bluuebunny/crossref_metadata_2025 huggingface dataset:
Mathematical Modeling for the Process of Smelting Reduction Ironmaking Integrated with Hydrogen-Rich Coal Gasification
Authors: Qu Yingxia , Song Shihao , Zou Zongshu , Shao Lei
Published: February 2024

To reduce pollution and improve the efficiency of coal resource utilization, this study proposed an integrated process for smelting reduction ironmaking and coal gasification. A multi-zone constrained mathematical model, based on heat and mass balance calculations, was developed to predict the energy and material flows required to produce 1 ton of hot metal. Two scenarios were examined: one using pure O2 as the gasification agent (referred to as the non-hydrogen-rich process) and the other using a combination of pure O2 and pure steam (referred to as the hydrogen-rich process). In the non-hydrogen rich process, as the PCR (Post Combustion Ratio) varies from 0% to 8%, the total coal consumption, O2 consumption, and volume of exported gas decrease by 57%, 57% and 53%, respectively. In the hydrogen-rich process, as the H2 content increases from 30% to 50%, the exported gas volume increases by 38%. The upper limit of H2 content in the SRV (Smelting Reduction Vessel) off-gas is mainly determined by the PCR, which decreases from 52.7% to 45.2% as the PCR varies from 0% to 8%. The findings of this work can serve as a theoretical basis for further investigation of the new process.
Study on Drying of Seaweeds and Importance of Glass Transition and Stabilization
Authors: Tolstorebrov Ignat , Senadeera Wijitha , Eikevik Trygve Magne , Bantle Michael , Sæther Maren , Petrova Inna
Published: February 2024

In this study, the thermal transition of seaweed Saccharina latissima (raw and blanched) during drying and quality stabilization was considered in view of understanding physico-chemical changes, color changes, sorption changes and thermal property changes with respect to drying kinetics. The variations in the effective moisture diffusivity coefficient with shrinkage changes and temperature lie between 1.0 and 5.0 × 10−10 m2 s−1 (raw) and 0.5 and 3.6 × 10−10 m2 s−1 (blanched), respectively. Noticeable physical and chemical changes were observed during longer drying times, especially in the case of blanched seaweeds. At the temperature of 38.0 °C, a more yellow-colored product was obtained from raw form input materials. The blanched seaweeds accumulated moisture in a linear manner with an increase in the relative humidity of the drying air in the range of 20.0~80.0%, which resulted in high level of hysteresis between the sorption and desorption behavior. Shrinkage changes during the drying of blanched and raw samples were also calculated. The thermal stabilization of raw and blanched forms started at different initial moisture contents showed changed glass transition phenomena during a wide range of temperature sand one melting endotherm between 141.9 and 167.9 °C. Some glass transitions were driven by the presence of water-soluble contents in the material. The dried seaweeds at low temperatures showed a partial glassy state and an amorphous/crystalline state. This study evaluated the effects of process parameters on the properties of dried product.
Classification of Typical Static Objects in Road Scenes Based on LO-Net
Authors: Li Yongqiang , Wu Jiale , Liu Huiyun , Ren Jingzhi , Xu Zhihua , Zhang Jian , Wang Zhiyao
Published: February 2024

Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.
An Innovative Smart and Sustainable Low-Cost Irrigation System for Anomaly Detection Using Deep Learning
Authors: Benameur Rabaie , Dahane Amine , Kechar Bouabdellah , Benyamina Abou El Hassan
Published: February 2024

The agricultural sector faces several difficulties today in ensuring the safety of food supply, including water scarcity. This study presents the design and development of a low-cost and full-featured fog-IoT/AI system targeted towards smallholder farmer communities (SFCs). However, the smallholder community is hesitant to adopt technology-based solutions. There are many overwhelming reasons for this, but the high cost, implementation complexity, and malfunctioning sensors cause inappropriate decisions. The PRIMA INTEL-IRRIS project aims to make digital and innovative agricultural technologies more appealing and available to these communities by advancing the intelligent irrigation “in-the-box” concept. Considered a vital resource, collected data are used to detect anomalies or abnormal behavior, providing information about an occurrence or a node failure. To prevent agro-field data leakage, this paper presents an innovative, smart, and sustainable low-cost irrigation system that employs artificial intelligence (AI) techniques to analyze anomalies and problems in water usage. The sensor anomaly can be detected using an autoencoder (AE) and a generative adversarial network (GAN). We will feed the autoencoders’ anomaly detection models with time series records from the datasets and replace detected anomalies with the reconstructed outputs. When integrated with an IoT platform, this methodology is a tool for easing the labeling of sensor anomalies and can help create supervised datasets for future research. In addition, anomalies can be corrected by prediction models based on deep learning approaches, applying CNN/BiLSTM architecture. The results show that AEs outperform the GANs, achieving an accuracy of 90%, 95%, and 97% for soil moisture, air temperature, and air humidity, respectively. The proposed system is designed to ensure that the data are of high quality and reliable enough to make sound decisions compared to the existing platforms.
Impact of Fatigue on Ergonomic Risk Scores and Foot Kinetics: A Field Study Employing Inertial and In-Shoe Plantar Pressure Measurement Devices
Authors: Simon Steven , Dully Jonas , Dindorf Carlo , Bartaguiz Eva , Becker Stephan , Fröhlich Michael
Published: February 2024

