bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time Context-Aware Recommendation System for Tourism
Authors: Yoon JunHo , Choi Chang
Published: April 2023

Recently, the tourism trend has been shifting towards the Tourism 2.0 paradigm due to increased travel experiences and the increase in acquiring and sharing information through the Internet. The Tourism 2.0 paradigm requires developing intelligent tourism service tools for positive effects such as time savings and marketing utilization. Existing tourism service tools recommend tourist destinations based on the relationship between tourists and tourist destinations or tourism patterns, so it is difficult to make recommendations in situations where information is insufficient or changes in real time. In this paper, we propose a real-time recommendation system for tourism (R2Tour) that responds to changing situations in real time, such as external factors and distance information, and recommends customized tourist destinations according to the type of tourist. R2Tour trains a machine learning model with situational information such as temperature and precipitation and tourist profiles such as gender and age to recommend the top five nearby tourist destinations. To verify the recommendation performance of R2Tour, six machine learning models, including K-NN and SVM, and information on tourist attractions in Jeju Island were used. As a result of the experiment, R2Tour was verified with accuracy of 77.3%, micro-F1 0.773, and macro-F1 0.415. Since R2Tour trains tourism patterns based on situational information, it is possible to recommend new tourist destinations and respond to changing situations in real time. In the future, R2Tour can be installed in vehicles to recommend nearby tourist destinations or expanded to tasks in the tourism industry, such as a smart target advertising system.
Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka
Authors: Herath Madhawa , Jayathilaka Tharaka , Azamathulla Hazi Mohammad , Mandala Vishwanadham , Rathnayake Namal , Rathnayake Upaka
Published: April 2023

Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall–nighttime relative humidity, rainfall–evaporation, daytime relative humidity–evaporation, and rainfall–nighttime relative humidity–evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.
Cybersecurity and Cyber Forensics for Smart Cities: A Comprehensive Literature Review and Survey
Authors: Kim Kyounggon , Alshenaifi Istabraq Mohammed , Ramachandran Sundaresan , Kim Jisu , Zia Tanveer , Almorjan Abdulrazaq
Published: April 2023

Smart technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are being adopted in cities and transforming them into smart cities. In smart cities, various network technologies, such as the Internet and IoT, are combined to exchange real-time information, making the everyday lives of their residents more convenient. However, there is a lack of systematic research on cybersecurity and cyber forensics in smart cities. This paper presents a comprehensive review and survey of cybersecurity and cyber forensics for smart cities. We analysed 154 papers that were published from 2015 to 2022 and proposed a new framework based on a decade of related research papers. We identified four major areas and eleven sub-areas for smart cities. We found that smart homes and the IoT were the most active research areas within the cybersecurity field. Additionally, we found that research on cyber forensics for smart cities was relatively limited compared to that on cybersecurity. Since 2020, there have been many studies on the IoT (which is a technological component of smart cities) that have utilized machine learning and deep learning. Due to the transmission of large-scale data through IoT devices in smart cities, ML and DL are expected to continue playing critical roles in smart city research.
A New Recursive Trigonometric Technique for FPGA-Design Implementation
Authors: Xing Xing , Wang Wilson
Published: April 2023

This paper presents a new recursive trigonometric (RT) technique for Field-Programmable Gate Array (FPGA) design implementation. The traditional implementation of trigonometric functions on FPGAs requires a significant amount of data storage space to store numerous reference values in the lookup tables. Although the coordinate rotation digital computer (CORDIC) can reduce the required FPGA storage space, their implementation process can be very complex and time-consuming. The proposed RT technique aims to provide a new approach for generating trigonometric functions to improve communication accuracy and reduce response time in the FPGA. This new RT technique is based on the trigonometric transformation; the output is calculated directly from the input values, so its accuracy depends only on the accuracy of the inputs. The RT technique can prevent complex iterative calculations and reduce the computational errors caused by the scale factor K in the CORDIC. Its effectiveness in generating highly accurate cosine waveform is verified by simulation tests undertaken on an FPGA.
Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau
Authors: Jiang Chuanli , Zhao Jianyun , Ding Yuanyuan , Li Guorong
Published: April 2023

Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis–NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis–NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis–NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30–40, 50–60, 30–35, and 25–35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM.
LeafSpec-Dicot: An Accurate and Portable Hyperspectral Imaging Device for Dicot Leaves
Authors: Li Xuan , Chen Ziling , Wang Jialei , Jin Jian
Published: April 2023

