bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on an Algorithm of Express Parcel Sorting Based on Deeper Learning and Multi-Information Recognition
Authors: Xu Xing , Xue Zhenpeng , Zhao Yun
Published: September 2022

With the development of smart logistics, current small distribution centers have begun to use intelligent equipment to indirectly read bar code information on courier sheets to carry out express sorting. However, limited by the cost, most of them choose relatively low-end sorting equipment in a warehouse environment that is complex. This single information identification method leads to a decline in the identification rate of sorting, affecting efficiency of the entire express sorting. Aimed at the above problems, an express recognition method based on deeper learning and multi-information fusion is proposed. The method is mainly aimed at bar code information and three segments of code information on the courier sheet, which is divided into two parts: target information detection and recognition. For the detection of target information, we used a method of deeper learning to detect the target, and to improve speed and precision we designed a target detection network based on the existing YOLOv4 network, Experiments show that the detection accuracy and speed of the redesigned target detection network were much improved. Next for recognition of two kinds of target information we first intercepted the image after positioning and used a ZBAR algorithm to decode the barcode image after interception. The we used Tesseract-OCR technology to identify the intercepted three segments code picture information, and finally output the information in the form of strings. This deeper learning-based multi-information identification method can help logistics centers to accurately obtain express sorting information from the database. The experimental results show that the time to detect a picture was 0.31 s, and the recognition accuracy was 98.5%, which has better robustness and accuracy than single barcode information positioning and recognition alone.
Design of FOPID Controller for Pneumatic Control Valve Based on Improved BBO Algorithm
Authors: Zhu Min , Xu Zihao , Zang Zhaoyu , Dong Xueping
Published: September 2022

Aiming at the problems of nonlinearity and inaccuracy in the model of the pneumatic control valve position in the industrial control process, a valve position control method based on a fractional-order PID controller is proposed. The working principle of the pneumatic control valve is analyzed, and its mathematical model is established. In order to improve the accuracy of the model, an improved biogeography-based optimization algorithm is proposed to tune the parameters of the fractional-order PID controller in view of the wide range and high complexity of the fractional-order PID controller. The initialization of the chaotic graph, the adjustment of the migration model, and the improvement of the migration operator and the mutation operator are introduced to improve the algorithm optimization ability, which is used for the model identification of the control valve control system. The simulation and experimental results clearly show that, compared with the integer-order PID controller, the designed fractional-order PID controller has faster response speed and control accuracy, which can better meet the requirements of pneumatic control valve position control.
A Method of Calibration for the Distortion of LiDAR Integrating IMU and Odometer
Authors: Wu Qiuxuan , Meng Qinyuan , Tian Yangyang , Zhou Zhongrong , Luo Cenfeng , Mao Wandeng , Zeng Pingliang , Zhang Botao , Luo Yanbin
Published: September 2022

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy.
An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images
Authors: Daoud Mohammad I. , Al-Ali Aamer , Alazrai Rami , Al-Najar Mahasen S. , Alsaify Baha A. , Ali Mostafa Z. , Alouneh Sahel
Published: September 2022

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of 98%, 0.91, 0.90, and 0.90, respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region.
Research on Ultra-Wideband Radar Echo Signal Processing Method Based on P-Order Extraction and VMD
Authors: Qi Qingjie , Zhao Youxin , Zhang Liang , Yang Zhen , Sun Lifeng , Jia Xinlei
Published: September 2022

As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.
A Customer Behavior Recognition Method for Flexibly Adapting to Target Changes in Retail Stores
Authors: Wen Jiahao , Abe Toru , Suganuma Takuo
Published: September 2022

To provide analytic materials for business management for smart retail solutions, it is essential to recognize various customer behaviors (CB) from video footage acquired by in-store cameras. Along with frequent changes in needs and environments, such as promotion plans, product categories, in-store layouts, etc., the targets of customer behavior recognition (CBR) also change frequently. Therefore, one of the requirements of the CBR method is the flexibility to adapt to changes in recognition targets. However, existing approaches, mostly based on machine learning, usually take a great deal of time to re-collect training data and train new models when faced with changing target CBs, reflecting their lack of flexibility. In this paper, we propose a CBR method to achieve flexibility by considering CB in combination with primitives. A primitive is a unit that describes an object’s motion or multiple objects’ relationships. The combination of different primitives can characterize a particular CB. Since primitives can be reused to define a wide range of different CBs, our proposed method is capable of flexibly adapting to target CB changes in retail stores. In experiments undertaken, we utilized both our collected laboratory dataset and the public MERL dataset. We changed the combination of primitives to cope with the changes in target CBs between different datasets. As a result, our proposed method achieved good flexibility with acceptable recognition accuracy.
A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks
Authors: Singamaneni Kranthi Kumar , Dhiman Gaurav , Juneja Sapna , Muhammad Ghulam , AlQahtani Salman A. , Zaki John
Published: September 2022

