bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of In-Cloth versus On-Skin Sensors for Measuring Trunk and Upper Arm Postures and Movements
Authors: Hoareau Damien , Fan Xuelong , Abtahi Farhad , Yang Liyun
Published: April 2023

Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (±SD) absolute cloth–skin sensor differences of the median dominant arm elevation angle ranged between 1.2° (±1.4) and 4.1° (±3.5). For the median trunk flexion angle, the mean absolute cloth–skin sensor differences ranged between 2.7° (±1.7) and 3.7° (±3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners.
Sensing of Catecholamine in Human Urine Using a Simple Colorimetric Assay Based on Direct Melanochrome and Indolequinone Formation
Authors: Lettieri Mariagrazia , Spinelli Michele , Caponi Laura , Scarano Simona , Palladino Pasquale , Amoresano Angela , Minunni Maria
Published: April 2023

We used the first enzyme-free synthesis and stabilization of soluble melanochrome (MC) and 5,6-indolequinone (IQ) derived from levodopa (LD), dopamine (DA), and norepinephrine (NE) oxidation to develop a simple colorimetric assay for catecholamine detection in human urine, also elucidating the time-dependent formation and molecular weight of MC and IQ using UV–Vis spectroscopy and mass spectrometry. The quantitative detection of LD and DA was achieved in human urine using MC as a selective colorimetric reporter to demonstrate the potential assay applicability in a matrix of interest in therapeutic drug monitoring (TDM) and in clinical chemistry. The assay showed a linear dynamic range between 5.0 mg L−1 and 50.0 mg L−1, covering the concentration range of DA and LD found in urine samples from, e.g., Parkinson’s patients undergoing LD-based pharmacological therapy. The data reproducibility in the real matrix was very good within this concentration range (RSDav% 3.7% and 6.1% for DA and LD, respectively), also showing very good analytical performances with the limits of detection of 3.69 ± 0.17 mg L−1 and 2.51 ± 0.08 mg L−1 for DA and LD, respectively, thus paving the way for the effective and non-invasive monitoring of dopamine and levodopa in urine from patients during TDM in Parkinson’s disease.
Phase-Resolved Optical Coherence Elastography: An Insight into Tissue Displacement Estimation
Authors: Batista Ana , Serranho Pedro , Santos Mário J. , Correia Carlos , Domingues José P. , Loureiro Custódio , Cardoso João , Barbeiro Sílvia , Morgado Miguel , Bernardes Rui
Published: April 2023

Robust methods to compute tissue displacements in optical coherence elastography (OCE) data are paramount, as they play a significant role in the accuracy of tissue elastic properties estimation. In this study, the accuracy of different phase estimators was evaluated on simulated OCE data, where the displacements can be accurately set, and on real data. Displacement (∆d) estimates were computed from (i) the original interferogram data (Δφori) and two phase-invariant mathematical manipulations of the interferogram: (ii) its first-order derivative (Δφd) and (iii) its integral (Δφint). We observed a dependence of the phase difference estimation accuracy on the initial depth location of the scatterer and the magnitude of the tissue displacement. However, by combining the three phase-difference estimates (Δdav), the error in phase difference estimation could be minimized. By using Δdav, the median root-mean-square error associated with displacement prediction in simulated OCE data was reduced by 85% and 70% in data with and without noise, respectively, in relation to the traditional estimate. Furthermore, a modest improvement in the minimum detectable displacement in real OCE data was also observed, particularly in data with low signal-to-noise ratios. The feasibility of using Δdav to estimate agarose phantoms’ Young’s modulus is illustrated.
Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques
Authors: Tiwari Divya , Miller David , Farnsworth Michael , Lambourne Alexis , Jewell Geraint W. , Tiwari Ashutosh
Published: April 2023

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s.
Bottleneck Detection in Modular Construction Factories Using Computer Vision
Authors: Panahi Roshan , Louis Joseph , Podder Ankur , Swanson Colby , Pless Shanti
Published: April 2023