(1) Background: Occupational fatigue is a primary factor leading to work-related musculoskeletal disorders (WRMSDs). Kinematic and kinetic experimental studies have been able to identify indicators of WRMSD, but research addressing real-world workplace scenarios is lacking. Hence, the authors of this study aimed to assess the influence of physical strain on the Borg CR-10 body map, ergonomic risk scores, and foot pressure in a real-world setting. (2) Methods: Twenty-four participants (seventeen men and seven women) were included in this field study. Inertial measurement units (IMUs) (n = 24) and in-shoe plantar pressure measurements (n = 18) captured the workload of production and office workers at the beginning of their work shift and three hours later, working without any break. In addition to the two 12 min motion capture processes, a Borg CR-10 body map and fatigue visual analog scale (VAS) were applied twice. Kinematic and kinetic data were processed using MATLAB and SPSS software, resulting in scores representing the relative distribution of the Rapid Upper Limb Assessment (RULA) and Computer-Assisted Recording and Long-Term Analysis of Musculoskeletal Load (CUELA), and in-shoe plantar pressure. (3) Results: Significant differences were observed between the two measurement times of physical exertion and fatigue, but not for ergonomic risk scores. Contrary to the hypothesis of the authors, there were no significant differences between the in-shoe plantar pressures. Significant differences were observed between the dominant and non-dominant sides for all kinetic variables. (4) Conclusions: The posture scores of RULA and CUELA and in-shoe plantar pressure side differences were a valuable basis for adapting one-sided requirements in the work process of the workers. Traditional observational methods must be adapted more sensitively to detect kinematic deviations at work. The results of this field study enhance our knowledge about the use and benefits of sensors for ergonomic risk assessments and interventions.
Pullulan-1-Ethyl-3-Methylimidazolium Tetrafluoroborate Composite as a Water-Soluble Active Component of a Vibration Sensor
Authors: Di Pasquale Giovanna , Graziani Salvatore , Pollicino Antonino , Trigona Carlo
Published: February 2024

In recent years, the issue of electronic waste production has gained significant attention. To mitigate the environmental impact of e-waste, one approach under consideration involves the development of biodegradable electronic devices or devices that dissolve in the environment at the end of their life cycle. This study presents results related to the creation of a sensor that effectively addresses both criteria. The device was constructed using a composite material formed by impregnating a pullulan membrane (a biodegradable water-soluble biopolymer) with 1-Ethyl-3-Methylimidazolium tetrafluoroborate (a water-soluble ionic liquid) and coating the product with a conductive silver-based varnish. Capitalizing on the piezoionic effect, the device has demonstrated functionality as a vibration sensor with a sensitivity of approximately 5.5 × 10−5 V/mm and a resolution of about 1 mm. The novelty of this study lies in the unique combination of materials. Unlike the use of piezoelectric materials, this combination allows for the production of a device that does not require an external potential difference generator to function properly as a sensor. Furthermore, the combination of a biopolymer, such as pullulan, and an ionic liquid, both readily soluble in water, in creating an active electronic component represents an innovation in the field of vibration sensors.
A TRIZ Approach for Designing a Smart Lighting and Control System for Classrooms Based on Counter Application with Dual PIR Sensors
Authors: Chong Peng Lean , Ismail Daniel , Ng Poh Kiat , Kong Feng Yuan , Basir Khan Mohammed Reyasudin , Thirugnanam Sargunam
Published: February 2024

Electrical energy is often wasted through human negligence when people do not switch off electrical appliances such as lighting after leaving a place. Such a scenario often happens in a classroom when the last person leaves the class and forgets to switch off the electrical appliances. Such wastage may not be able to be afforded by schools that are limited financially. Therefore, this research proposed a simple and cost-effective system that can analyze whether there is or is not a human presence in the classroom by applying a counter to count the total number of people entering and leaving the classroom based on the sensing signals of a set of dual PIR sensors only and then correlating this to automatically turn on or off the electrical appliances mentioned. The total number of people identified in the classroom is also displayed on an LCD screen. A TRIZ approach is used to support the ideation of the system. The system can switch on several electrical output loads simultaneously when the presence of people is detected and switch them off when there are no people in the classroom. The proposed system can be expanded to be used in homes, offices, and buildings to prevent the high cost of electricity consumption caused by the negligence of people. This enables smarter control of electricity consumption.
Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments
Authors: Mg Wai Hnin Eaindrar , Tin Pyke , Aikawa Masaru , Kobayashi Ikuo , Horii Yoichiro , Honkawa Kazuyuki , Zin Thi Thi
Published: February 2024