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber–physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information.
Accuracy Assessment of the 2D-FFT Method Based on Peak Detection of the Spectrum Magnitude at the Particular Frequencies Using the Lamb Wave Signals
Authors: Draudvilienė Lina , Meškuotienė Asta , Raišutis Renaldas , Tumšys Olgirdas , Surgautė Lina
Published: September 2022

The 2D-FFT is described as a traditional method for signal processing and analysis. Due to the possibility to determine the time and frequency (t,f) domains, such a method has a wide application in various industrial fields. Using that method, the obtained results are presented in images only; thus, for the extraction of quantitative values of phase velocities, additional algorithms should be used. In this work, the 2D-FFT method is presented, which is based on peak detection of the spectrum magnitude at particular frequencies for obtaining the quantitative expressions. The radiofrequency signals of ULWs (ultrasonic Lamb waves) were used for the accuracy evaluation of the method. An uncertainty evaluation was conducted to guarantee the metrological traceability of measurement results and ensure that they are accurate and reliable. Mathematical and experimental verifications were conducted by using signals of Lamb waves propagating in the aluminum plate. The obtained mean relative error of 0.12% for the A0 mode (160 kHz) and 0.05% for the S0 mode (700 kHz) during the mathematical verification indicated that the proposed method is particularly suitable for evaluating the phase-velocity dispersion in clearly expressed dispersion zones. The uncertainty analysis showed that the plate thickness, the mathematical modeling, and the step of the scanner have a significant impact on the estimated uncertainty of the phase velocity for the A0 mode. Those components of uncertainty prevail and make about ~92% of the total standard uncertainty in a clearly expressed dispersion range. The S0 mode analysis in the non-dispersion zone indicates that the repeatability of velocity variations, fluctuations of the frequency of Lamb waves, and the scanning step of the scanner influence significantly the combined uncertainty and represent 98% of the total uncertainty.
Semi-Supervised Deep Learning Model for Efficient Computation of Optical Properties of Suspended-Core Fibers
Authors: Wang Gao , Ren Sufen , Li Shuna , Chen Shengchao , Yu Benguo
Published: September 2022

Suspended-core fibers (SCFs) are considered the best candidates for enhancing fiber nonlinearity in mid-infrared applications. Accurate modeling and optimization of its structure is a key part of the SCF structure design process. Due to the drawbacks of traditional numerical simulation methods, such as low speed and large errors, the deep learning-based inverse design of SCFs has become mainstream. However, the advantage of deep learning models over traditional optimization methods relies heavily on large-scale a priori datasets to train the models, a common bottleneck of data-driven methods. This paper presents a comprehensive deep learning model for the efficient inverse design of SCFs. A semi-supervised learning strategy is introduced to alleviate the burden of data acquisition. Taking SCF’s three key optical properties (effective mode area, nonlinear coefficient, and dispersion) as examples, we demonstrate that satisfactory computational results can be obtained based on small-scale training data. The proposed scheme can provide a new and effective platform for data-limited physical computing tasks.
A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults
Authors: Chen Manting , Wang Hailiang , Yu Lisha , Yeung Eric Hiu Kwong , Luo Jiajia , Tsui Kwok-Leung , Zhao Yang
Published: September 2022

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.
Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology
Authors: Nasir Muhammad Umar , Khan Safiullah , Mehmood Shahid , Khan Muhammad Adnan , Zubair Muhammad , Hwang Seong Oun
Published: September 2022

The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.
A Miniaturized MicroRNA Sensor Identifies Targets Associated with Weight Loss in a Diet and Exercise Intervention among Healthy Overweight Individuals
Authors: Jayasooriya Vidura , Johnson Nathaniel , Bradley Adam , Kotarsky Christopher , Jepng’etich Lizzy , Friesner Daniel , Stastny Sherri , Hackney Kyle J. , Nawarathna Dharmakeerthi
Published: September 2022