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.
Signal Novelty Detection as an Intrinsic Reward for Robotics
Authors: Kubovčík Martin , Dirgová Luptáková Iveta , Pospíchal Jiří
Published: April 2023

In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task’s completion or failure, leading to slow convergence. Additional intrinsic rewards based on the state visitation frequency can provide more feedback. In this study, an Autoencoder deep learning neural network was utilized as novelty detection for intrinsic rewards to guide the search process through a state space. The neural network processed signals from various types of sensors simultaneously. It was tested on simulated robotic agents in a benchmark set of classic control OpenAI Gym test environments (including Mountain Car, Acrobot, CartPole, and LunarLander), achieving more efficient and accurate robot control in three of the four tasks (with only slight degradation in the Lunar Lander task) when purely intrinsic rewards were used compared to standard extrinsic rewards. By incorporating autoencoder-based intrinsic rewards, robots could potentially become more dependable in autonomous operations like space or underwater exploration or during natural disaster response. This is because the system could better adapt to changing environments or unexpected situations.
Left and Right Cortical Activity Arising from Preferred Walking Speed in Older Adults
Authors: Greenfield Julia , Delcroix Véronique , Ettaki Wafae , Derollepot Romain , Paire-Ficout Laurence , Ranchet Maud
Published: April 2023

Cortical activity and walking speed are known to decline with age and can lead to an increased risk of falls in the elderly. Despite age being a known contributor to this decline, individuals age at different rates. This study aimed to analyse left and right cortical activity changes in elderly adults regarding their walking speed. Cortical activation and gait data were obtained from 50 healthy older individuals. Participants were then grouped into a cluster based on their preferred walking speed (slow or fast). Analyses on the differences of cortical activation and gait parameters between groups were carried out. Within-subject analyses on left and right–hemispheric activation were also performed. Results showed that individuals with a slower preferred walking speed required a higher increase in cortical activity. Individuals in the fast cluster presented greater changes in cortical activation in the right hemisphere. This work demonstrates that categorizing older adults by age is not necessarily the most relevant method, and that cortical activity can be a good indicator of performance with respect to walking speed (linked to fall risk and frailty in the elderly). Future work may wish to explore how physical activity training influences cortical activation over time in the elderly.
Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
Authors: An SangHoon , Chang KyungHi
Published: April 2023

In the development of autonomous driving technology, 5G-NR vehicle-to-everything (V2X) technology is a key technology that enhances safety and enables effective management of traffic information. Road-side units (RSUs) in 5G-NR V2X provide nearby vehicles with information and exchange traffic, and safety information with future autonomous vehicles, enhancing traffic safety and efficiency. This paper proposes a communication system for vehicle networks based on a 5G cellular network with RSUs consisting of the base station (BS) and user equipment (UE), and validates the system performance when providing services from different RSUs. The proposed approach maximizes the utilization of the entire network and ensures the reliability of V2I/V2N links between vehicles and each RSU. It also minimizes the shadowing area in the 5G-NR V2X environment, and maximizes the average throughput of vehicles through collaborative access between BS- and UE-type RSUs. The paper applies various resource management techniques, such as dynamic inter-cell interference coordination (ICIC), coordinated scheduling coordinated multi-point (CS-CoMP), cell range extension (CRE), and 3D beamforming, to achieve high reliability requirements. Simulation results demonstrate improved performance in outage probability, reduced shadowing area, and increased reliability through decreased interference and increased average throughput when collaborating with BS- and UE-type RSUs simultaneously.
Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case
Authors: Abbasi Ali , Queirós Sandro , da Costa Nuno M. C. , Fonseca Jaime C. , Borges João
Published: April 2023

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.
Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks
Authors: Khan Sajid , Ullah Imdad , Khan Faheem , Lee Youngmoon , Ullah Shahid
Published: April 2023

Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.
Multidimensional Fractionation of Particles
Authors: Frank Uwe , Dienstbier Jana , Tischer Florentin , Wawra Simon E. , Gromotka Lukas , Walter Johannes , Liers Frauke , Peukert Wolfgang
Published: April 2023