Soybean is one of the world’s most consumed crops. As the human population continuously increases, new phenotyping technology is needed to develop new soybean varieties with high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most used technologies for phenotyping. The current HSI techniques with indoor imaging towers and unmanned aerial vehicles (UAVs) suffer from multiple major noise sources, such as changes in ambient lighting conditions, leaf slopes, and environmental conditions. To reduce the noise, a portable single-leaf high-resolution HSI imager named LeafSpec was developed. However, the original design does not work efficiently for the size and shape of dicot leaves, such as soybean leaves. In addition, there is a potential to make the dicot leaf scanning much faster and easier by automating the manual scan effort in the original design. Therefore, a renovated design of a LeafSpec with increased efficiency and imaging quality for dicot leaves is presented in this paper. The new design collects an image of a dicot leaf within 20 s. The data quality of this new device is validated by detecting the effect of nitrogen treatment on soybean plants. The improved spatial resolution allows users to utilize the Normalized Difference Vegetative Index (NDVI) spatial distribution heatmap of the entire leaf to predict the nitrogen content of a soybean plant. This preliminary NDVI distribution analysis result shows a strong correlation (R2 = 0.871) between the image collected by the device and the nitrogen content measured by a commercial laboratory. Therefore, it is concluded that the new LeafSpec-Dicot device can provide high-quality hyperspectral leaf images with high spatial resolution, high spectral resolution, and increased throughput for more accurate phenotyping. This enables phenotyping researchers to develop novel HSI image processing algorithms to utilize both spatial and spectral information to reveal more signals in soybean leaf images.
Investigation of Optimal Light Source Wavelength for Cuffless Blood Pressure Estimation Using a Single Photoplethysmography Sensor
Authors: Toda Sogo , Matsumura Kenta
Published: April 2023

Routine blood pressure measurement is important for the early detection of various diseases. Recently, cuffless blood pressure estimation methods that do not require cuff pressurization have attracted attention. In this study, we investigated the effect of the light source wavelength on the accuracy of blood pressure estimation using only two physiological indices that can be calculated with photoplethysmography alone, namely, heart rate and modified normalized pulse volume. Using a newly developed photoplethysmography sensor that can simultaneously measure photoplethysmograms at four wavelengths, we evaluated its estimation accuracy for systolic blood pressure, diastolic blood pressure, and mean arterial pressure against a standard cuff sphygmomanometer. Mental stress tasks were used to alter the blood pressure of 14 participants, and multiple linear regression analysis showed the best light sources to be near-infrared for systolic blood pressure and blue for both diastolic blood pressure and mean arterial pressure. The importance of the light source wavelength for the photoplethysmogram in cuffless blood pressure estimation was clarified.
Clinical Study of Continuous Non-Invasive Blood Pressure Monitoring in Neonates
Authors: Rao Anoop , Eskandar-Afshari Fatima , Weiner Ya’el , Billman Elle , McMillin Alexandra , Sella Noa , Roxlo Thomas , Liu Junjun , Leong Weyland , Helfenbein Eric , Walendowski Alan , Muir Arthur , Joseph Alexandria , Verma Archana , Ramamoorthy Chandra , Honkanen Anita , Green Gabrielle , Drake Keith , Govindan Rathinaswamy B. , Rhine William , Quan Xina
Published: April 2023

The continuous monitoring of arterial blood pressure (BP) is vital for assessing and treating cardiovascular instability in a sick infant. Currently, invasive catheters are inserted into an artery to monitor critically-ill infants. Catheterization requires skill, is time consuming, prone to complications, and often painful. Herein, we report on the feasibility and accuracy of a non-invasive, wearable device that is easy to place and operate and continuously monitors BP without the need for external calibration. The device uses capacitive sensors to acquire pulse waveform measurements from the wrist and/or foot of preterm and term infants. Systolic, diastolic, and mean arterial pressures are inferred from the recorded pulse waveform data using algorithms trained using artificial neural network (ANN) techniques. The sensor-derived, continuous, non-invasive BP data were compared with corresponding invasive arterial line (IAL) data from 81 infants with a wide variety of pathologies to conclude that inferred BP values meet FDA-level accuracy requirements for these critically ill, yet normotensive term and preterm infants.
The Role of Silver Nanoparticles in Electrochemical Sensors for Aquatic Environmental Analysis
Authors: Ivanišević Irena
Published: April 2023