Weight loss through dietary and exercise intervention is commonly prescribed but is not effective for all individuals. Recent studies have demonstrated that circulating microRNA (miR) biomarkers could potentially be used to identify individuals who will likely lose weight through diet and exercise and attain a healthy body weight. However, accurate detection of miRs in clinical samples is difficult, error-prone, and expensive. To address this issue, we recently developed iLluminate—a low-cost and highly sensitive miR sensor suitable for point-of-care testing. To investigate if miR testing and iLluminate can be used in real-world obesity applications, we developed a pilot diet and exercise intervention and utilized iLluminate to evaluate miR biomarkers. We evaluated the expression of miRs-140, -935, -let-7b, and -99a, which are biomarkers for fat loss, energy metabolism, and adipogenic differentiation. Responders lost more total mass, tissue mass, and fat mass than non-responders. miRs-140, -935, -let-7b, and -99a, collectively accounted for 6.9% and 8.8% of the explained variability in fat and lean mass, respectively. At the level of the individual coefficients, miRs-140 and -935 were significantly associated with fat loss. Collectively, miRs-140 and -935 provide an additional degree of predictive capability in body mass and fat mass alternations.
An Interpretable Two-Phase Modeling Approach for Lung Cancer Survivability Prediction
Authors: Sedighi-Maman Zahra , Heath Jonathan J.
Published: September 2022

Although lung cancer survival status and survival length predictions have primarily been studied individually, a scheme that leverages both fields in an interpretable way for physicians remains elusive. We propose a two-phase data analytic framework that is capable of classifying survival status for 0.5-, 1-, 1.5-, 2-, 2.5-, and 3-year time-points (phase I) and predicting the number of survival months within 3 years (phase II) using recent Surveillance, Epidemiology, and End Results data from 2010 to 2017. In this study, we employ three analytical models (general linear model, extreme gradient boosting, and artificial neural networks), five data balancing techniques (synthetic minority oversampling technique (SMOTE), relocating safe level SMOTE, borderline SMOTE, adaptive synthetic sampling, and majority weighted minority oversampling technique), two feature selection methods (least absolute shrinkage and selection operator (LASSO) and random forest), and the one-hot encoding approach. By implementing a comprehensive data preparation phase, we demonstrate that a computationally efficient and interpretable method such as GLM performs comparably to more complex models. Moreover, we quantify the effects of individual features in phase I and II by exploiting GLM coefficients. To the best of our knowledge, this study is the first to (a) implement a comprehensive data processing approach to develop performant, computationally efficient, and interpretable methods in comparison to black-box models, (b) visualize top factors impacting survival odds by utilizing the change in odds ratio, and (c) comprehensively explore short-term lung cancer survival using a two-phase approach.
Textronic Glove Translating Polish Sign Language
Authors: Korzeniewska Ewa , Kania Marta , Zawiślak Rafał
Published: September 2022

Communication between people is a basic social skill used to exchange information. It is often used for self-express and to meet basic human needs, such as the need for closeness, belonging, and security. This process takes place at different levels, using different means, with specific effects. It generally means a two-way flow of information in the immediate area of contact with another person. When people are communicating using the same language, the flow of information is much easier compared to the situation when two people use two different languages from different language families. The process of social communication with the deaf is difficult as well. It is therefore essential to use modern technologies to facilitate communication with deaf and non-speaking people. This article presents the results of work on a prototype of a glove using textronic elements produced using a physical vacuum deposition process. The signal from the sensors, in the form of resistance changes, is read by the microcontroller, and then it is processed and displayed on a smartphone screen in the form of single letters. During the experiment, 520 letters were signed by each author. The correctness of interpreting the signs was 86.5%. Each letter was recognized within approximately 3 s. One of the main results of the article was also the selection of an appropriate material (Velostat, membrane) that can be used as a sensor for the proposed application solution. The proposed solution can enable communication with the deaf using the finger alphabet, which can be used to spell single words or the most important key words.
A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring
Authors: Cinar Eyup
Published: September 2022