The increasing complexity in particle science and technology requires the ability to deal with multidimensional property distributions. We present the theoretical background for multidimensional fractionations by transferring the concepts known from one dimensional to higher dimensional separations. Particles in fluids are separated by acting forces or velocities, which are commonly induces by external fields, e.g., gravitational, centrifugal or electro-magnetic fields. In addition, short-range force fields induced by particle interactions can be employed for fractionation. In this special case, nanoparticle chromatography is a recent example. The framework for handling and characterizing multidimensional separation processes acting on multidimensional particle size distributions is presented. Illustrative examples for technical realizations are given for shape-selective separation in a hydrocyclone and for density-selective separation in a disc separator.
Dynamic Response of a Flexible Multi-Body in Large Wind Turbines: A Review
Authors: Xue Zhanpu , Zhang Hao , Ji Yunguang
Published: April 2023

Dynamic response of flexible multi-body large wind turbines has been quickly growing in recent years. With the new normal economic policy, the economy of China is developing innovatively and stably. New energy development and utilization is an important strategy for people’s lives and economic development around the world. It is feasible to analyze from a broad perspective. In particular, the development and application of wind power is affecting the economic development of industry to a certain extent. Persistent and significant large wind turbines have cast concern over the prospects of wind power technology, and a comprehensive development potential of wind power technology has been analyzed its potential use in the future. The multi-body dynamics method can better analyze and describe the impact of flexible blade elastic deformation on motion characteristics and provides a practical analysis method for the aeroelastic stability analysis and control system design of wind turbines.
Numerical Analysis of Sulfamerazine Solubility in Acetonitrile + 1-Propanol Cosolvent Mixtures at Different Temperatures
Authors: Ortiz Claudia Patricia , Cardenas-Torres Rossember Edén , Herrera Mauricio , Delgado Daniel Ricardo
Published: April 2023

The current challenges of the pharmaceutical industry regarding the environmental impact caused by its waste have led to the design and development of more efficient industrial processes. In this context, solubility studies are at the core of different processes, such as formulation, preformulation, synthesis, purification, recrystallization, quantification, and quality control. This research evaluates the solubility of sulfamerazine (SMR) in acetonitrile + 1-propanol cosolvent mixtures at nine temperature levels with UV/vis spectrophotometry using the vial-shake method. According to the analysis of the solid phase in equilibrium using differential scanning calorimetry, there were no polymorphic changes. The minimal solubility of SMR was reached in 1-propanol at 278.15 K, and the maximal solubility in acetonitrile at 313.15 K. In all cases, the process was endothermic and dependent on the cosolvent composition, and the solution enthalpy drove the solution process. The solubility data were well correlated with the van’t Hoff, Yalkowsky–Roseman–van’t Hoff, Apelblat, Buchowski–Ksiazczak λh, Yaws, NRTL, Wilson, and modified Wilson models, with the YR model being one of the most attractive because it presented an excellent prediction percentage from four sets of experimental data. The solution process of SMR in acetonitrile + 1-propanol cosolvent mixtures depends on the affinity of SMR for acetonitrile and temperature increase.
A Comprehensive Evaluation and Empirical Research on Dual Carbon Emission Reduction under Digital Empowerment
Authors: Xu Guoli , Wong Cora Un In , Xu Xintong
Published: April 2023