With rapidly increasing environmental pollution, there is an urgent need for the development of fast, low-cost, and effective sensing devices for the detection of various organic and inorganic substances. Silver nanoparticles (AgNPs) are well known for their superior optoelectronic and physicochemical properties, and have, therefore, attracted a great deal of interest in the sensor arena. The introduction of AgNPs onto the surface of two-dimensional (2D) structures, incorporation into conductive polymers, or within three-dimensional (3D) nanohybrid architectures is a common strategy to fabricate novel platforms with improved chemical and physical properties for analyte sensing. In the first section of this review, the main wet chemical reduction approaches for the successful synthesis of functional AgNPs for electrochemical sensing applications are discussed. Then, a brief section on the sensing principles of voltammetric and amperometric sensors is given. The current utilization of silver nanoparticles and silver-based composite nanomaterials for the fabrication of voltammetric and amperometric sensors as novel platforms for the detection of environmental pollutants in water matrices is summarized. Finally, the current challenges and future directions for the nanosilver-based electrochemical sensing of environmental pollutants are outlined.
DV-Hop Algorithm Based on Multi-Objective Salp Swarm Algorithm Optimization
Authors: Liu Weimin , Li Jinhang , Zheng Aiyun , Zheng Zhi , Jiang Xinyu , Zhang Shaoning
Published: April 2023

The localization of sensor nodes is an important problem in wireless sensor networks. The DV-Hop algorithm is a typical range-free algorithm, but the localization accuracy is not high. To further improve the localization accuracy, this paper designs a DV-Hop algorithm based on multi-objective salp swarm optimization. Firstly, hop counts in the DV-Hop algorithm are subdivided, and the average hop distance is corrected based on the minimum mean-square error criterion and weighting. Secondly, the traditional single-objective optimization model is transformed into a multi-objective optimization model. Then, in the third stage of DV-Hop, the improved multi-objective salp swarm algorithm is used to estimate the node coordinates. Finally, the proposed algorithm is compared with three improved DV-Hop algorithms in two topologies. Compared with DV-Hop, The localization errors of the proposed algorithm are reduced by 50.79% and 56.79% in the two topology environments with different communication radii. The localization errors of different node numbers are decreased by 38.27% and 56.79%. The maximum reductions in localization errors are 38.44% and 56.79% for different anchor node numbers. Based on different regions, the maximum reductions in localization errors are 56.75% and 56.79%. The simulation results show that the accuracy of the proposed algorithm is better than that of DV-Hop, GWO-DV-Hop, SSA-DV-Hop, and ISSA-DV-Hop algorithms.
Insights into Batch Selection for Event-Camera Motion Estimation
Authors: Valerdi Juan L. , Bartolozzi Chiara , Glover Arren
Published: April 2023

Event cameras measure scene changes with high temporal resolutions, making them well-suited for visual motion estimation. The activation of pixels results in an asynchronous stream of digital data (events), which rolls continuously over time without the discrete temporal boundaries typical of frame-based cameras (where a data packet or frame is emitted at a fixed temporal rate). As such, it is not trivial to define a priori how to group/accumulate events in a way that is sufficient for computation. The suitable number of events can greatly vary for different environments, motion patterns, and tasks. In this paper, we use neural networks for rotational motion estimation as a scenario to investigate the appropriate selection of event batches to populate input tensors. Our results show that batch selection has a large impact on the results: training should be performed on a wide variety of different batches, regardless of the batch selection method; a simple fixed-time window is a good choice for inference with respect to fixed-count batches, and it also demonstrates comparable performance to more complex methods. Our initial hypothesis that a minimal amount of events is required to estimate motion (as in contrast maximization) is not valid when estimating motion with a neural network.
Green Bio-Analytical Study of Gabapentin in Human Plasma Coupled with Pharmacokinetic and Bioequivalence Assessment Using UPLC-MS/MS
Authors: Tony Rehab Moussa , El Hamd Mohamed A. , Gamal Mohammed , Saleh Safaa F. , Maslamani Nujud , Alsaggaf Wejdan T. , El-Zeiny Mohamed B.
Published: April 2023

