bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN
Authors: Alshamrani Khalaf , Alshamrani Hassan A. , Alqahtani Fawaz F. , Almutairi Bander S.
Published: December 2022

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.
Estimation of Number of Pigs Taking in Feed Using Posture Filtration
Authors: Kim Taeho , Kim Youjin , Kim Sehan , Ko Jaepil
Published: December 2022

Pork production is hugely impacted by the health and breeding of pigs. Analyzing the eating pattern of pigs helps in optimizing the supply chain management with a healthy breeding environment. Monitoring the feed intake of pigs in a barn provides information about their eating habits, behavioral patterns, and surrounding environment, which can be used for further analysis to monitor growth in pigs and eventually contribute to the quality and quantity of meat production. In this paper, we present a novel method to estimate the number of pigs taking in feed by considering the pig’s posture. In order to solve problems arising from using the pig’s posture, we propose an algorithm to match the pig’s head and the corresponding pig’s body using the major-and-minor axis of the pig detection box. In our experiment, we present the detection performance of the YOLOv5 model according to the anchor box, and then we demonstrate that the proposed method outperforms previous methods. We therefore measure the number of pigs taking in feed over a period of 24 h and the number of times pigs consume feed in a day over a period of 30 days, and observe the pig’s feed intake pattern.
Efficient Transformer-Based Compressed Video Modeling via Informative Patch Selection
Authors: Suzuki Tomoyuki , Aoki Yoshimitsu
Published: December 2022

Recently, Transformer-based video recognition models have achieved state-of-the-art results on major video recognition benchmarks. However, their high inference cost significantly limits research speed and practical use. In video compression, methods considering small motions and residuals that are less informative and assigning short code lengths to them (e.g., MPEG4) have successfully reduced the redundancy of videos. Inspired by this idea, we propose Informative Patch Selection (IPS), which efficiently reduces the inference cost by excluding redundant patches from the input of the Transformer-based video model. The redundancy of each patch is calculated from motions and residuals obtained while decoding a compressed video. The proposed method is simple and effective in that it can dynamically reduce the inference cost depending on the input without any policy model or additional loss term. Extensive experiments on action recognition demonstrated that our method could significantly improve the trade-off between the accuracy and inference cost of the Transformer-based video model. Although the method does not require any policy model or additional loss term, its performance approaches that of existing methods that do require them.
Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game
Authors: Lee Wonjun , Lee Deokwon , Lee Sanghyub , Jun Kooksung , Kim Mun Sang
Published: December 2022

The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD. However, there has been no study of ADHD classification using only skeleton data. It was hypothesized that the main symptoms of ADHD, such as distraction, hyperactivity, and impulsivity, could be differentiated through skeleton data. Thus, we devised a game system for the screening and diagnosis of children’s ADHD and acquired children’s skeleton data using five Azure Kinect units equipped with depth sensors, while the game was being played. The game for screening diagnosis involves a robot first travelling on a specific path, after which the child must remember the path the robot took and then follow it. The skeleton data used in this study were divided into two categories: standby data, obtained when a child waits while the robot demonstrates the path; and game data, obtained when a child plays the game. The acquired data were classified using the RNN series of GRU, RNN, and LSTM algorithms; a bidirectional layer; and a weighted cross-entropy loss function. Among these, an LSTM algorithm using a bidirectional layer and a weighted cross-entropy loss function obtained a classification accuracy of 97.82%.
Properties and Applications of Random Lasers as Emerging Light Sources and Optical Sensors: A Review
Authors: Ni Dongqin , Späth Moritz , Klämpfl Florian , Hohmann Martin
Published: December 2022

In a random laser (RL), optical feedback arises from multiple scattering instead of conventional mirrors. RLs generate a laser-like emission, and meanwhile take advantage of a simpler and more flexible laser configuration. The applicability of RLs as light sources and optical sensors has been proved. These applications have been extended to the biological field, with tissues as natural scattering materials. Herein, the current state of the RL properties and applications was reviewed.
Weigh-in-Motion System Based on an Improved Kalman and LSTM-Attention Algorithm
Authors: Shi Baidi , Jiang Yongfeng , Bao Yefeng , Chen Bingyan , Yang Ke , Chen Xianming
Published: December 2022