Through in-depth analysis of the background and connotation of carbon peaking and carbon neutral development goals, this paper constructs a comprehensive evaluation system of carbon neutral development goals from five secondary indicators of innovation, coordination, green, open and shared development and 15 tertiary indicators. By collecting characteristic indicator data of 10 typical provinces in China in the period of 2016–2020, using the entropy weight method, the analytic hierarchy process and the TOPSIS model, to provide research and a comprehensive analysis on the carbon neutral development level of each province. The research results show that: in terms of time series distribution, it can be observed from the index trends of five secondary indicators’ closeness and comprehensive closeness that the development of the 10 typical provinces screened in this paper is generally in a rising trend year by year, but there are different development differences in a small part of the regions. In terms of spatial distribution, due to the geographical location of various regions and resource allocation and other factors, the economic development is unbalanced. With the rapid development of the digital economy, the investment in 5G, big data technology and artificial intelligence can be increased to achieve the development goal of carbon neutrality. Cloud computing can be used to predict the development trend, provide a guarantee for quantitative tracking of carbon neutrality, and monitor and optimize carbon emissions. Based on the research results, this paper draws the corresponding conclusions and puts forward constructive suggestions to achieve the goal of carbon neutral development.
Islamic Law, Islamic Finance, and Sustainable Development Goals: A Systematic Literature Review
Authors: Harahap Burhanudin , Risfandy Tastaftiyan , Futri Inas Nurfadia
Published: April 2023

In essence, Islamic law (Maqasid al-Shariah) and the sustainable development goals (SDGs) initiated by the United Nations have the same goal: to achieve the perfection of a sustainable human life. Meanwhile, Islamic finance is regarded as an implementation of Islamic law, as many Islamic finance products and instruments are derived from Islamic law. Prior studies on Islamic law, Islamic finance, and SDGs tend to be scattered, and the role of Islamic finance in SDGs is still questionable. This paper uses a systematic literature review to investigate the intersection of Islamic finance, Islamic law, and SDGs. We selected papers that focused on Islamic finance as an inclusion criterion and excluded papers that only discussed Islamic countries as an exclusion criterion. We retrieved 65 papers and book chapters published from 2008 to 2022 from the Scopus database to analyze which parts of Islamic finance and law can contribute to the SDGs. We use thematic analysis for data synthesis by grouping findings into their relation to Islamic law using Al-Ghazali’s Framework of Maqashid Al-Shariah and SDGs from the UN, and then explaining the research results using a narrative method. Through this study, we found that Islamic finance supports the SDGs with the most significant contribution to humanity. In addition, it is essential to know that the support of the government, regulators, and related institutions is much needed to improve Islamic finance for the achievement of SDGs.
The Performance and Distribution of Polyurethane-Modified Asphalt That Exhibits Different Molecular Weights
Authors: Ban Xiaoyi , Zhang Zengping , Chang Pengtao , Zhang Suyu , Liu Hao , Liang Yuzi , Chen Yujing
Published: April 2023

To analyze the effect of polyol on polyurethane (PU)-modified asphalt, three different soft segments of polyurethane were synthesized, and we utilized the reaction of MDI (diphenylmethane diisocyanate) with PU650, PU1000, and PU1400. With respect to molecular weight, the effect of polyol on the performance of modified asphalt was analyzed, and the asphalt was modified by using three different polyurethanes. To analyze the PU samples, the Fourier transform infrared spectroscopy (FTIR) tests and gel permeation chromatography (GPC) tests were selected; by contrast, to analyze the rheological properties and modification mechanism of asphalt, the dynamic rheology test (DSR), low-temperature bending creep test (BBR), multi-stress repetitive creep test (MSCR), FTIR, and differential scanning calorimetry (DSC) were selected. The results indicate that the molecular weight of polyol affects the molecular structure of polyurethane, the distribution of soft and hard segments, the content of soft segments, and the distribution of asphaltene in asphalt; thus, the asphalt modification effect occurs differently. The storage stability and high-temperature stability of the polyurethane-modified asphalts that were synthesized using three different polyols (i.e., polyols that exhibit different molecular weights) did not differ considerably, and the PU1400-modified asphalt exhibited the best low-temperature performance.
A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data
Authors: Sun Ying , Lao Dazhao , Ruan Yongjian , Huang Chen , Xin Qinchuan
Published: April 2023