Ensuring precise calving time prediction necessitates the adoption of an automatic and precisely accurate cattle tracking system. Nowadays, cattle tracking can be challenging due to the complexity of their environment and the potential for missed or false detections. Most existing deep-learning tracking algorithms face challenges when dealing with track-ID switch cases caused by cattle occlusion. To address these concerns, the proposed research endeavors to create an automatic cattle detection and tracking system by leveraging the remarkable capabilities of Detectron2 while embedding tailored modifications to make it even more effective and efficient for a variety of applications. Additionally, the study conducts a comprehensive comparison of eight distinct deep-learning tracking algorithms, with the objective of identifying the most optimal algorithm for achieving precise and efficient individual cattle tracking. This research focuses on tackling occlusion conditions and track-ID increment cases for miss detection. Through a comparison of various tracking algorithms, we discovered that Detectron2, coupled with our customized tracking algorithm (CTA), achieves 99% in detecting and tracking individual cows for handling occlusion challenges. Our algorithm stands out by successfully overcoming the challenges of miss detection and occlusion problems, making it highly reliable even during extended periods in a crowded calving pen.
Enhanced YOLOv5s-Based Algorithm for Industrial Part Detection
Authors: Fang Yingjian , Wu Qingxiao , Li Sicong , Guan Jian , Cui Yunge
Published: February 2024

In complex industrial environments, accurate recognition and localization of industrial targets are crucial. This study aims to improve the precision and accuracy of object detection in industrial scenarios by effectively fusing feature information at different scales and levels, and introducing edge detection head algorithms and attention mechanisms. We propose an improved YOLOv5-based algorithm for industrial object detection. Our improved algorithm incorporates the Crossing Bidirectional Feature Pyramid (CBiFPN), effectively addressing the information loss issue in multi-scale and multi-level feature fusion. Therefore, our method can enhance detection performance for objects of varying sizes. Concurrently, we have integrated the attention mechanism (C3_CA) into YOLOv5s to augment feature expression capabilities. Furthermore, we introduce the Edge Detection Head (EDH) method, which is adept at tackling detection challenges in scenes with occluded objects and cluttered backgrounds by merging edge information and amplifying it within the features. Experiments conducted on the modified ITODD dataset demonstrate that the original YOLOv5s algorithm achieves 82.11% and 60.98% on mAP@0.5 and mAP@0.5:0.95, respectively, with its precision and recall being 86.8% and 74.75%, respectively. The performance of the modified YOLOv5s algorithm on mAP@0.5 and mAP@0.5:0.95 has been improved by 1.23% and 1.44%, respectively, and the precision and recall have been enhanced by 3.68% and 1.06%, respectively. The results show that our method significantly boosts the accuracy and robustness of industrial target recognition and localization.
A Millimeter-Wave Broadband Multi-Mode Substrate-Integrated Gap Waveguide Traveling-Wave Antenna with Orbit Angular Momentum
Authors: Lin Qiu-Hua , Hou Da , Wang Lihui , Chen Pengpeng , Luo Zhiyong
Published: February 2024

Orbit angular momentum (OAM) has been considered a new dimension for improving channel capacity in recent years. In this paper, a millimeter-wave broadband multi-mode waveguide traveling-wave antenna with OAM is proposed by innovatively utilizing the transmitted electromagnetic waves (EMWs) characteristic of substrate-integrated gap waveguides (SIGWs) to introduce phase delay, resulting in coupling to the radiate units with a phase jump. Nine “L”-shaped slot radiate elements are cut in a circular order at a certain angle on the SIGW to generate spin angular momentum (SAM) and OAM. To generate more OAM modes and match the antenna, four “Π”-shaped slot radiate units with a 90° relationship to each other are designed in this circular array. The simulation results show that the antenna operates at 28 GHz, with a −10 dB fractional bandwidth (FBW) = 35.7%, ranging from 25.50 to 35.85 GHz and a VSWR ≤ 1.5 dB from 28.60 to 32.0 GHz and 28.60 to 32.0 GHz. The antenna radiates a linear polarization (LP) mode with a gain of 9.3 dBi at 34.0~37.2 GHz, a l = 2 SAM–OAM (i.e., circular polarization OAM (CP–OAM)) mode with 8.04 dBi at 25.90~28.08 GHz, a l = 1 and l = 2 hybrid OAM mode with 5.7 dBi at 28.08~29.67 GHz, a SAM (i.e., left/right hand circular polarization (L/RHCP) mode with 4.6 dBi at 29.67~30.41 GHz, and a LP mode at 30.41~35.85 GHz. In addition, the waveguide transmits energy with a bandwidth ranging from 26.10 to 38.46 GHz. Within the in-band, only a quasi-TEM mode is transmitted with an energy transmission loss |S21| ≤ 2 dB.
Wearable-Sensor-Based Weakly Supervised Parkinson’s Disease Assessment with Data Augmentation
Authors: Yue Peng , Li Ziheng , Zhou Menghui , Wang Xulong , Yang Po
Published: February 2024

