test our hypotheses. The results demonstrated three positive correlations between psychological factors (including perceived severity, parasocial relationship, and response efficacy) and Chinese young adults’ willingness to engage in liver cancer prevention. Furthermore, we found that reduced unrealistic optimism mediated these relationships. These findings provide valuable practical insights for Chinese health departments and experts to develop effective health campaign strategies that utilize multiple media platforms for optimal promotion.
Repeated Bout Effect of Downhill Running on Physiological Markers of Effort and Post Exercise Perception of Soreness in Trained Female Distance Runners
Authors: Tallis Jason , McMorrow Caitlin , Shelley Sharn P. , Eustace Steven J.
Published: June 2024

This study examined the effect of repeated bouts of level and downhill running on physiological markers of effort and exercise-induced muscle soreness in trained female distance runners. Ten participants (Age: 24.4 ± 2.0 years; V̇O2peak: 52.9 ± 1.1 mL·kg−1·min−1), naïve to downhill running, completed six alternate 5 min trials of level and downhill running (−15%) at a 70% velocity at V̇O2peak on two occasions, three weeks apart. Perceived muscle soreness was measured upon completion and in the 72 h post exercise. V̇O2, Heart Rate (HR), Blood Lactate (BLa), and Respiratory Exchange Ratio (RER) were lower running downhill (p < 0.016, ηp2 > 0.541). For the first downhill run, Rating of Perceived Exertion (RPE) was higher compared to that for level running (p = 0.051; d = 0.447), but for the remaining trials, RPE was lower when running downhill (p < 0.004; d > 0.745). V̇O2, HR, and RER were not different in the second bout (p > 0.070, ηp2 < 0.318); however, V̇O2 was lower in each downhill trial (Δ = 1.6–2.2 mL·kg−1·min−1; d = 0.382–0.426). In the second bout, BLa was lower (p = 0.005, ηp2 = 0.602), RPE in the first trial was lower (p = 0.002; d = 0.923), and post exercise perceived soreness of the gastrocnemius, quadriceps, and hamstrings was attenuated (p < 0.002; ηp2 > 0.693). Perceived soreness of the gluteal muscles was lower in the second bout immediately post exercise, 24 h, and 48 h post exercise (p < 0.025; d > 0.922). A repeated bout of downhill running attenuated perceived muscle soreness and may modulate the physiological and perceived physical demand of a second bout of level and downhill running.
Redefining Significance: Robustness and Percent Fragility Indices in Biomedical Research
Authors: Heston Thomas F.
Published: June 2024

The p-value has long been the standard for statistical significance in scientific research, but this binary approach often fails to consider the nuances of statistical power and the potential for large sample sizes to show statistical significance despite trivial treatment effects. Including a statistical fragility assessment can help overcome these limitations. One common fragility metric is the fragility index, which assesses statistical fragility by incrementally altering the outcome data in the intervention group until the statistical significance flips. The robustness index takes a different approach by maintaining the integrity of the underlying data distribution while examining changes in the p-value as the sample size changes. The percent fragility index is another useful alternative that is more precise than the fragility index and is more uniformly applied to both the intervention and control groups. Incorporating these fragility metrics into routine statistical procedures could address the reproducibility crisis and increase research efficacy. Using these fragility indices can be seen as a step toward a more mature phase of statistical reasoning, where significance is a multi-faceted and contextually informed judgment.
Enhancing Sustainable Development Goal Integration in Chilean Citizenship Education: A Thematic Analysis of Textbook Content and Instructional Strategies
Authors: Lasekan Olusiji Adebola , Opazo Felipe , Méndez Alarcón Claudia Myrna
Published: June 2024