Sensor fusion is becoming increasingly popular in condition monitoring. Many studies rely on a fusion-level strategy to enable the most effective decision-making and improve classification accuracy. Most studies rely on feature-level fusion with a custom-built deep learning architecture. However, this may limit the ability to use the widely available pre-trained deep learning architectures available to users today. This study proposes a new method for sensor fusion based on concepts inspired by image fusion. The method enables the fusion of multiple and heterogeneous sensors in the time-frequency domain by fusing spectrogram images. The method’s effectiveness is tested with transfer learning (TL) techniques on four different pre-trained convolutional neural network (CNN) based model architectures using an original test environment and data acquisition system. The results show that the proposed sensor fusion technique effectively classifies device faults and the pre-trained TL models enrich the model training capabilities.
Cluster Migration Distance for Performance Degradation Assessment of Water Pump Bearings
Authors: Zhai Zhongping , Zhu Zihao , Xu Yifan , Zhao Xinhang , Liu Fang , Feng Zhihua
Published: September 2022

Because the signal of water pump bearing is seriously disturbed by noise and the fault evolution is complex, it is difficult to describe the performance degradation trend of water pump bearing in a timely and accurate manner using the traditional performance degradation index (PDI). In this paper, a new Cluster Migration Distance (CMD) algorithm is proposed. The extraction of the indicator includes the following four steps: First, the relevant blind separation is used to extract the useful signal of the monitored bearing from the mixed signal; secondly, the impact component is further enhanced by wavelet packet analysis. Then, the redundancy of the original feature vectors is eliminated using our previously proposed KJADE (Kernel Joint Approximate Diagonalization of Eigen-matrices) method. Finally, the newly proposed CMD index is computed as PDI. By calculating the offset trajectory of the feature cluster centroid in the continuous running process of the bearing, CMD can aptly deal with the complex and variable features in the fault evolution process of the water pump bearing. The whole-life monitoring data of a 220 KW water pump system are processed. The results show that the proposed CMD index has better early-warning ability and monotonicity than the traditional kurtosis index.
Exploring the Direct and Indirect Influence of Perceived Organizational Support on Affective Organizational Commitment
Authors: Silva Rui , Dias Álvaro , Pereira Leandro , da Costa Renato Lopes , Gonçalves Rui
Published: September 2022

As a result of the pandemic and the consequent changes in labor market patterns, firms are facing a difficult moment in attracting and retaining talented employees. In these new patterns, remuneration factors are increasingly a necessary but not sufficient condition to address this challenge. Given this background, this study seeks to identify the role of perceived organizational support in affective organizational commitment. In order to achieve this objective, structural equation models were used based on survey data from a sample of 333 respondents. The findings of this study reveal that perceived organizational support positively influences affective organizational commitment, job involvement, and job satisfaction. Furthermore, job involvement and job satisfaction were found to play a mediating effect in the relation between perceived organizational support and affective organizational commitment.
The High Note of Meaning: A Case Study of Public Service Motivation of Local Government Officials
Authors: Forte Teresa , Santinha Gonçalo , Oliveira Mariana , Patrão Marta
Published: September 2022

Public service motivation (PSM) has provided a new breadth to the study of what attracts and retains workers in public organizations committed to the public mission. The present research contributes to the topic by exploring local government workers’ motivation for public service, the meaning they attribute to their activity as public servants, and the relations between PSM and the meaning of work (MOW). An adaptation of the PSM scale to the Portuguese language and context and the local level of public administration is proposed based on a sample of seventeen surveyed municipalities involving 252 participants. Within the analyzed context, dedication to the public interest is the most important factor of public service motivation and, alongside self-sacrifice, more common in older public servants and those with higher educational degrees. Workers with temporary job contracts rank higher in indifference and disbelief in politics as opposed to those workers with tenure who show a higher dedication to the public interest. The majority of the participants consider their work to be purposeful and meaningful, a state that is significantly positively correlated with the more altruistic dimensions of PSM, self-sacrifice and dedication to the public interest, suggesting a profitable venue of organizational research and work policy benchmarking.
Financial Efficiency and Its Impact on Renewable Energy Demand and CO2 Emissions: Do Eco-Innovations Matter for Highly Polluted Asian Economies?
Authors: Hafeez Muhammad , Rehman Saif Ur , Faisal C. M. Nadeem , Yang Juan , Ullah Sana , Kaium Md. Abdul , Malik Muhammad Yousaf
Published: September 2022