Parkinson’s disease (PD) is the second most prevalent dementia in the world. Wearable technology has been useful in the computer-aided diagnosis and long-term monitoring of PD in recent years. The fundamental issue remains how to assess the severity of PD using wearable devices in an efficient and accurate manner. However, in the real-world free-living environment, there are two difficult issues, poor annotation and class imbalance, both of which could potentially impede the automatic assessment of PD. To address these challenges, we propose a novel framework for assessing the severity of PD patient’s in a free-living environment. Specifically, we use clustering methods to learn latent categories from the same activities, while latent Dirichlet allocation (LDA) topic models are utilized to capture latent features from multiple activities. Then, to mitigate the impact of data imbalance, we augment bag-level data while retaining key instance prototypes. To comprehensively demonstrate the efficacy of our proposed framework, we collected a dataset containing wearable-sensor signals from 83 individuals in real-life free-living conditions. The experimental results show that our framework achieves an astounding 73.48% accuracy in the fine-grained (normal, mild, moderate, severe) classification of PD severity based on hand movements. Overall, this study contributes to more accurate PD self-diagnosis in the wild, allowing doctors to provide remote drug intervention guidance.
Spatial-Temporal Dynamic Evolution of Land Deformation Driven by Hydrological Signals around Chaohu Lake
Authors: Tao Tingye , Dai Ju , Song Zichen , Li Shuiping , Qu Xiaochuan , Zhu Yongchao , Li Zhenxuan , Zhu Mingming
Published: February 2024

The frequent occurrence of extreme climate events has a significant impact on people’s lives. Heavy rainfall can lead to an increase of regional Terrestrial Water Storage (TWS), which will cause land subsidence due to the influence of hydrological load. At present, regional TWS is mostly obtained from Gravity Recovery and Climate Experiment (GRACE) data, but the method has limitations for small areas. This paper used water level and flow data as hydrological signals to study the land subsidence caused by heavy rainfall in the Chaohu Lake area of East China (June 2016–August 2016). Pearson’s correlation coefficient was used to study the interconnection between water resource changes and Global Navigation Satellites System (GNSS) vertical displacement. Meanwhile, to address the reliability of the research results, combined with the Coefficient of determination method, the research findings were validated by using different institutional models. The results showed that: (1) During heavy rainfall, the vertical displacement caused by atmospheric load was larger than non-tidal oceanic load, and the influence trends of the two were opposite. (2) The rapidly increasing hydrologic load in the Chaohu Lake area resulted in greater subsidence displacement at the closer CORS station (CHCH station) than the more distant CORS station (LALA station). The Pearson correlation coefficients between the vertical displacement and water level were as high as −0.80 and −0.64, respectively. The phenomenon confirmed the elastic deformation principle of disc load. (3) Although there was a systematic bias between the different environmental load deformation models, the deformation trends were generally consistent with the GNSS monitoring results. The average Coefficients of determination between the different models and the GNSS results were 0.63 and 0.77, respectively. The results demonstrated the effectiveness of GNSS in monitoring short-term hydrological load. This study reveals the spatial-temporal evolution of land deformation during heavy rainfall around Chaohu Lake, which is of reference significance for water resource management and infrastructure maintenance in this area.
Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis
Authors: Li Wei , Zhou Haozhou , Lu Zhenyuan , Kamarthi Sagar
Published: February 2024

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.
Physiological and Thermal Sensation Responses to Severe Cold Exposure (−20 °C)
Authors: Zlatar Tomi , Bustos Denisse , Costa José Torres , Baptista João Santos , Guedes Joana
Published: February 2024