Vegetation activities and stresses are crucial for vegetation health assessment. Changes in an environment such as drought do not always result in vegetation drought stress as vegetation responses to the climate involve complex processes. Satellite-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been widely used to monitor vegetation activities. As satellites only carry information for understanding past and current vegetation conditions, there is a need to model vegetation dynamics to make future predictions. Although many other factors are related, we attempt to predict the vegetation activities and stresses via simulating NDVI, based on only meteorological data and using a deep learning method (bidirectional long short-term memory model, BiLSTM). The BiLSTM is a sequence processing model that can predict NDVI by establishing the relationship between meteorological variables and vegetation activities. Experimental results show that the predicted NDVI is consistent with the reference data (R2 = 0.69 ± 0.28). The best accuracy was achieved in the deciduous forest (R2 = 0.87 ± 0.16). The vegetation condition index (VCI) calculated from the BiLSTM-predicted NDVI also agreed with the satellite-based ones (R2 = 0.70 ± 0.28). Both the monitored and predicted VCI indicated an upward but insignificant trend of vegetation activity in the past decade and increased vegetation stresses in the early growing season over northern China. Based on meteorological data, the deep learning-based solution shows the potential for not only retrospective analysis, but also future prediction of vegetation activities and stresses under varied climate conditions as compared with remote sensing data.
Understanding How Organizational Culture Affects Innovation Performance: A Management Context Perspective
Authors: Zhang Wen , Zeng Xiaoshuang , Liang Huigang , Xue Yajiong , Cao Xuanze
Published: April 2023

This study takes the management context perspective to investigate how the social context and performance management context can jointly improve organizational innovation performance. We propose that organizational culture factors, including psychological safety, collectivism, and power distance, are antecedents of social context and performance management context. We collected survey data from 301 employees in Chinese organizations. Our data analysis results show that the social context and performance management context fully mediate the effects of psychological safety, collectivism, and power distance on innovation performance. Specifically, psychological safety and collectivism have positive indirect effects, whereas power distance has a negative indirect effect on innovation performance. Our findings offer insights regarding how firms can develop management contexts to enhance their innovation performance, which contributes to both research and practice in innovation management.
Relative Importance of Barriers and Levers to Intercropping Systems Adoption: A Comparison of Farms and Co-Operatives
Authors: Fares M’hand , Mamine Fateh
Published: April 2023

This paper focuses on the barriers and levers to the adoption of Wheat–Pea intercropping systems. More precisely, we define a hierarchy of the main barriers and levers to adoption using the Relative Importance Index (RII) method. This method allows comparison of incentives, negative (brakes) and positive (levers), for adoption at two levels of the value chain, i.e., the farmer and the co-operative level. For this comparison, we conducted two surveys: one on 71 Belgian farmers and the other on 19 French co-operatives. Our results show that the barriers of high importance for the farmers are both internal and external, while the co-operatives consider only internal barriers. That is, the farmers mainly focus on external (market access and public subsidies) and internal (lack of technical advice and extension, as well as collection and storage problems) obstacles to evaluate the intercropping system. For the co-operatives, the most important barriers are related to the sorting and storage of the mixture (internal barriers). Regarding levers, farmers and co-operatives converge on the importance of almost the same external levers, e.g., building new value chains through contracts and labeling, specific extension services for farmers and logistical support for co-operatives.
A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence
Authors: Amjad Ayesha , Kordel Piotr , Fernandes Gabriela
Published: April 2023