A weigh-in-motion (WIM) system continuously and automatically detects an object’s weight during transmission. The WIM system is used widely in logistics and industry due to increasing labor and time costs. However, the accuracy and stability of WIM system measurements could be affected by shock and vibration under high speed and heavy load. A novel six degrees-of-freedom (DOF), mass–spring damping-based Kalman filter with time scale (KFTS) algorithm was proposed to filter noise due to the multiple-input noise and its frequency that is highly coupled with the basic sensor signal. Additionally, an attention-based long short-term memory (LSTM) model was built to predict the object’s mass by using multiple time-series sensor signals. The results showed that the model has superior performance compared to support vector machine (SVM), fully connected network (FCN) and extreme gradient boosting (XGBoost) models. Experiments showed this improved deep learning model can provide remarkable accuracy under different loads, speed and working situations, which can be applied to the high-precision logistics industry.
NG-GAN: A Robust Noise-Generation Generative Adversarial Network for Generating Old-Image Noise
Authors: Hossain Sadat , Lee Bumshik
Published: December 2022

Numerous old images and videos were captured and stored under unfavorable conditions. Hence, old images and videos have uncertain and different noise patterns compared with those of modern ones. Denoising old images is an effective technique for reconstructing a clean image containing crucial information. However, obtaining noisy-clean image pairs for denoising old images is difficult and challenging for supervised learning. Preparing such a pair is expensive and burdensome, as existing denoising approaches require a considerable number of noisy-clean image pairs. To address this issue, we propose a robust noise-generation generative adversarial network (NG-GAN) that utilizes unpaired datasets to replicate the noise distribution of degraded old images inspired by the CycleGAN model. In our proposed method, the perception-based image quality evaluator metric is used to control noise generation effectively. An unpaired dataset is generated by selecting clean images with features that match the old images to train the proposed model. Experimental results demonstrate that the dataset generated by our proposed NG-GAN can better train state-of-the-art denoising models by effectively denoising old videos. The denoising models exhibit significantly improved peak signal-to-noise ratios and structural similarity index measures of 0.37 dB and 0.06 on average, respectively, on the dataset generated by our proposed NG-GAN.
Bias-Repeatability Analysis of Vacuum-Packaged 3-Axis MEMS Gyroscope Using Oven-Controlled System
Authors: Din Hussamud , Iqbal Faisal , Park Jiwon , Lee Byeungleul
Published: December 2022

The performance of microelectromechanical system (MEMS) inertial measurement units (IMUs) is susceptible to many environmental factors. Among different factors, temperature is one of the most challenging issues. This report reveals the bias stability analysis of an ovenized MEMS gyroscope. A micro-heater and a control system exploiting PID/PWM were used to compensate for the bias stability variations of a commercial MEMS IMU from BOSCH “BMI 088”. A micro-heater made of gold (Au) thin film is integrated with the commercial MEMS IMU chip. A custom-designed micro-machined glass platform thermally isolates the MEMS IMU from the ambient environment and is vacuum sealed in the leadless chip carrier (LCC) package. The BMI 088 built-in temperature sensor is used for temperature sensing of the device and the locally integrated heater. The experimental results reveal that the bias repeatability of the devices has been improved significantly to achieve the target specifications, making the commercial devices suitable for navigation. Furthermore, the effect of vacuum-packaged and non-vacuum-packaged devices was compared. It was found that the bias repeatability of vacuum-packaged devices was improved by more than 60%.
Switched-Delay Smith Predictor for the Control of Plants with Response-Delay Asymmetry
Authors: Baskys Algirdas
Published: December 2022

The modification of a Smith predictor for the control of plants with response-delay asymmetry has been proposed. It was developed for application in frequency converters for the control of the speed of the AC induction motor drives of pumps used in water- and liquefied-petroleum-gas-supply systems. Such plants are characterized by long response delays, and often these delays are asymmetric, i.e., the response delay to the rising and falling plant-control signal is different. A distinctive feature of the proposed modification is that the value of the response delay in the model of the plant used for the realization of the Smith predictor is switched. The operation of the proposed switched-delay Smith predictor, which was used with the proportional-integral controller, was analyzed using a simulation and experimentally in a real water-supply system. The obtained results prove an advantage of the suggested solution.
Application of Gas Chromatography Mass Spectrometry in Tar Analysis from Underground Gasification
Authors: Feng Lele , Liu Jie , Xin Haihui , Pang Jiabao
Published: December 2022