This study examines the integration level of the three dimensions of the Sustainable Development Goals (SDGs) (social, economic, and environmental) in Chilean citizenship education textbooks for third and fourth grades’ students in high schools, aiming to identify gaps and propose a comprehensive model for thematic instruction. Utilizing the citizenship education student textbook designed by the Faculty of Educational Sciences at the San Sebastián University in Chile, this research employs thematic analysis to assess the representation of the SDGs within the educational content. The results reveal a positive aspect: the textbooks showcase all three SDG dimensions, highlighting their potential to foster comprehensive SDG comprehension. However, the emphasis predominantly lies on social inclusion, with economic and environmental dimensions receiving less attention. This imbalance could impede the provision of holistic sustainability education, especially as critical ecological issues and certain goals (SDGs 2, 7, 12, 14, and 15) remain under-represented. The proposed thematic instruction model merges sustainability with citizenship education through concept mapping, structured lesson plans, and active collaboration, enhancing SDG literacy. This research contributes to Education for Sustainable Development (ESD) by providing a comprehensive framework that transforms sustainability education globally, cultivating knowledgeable and proactive individuals prepared to address and resolve pressing global challenges.
Barriers and Enablers for Green Concrete Adoption: A Scientometric Aided Literature Review Approach
Authors: Al-Otaibi Ali
Published: June 2024

Green concrete is a concept of concrete that uses waste materials to reduce its environmental impact and has various benefits for the environment, economy, and society, such as lower construction cost, less landfill waste, new waste markets, and better quality of life. This study aims to investigate and analyze the barriers and enablers for green concrete development and implementation, based on a mixed-method approach that combines a scientometric analysis and a literature review. The Scopus database was explored first and then these data were used to investigate and capture six categories of barriers and enablers: awareness, technical, economic and market, implementation, support/promotion, and social. Results reveal that the technical and operational aspects are the main challenges for green concrete, while the awareness and social acceptance are not major issues. The current study surpasses the mere popularization of green concrete. Instead, it delves into its multifaceted dimensions, that is, technical, economic, social, and institutional. By meticulously analyzing a diverse group of research articles, key challenges and opportunities associated with green concrete are pinpointed. The findings not only deepen our understanding of the barriers impeding the widespread adoption of green concrete, but also shed light on potential solutions. In summary, this work bridges theory and practice, providing invaluable insights for future researchers, practitioners, and policymakers in the sustainable construction domain.
Comparative Analysis: Resource Consumption and Environmental Pollution by Subsoil Users in Northern Asia (Russia)
Authors: Bilgaev Alexey , Li Zehong , Sadykova Erzhena , Dong Suocheng , Tulokhonov Arnold , Li Fujia , Garmaev Endon , Mikheeva Anna , Sanzheev Erdeni
Published: June 2024

This article examines resource use issues and the anthropogenic impact on the environment of companies operating in the mineral resource sector of northern Asian regions. The research aims to comprehensively analyze resource consumption and environmental intensity indicators for leading companies in mineral extraction. A set of indicators reflecting the necessary parameters for mineral resource consumption and pollution intensity due to their production activities is constructed based on quantitative and cost indicators. A pollution intensity assessment is carried out based on environmental intensity indicators. The sectoral and corporate-level indicators are of the most interest in industrial production. The study identified seven critical northern Asian companies that play a decisive role in the mining industry, which were examples for analyzing material resource consumption and production greening pace. Reducing resource use and environmental impact is a crucial task for sustainable development. This study emphasizes the importance of evaluating resource consumption and environmental impact for companies in the mineral resource sector, providing valuable insights for companies, government bodies, and international organizations working towards the region’s sustainable development and efficient resource management.
Integrating Industry 4.0 for Sustainable Localized Manufacturing to Support Saudi Vision 2030: An Assessment of the Saudi Arabian Automotive Industry Model
Authors: Aljuaid Abdullah Awadh , Masood Syed Athar , Tipu Javed Ahmed
Published: June 2024