Various jobs, indoors and outdoors, are subjected to severe cold temperatures during daily activities. Extremely low-temperature exposure and work intensity affect health, safety, and occupational performance. This work aimed to assess the physiological and thermal sensation responses before, during, and following a 60 min exposure to cold (−20 °C), during which occupational activities were developed. Using ingestible telemetric temperature pills, eight skin temperature sensors, blood pressure equipment, and the Thermal Sensation Questionnaire, experiments were conducted with 11 healthy male volunteers wearing highly insulating cold protective clothing. The most notorious alterations were reported in mean skin temperatures and thermal sensation responses during the first 20 min of cold exposure. Among the eight skin temperature points, the forehead and left hand showed a higher sensitivity to cold. The mean core temperature reported significant variations throughout the protocol, with decreases during the initial 10 min of cold exposure and posterior increases despite the cold environment. Blood pressure showed slight increases from the initial to the recovery period. Overall, outcomes contribute to current scientific knowledge on physiological and perception responses in extremely cold environments while describing the influence of protective clothing and occupational activities on these responses. Future research should be developed with additional skin temperature measurements in the extremities (fingers, face, and toes) and the analysis of thermal sensation potential associations with performance changes, which can also be of great significance for future thermal comfort models.
Role of Smartphone Applications in the Assessment and Management of Fatigue in Patients with Multiple Sclerosis: A Scoping Review
Authors: Antonioni Annibale , Baroni Andrea , Milani Giada , Cordioli Irene , Straudi Sofia
Published: February 2024

Fatigue is a common symptom in Multiple Sclerosis (MS), and its assessment depends entirely on patient reports. Importantly, managing MS symptoms is increasingly supported by Digital Health Technology (DHT), which includes Mobile Health Technology (mHT). Considering the growing interest, we aimed to synthesise evidence about smartphone applications for the assessment and management of fatigue in MS, as well as to investigate their usability, feasibility, and reliability. We performed a literature search in PubMed, Science Direct, and Embase using a scoping review approach. We included 16 articles and, although many lacked crucial methodological details, DHT was evaluated in all MS clinical subtypes and with disease durations up to more than 20 years. Despite the marked heterogeneity in terms of the employed methods, all documented a high degree of usability, assessed both as feedback from participants and completed tasks. Moreover, the feasibility assessment also showed good results, as apps were able to discriminate between patients with and without fatigue. Importantly, most also showed excellent results in terms of reliability, and some patients reported a reduction in fatigue thanks to mHT. Despite limitations, mHT has been positively evaluated by patients, suggesting a promising role of DHT in the self-management of MS.
Validation Using Structural Equations of the “Cursa-T” Scale to Measure Research and Digital Competencies in Undergraduate Students
Authors: Duarte Ayala Rocío Elizabeth , Palacios-Rodríguez Antonio , Guzmán-Cedillo Yunuen Ixchel , Segura Leticia Rodríguez
Published: February 2024

Research competencies are considered essential in fields such as science, academia, and technology, and this research seeks to provide a reliable tool to evaluate them. Therefore, the main objective of this study is to validate the “Cursa-T” scale through an exploratory and confirmatory factor analysis, as well as through structural equations, to ensure that the data collected fit the proposed theoretical model. The study sample consists of 1104 university students, mostly female, and a questionnaire based on previous studies is used. The most important results of the research include the validation of the “Cursa-T” scale through advanced statistical methods, such as exploratory and confirmatory factor analysis. The scale is found to be reliable and valid for measuring undergraduate research and digital competencies, and the data collected fit the proposed theoretical model. The discussion of the research highlights the importance of technology, devices, software, and the use of platforms in the development of research and digital competencies in Health Sciences students. It also reflects on the role of social networks in these competencies, as they can facilitate participation in academic communities. Ultimately, the research underlines the relevance of preparing undergraduate students in health areas.
Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China
Authors: Zhang Huayong , Zheng Jiayu , Zou Hengchao , Wang Zhongyu , Ji Xiande , Zhang Shijia , Liu Zhao
Published: February 2024

Understanding habitat quality patterns and their drivers in arid zones is of fundamental importance to the sustainability maintenance of terrestrial ecosystems, but remains elusive. Here, we applied the InVEST model to investigate the spatiotemporal patterns of habitat quality in the northern sand-prevention belt (NSPB) across five time periods (2000, 2005, 2010, 2015, 2018), coupled with the structural equation model (SEM) and boosted regression tree (BRT) model to identify their integrated driving forces. The results exhibited that habitat quality in high-level zones expanded gradually from 2000 to 2018, while the middle- and low-level zones shrank. Climate, soil, topography, and human activities were significantly correlated with habitat quality, with mean annual temperature (MAT) and human activities being key contributing factors in the high-level and low-level zones, respectively, whereas the contribution of factors varied considerably in the middle-level zones. The interactions among climate, soil, topography, and human activities jointly drive habitat quality changes. Climate intensified the positive effects of soil on habitat quality, while the topographic and human activities mainly affected habitat quality indirectly through climate and soil. Our findings offer a scientific guidance for the restoration and sustainable management of desertification ecosystems in northern China.
Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets
Authors: Almutairi Mubarak Saad
Published: February 2024