The analysis aims to examine the impact of eco-innovation and financial efficiency on CO2 emissions and renewable energy consumption in highly polluted Asian economies, including China, India, Russia, and Japan. For empirical analysis, we have applied the ARDL pooled mean group (ARDL-PMG) model. The long-run estimated coefficient of environmental innovations is positively significant in both renewable energy models and negatively significant in the CO2 emissions model. These results imply that environmental innovations help facilitate renewable energy consumption and reduce CO2 emissions. On the other side, the estimates of financial development are insignificant in both renewable energy and CO2 emissions models. However, the estimates of financial institution efficiency and financial markets are positively significant in both renewable energy and CO2 emissions models, implying that financial institutions and market efficiency increase renewable energy consumption and decrease CO2 emissions.
Techno-Economic Comprehensive Review of State-of-the-Art Geothermal and Solar Roadway Energy Systems
Authors: Cui Yuanlong , Zhang Fan , Shao Yiming , Twaha Ssennoga , Tong Hui
Published: September 2022

Road infrastructure is a vital constituent element in the transportation network; however, roadway surface ice and snow accumulation leads to huge traffic accidents in winter. Geothermal roadway energy systems (GRES) and solar roadway energy systems (SRES) can increase or decrease roadway surface temperature for the de-icing and removal of snow in winter, or mitigation of heat in summer. Technology performance and economic evaluation of the GRES and SRES are reviewed in this paper based on numerical and economic models, and experimental analyses. Three crucial aspects of the technology performance assessment, i.e., roadway surface temperature, energy consumption and key factors, are explored in different regions and countries. Economic evaluation approaches for net present values and payback periods of the GRES and SRES are investigated. The recommendations and potential future developments on the two technologies are deliberated; it is demonstrated that the GRES and SRES could increase roadway surface temperature by around 5 °C in winter and decrease it by about 6 °C in summer, with the payback periods of 4 to 8 years and 2.3 to 5 years, respectively.
Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization
Authors: Dayalan Suchitra , Gul Sheikh Suhaib , Rathinam Rajarajeswari , Fernandez Savari George , Aleem Shady H. E. Abdel , Mohamed Mohamed A. , Ali Ziad M.
Published: September 2022

Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters.
MSE Response during Times of Crisis: The Roles of Budgeting Micro Functions and Guanxi
Authors: Marjerison Rob Kim , Kim Jong Min
Published: September 2022

This study explores how micro and small enterprises (MSEs) rely on and adapt their budgetary practices to guide their activities during times of crisis, specifically during the COVID-19 global pandemic that began in early 2020. While the existing literature has mainly focused on large enterprises during economic crises, this paper will specifically consider MSEs in Southeast China during and since the pandemic. The primary data were collected via an online survey distributed in Wenzhou municipality, with 397 responses included in the analysis. The results indicate that within the budgeting process, the micro functions of planning and performance evaluation had less importance during the pandemic while the importance placed on the function of resource allocation was unchanged. The findings suggest that MSEs during crises placed less importance on the budgeting process and more on guanxi-related considerations for maintaining financial liquidity, including customer and government relationship building. Our findings add a resource-based view of the capability of guanxi during times of crisis. In addition, the limitations and future research are discussed.
The Origins, Evolution, Current State, and Future of Green Products and Consumer Research: A Bibliometric Analysis
Authors: Bravo Alencar , Vieira Darli , Rebello Thais Ayres
Published: September 2022

Green products are crucial for a sustainable future. Without a strong understanding of consumer intent toward green products and research gaps, translating the availability of green products into actual consumer and market acceptance is hampered. This article reviews the available literature on green products and their relationship to consumers through bibliometric analysis. We used VosViewer to globalize the topic mapping and Scimat for longitudinal analysis. The results show that the available literature can be divided into four clusters, and five periods representing four distinct eras can be defined. Published studies were found in only 15 of the 36 calendar years constituting the first era. The second era started a wave of increasing green product research. In the third era, the number of journals with publications related to green products peaked. After the diversification of the third era, the fourth era saw the consolidation of the main vectors of publication. Despite a slow start in 1974, the research on eco-friendly products has expanded significantly over the past decade. Nonetheless, one persistent weakness of the literature is that most studies use customer intent, not the purchase itself, as the dependent variable. Consequently, there is still enormous potential for further research.
Contemporary Management Practice Applying the Dynamic Absorptive Capacity Measurement Model (PM4AC) for Improved Business Sustainability
Authors: Acosta-Velásquez Rubén Dario , León-Pulido Jeffrey , García-Pérez Alexander , Fajardo-Moreno William Stive , Espinosa-Leal Leonardo
Published: September 2022