Saudi Arabia aims to build a sustainable and diversified economy by 2030. The automotive industry presents a key opportunity to achieve these goals through localization and the adoption of Industry 4.0 technologies. This research analyzes the impact of a developed localized production model for the automotive industry. The model promotes economic sustainability through domestic manufacturing, job creation and reduced oil dependence; environmental sustainability via the Industry 4.0 approach, which enhances customization, and social sustainability by developing human capital, transferring advanced technologies and stimulating job creation, thereby balancing the three spheres of sustainability. To attain the aim of the research, data were collected from 106 Saudi automobile companies. For the data collection, a questionnaire was designed, and authentication was performed according to the average level of use of Industry 4.0 and analysis of the level of implementation of the developed localized production model for the automotive industry of the Kingdom of Saudi Arabia. The results shows that large automobile companies in the Kingdom of Saudi Arabia are employing the developed localized production model more than medium and smaller automobile companies, and as a result of this model employment, automobile companies’ customer satisfaction through customization and revenue has improved in an agile and lean manner.
Microscopic Mechanism and Road Performance Analysis of MgO Carbonation–Solidification of Dredged Sediment
Authors: Kong Xianghui , Wang Xiaokang , Zhang Zhibin , Sun Aoqi , Yang Lei , Zhang Fengrong , Xie Bingquan , Li Yutong
Published: June 2024

MgO carbonization is a green and low-carbon soil improvement technology. The use of MgO carbonization to solidify dredged sediment and transform it into road-building materials has significant environmental sustainability advantages. A series of microscopic characterization tests, including X-ray Diffraction (XRD), Scanning Electron Microscope–Energy Dispersive Spectrometer (SEM-EDS), and Mercury-in-Pressure (MIP) tests, were conducted to elucidate the evolution characteristics of mineral composition, microscopic morphology, and pore structure of sediment under carbonation. Based on the results, the mechanism of MgO carbonation–solidification of dredged sediment was explored. In order to verify the improvement of carbonation on the road performance of sediment, comparative tests were carried out on sediment, non-carbonated sediment, and carbonated sediment. The results indicate a significant improvement in the solidification of MgO-treated sediment through carbonation, with enhanced macroscopic strength and densified microscopic structure. This can be attributed to the encapsulation, cementation, and pore-filling effects of the hydration products and carbonation products of MgO on soil particles. The rebound modulus and splitting strength of carbonated sediment were 3.53 times and 2.16 times that of non-carbonated sediment, respectively. Additionally, the carbonated sediment showed improved saturated stability, resistance to salt solution wet–dry cycles, and resistance to freeze–thaw cycles.
Enhancing Sustainable Safety Practices in Construction: Insights from Cultural and Socio-Economic Analysis
Authors: Alqahtani Fahad M. , Abas Muhammad , Alkahtani Mohammed , Hayat Mubashir , Farrukh Aasia
Published: June 2024

Exploring cultural diversity and socio-economic factors offers a holistic approach to fostering sustainable safety practices within the construction industry. This study investigates the relationship between cultural and socio-economic factors and their influence on safety practices within the construction industry, focusing specifically on Saudi Arabian construction. Employing a comparative research design, data were gathered through a questionnaire distributed among various stakeholders, including construction workers, safety officers, project managers, supervisors, and other professionals involved in construction projects. Structural equation modeling (SEM) was utilized to analyze the relationships between cultural, socio-economic, and safety practices. The findings showed significant associations of cultural and socio-economic factors with safety practices and attitudes. Specifically, positive workplace norms and values, effective communication styles, higher income levels, and better education and literacy levels were found to positively influence adherence to safety guidelines and the effectiveness of training programs. Conversely, job insecurity exhibited a negative impact on safety-related outcomes and training effectiveness. This study demonstrates the importance of fostering a positive safety culture within organizations, emphasizing leadership commitment, effective communication, and employee engagement. It also highlights the significance of addressing socio-economic factors, such as job security, income levels, and education, to promote a safer work environment and enhance safety practices. Practical implications derived from the research findings offer actionable insights for organizations to develop targeted interventions aimed at improving sustainable safety practices and training effectiveness within the construction industry in Saudi Arabia.
Association Analysis of the Spatial and Temporal Changes in Vegetation Photosynthesis Levels with Land Cover Changes in China Based on Solar-Induced Fluorescence
Authors: Liao Weihua , Wei Yifang , Huang Shengxia , Wei Zhiyan , Hu Xingwang
Published: June 2024