The conceptual fusion of smart city and sustainability indicators has inspired the emergence of the smart sustainable city (SSC). Given the early stage of development in this field, most SSC studies have been primarily theoretical. Notably, existing empirical studies have overlooked the crucial aspect of feature engineering in the context of SSC, despite its significance in advancing SSC initiatives. This paper introduces an approach advocating for feature subset selection to maximize prediction accuracy and minimize computational time across diverse SSC indicators encompassing socio-cultural, economic, environmental, and governance categories. The study systematically collected multiple datasets on SSC indicators, covering various themes within the SSC framework. Employing six carefully chosen multiple-objective evolutionary feature selection algorithms, the research selected feature subsets. These subsets were then utilized in modeling algorithms to predict SSC indicators. The proposal enhanced prediction accuracy for life expectancy, online shopping intentions, energy consumption, air quality, water quality, and traffic flow for a smart and sustainable city by minimizing the subset features. The findings underscore the efficacy of feature subset selection in generating minimal features, thereby enhancing both prediction accuracy and computational efficiency in the realm of SSC indicators. For researchers aiming to develop sustainable systems for real-time data monitoring within SSC, the identified subset features offer a valuable resource, negating the necessity for extensive dataset collection. The provided SSC datasets are anticipated to serve as a catalyst, inspiring researchers to embark on empirical studies that explore SSC development from diverse perspectives, ultimately contributing to a more profound understanding of the SSC dynamics.
Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach
Authors: Nicolis Orietta , Maidana Jean Paul , Contreras Fabian , Leal Danilo
Published: February 2024

This work presents a comprehensive analysis of the economic impact of the COVID-19 pandemic, with a focus on OECD countries and the Chilean case. Utilizing a clustering approach, the research aims to investigate how countries can be categorized based on their pandemic mitigation strategies, economic responses, and infection rates. The methodology incorporates k-means and hierarchical clustering techniques, along with dynamic time warping, to account for the temporal variations in the pandemic’s progression across different nations. The study integrates the GDP into the analysis, thereby offering a perspective on the relationship between this economic indicator and health measures. Special attention is given to the case of Chile, thus providing a detailed examination of its economic and financial indicators during the pandemic. In particular, the work addresses the following main research questions: How can the OECD countries be clustered according to some health and economical indicators? What are the impacts of mitigation measures and the pension fund withdrawals on the Chilean economy? The study identifies significant differences (p-value < 0.05%) in the GDPs and infection rates between the two identified clusters that are influenced by government measures, particularly in the banking sector (55% and 60% in clusters 1 and 2, respectively). In Chile, a rebound in the IMACEC index is noted after increased liquidity, especially following partial pension fund withdrawals, thereby aligning with discrepancies between model forecasts and actual data. This study provides important insights for evidence-based public policies, thus aiding decision makers in mitigating the socioeconomic impact of global health crises and offering strategic advice for a sustainable economy.
Impact of the Citrus Industry on the Water Quality of the Filobobos River in Veracruz, Mexico
Authors: Sandoval Herazo Luis Carlos , González-Moreno Humberto Raymundo , Carreto-Hernandez Luis Guillermo , Zurita Florentina , Nani Graciela , Zamora Sergio , Sandoval-Herazo Mayerlin , Martínez-Reséndiz Georgina
Published: February 2024

Veracruz, Mexico, boasts abundant water resources. However, the region is facing challenges stemming from population growth and industrial expansion, leading to a notable increase in wastewater discharge into surface water bodies. This discharge has resulted in significant alterations to water quality. In this study, we analyzed the water quality in the Bobos River, Veracruz, with a focus on sustainability. A total of 12 samples were collected from various points along the reservoir. Parameters including temperature, pH, dissolved oxygen (DO), conductivity, total nitrogen (TN), nitrate, chemical oxygen demand (COD), total phosphorus (TP), phosphate, dissolved solids (DSs), and fecal coliforms (FCs) were measured and analyzed. The pH levels ranged from slightly acidic (6.40 ± 0.71) to slightly alkaline (8.65 ± 0.07), with the lowest dissolved oxygen concentration recorded at 0.15 ± 0.07 mg L−1. Conductivity varied between 0.26 and 3.81 mS. Total nitrogen concentrations ranged from 0.21 ± 3.8 to 0.491 ± 1.3 mg L−1. Dissolved solid concentrations were measured at 0.39 ± 0.05 mg L−1, while the final sampling point exhibited elevated values for temperature, nitrate, chemical oxygen demand, total phosphorus, and coliforms (30.35 ± 0.21 °C, 0.35, 389.00 ± 11.31, 13.20 ± 0.85 mg L−1, and 3.2 × 105 ± 3.707107 × 104, respectively). To address these concerning trends and safeguard public health and environmental integrity, the implementation of continuous monitoring and stringent control measures is imperative.
Intervention Works Conducted to Ensure the Stability of a Slope: A Sustainability Study
Authors: Tudorica Mircea Raul , Bob Corneliu Ioan
Published: February 2024