The study of tar behaviors in underground coal gasification (UCG) is essential for pollution control, system safety and conversion efficiency; however, existing studies have only focused on tar in products without revealing tar evolution in the reaction zone, and the experimental conditions in reported work are far from those in the real situation. In this work, tar behaviors were studied with a self-developed apparatus to simulate the UCG process. During the experiments, the sampling method along the gasification channel was used to collect tar at different positions; the gasification object was a large raw coal block 460 mm × 230 mm × 230 mm in size, and the flow rate of the inlet gas was adjusted according to the composition of products. The tar samples were not only taken from the outlet, but also from the reaction zone, and then analyzed using gas chromatography mass spectrometry. For all the tar samples, C15H13N and its isomer were the most abundant compounds, with a total percentage greater than 14%. Most of the top five chemicals contained more than nine carbon atoms in their molecular formulae, indicating that more heavy tar than light tar is formed by low-temperature pyrolysis. Compared with the upstream tar, the downstream tar had fewer PAHs and a lower boiling point, due to the decomposition of the heavy tar. The downstream tar contained more of the element fluorine (F) than upstream and outlet tars, indicating that tar pollution remaining in the reaction zone cannot be evaluated by monitoring the outlet tar.
Assistive Technology Is a Resource for Building Capabilities, but Is It Just Addressing the Symptoms of Inequality?
Authors: Steel Emily J
Published: December 2022

Advocacy for assistive technology interventions is compatible with the capabilities approach but is insufficient for addressing the disadvantage experienced by people with disability. This paper reflects on equality as an objective of the capabilities approach arising from economics, and it summarises how assistive technology and accessibility are mechanisms for achieving equality in the contemporary legal context of international disability rights. Research and advocacy for assistive technology have failed to communicate a coherent set of actions for policy makers to adopt. Defined concepts and interventions are required to prioritise and coordinate action to support individuals with assistive technology in parallel with improving collective resources by improving accessibility. Radical change in economic paradigms and societal structures that drive poverty and disability may be required for the effective adoption of assistive technology and closure of capability gaps.
The COVID-19 Risk Perception: A Qualitative Study among the Population in an Urban Setting in Burkina Faso
Authors: Bocoum Fadima Yaya , Kadio Kadidiatou , Bila Alice , Drabo Koiné Maxime , Kouanda Seni
Published: December 2022

Background: The population’s adherence to public health recommendations depends on many individual and collective cultural, socioeconomic, institutional, and environmental factors and the perception of the risks involved. This study aimed to describe and analyze the perception of risk related to coronavirus in the general population. Methods: A qualitative, exploratory cross-sectional study was conducted in Ouagadougou, the capital city of Burkina Faso. It involved the general population (youth and adults, men and women, traditional practitioners, religious leaders, and opinion leaders). The data were analyzed using the thematic analysis method. Results: Respondents perceived COVID-19 as deadly, dangerous, and highly contagious. It emerged that respondents perceived themselves to be at risk of being infected by the virus. This risk exposure is linked to several factors. These include the dangerousness of the disease contaminated by direct contact, the feeling of vulnerability linked to working conditions (traders, exposing themselves and others to the disease), status (elderly, and sick,), gender (mobility of men compared to women), and the risk relationship (the individual perceived as being a potential danger to his family/relatives). Vulnerability depended on the immune status of the people (elderly people, pregnant women, and people with chronic diseases), working conditions (shopkeepers, and traders), and also socioeconomic categories (wealthy people). Conclusion: Policymakers and actors in the response to COVID-19 should develop communication strategies to better address ongoing challenges.
Optimization of Truck–Loader Matching Based on a Simulation Method for Underground Mines
Authors: Hou Jie , Li Guoqing , Chen Lianyun , Wang Hao , Hu Nailian
Published: December 2022