Understanding the relationship between vegetation photosynthesis levels and land use changes is crucial for assessing ecosystem health and plant growth status. Existing studies have not fully considered temporal and spatial dimensions, resulting in an incomplete understanding of the relationship between vegetation photosynthesis levels and land use. Based on solar-induced fluorescence (SIF) data from 2001 to 2022, this study used the Mann-Kendall (MK) test and spatial association analysis to explore the associations between temporal and spatial changes in vegetation photosynthesis levels and land cover change (LCC) in China. The contributions and findings are as follows: (1) A computational framework was utilized to comprehensively measure the spatial correlation between LCCs and chlorophyll levels based on their spatial co-occurrence. (2) The MK test results of the annual and monthly average vegetation photosynthesis levels revealed that most regions in China exhibited increasing trends, accounting for 90.01% and 91.78%, respectively. Moreover, the vegetation photosynthesis levels in western China had a downward trend, indicating that the vegetation ecosystem in this region may be under a certain degree of pressure or may face the risk of degradation. (3) Some economically developed provinces are facing ecological pressures caused by urbanization and industrialization, which have led to the degradation of vegetation ecosystems and a decrease in vegetation photosynthesis levels. (4) Highly supportive areas of the land use–vegetation photosynthesis level association analysis were mainly distributed in grassland and forest areas, indicating the effectiveness of forest protection and grassland management policies. Moreover, the decrease in vegetation photosynthesis mainly occurred in barren areas, illustrating that the management and protection of this type of land still need to be strengthened. These findings underscore the complex interplay between land use and vegetation health, providing insights for sustainable land management policies.
Evaluating the Special Education Professional Development Program Based on the Council of Exceptional Children’s Advanced Preparation Standards: A Mixed-Method Inquiry
Authors: Alabdulwahab Reem A.
Published: June 2024

A national call to promote the capability of the educational system to meet the development and job market demands initiated the development of the Special Education Higher Diploma Program (SEHDP) to create robust special education (SE) school programs. A concurrent explanatory mixed-method approach was employed. A web-based survey based on the Council of Exceptional Children (CEC)’s advanced preparation standards (2015) was sent to 561 Special Education Teachers (SETs) who were registered in the SEHDP. One hundred-and-fifty-six SETs responded to the survey (27%). Differences among participants’ responses based on gender, geographical region, educational qualifications, disability programs, and years of experience were examined. Furthermore, 10 interviews were held with SETs to explore their perspectives regarding the SEHDP strengths and challenges they encountered. Data were analyzed using descriptive and thematic analyses. The study found a consistently high rating of the seven CEC standards with an overall mean of 4.190. Statistically significant differences in terms of gender, educational qualifications, and years of experience were identified. SETs’ overall perspectives about the program were positive, indicating the program’s impact on the knowledge and skills they gained, which aligned with other studies, along with the program’s challenges and suggestions. Study limitations and implications for practice were included.
Deactivation Patterns of Potassium-Based γ-Alumina Dry Sorbents for CO2 Capture
Authors: In Soo Yeong , Min Ji Hwan , Kim Jae Chang , Lee Soo Chool
Published: June 2024

Gamma-alumina (γ-Al2O3) is an essential support material in dry sorbents used to capture CO2 from flue gas. This study explores the deactivation of potassium-based γ-Al2O3 sorbents due to by-products such as KAl(CO3)(OH)2 during CO2 capture. We synthesized sorbents with K2CO3 loadings of 5, 10, 20, and 30 wt% and subjected them to repeated capture and regeneration cycles. The results show significant variations in the deactivation degree: the sorbent with 5 wt% K2CO3 exhibited a 100% deactivation rate, while the 30 wt% variant showed a markedly reduced rate of 44.6%. These findings highlight the impact of the formation of KAl(CO3)(OH)2 at the interface between K2CO3 and γ-Al2O3 on sorbent deactivation. An equation that can be used to predict the final CO2 capture capacity based on the ratio of active material to support was proposed using these results.
Spatial Patterns and Determinants of PM2.5 Concentrations: A Land Use Regression Analysis in Shenyang Metropolitan Area, China
Authors: Shi Tuo , Zhang Yang , Yuan Xuemei , Li Fangyuan , Yan Shaofang
Published: June 2024