Gabapentin (GAB) is a cyclohexane acetic acid, structurally related to the neurotransmitter gamma-aminobutyric acid (GABA), and considered the principal inhibitory neurotransmitter in the central nervous system (CNS) of mammals. An ultra-performance liquid chromatography–tandem mass spectrophotometry (UPLC-MS/MS) method for assessing pregabalin (PRE) in human plasma, was developed and validated, via PRE usage as an internal standard. The plasma underwent protein precipitation using methanol, prior to analysis. Chromatographic separation was completed using a mobile phase of methanol: 0.1% formic acid solution, (65:35, v/v), at a flow rate of 0.2 mL/min, with an isocratic approach, on an Agilent Eclipse plus column (50 × 2.1 mm and 1.8 μm), in 1.6 min of running time. An Agilent triple quadrupole was used for mass analysis, to detect the ion transitions for GAB and PER, respectively, at m/z of 172.1 → 154.1 and 160.10 → 142.10. The calibration curve, over the linear range of 0.050–10.0 μg/mL, showed a high correlation coefficient, r = 0.9993. The limits of detection and quantitation were 13.37 ng/mL and 40.52 ng/mL, respectively, based on the standard deviation and slope equation. The results for intra- and inter-day measurement accuracy and precision were in acceptable ranges. The method was extended into the assessment of oral administrations of GAB at different doses, of one 600 mg/tablet and two capsules (each one of them has 300 mg of GAB), to volunteers who were used in pharmacokinetics and bioequivalent studies. The AGREE assessment tool was used to visualize the proposed method’s greenness degree, which revealed a high AGREE rating score, supporting the accepted method’s greenness profile.
Magnet Integrated Fabric Phase Sorptive Extraction for the Extraction of Resin Monomers from Human Urine Prior to HPLC Analysis
Authors: Antoniou Georgios , Alampanos Vasileios , Kabir Abuzar , Zughaibi Torki , Furton Kenneth G. , Samanidou Victoria
Published: April 2023

In this work, a method for the simultaneous determination of four resin monomers: Bisphenol A, bisphenol A methacrylate glycidate, triethyleneglycol-dimethacrylate, and urethane dimethacrylate, from human urine using magnet integrated fabric phase sorptive extraction (MI-FPSE), followed by high performance liquid chromatography (HPLC) diode array detection (HPLC-DAD), is presented. MI-FPSE is a novel configuration of FPSE that incorporates the stirring and extraction mechanism into one device, resulting in an improved extraction kinetic factor. FPSE is a green sample preparation technique that uses a flexible surface, such as cellulose, coated with a polymeric material using sol–gel technology. Poly(tetrahydrofuran) (PTHF) material was selected, due to its higher efficiency in terms of recovery rate among the studied MI-FPSE membranes. Optimization of the extraction process was performed based on several extraction and elution parameters. The method was validated for its linearity, selectivity, accuracy, precision, and stability of the samples. For the four compounds, the LOD and LOQ were 0.170 ng/μL and 0.050 ng/μL, respectively. The relative standard deviation of the method was less than 9.8% and 11.9%, for the within-day and between-day precision, respectively. The relative recoveries were between 85.6 and 105.2% in all cases, showing a good accuracy. The effectiveness of the proposed method was confirmed through successful application to the bioanalysis of real urine samples.
Impacts of Product Variety and Supply Chain Networks on the Influx of Information Exchange in Industry Applications
Authors: Huddiniah Estu Rizky , Pradana Hilmil
Published: April 2023

Managing product variety is a challenging problem given the increasing complexity of supply chain networks. To overcome this complexity, managing integration in the supply chain is essential for companies to coordinate effectively. By managing the influx of information exchange between the various entities involved in the supply chain network, integration can be achieved successfully. In this paper, we are targeting research questions regarding the impact of the influx of information exchange on product variety and supply chain networks and the key factors influencing its exchange from different industries’ perspectives. To investigate our research questions and to conduct a case study across different industries and companies, this study aims to explore the impact of supply chain network complexity, which causes an influx of information exchange due to increasing product variety through qualitative research. In our results, by categorizing the raw interview data, we visualize correspondent opinions to facilitate deep analysis, including factors such as product variety, supply chain networks, and information exchange. The key factors that can influence the influx of information exchange from different industries’ and companies’ perspectives are presented in our results to provide valuable insights into the significant factors affecting the success of the smart business.
“We Can Manage This Corona Disaster”: Psycho-Social Experiences of a Diverse Suburban Middle-Class Community in South Africa: Interview-Based Study
Authors: Jacobs Susanne
Published: April 2023