The choice of transportation system has an important impact on the production efficiency and economic behavior of underground mines. Trackless vehicle transportation has gradually become the main method in underground mines because mining companies have realized that mining efficiency can be improved using advanced vehicle mechanization and automation in the mining process. The extracted ore is loaded onto trucks by loaders in situ, then the trucks drive to ore passes for unloading Trucks load and unload ore in a cyclical manner between stopes and ore passes. Numerous trucks drive in tunnels simultaneously to achieve production targets, and there are interactions and influences among trucks, such as blocking and queuing, due to limited underground space. To address this issue, a transportation route model was built, and the ore transportation process was divided into three parts, including ore loading, truck transportation, and ore unloading. The simulation method was applied to optimize the number of loaders and trucks under the constraints of stope production capacity, transportation route and capacity, and vehicle capacity, to achieve the optimal vehicle utilization rate and transportation capability. The Monte Carlo simulation method was utilized to take the uncertainties of the transportation parameters into account to improve the robustness of the simulation results. The model was verified using the case study of an underground gold mine located in Shandong Province, China, with the objective of accomplishing optimal truck–loader matching considering various stopes in a mining area.
Institutional and Individual Effects of Greenwashing on Food Waste
Authors: Cavazos Robert Lee , Taylor Keelyn , Eary R. Brandon , Doty Scott
Published: December 2022

Greenwashing is when organizations deceive consumers by making false claims regarding the environmental advantages their products have over other products. Institutional, greenwashing leads to “individual greenwashing”, which perpetuates the problem of people believing they are environmentally conscious when, in reality, they are not. This is evident in individuals’ behavior regarding food waste; therefore, our study examines the effects of individual greenwashing on food waste. It uses a mixed-methods approach to demonstrate how institutional barriers socialize patrons into routines, making it difficult to implement sustainable practices. We conducted a plate-waste audit at a university over an eight-day period, collecting 1443 pounds (≈654.5 kg) of food waste from 7775 patrons. The audit demonstrated that individuals generated 0.79 ounces (≈22.4 g) less plate waste when they controlled their portions than when the institution controlled their portions. Our surveys revealed that only 15.6% of patrons were aware of composting options; however, 83.3% were willing to compost. The field experiment increased patrons’ awareness of the compost bin, creating a 2.5 percentage point increase in usage, resulting in 13.4 pounds (≈6.1 kg) of waste being diverted from the landfill. Therefore, institutions should recognize their role in socializing individuals to adopt environmentally sound food-waste habits.
Health Effects of PM2.5 Exposure in China from 2004 to 2018: A Systematic Review and Meta-Analysis
Authors: Ruan Fangfang , Zeng Xiangang
Published: December 2022

In order to comprehensively evaluate the impact of fine particulate matter (PM2.5) exposure on public health in China, and to obtain a quantitative concentration–response relationship, the literature published in multiple databases from 1980 to 2019 was searched to obtain studies on the health effects of PM2.5 on the Chinese population in this paper. According to the inclusion and exclusion criteria, 67 studies were included in the research, which covered the study period from 2004 to 2018. A systematic review shows that there are 30 diseases and 46 health effect outcomes with clear concentration–response relationships with PM2.5 in China. Seven health effects were investigated by meta-analysis. For each 10 μg/m3 increase in PM2.5, the combined overall random-effects relative risk (RR) of non-accidental mortality, mortality due to cardiovascular disease, and mortality due to respiratory disease was 1.006 (95% CI = 1.004, 1.007), 1.007 (95% CI = 1.005, 1.008), and 1.008 (95% CI = 1.006, 1.010), respectively. The RR of hospital admission due to cardiovascular disease and respiratory disease was 1.006 (95% CI = 0.999, 1.014) and 1.006 (95% CI = 1.003, 1.010), respectively. In terms of outpatient visits, a 10 μg/m3 increase in PM2.5 corresponds to a 1.004 (95% CI = 1.002, 1.006) and 1.008 (95% CI = 1.005, 1.010) RR for cardiovascular disease and respiratory disease, respectively.
Smart City, Digitalization and CO2 Emissions: Evidence from 353 Cities in China
Authors: Ma Zhongxin , Wu Fenglan
Published: December 2022