Identifying impact factors and spatial variability of pollutants is essential for understanding environmental exposure and devising solutions. This research focused on PM2.5 as the target pollutant and developed land use regression models specific to the Shenyang metropolitan area in 2020. Utilizing the Least Absolute Shrinkage and Selection Operator approach, models were developed for all seasons and for the annual average, explaining 62–70% of the variability in PM2.5 concentrations. Among the predictors, surface pressure exhibited a positive correlation with PM2.5 concentrations throughout most of the year. Conversely, both elevation and tree cover had negative effects on PM2.5 levels. At a 2000 m scale, landscape aggregation decreased PM2.5 levels, while at a larger scale (5000 m), landscape splitting facilitated PM2.5 dispersion. According to the partial R2 results, vegetation-related land use types were significant, with the shrubland proportion positively correlated with local-scale PM2.5 concentrations in spring. Bare vegetation areas were the primary positive factor in autumn, whereas the mitigating effect of tree cover contrasted with this trend, even in winter. The NDVI, an index used to assess vegetation growth, was not determined to be a primary influencing factor. The findings reaffirm the function of vegetation cover in reducing PM2.5. Based on the research, actionable strategies for PM2.5 pollution control were outlined to promote sustainable development in the region.
Landscape Restoration Using Individual Tree Harvest Strategies
Authors: Schriver Robert , Sessions John , Strimbu Bogdan M.
Published: June 2024

Western juniper (Juniperus occidentalis Hook.) is a native species west of the Rocky Mountains that has become noxious as its area increased ten times in the last 140 years. Restoration of the landscapes affected by the spread of juniper through harvesting poses several challenges related to the sparse spatial distribution (trees per hectare) of the resource. Therefore, the objective of the present study is to develop a harvest scheduling strategy that converts the western juniper from a noxious species to a timber resource. We propose a procedure that aggregates individual trees into elementary harvest units by considering the location of each tree. Using the coordinates of each harvest unit and its corresponding landing, we developed a spatially explicit algorithm that aims at the maximization of net revenue from juniper harvest. We applied the proposed landscape restoration approach to two areas of similar size and geomorphology. We implemented the restoration algorithm using two heuristics: simulated annealing and record-to-record travel. To account for the closeness to the mill, we considered two prices at the landing for the juniper: 45 USD/ton and 65 USD/ton. Our results suggest that restoration is possible at higher prices, but it is economically infeasible when prices are low. Simulated annealing outperformed record-to-record travel in both study areas and for both prices. Our approach and formulation to the restoration of landscapes invaded by western juniper could be applied to similar instances where complex stand structures preclude the use of traditional forest stand-level harvest scheduling and require a more granular approach.
Failure Mechanisms and Protection Measures for Expansive Soil Slopes: A Review
Authors: Luo Peng , Ma Min
Published: June 2024

Due to the significant hydrophilicity and cracking properties of expansive soils, expansive soil slopes are prone to destabilization and landslides after rainfall, seriously threatening the safety of buildings, highways, and railroads. Substantial economic losses often accompany the occurrence of expansive soil slope disasters; thus, it is of great significance to understand the slope failure mechanisms experienced by expansive soil slopes and to prevent expansive soil slope disasters. In this paper, the current research status of the landslide failure mechanism of expansive soil slopes is systematically reviewed based on three research methods: field test, model test, and numerical simulation. The failure mechanisms of expansive soil slopes and the main influencing factors are summarized. Based on the failure mechanisms, three protection principles (waterproofing and water blocking, swelling–shrinkage deformation limitation, and crack inhibition and strength enhancement) that can be followed for disaster prevention of expansive soil slopes are proposed. The research status and advantages and disadvantages of these protection methods are reviewed, and future researchable directions of the stability of expansive soil slopes and slope protection methods are explored. Based on the previous work, a new flexible ecological slope protection system with a double waterproof layer is proposed for expansive soil slopes to realize ecological, efficient, and long-term protection. This paper thus aims to provide technical reference for the prevention and control of slope engineering disasters in expansive soil areas.