The study concerns psycho-social domains experienced in a diverse suburban middle-class community, reporting the most positive cases and the deepest suffering, and interactions towards adaptation in stressful situations, such as the Coronavirus pandemic. This qualitative investigation used a descriptive design, with a strengths-based perspective directing a two-phased method. Through non-probability convenience sampling, 80 participants completed a web-based qualitative questionnaire (phase one). From those, 20 purposely selected volunteers participated in individual, face-to-face, open-ended, and unstructured interviews (phase 2). Themes, interpreted as one set, show how strengths and resilience appear, despite extreme shock and uncertainty. Transitional processes in psycho-social spheres reveal conscious decisions towards dynamic engagement, embracing change, reflecting on life’s value, and regarding novel meaningful priorities in contrast with “before”. Most prominent relational spaces have human connections in the inner (close) and outer (community) circles. Personal, meaningful relationships strengthen social bonds. Appreciative inquiry (AI) assisted in the transitional process to co-construct awareness of the positive core, emotional agility, and pride in embracing and expanding on newly developed strengths. Interpreted inductively, meaning described in abstracted knowledge can be transferred to and integrated with other contexts, identifying new initiatives and trans-, multi-, and inter-disciplinary debates mitigating psycho-social consequences and fostering resilience during disasters.
Official Information on Twitter during the Pandemic in Spain
Authors: García-García Soledad , Rodríguez-Díaz Raquel
Published: April 2023

This article shows the use of Twitter that the main official spokespersons of the Spanish government made during the first weeks of the pandemic, with the aim of analyzing how government health campaigns were managed during the exceptional period of the state of alarm to deal with the COVID-19 pandemic and whether the instructions in terms of institutional management of communication to combat the infodemic set by the World Health Organization (WHO) were followed. This research considers the diffusion of official information in different phases of the first three months of the government’s action (102 days) from the outbreak of COVID-19 in Spain (March 2020) and how it developed its approach to crisis communication using the Twitter accounts of the President of the Spanish government (@sanchezcastejon), front-line leaders and the Ministry of Health (@sanidadgob), the main public institution responsible for health crisis management with the hashtags #EsteVirusLoParamosUnidos and #COVID-19. The results of a sample of 750 tweets reveal how the official sources used a model of online communication with a particular emphasis on informative and motivational tweets from leaders aimed at audiences (media and the general public). At the same time, there is also an instructive function about the pandemic towards audiences (general public and companies), with the Ministry and health authorities playing a key, proactive role in an attempt to achieve informative transparency to mitigate the pandemic and infodemic.
Nine Months of Hybrid Intradialytic Exercise Training Improves Ejection Fraction and Cardiac Autonomic Nervous System Activity
Authors: Giannaki Christoforos D. , Grigoriou Stefania S. , George Keith , Karatzaferi Christina , Zigoulis Paris , Lavdas Eleftherios , Chaniotis Dimitrios , Stefanidis Ioannis , Sakkas Giorgos K.
Published: April 2023

Cardiovascular disease is the most common cause of death in hemodialysis (HD) patients. Intradialytic aerobic exercise training has a beneficial effect on cardiovascular system function and reduces mortality in HD patients. However, the impact of other forms of exercise on the cardiovascular system, such as hybrid exercise, is not clear. Briefly, hybrid exercise combines aerobic and strength training in the same session. The present study examined whether hybrid intradialytic exercise has long-term benefits on left ventricular function and structure and the autonomous nervous system in HD patients. In this single-group design, efficacy-based intervention, twelve stable HD patients (10M/2F, 56 ± 19 years) participated in a nine-month-long hybrid intradialytic training program. Both echocardiographic assessments of left ventricular function and structure and heart rate variability (HRV) were assessed pre, during and after the end of the HD session at baseline and after the nine-month intervention. Ejection Fraction (EF), both assessed before and at the end of the HD session, appeared to be significantly improved after the intervention period compared to the baseline values (48.7 ± 11.1 vs. 58.8 ± 6.5, p = 0.046 and 50.0 ± 13.4 vs. 56.1 ± 3.4, p = 0.054 respectively). Regarding HRV assessment, hybrid exercise training increased LF and decreased HF (p < 0.05). Both conventional Doppler and tissue Doppler imaging indices of diastolic function did not change after the intervention period (p > 0.05). In conclusion, long-term intradialytic hybrid exercise training was an effective non-pharmacological approach to improving EF and the cardiac autonomous nervous system in HD patients. Such exercise training programs could be incorporated into HD units to improve the patients’ cardiovascular health.
An Integrated Method Based on Convolutional Neural Networks and Data Fusion for Assembled Structure State Recognition
Authors: Luo Jianbin , Jiang Shaofei , Zhao Jian , Zhang Zhangrong
Published: April 2023