Artificial intelligence (AI) has entered the mainstream as computing power has improved. The healthcare industry is undergoing dramatic transformations at present. One of the most recent industries to heavily use AI is telehealth, which is used for anything from issuing electronic healthcare cards to providing individual counselling. Artificial intelligence (AI) is influencing telehealth in the United States in a major way. Using AI in telehealth to allow clinicians to make real-time, data-driven rich choices is critical to offering a better patient experience and improved health outcomes as practitioners strive toward expanding virtual care options along the care continuum. Research in the medical industry has started to use AI’s strengths in data processing and analysis in telehealth, reflecting the widespread adoption of AI in other sectors. Because of the difficulties inherent in telemedicine’s deployment, there is an urgent need to broaden its capabilities and enhance its processes so that they may be tailored to address particular issues. This article is aimed to study different areas of telemedicine and analyze the effect of AI in the field of health and medicine. The literature surveyed in this study demonstrates the infinite growth potential afforded by the combination of AI and telemedicine. There are four main directions that the expanding use of this technology is heading: patient monitoring, healthcare IT, intelligent aid in diagnosis, and information analysis with other specialists.
A Causal Model of Ethical Leadership Affecting the Organizational Citizenship Behavior of Teachers in the Office of the Basic Education Commission
Authors: Phetsombat Pimolpun , Na-Nan Khahan
Published: April 2023

The objective of this study was to examine the direct and indirect effects of ethical leadership on organizational citizenship behavior while considering job stress, organizational commitment, and job satisfaction as full and partial mediators through which these effects are transmitted. The study comprised a sample of 400 teachers working at the Office of the Basic Education Commission. The instruments utilized in this study were adapted from previous studies by scholars, and their content validity and reliability were tested before data collection. Analysis of the direct and indirect effects of full and partial mediators was conducted using the PROCESS program. The results indicated that ethical leadership had a direct effect on organizational citizenship behavior with statistical significance, as well as an indirect effect transmitted through job stress and job satisfaction. Job stress and job satisfaction functioned as partial mediators between ethical leadership and organizational citizenship behavior with statistical significance. However, organizational commitment did not show statistical significance as a mediator. The developed model suggested that ethical leadership theory effectively increased organizational citizenship behavior, with job stress and job satisfaction playing vital roles as mediators in transmitting organizational citizenship behavior with higher effectiveness. The mediators identified in this study were helpful in more accurately explaining organizational citizenship behavior.
Based on AFC Data Calculation of Walking Time in Metro Stations Considering the Impact of Passenger Flows
Authors: Zou Liang , Hu Suiying , Zhu Lingxiang
Published: April 2023

The walking time in metro stations is influenced by passenger flow with large fluctuation. Therefore, this paper proposes a method of station walking time calculation considering the influence of passenger flow: firstly, the time, entry, and exit direction and volume distribution characteristics of station passenger flow are analyzed, and the threshold of passenger flow that affects walking time is defined; then, an entry and exit walking time calculation model is established by five independent travel time chains and three station walking time constraints, and the transfer walking time is calculated based on the entry and exit walking time results; finally, the accuracy and validity of the walking time projection model are verified by using the calculation results. The results show that the calculation results are accurate and the validity of the model reaches 91.5%, and the constraint effect on walking time at station time is most obvious when the passenger flow reaches 3000 passengers per 30 min.
Twitter Bot Detection Using Diverse Content Features and Applying Machine Learning Algorithms
Authors: Alarfaj Fawaz Khaled , Ahmad Hassaan , Khan Hikmat Ullah , Alomair Abdullah Mohammaed , Almusallam Naif , Ahmed Muzamil
Published: April 2023

A social bot is an intelligent computer program that acts like a human and carries out various activities in a social network. A Twitter bot is one of the most common forms of social bots. The detection of Twitter bots has become imperative to draw lines between real and unreal Twitter users. In this research study, the main aim is to detect Twitter bots based on diverse content-specific feature sets and explore the use of state-of-the-art machine learning classifiers. The real-world data from Twitter is scrapped using Twitter API and is pre-processed using standard procedure. To analyze the content of tweets, several feature sets are proposed, such as message-based, part-of-speech, special characters, and sentiment-based feature sets. Min-max normalization is considered for data normalization and then feature selection methods are applied to rank the top features within each feature set. For empirical analysis, robust machine learning algorithms such as deep learning (DL), multilayer perceptron (MLP), random forest (RF), naïve Bayes (NB), and rule-based classification (RBC) are applied. The performance evaluation based on standard metrics of precision, accuracy, recall, and f-measure reveals that the proposed approach outperforms the existing studies in the relevant literature. In addition, we explore the effectiveness of each feature set for the detection of Twitter bots.
Understanding Idle Land Using Local Environmental Characteristics: A Case Study of Liuyang, China
Authors: Zhou Kaichun , Fu Anzhou , Xiao Can , Tang Ping , Zhang Junyi , Xie Binggeng
Published: April 2023