The Colombian industrial sector faces various problems, such as contributing to the development of business and innovation capacities to overcome the difficulties associated with poverty, low competitiveness and low complexity. A key challenge is to develop mechanisms that allow companies to adapt to a globalized competitive environment. In this regard, projects and their management represent an opportunity for greater flexibility. This work presents a model developed to quantify dynamic absorption, understood as the ability to identify the value of new external knowledge, absorb it as internal knowledge and apply it to serve business purposes. The measured indicator is adapted to a dynamic organization environment and provides a project with the ability to interact with and monitor variables. For modeling, variables observed across 148 small and medium-sized enterprises (SMEs), belonging to Colombian organizations, were collected using questionnaires and structural equation modeling (SEM) to determine analysis dimensions, and subsets of dynamic absorptive capacity as latent variables from this information. The dynamic absorptive capacity measurement model (PM4AC) describes a normalized fit index (NFI), comparative fit index (CFI) and RMSEA of 0.935, 0.986 and 0.042, correspondingly. The contribution of this model is designed to improve and make available a new framework of business sustainability leadership, using the PM4AC tool. Finally, the objective of this study is to provide a model developed to quantify the dynamic absorption capacity for SMES. Furthermore, as most of the research involves technology firms, we seek to better understand the business sustainability in SMEs.
Innovation Input and Firm Value: Based on the Moderating Effect of Internal Control
Authors: Ma Qianhui , Ju Lan , Zhang Zishi
Published: September 2022

Using the data of Chinese A-share listed firms with non-zero innovation investment between 2007 and 2017, this paper links the value relevance of innovation investment with internal control from the perspective of operating performance and market value, respectively. This paper empirically verifies that internal control significantly increases the value relevance of innovation input, that is, the better the internal control, the more innovation investment contributes to the operating performance and market value of a firm. Then, based on the potential mechanisms of alleviating agency problems and conducting better risk management, further investigation in this paper also indicates that internal control’s moderating effect on the value relevance of innovation input is more prominent for firms with relatively more severe agency problems and for expensed R&D expenditure which represents the part of innovation investment with higher uncertainty.
Water Scarcity and Climate Change in Mykonos (Greece): The Perceptions of the Hospitality Stakeholders
Authors: Atay Itri , Saladié Òscar
Published: September 2022

Climate change, unsustainable water use by the tourism sector, and short-term-based decisions by policymakers create additional stress on limited water resources in arid and semi-arid tourism destinations. Non-conventional water resources are playing an important role in making additional water available for these destinations, and the use of desalination plants has gradually increased. However, they can create additional externalities on the environment that can compromise the sustainability of the tourist destination. The decisions of key stakeholders and cooperation among them hold vital importance for the sustainability of tourism and the availability of water resources. Mykonos Island (Greece) is a globally recognized tourist destination in the Eastern Mediterranean Sea. The island has suffered significant water-scarcity problems in recent years, in particular during the peak tourist season, and desalination plants are being built to reduce stress on the water supply. The first objective of this work was to analyze the perceptions of hospitality stakeholders regarding water shortages and climate change in Mykonos. A second objective was to assess whether the same hospitality stakeholders feel that desalination plants are the solution to water scarcity in island-tourism destinations such as Mykonos. The results show that (i) hospitality stakeholders have significant awareness regarding water-scarcity problems and the impacts of climate change on tourism activity, (ii) they do not view desalination plants as the only solution to water scarcity, and (iii) they perceive a significant lack of coordination among actors who participate in the decision-making process. These results provide clues regarding the importance of awareness, coordination, and cooperation of each actor involved in the decision-making process and can be of interest to policymakers and public authorities in tourism destinations facing water-scarcity problems.
Drivers of Post-Harvest Aflatoxin Contamination: Evidence Gathered from Knowledge Disparities and Field Surveys of Maize Farmers in the Rift Valley Region of Kenya
Authors: Gachara Grace , Suleiman Rashid , El Kadili Sara , Ait Barka Essaid , Kilima Beatrice , Lahlali Rachid
Published: September 2022