This article focuses on the Assembled Structure (AS) state recognition method based on vibration data. The difficulty of AS state recognition is mainly the extraction of effective classification features and pattern classification. This paper presents an integrated method based on Convolutional Neural Networks (CNNs) and data fusion for AS state recognition. The method takes the wavelet transform time-frequency images of the denoised vibration signal as input, uses CNNs to supervise and learn the data, extracts the deep data structure layer by layer, and improves the classification results through data fusion technology. The method is tested on an assembly concrete shear wall using shake-table testing, and the results show that it has a good overall identification accuracy (IA) of 94.7%, indicating that it is robust and capable of accurately recognizing very small changes in AS state recognition.
Using the Morgenstern–Price Method and Cloud Theory to Invert the Shear Strength Index of Tailings Dams and Reveal the Coupling Deformation and Failure Law under Extreme Rainfall
Authors: Chen Ziwei , Xie Chengyu , Xiong Guanpeng , Shen Jinbo , Yang Baolin
Published: April 2023

It is difficult to obtain reliable shear strength parameters for the stability analysis and evaluation of tailings dams in an unstable state. In this study, the sensitivity of the shear strength index to the safety factor of a tailings dam was evaluated. The cohesion C range of a tailings earth rock dam in an unstable state is determined by the safety factor, and the Morgenstern–Price method is used for inversion. During parameter inversion, uncertainty reasoning is established based on cloud theory, which overcomes the problem that the fuzziness and randomness of the quantitative cohesion value are transferred to the qualitative concept of the safety factor. The results show that the change in cohesion C has a greater influence on the safety factor Fs of the tailings dam, and the value of parameter inversion is 8.6901 kPa. The deformation and failure of tailings dams under extreme rainfall conditions are analyzed by using the modified cohesion C value. The dam toe becomes the main response area of plastic deformation and slowly expands to the interior, showing creep deformation. The displacement field gradually transfers from the accumulated tailings to the tailings dam with the flow direction, causing erosion damage. This study provides a new idea and method for parameter inversion of the shear strength index of tailings dams and provides a reference for the disaster prediction and prevention of tailings dams subjected to extreme rainfall.
Employer Branding and Employee Retention in the Banking Sector in Saudi Arabia: Mediating Effect of Relational Psychological Contracts
Authors: Alzaid Deemah , Dukhaykh Suad
Published: April 2023

The main purpose of this study is to examine the impact of employer branding on employee retention and the mediating effect of the relational psychological contract between employer branding and employee retention among employees of the banking sector in Saudi Arabia. A cross-sectional survey is used to gather data from 459 employees working in the banking sector. The study findings reveal that employer branding is positively and significantly related to relational psychological contracts and employee retention. Furthermore, a relational psychological contract significantly mediates the relationship between employer branding and employee retention. The current study is considered the first to provide empirical evidence of the role of the relational psychological contract as a mediator between employer branding and employee retention. As a result, the current study will extend the employer branding literature by recognizing a new way through which employer branding impacts employee retention. Theoretical and practical implications are discussed.
A Blockchain-Based Recycling Platform Using Image Processing, QR Codes, and IoT System
Authors: Borandag Emin
Published: April 2023

The climate crisis is one of the most significant challenges of the twenty-first century. The primary cause of high carbon emissions is industrial production that relies on carbon-based energy sources such as fuel oil, paraffin, coal, and natural gas. One of the effective methods to minimize carbon emissions originating from the use of energy resources is using recycling systems. A blockchain-based recycling platform was developed in this regard, adhering to the basic principles of Industry 4.0, which Robert Bosch GmbH and Henning Kagermann’s working group described as an industrial strategy plan at the Hannover Fair in 2013. Concurrently, the recycling platform has set up an infrastructure that combines blockchain, AI, and IoT technologies for recycling objects. An IoT-based smart device was developed to collect recyclable objects. Thanks to the embedded artificial intelligence software and QR code sensor on the device, recyclable objects can be collected in different hoppers. In the laboratory studies, correct object recognition success was achieved at a rate of 98.2%.
Adoption of Building Information Modelling in the Saudi Construction Industry: An Interpretive Structural Modelling
Authors: Alaboud Naif , Alshahrani Adnan
Published: April 2023