Idle land represents a major challenge to sustainable land management and the effective utilization of land resources. Achieving a comprehensive understanding of idle land is crucial to reduce land waste and promoting sustainable socio-economic development. While previous research has mainly focused on the concepts and governance of idle land, limited attention has been paid to its local environmental characteristics (LECs), this study aims to research idle land in Liuyang City to better understand its spatial distribution pattern and association with LESc for sustainable land use practices. The research employs various techniques, including testing the validity of Zipf’s law in relation to idle land, using spatial autocorrelation methods to determine its spatial distribution at the town/subdistrict level, and employing spatial-semantic clustering to identify spatio-LEC patterns of idle land. The results show that the areal distribution of idle land at the town/subdistrict level is close to the ideal state. Additionally, the study identifies non-randomness in the spatial patterns of idle land and observes links among different types of idle land with varying uses.
Local Context of Climate Change Adaptation in the South-Western Coastal Region of Bangladesh
Authors: Ashrafuzzaman Md
Published: April 2023

This study was conducted in 12 unions of the Shyamnagar upazila in the Shatkira District, located in the south-western coastal region of Bangladesh (SWCRB). The inhabitants of the SWCRB are affected by different climate-influenced events such as high-intensity cyclones, saltwater intrusion, sea-level rise, and weather pattern-affected agriculture. This study focused on how the local inhabitants are coping with climate change using multilevel adaptation. A mixed approach of data collection, including quantitative and qualitative data, was followed for both primary and secondary sources. Individual-level data collection, key informant interviews, close-ended questions, focus groups, life history of SWCRB residents, and workshops were used to understand vulnerability and social perceptions at the local level. The findings indicated that multiple adaptation practices are employed by people in the SWCRB, such as rainwater harvesting, plantation of different rice varieties, gardening of indigenous vegetables, and pond sand filtering. However, the construction of multipurpose cyclone shelters along with coastal afforestation contributes to building resilience in the SWRCB from the socio-economic and environmental perspectives. Therefore, this study will help to find the most adequate strategy for climate change adaptation and sustainability.
Soil Arsenic Toxicity Impact on the Growth and C-Assimilation of Eucalyptus nitens
Authors: Ramalho José C. , Pelica João , Lidon Fernando C. , Silva Maria M. A. , Simões Maria M. , Guerra Mauro , Reboredo Fernando H.
Published: April 2023

The selection of adequate plants that can cope with species that can live in contaminated/degraded and abandoned mining areas is of utmost importance, especially for environmental management and policymakers. In this framework, the use of a fast-growing forestry species, such as Eucalyptus nitens, in the recovery of arsenic (As) from artificially contaminated soils during a long-term experiment was studied. Roots can accumulate to levels ranging between 69.8 and 133 μg g−1 for plants treated with 100 and 200 µg As mL−1, respectively, while leaves between 9.48 μg g−1 (200 As) and 15.9 μg g−1 (100 As) without apparent morphological damage and toxicity symptoms. The C-assimilation machinery performance revealed a gradual impact, as evaluated through some gas exchange parameters such as the net photosynthetic rate (Pn), stomatal conductance to H2O (gs), and transpiration rate (E), usually with the greater impacts at the highest As concentration (200 As), although without significantly impacting the PSII performance. The As effects on the uptake and translocation of Ca, Fe, K, and Zn revealed two contrasting interferences. The first one was associated with Zn, where a moderate antagonism was detected, whereas the second one was related to Fe, where a particular enrichment in leaves was noted under both As treatments. Thus, it seems to exist a synergistic action with an impact on the levels of the photosynthetic pigments in As-treated plant leaves, compared with control plants. E. nitens must be considered as an alternative when phytoremediation processes are put into practice in our country, particularly in areas with cool climatic conditions.
Investigation of Changes in Packaging Properties of Refrigerated Food Products
Authors: Iljin Igor , Stonkus Rimantas , Jasevičius Raimondas
Published: April 2023