Challenges related to sustainability arise in all areas of human activity, but with a significant impact on the environment considering that the construction industry is held accountable for nearly one-third of the world’s final energy consumption. The aim of this paper is to assess through the use of the Bob–Dencsak specific model a sustainable slope design taking into account environmental, economic, and safety variables. Thus, analysis was performed on four intervention works, two versions of reinforced concrete retaining walls and two versions of reinforced soil with a biaxial geogrid, which ensure the stability of a slope that serves as a base for an access road to an ecological landfill located in Alba County, Romania. The study’s analysis points out that reinforced soil retaining walls are far more sustainable, providing the best sustainability indices, which is also supported by the impact of geogrids compared to reinforced concrete, thus resulting in the finding that reinforced concrete is less sustainable, achieving increases of up to 23% for embodied energy and 66% of CO2 emissions in the atmosphere. Finally, the paper provides recommendations for future research on the sustainability assessment of slopes, with the intention of reducing environmental damage, while keeping costs to a minimum.
Assessing Shrub Patch Characteristics and Soil Nutrient Distribution Patterns of Four Typical Alpine Shrub Plants in the Eastern Qilian Mountains
Authors: Zhao Jinmei , Adu Benjamin , Wang Jingnan , Fan Yuhang
Published: February 2024

Shrub patches have an impact on soil fertility and vegetation, influencing species composition and diversity. The unique context of the Eastern Qilian Mountains provides insights into alpine ecosystems’ responses to environmental challenges. This study aimed to evaluate the physical characteristics and soil nutrient contents of shrub patches for four different shrub species (Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF)). We assessed their patch characteristics and soil nutrients at different depths within three patch microsites (the center (CS), the edge (ES), and the midpoint between the center and the edge of the shrub patch (BC)). Soil samples were collected and analyzed for organic matter, nitrogen, phosphorus, and potassium content. Statistical analyses were conducted to evaluate differences among shrub species and locations within the shrub patches. Our results showed that the shrub species exhibited variations in patch characteristics and soil nutrient distribution. Soil nutrient content varied by depth and location within the shrub patches, with higher concentrations at the center. The relative interaction intensity (RII) revealed nutrient aggregation or dispersion trends. The study highlighted the complex interactions between shrub characteristics and soil nutrients, emphasizing their influence on nutrient cycling, vegetation dynamics, and soil properties. These findings contribute to our understanding of alpine ecosystem dynamics and inform conservation, sustainability, and management strategies.
The Assessment of Attitudes towards Retirement from a Psychosocial Approach
Authors: Elvira-Zorzo Maria Natividad , Merino-Tejedor Enrique , Lorenzo Miguel
Published: February 2024

The aim of this research is, on the one hand, to develop a scale that can be used for assessing attitudes towards retirement as a prevention and intervention tool for improving the life of people in retirement, thereby enhancing a sustainable world that offers quality of life, as well as personal, social, and community well-being, and the efficient use of available materials and socio-sanitary resources. On the other hand, the aim of this research is to analyze the psychometric properties of such a scale, i.e., the reliability and validity of a sample of people at an age that is close to retirement age. Hence, the factorial validity was tested using the confirmatory factor analysis (CFA) technique, and the criterion validity was tested by considering general self-efficacy, self-regulation, state of irritation, and certain dimensions of health and personality. The obtained results confirmed the existence of four factors in the scale of attitudes towards retirement as follows: (i) leisure, (ii) economy, (iii) status, and (iv) health. The obtained correlations showed that attitudes towards retirement are positively linked to variables such as self-esteem and self-regulation, whereas significant and negative correlations related to irritation and fatigue were found. Hence, according to the obtained results, the proposed scale is an easy and relevant tool for working on a better and more profitable psychological adaptation to retirement from work. Thus, a society where tools of psychological evaluation, such as the Attitudes toward Retirement Scale (ARS) proposed in this study, are utilized allows for the detection of problems among people facing retirement, which will lead to a more sustainable and evolved society that provides quality of life as well as personal, social, and community well-being.
How Information Technology (IT) Is Shaping Consumer Behavior in the Digital Age: A Systematic Review and Future Research Directions
Authors: Singh Prakash , Khoshaim Lama , Nuwisser Bader , Alhassan Ibrahim
Published: February 2024