The Saudi Vision 2030 is a program of change management on a national level driven mostly by the use of digital technology. The implementation of building information modelling (BIM) is part of this change, and there is general agreement that its use improves the productivity and quality of the architecture, engineering, and construction (AEC) industries. Despite its extensive construction projects, Saudi Arabia has only recently planned for BIM. Several conditions must apply before it is successfully implemented. While the literature has identified the factors critical to its success and the barriers to its adoption, few studies have rated these factors in terms of their importance and their contingent impact. Furthermore, the interactional relationship between the factors has rarely been investigated. As a result, an interpretative structural model (ISM) was conducted to establish a hierarchy of variables, while accounting for the dynamic interaction between each. For factor selection, the matrix-based multiplication applied to a classification (MICMAC) method was utilized. Therefore, awareness of BIM and sustainability benefits are expected to be the most important variables in acceptance. Furthermore, the dynamic method is gradually shifting from bottom-down to a combined effect of top-down and bottom-up leadership.
Research on the Pro-Poorness of Economic Growth in Rural China
Authors: Jiang Kezhong , Zhou Qian , Yao Jin
Published: April 2023

Based on the pro-poor index and pro-poor curve measurement method of economic growth, this paper uses the data of Chinese Family Panel Studies to measure the pro-poorness of economic growth in rural areas of China. The study found that, from 2014 to 2018, the economic growth of rural areas in China was absolutely pro-poor, the economic growth in 2014–2016 was relatively non-pro-poor, and the economic growth in 2016–2018 was relatively pro-poor. From the perspective of household income structure, optimizing the pro-poorness of economic growth requires continuously increasing the wage income of low-income households, and continuing to implement fiscal transfer policies that are more favorable to low-income households. From the perspective of the human capital of households, optimizing the pro-poorness of economic growth requires active family support policies to enable workers to better balance family and work responsibilities, and adopt target location strategies to give special care to families with special difficulties while increasing investment in public services.
A Bibliometric Review of Household Carbon Footprint during 2000–2022
Authors: Shen Fang , Simayi Zibibula , Yang Shengtian , Mamitimin Yusuyunjiang , Zhang Xiaofen , Zhang Yunyi
Published: April 2023

With the increasing contribution of household carbon footprint to the global carbon footprint, household carbon footprint as a critical component of future carbon reduction has become a research hotspot to mitigate global warming and maintain sustainable economic development. The Web of Science (WOS) database is the literature data source. The literature on household carbon footprint is analyzed with the help of the visualization software CiteSpace. The collected data are analyzed using bibliometric analysis tools for knowledge base analysis, cooperative network analysis, and knowledge evolution analysis to grasp the developmental pulse of household carbon footprint. The findings show that the co-citation analysis reveals that household carbon footprint research has a clear knowledge base and shows a clear trend over time. The classic literature lays the foundation for subsequent diversified and interdisciplinary articles. The subsequent research hotspots show apparent inheritance and development characteristics, with many diversified and interdisciplinary studies appearing after 2008. Based on the three-level cooperation network analysis, household carbon footprint research has a clear structure of research cooperation network. Cross-institution and cross-country articles collaborate frequently; most authors tend to co-author articles, and there are still a small number of authors who write independently, among which China, Chinese institutions, and Chinese authors make significant contributions. Developed countries represented by the U.S. have chosen cross-region cooperation at the macro level through multifaceted research. The co-word and journal overlay analyses reveal that household carbon footprint research encompasses various research topics. The apparent shift of keywords within each research phase directly proves that household carbon footprint research is gradually developing into an interdisciplinary and diversified homogeneous field. This paper analyzes the evolution of household carbon footprint in detail and composes the basic knowledge which can provide a reliable reference for subsequent scholars.
Circular Economy Approaches for Electrical and Conventional Vehicles
Authors: Kifor Claudiu Vasile , Grigore Niculina Alexandra
Published: April 2023

The purpose of this article is to analyze the current state of the research and the trends in the field of Circular Economy (CE) for the automotive industry, with electric vehicles (EVs) and conventional vehicles (CVs), being analyzed separately. A systematic literature search was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, to screen and select articles from the Clarivate Web of Science database. For the detailed analysis, the information from the selected articles was structured based on such criteria as study objective, findings, limitations, and research proposals, and was further organized into nine categories for EVs, and into four categories for CVs. There were several CE strategies identified in the review, but a major challenge remains the evaluation of these strategies, in order to determine the most relevant, effective, and efficient ones, starting from the goal of minimizing the resource functionality loss. Future studies should focus on promoting new green technologies in the automotive industry, with an emphasis on circularity, in terms of raw materials, energy, production systems, product life extension, second use application, reuse, recycling, and end of life.
Fomes fomentarius as a Bio-Template for Heteroatom-Doped Carbon Fibers for Symmetrical Supercapacitors
Authors: Chernysheva Daria , Konstantinov Maksim , Sidash Ekaterina , Baranova Tatiana , Klushin Victor , Tokarev Denis , Andreeva Veronica , Kolesnikov Evgeny , Kaichev Vasily , Gorshenkov Mikhail , Smirnova Nina
Published: April 2023