Maize-dependent populations in sub-Saharan Africa are continually exposed to aflatoxin poisoning owing to their regular consumption of this dietetic cereal. Being a staple in Kenyan households, consumption of maize-based meals is done almost daily, thereby exposing consumers to aflatoxicoses. This study assessed awareness levels, knowledge disparities, and perceptions regarding aflatoxin contamination at the post-harvest phase among farmers in the Rift Valley Region of Kenya. Households were randomly selected using a geographical positioning system (GPS) overlay of the agro-ecological zones within Uasin Gishu and Elgeyo Marakwet counties. Face-to-face interviews were conducted in 212 smallholder and large-scale farms. The study documented the demographic profiles of farmers and knowledge, awareness, and perceptions of aflatoxin contamination using a pre-designed structured questionnaire. Most farmers were familiar with aflatoxins and the adverse effects they present to health (61.32%). Almost all the farmers (94.37%) were aware of storage molds and food-spoilage fungi. However, few farmers adopted good post-harvest practices (PHPs), such as avoiding premature harvests (49.8%), using well-ventilated storage spaces (44.6%), grain sorting (30.5%), proper drying of maize (17.8%), and using hermetic bags for storage (30.5%). Conclusively, intensified farmer education is required to train farmers on good PHPs to protect their maize from aflatoxigenic fungi and aflatoxin accumulation.
Radiographic Evaluation of Distal Radius Fracture Healing by Time: Orthopedist versus Qualitative Assessment of Image Processing
Authors: Oulianski Maria , Avraham Dana , Lubovsky Omri
Published: September 2022

Distal radius fractures are among the most prevalent long-bone fractures in the body. Fracture healing assessment is based on clinical evaluation and radiological examinations. A lack of consensus exists regarding the radiographic criteria for fracture union. Our work examined the commonly used criteria for the assessment of fracture healing. Thirty-two patients, conservatively treated for distal radius fracture, participated in a prospective study. Enrolled patients followed protocol for 26 weeks. Four orthopedic surgeons with similar ranks were asked to evaluate three parameters of radiographic measurements for each set of radiographs, including callus formation, the presence of a fracture line, and bridging of fracture sites or sites of fracture edges in 70 radiographs. Ten patients were eligible for the study. The degree of agreement among surgeons was “good” (Cronbach’s alpha): callus formation—0.8, bridging of fracture sites—0.775, blurring of fracture line gap—0.795. A timeline based on the specific week and grading system was made. Radiographic detection of callus formation was seen after the second film, between 6 and 9 weeks, and an agreement among surgeons was achieved for more than half of the patients for the blurring of the fracture gap. The radiographic healing progression of the distal radius can be detected after 6 and 9 weeks in all three parameters with good agreement between different surgeons. A timeline graph such as the one that was made in this model can be used for the follow-up of patients’ fracture healing or early detection of non-union.
La formación profesional de psicólogos durante la pandemia
Authors: Cuevas Jiménez Adrián , Covarrubias Terán María Antonieta , Gómez Herrera José Trinidad
Published: September 2022

La pandemia por COVID-19, ha irrumpido y alterado la vida cotidiana de miles de millones de personas en el mundo, afectando la salud, la economía, así como la educación. Ante el confinamiento repentino se suspendieron los cursos escolares presenciales y, por lo tanto, la interacción directa profesor-alumno, suscitando cambios en la percepción, valoración y estados afectivos, los cuales han generado condiciones de vida marcadas por la incertidumbre y los riesgos de contagio. En particular, la formación profesional de estudiantes universitarios, inmersa de manera abrupta en una educación en línea, ha implicado vivencias inesperadas en los estudiantes que, a pesar de todo, aún permanecen en la educación superior. El objetivo de este trabajo es presentar un análisis sobre la formación profesional de estudiantes de psicología desde su intervención en el ámbito educativo, a partir de sus narraciones. El sustento es una metodología cualitativa, que implica la construcción de la información a partir de los resultados, resaltando las situaciones imprevistas, la búsqueda de estrategias alternativas adaptadas a problemáticas específicas, y su contribución a una formación rica en experiencia, creativa en didáctica y formativa al usar las nuevas tecnologías como desafíos en las dimensiones certidumbre-incertidumbre, generando distintos grados de insatisfacción–satisfacción. En este proceso se resalta el soporte afectivo, académico, de comunicación, respeto y empatía como contexto formativo.