The success of modern businesses hinges on their capability to recognize and explore emerging patterns in consumer behavior within the context of information technology (IT). While the study of consumer behavior has made notable progress, there remains a need to study the convergence between IT and consumer behavior to discover new prospects and insights that can improve business performance. Therefore, conducting a thorough study to evaluate the current state of research in this area is imperative. This endeavor can accumulate and map current research, extending knowledge and good practices for businesses and stakeholders. This study studied the intersection of IT and consumer behavior using the systematic literature review (SLR) approach and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The primary objective was to review the comprehensiveness of systematic review reports and meta-analysis studies in journals indexed with the SSCI and SCIE categories of the Web of Science. After applying the PRISMA approach, a sample of 40 eligible articles was finalized for further review. It identified four critical themes related to research: technological diffusion, disruptive consumer behavior, IT and consumer behavior, and the impact of IT on consumer behavior. The study’s results deliver practical implications for businesses and stakeholders and contribute to progressing knowledge in this domain.
Factors Determining the Choice of Pro-Ecological Products among Generation Z
Authors: Bełch Paulina , Hajduk-Stelmachowicz Marzena , Chudy-Laskowska Katarzyna , Vozňáková Iveta , Gavurová Beáta
Published: February 2024

(1) Background: This article aims to present the factors influencing purchasing decisions by Generation Z respondents (from Poland, Slovakia, the Czech Republic, and Hungary) relating to pro-ecological products from the electrical machinery industry. Additionally, a model was created allowing us to determine whether the pro-environmental approach is associated with other purchase determinants identified in the factor analysis. (2) Methods: Verification of the objective was carried out using, i.a., a survey method, Delphi and factor analysis, econometric modeling, and the Kruskal–Wallis test. (3) Results: The most significant factors declared by respondents from Generation Z of the V4 countries influencing pro-quality and pro-environmental purchasing decisions from this industry were product price (3.959), previous personal experience with the product (3.959), expected product life (durability) (3.809), and individual customer needs (3.615). The least important determinants were the period for which the product was on the market (2.843) then, surprisingly, product packaging (2.902), and ecolabel (2.921). (4) Conclusions: Interpreting the resulting model, it can be concluded that as the assessment of knowledge and experience as well as the assessment of the brand and image increased, the assessment of the importance of pro-ecological factors also increased. Branding and image are more linked to environmental activities.
Modulation of the Tumor Stroma and Associated Novel Nanoparticle Strategies to Enhance Tumor Targeting
Authors: Haze Hessel , Sier Cornelis F. M. , Vahrmeijer Alexander L. , Vuijk Floris A.
Published: February 2024

Growth of malignant cells in solid tumors induces changes to the tumor microenvironment (TME). These changes result in promotion of tumor growth, invasion, and metastasis, but also in tumor resistance to drugs and radiotherapy. The enhanced permeability and retention (EPR) effect in neo-angiogenic tumor tissue enables the transport of therapeutic molecules from the circulation into the tumor, but studies show that further diffusion of these agents is often not sufficient for efficient tumor eradication. Despite the hyperpermeable vasculature facilitating the delivery of drugs and tracers, the high density of stromal cells and matrix proteins, in combination with the elevated interstitial fluid pressure in the microenvironment of solid tumors, presents a barrier which limits the delivery of compounds to the core of the tumor. Reversing the cancer-cell-induced changes to the microenvironment as well as novel nanoparticle strategies to circumvent tumor-induced stromal changes have therefore been suggested as potential methods to improve the delivery of therapeutic molecules and drug efficacy. Strategies to modulate the TME, i.e., normalization of tumor vasculature and depletion of excessive stromal proteins and cells, show promising results in enhancing delivery of therapeutic compounds. Modulation of the TME may therefore enhance the efficacy of current cancer treatments and facilitate the development of novel treatment methods as an alternative for invasive resection procedures.
Determinants of Ecological Footprint: A Quantile Regression Approach
Authors: Akyol Özcan Kübra
Published: February 2024

Through the examination of the ecological consequences of human actions, policymakers are able to distinguish certain areas in which resource use can be increased and the generation of waste diminished. This study examines the effects of foreign direct investment, gross domestic product, industrialization, renewable energy consumption, and urban population on the ecological footprints in 131 countries between 1997 and 2020. The objective of this study is to establish a thorough understanding of the relationship between these variables and ecological footprints while considering temporal changes from economic and environmental aspects. The analysis of a substantial dataset encompassing many countries aims to uncover recurring patterns and trends that can provide valuable information for the formulation of policies and strategies pertaining to sustainable development on a global level. The study fills a significant gap in the knowledge on the ecological impact of different variables, providing a nuanced understanding of the interdependencies among these factors, thus guiding sustainable development strategies, and promoting global sustainability. The study utilizes quantile regression analysis, a nonparametric estimator, to estimate consistent coefficients. The statistical analysis reveals that FDI, urbanization, and GDP have statistically significant and positive effects on ecological footprints. Industrialization and renewable energy consumption show significant and negative relationships with ecological footprints. The findings of this study contribute to the understanding of the relationships among these variables and provide insight to inform policy and decision-making efforts focused on reducing ecological consequences and advancing sustainable development goals.