This study presents the experimental investigations of the thermal conductivity properties of refrigerated packages in an autonomous commercial open refrigerated display cabinet. The study aimed to determine whether the type of packaging can reduce the impact of a malfunctioning refrigeration unit on the product. The characteristics of refrigerated packages are studied during the freezing process on the level of loading and the arrangement of the packages in the display case. For the research, four types of packaging were used: combined, composite, plastic, and pulp. Based on the results obtained, the compliance of the packages with the established/existing requirements was determined during each test. It was found that during the refrigeration process, the permissible temperature norms in the case of individual packages complied with the most commonly used. None of the test packages met the norms during refrigeration by filling the products to the intended limit. As a result, simplified labeling of the respective cooling zones is proposed, and the issue of ensuring the uniformity of the cooling itself remains open. Given the cost of production, it is also proposed to take into account this issue (marking the corresponding cooling zones) when designing similar refrigeration equipment.
Political Discourses as A Resource for Climate Change Education: Promoting Critical Thinking by Closing the Gap between Science Education and Political Education
Authors: Mejía-Cáceres María Angélica , Rieckmann Marco , Folena Araújo Monica Lopes
Published: April 2023

This paper discusses political discourses as a resource for climate change education and the extent to which they can be used to promote critical thinking. To illustrate this, we present here an activity developed in the online course, Freirean Communicative Educational Situations for Climate Change Education, designed and developed as part of postdoctoral research at the Federal Rural University of Pernambuco, Brazil. The activity aimed to analyze the speeches of the Presidents of Colombia and Chile at the United Nations Climate Action Summit (2019) in a way which approached climate change as a socio-scientific issue. We argue that climate change education should not only involve learning about risk, adaptation, resilience, and basic scientific concepts, but also critical reflection on public policy and discourses and transformative content. This includes consideration of non-formal and informal communications and analysis of how power relations can restrict, motivate, or boost the impetus towards climate change education. These kinds of classroom activities enable teachers to work with a combination of core critical thinking skills, attitudes, and abilities, as well as discussing the details of science and scientific knowledge. This in turn enables the gap between the scientific and political aspects of climate change education to be bridged.
Monitoring of Location Parameters with a Measurement Error under the Bayesian Approach Using Ranked-Based Sampling Designs with Applications in Industrial Engineering
Authors: Khan Imad , Noor-ul-Amin Muhammad , Khan Dost Muhammad , AlQahtani Salman A. , Dahshan Mostafa , Khalil Umair
Published: April 2023

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error.
A Study on the Influencing Factors of China’s Ecological Footprint Based on EEMD–GeoDetector
Authors: Han Jiaxin , Dalaibaatar Enkhjargal
Published: April 2023

Ecological footprint (EF) is used to measure the energy and resources that are consumed by human beings, and it is used to calculate the energy that ecological services can provide to determine the gap between human behavior and what the earth can tolerate so as to ensure that human activities and sustainable development fall within this range. Therefore, it is crucial to research the influencing factors of EF. In this study, the ensemble empirical mode decomposition (EEMD) method was used to decompose China’s per capita ecological footprint from 1961 to 2018 into four intrinsic mode functions (IMFs) and a residual (R) and to conduct factor detection and interaction detection on both each obtained sequence and the original sequence. In order to examine the contributing factors, 15 independent variables representing the economic, social, and environmental pillars of sustainable development were chosen. The outcome under the interaction factor is more logical than the result under the single factor. Under the interaction factor of climate, the short-term changes in the number of doctors per 1000 people, long-term population density, carbon dioxide emissions, and average life expectancy interact with each other and the trend in CO2 emissions to affect the change in ecological footprint.