The development of digital technology provides new governance methods for achieving the goal of “carbon peaking and carbon neutrality”. Since 2013, the pilot construction of smart cities in China has strengthened the government’s digital governance capabilities and significantly influenced the reduction in carbon emissions. This paper provides empirical evidence for the driving effect of digitization on carbon emission reduction based on panel data from 353 cities in China. The results show that digital governance based on smart city construction pilots has significantly reduced regional carbon emissions, and the implementation of smart city construction pilots has reduced regional carbon emissions by an average of 6.6%, and this effect is sustainable over the long term. The increase in the level of digitalization has significantly promoted carbon emission reduction. From the perspective of the impact path, regional green patent innovation has played a significant partial intermediary effect in the process of digitization-driven carbon emission reduction. From a micro-mechanism standpoint, digitization plays a significant role in promoting the green innovation of high-polluting listed companies.
An Exploration of the Decline in E-Scooter Ridership after the Introduction of Mandatory E-Scooter Parking Corrals on Virginia Tech’s Campus in Blacksburg, VA
Authors: Buehler Ralph , Broaddus Andrea , White Elizabeth , Sweeney Ted , Evans Chris
Published: December 2022

We report shared e-scooter ridership and rider perceptions on Virginia Tech’s Blacksburg campus before and after introduction of mandatory e-scooter parking corrals in January 2022. The analysis relies on a panel of 131 e-scooter riders surveyed in Fall 2021 and Spring 2022. Although parking corrals were perceived favorably prior to implementation, perceptions became more negative afterwards. Respondents said corrals were not located where needed, difficult to find, fully occupied, and took too much extra time to use. After parking corrals were introduced, ridership declined 72% overall and also fell for all socio-economic subgroups. The heaviest user groups, like undergraduate males, were most likely to quit. The first study identifying desired and actual egress times for e-scooters, we found that roughly two-thirds of riders desired egress times under 2 min and one quarter under 1 min. Prior to the introduction of parking corrals, 82% of riders reported actual egress times under 2 min, and 43% under 1 min. Those who kept riding after the introduction of e-scooter corrals reported longer actual egress times and a stronger stated desire for egress times under 2 min. Communities should be careful when imposing e-scooter parking restrictions to ensure that e-scooter egress time is sufficiently low—ideally within an easy 2 min walk of popular origins and destinations.
Physics Students’ Innovation Readiness for Digital Learning within the University 4.0 Model: Essential Scientific and Pedagogical Elements That Cause the Educational Format to Evolve in the Context of Advanced Technology Trends
Authors: Jugembayeva Bakytgul , Murzagaliyeva Aliya
Published: December 2022

Students’ readiness for innovative educational formats is a determining factor that can affect the effectiveness of digital transformations in a higher education institution. Evaluation of students’ innovative readiness for the transition to the University 4.0 model can provide guidance on what actions and policies should be taken to achieve an educational institution’s digital transformation goals. This article aims to study the essential scientific and pedagogical elements that cause the educational format to evolve in the context of advanced technology trends and determine physics students’ innovation readiness for digital learning within the University 4.0 model. The survey was extended to the online forum University 4.0: Prospects for Modernising Approaches to Teaching Physics. The survey results showed the degree of readiness of physics students to implement digital approaches in teaching in four dimensions. Students had a high level of creativity and a positive attitude toward innovative educational activities (M = 3.89); they also had a high level of motivation for digital innovative training and sufficient knowledge of how the training will help them achieve their educational goals and professional and personal competence (M = 3.85); and they had a high level of digital literacy (M = 3.96). The scientific value of this article lies in demonstrating the developed methodology for determining and evaluating the degree of innovative readiness of students in the process of transition to a new educational format University 4.0. This methodology can be used in the process of developing an innovative strategy for organizing the educational process in the conditions of Industry 4.0 taking into account its challenges and opportunities.
Renewable Energy for Balancing Carbon Emissions and Reducing Carbon Transfer under Global Value Chains: A Way Forward
Authors: Guo Xixuan , Huang Kaixiang , Li Lanyu , Wang Xiaonan
Published: December 2022