Nowadays, commercial electric double-layer supercapacitors mainly use porous activated carbons due to their high specific surface area, electrical conductivity, and chemical stability. A feature of carbon materials is the possibility of obtaining them from renewable plant biomass. In this study, fungi (Fomes fomentarius) were used as a bio-template for the preparation of carbon fibers via a combination of thermochemical conversion approaches, including a general hydrothermal pre-carbonization step, as well as subsequent carbonization, physical, or chemical activation. The relationships between the preparation conditions and the structural and electrochemical properties of the obtained carbon materials were determined using SEM, TEM, EDAX, XPS, cyclic voltammetry, galvanostatic measurements, and EIS. It was shown that hydrothermal pretreatment in the presence of phosphoric acid ensured the complete removal of inorganic impurities of raw fungus hyphae, but at the same time, saved some heteroatoms, such as O, N, and P. Chemical activation using H3PO4 increased the amount of phosphorus in the carbon material and saved the natural fungus’s structure. The combination of a hierarchical pore structure with O, N, and P heteroatom doping made it possible to achieve good electrochemical properties (specific capacitance values of 220 F/g) and excellent stability after 25,000 charge/discharge cycles in a three-electrode cell. The electrochemical performance in both three- and two-electrode cells exceeded or was comparable to other biomass-derived porous carbons, making it a prospective candidate as an electrode material in symmetrical supercapacitors.
Special Functions and Its Application in Solving Two Dimensional Hyperbolic Partial Differential Equation of Telegraph Type
Authors: Ali Ishtiaq , Saleem Maliha Tehseen , Din Azhar ul
Published: April 2023

In this article, we use the applications of special functions in the form of Chebyshev polynomials to find the approximate solution of hyperbolic partial differential equations (PDEs) arising in the mathematical modeling of transmission line subject to appropriate symmetric Dirichlet and Neumann boundary conditions. The special part of the model equation is discretized using a Chebyshev differentiation matrix, which is centro-asymmetric using the symmetric collocation points as grid points, while the time derivative is discretized using the standard central finite difference scheme. One of the disadvantages of the Chebyshev differentiation matrix is that the resultant matrix, which is obtained after replacing the special coordinates with the derivative of Chebyshev polynomials, is dense and, therefore, needs more computational time to evaluate the resultant algebraic equation. To overcome this difficulty, an algorithm consisting of fast Fourier transformation is used. The main advantage of this transformation is that it significantly reduces the computational cost needed for N collocation points. It is shown that the proposed scheme converges exponentially, provided the data are smooth in the given equations. A number of numerical experiments are performed for different time steps and compared with the analytical solution, which further validates the accuracy of our proposed scheme.
System of Project Management at a Medical Hub as an Instrument for Implementation of Open Innovation
Authors: Ilin Igor , Voronova Olga , Pavlov Dmitriy , Kochkarov Azret , Tick Andrea , Khusainov Bulat
Published: April 2023

Globalization activates qualitative changes in multiple economic entities and requires the development of alternative forms of business organization. At present, one of the most promising development tracks is represented by the implementation of network structures, aimed at achieving common goals and obtaining a win-win outcome via joint effort. Business structures that invite dynamic and consistent transformations on a wide scale prove to be able to successfully compete in the market. In this regard, a project management system at a medical hub serves as a vital tool for implementation of open innovation. Participation in the medical hub allows coordinating intentions and establishing aligned communication between all stakeholders, suppliers and private institutions. In modern sectors of the economy, a developing hub becomes a unique structure, because it unites the contributions of the most important healthcare specialists in a single framework. This research examines the structure of healthcare business process models, and scrutinizes the communication between suppliers, partners and consumers of medical services. It also defines the main directions and outlines strategic goals. Assessment of performance of a project management system at a medical hub proves to be the issue of particular relevance, due to the fact that its tasks should be primarily aimed at increasing the share of successful projects and implementing only those ones that comply with the strategy. Based on the latter, a model for the project management system at a medical hub was designed. As a result, the authors developed an assessment mechanism for innovative projects using SNA methods that align with intra-communication interactions (transactions) between the participants in a medical hub. The conducted research allows concluding that in the current era of cutting-edge technologies, the project management system should be considered the most effective management tool for coordinating the actions of a corporate structure at a medical hub.