Research on the relationship between a country’s renewable energy consumption and carbon emissions is of great significance for reducing carbon emissions embodied in international trade. There always exists a gap between production-based and consumption-based carbon emissions. Accordingly, this paper investigates the influence of renewable energy consumption on carbon emission balance, the ratio of production-based emissions to consumption-based emissions, in various countries using the ordinary least square (OLS) method and generalized method of moments (GMM) method. We found that a 1% increase in renewable energy consumption can decrease the carbon emission balance by 5.8%. Furthermore, renewable energy consumption can help narrow the gap between production-based and consumption-based carbon emissions in net emission exporters. In addition, renewable energy consumption can also weaken the negative impact of the global value chains (GVCs) division system on the carbon emission balance. The findings in this study fill the research gap by analyzing the heterogeneous impacts of renewable energy consumption on carbon emission balance embodied within a GVC division system in various countries and provide policy suggestions that renewable energy consumption should be encouraged in net emission exporters to reduce the carbon emission transfers.
Evaluating the Efficiency of Non-Orthogonal MU-MIMO Methods in Smart Cities Technologies & 5G Communication
Authors: Dutta Papiya , Ramyasree Jvl , Sridhar V. , Minchula Vinodh Kumar , Mohanta Harish Chandra , Mahfoudh Saoucene , Shah Syed Bilal Hussain , Singh Santar Pal
Published: December 2022

Many cutting-edge technologies, such as MIMO, cognitive radio, multi-carrier modulation, and network coding, have been proposed for wireless communication to satisfy needs for a higher data rate in the upcoming time, leading to improved quality of service (QoS) regardless of the weather. Orthogonal and non-orthogonal multiple access techniques are two categories into which multiple access technologies can be subdivided. Large networking with effective implementation of wireless devices is supported by non-orthogonal multiple access techniques. Massive NOMA has been implemented to advance access efficiency by permitting several users to share a similar spectrum. Because of the robust co-channel interference between mobile users presented by NOMA, it offers important tasks for system model and resources management. In this study, two additional sets of demanding codes are explored. Multi-user shared access methods and expanded multi-user shared access (EMUSA) methods are both employed. In the MUSA technique, an algorithm is used for the allocation of resources to achieve minimum intercorrelation to the maximum extent in 5G networks. A novel idea proposed in this paper is to create complex codes starting from PN codes (i.e., ePN), thereby achieving promising results in the overall system performance. The first part of this paper describes the fundamental principles of MUSA, and in the next part the main idea of the proposed technique will be studied in detail. Using Monte-Carlo MATLAB simulation, the performance of the suggested approach is assessed in terms of BER vs. SNR. The efficiency of the proposed approach is evaluated in various settings, and the outcomes are contrasted with those of the traditional CDMA technique, using parameters, such as the number of active users and antennas at the receiver.
Nonlinear Integer Programming for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants with Production
Authors: Alhamad Khaled , Alkhezi Yousuf , Alhajri M. F.
Published: December 2022

Preventive maintenance (PM) is a maintenance program with activities created at a determined interval or according to certain principles, designed to reduce the likelihood of failure or deterioration of item performance. This aims to improve overall reliability and system availability. In this research, a preventive maintenance schedule (PMS) was designed for electricity and desalination of water in power plants, subject to meeting relevant constraints. The proposed methodology is used to generate a PMS for the boilers, turbines, and distillers. A nonlinear integer programming (NLIP) model was employed to address this problem. The results of the proposed method were compared with the PMS for a power station in Kuwait. The results were better in terms of the volume of production and in terms of the gap between the available production and demand in order to continue providing consumers with electricity and water without a shortage in the event of a breakdown in equipment. It produces an improvement of 12.12% and 16.58% respectively, for water and electricity. Furthermore, the sensitivity and robustness of the proposed method were analysed by increasing the maintenance duration for some equipment, increasing the demand, and adding various additional conditions. In addition, a comparison of additional conditions with a binary problem method in terms of computer time for the search for an optimal solution was carried out, where the model provided an optimal solution in a reasonable time. Among the most important benefits that the user can obtain for this technique are extending the life of the equipment, increasing efficiency, and reducing expenses.
Risk Evaluation of Overseas Mining Investment Based on a Support Vector Machine
Authors: He Hujun , Zhao Yichen , Tian Hongxu , Li Wei
Published: December 2022

Analyzing the general method of establishing a support vector machine evaluation model, this paper discusses the application of this model in the risk assessment of overseas mining investment. Based on the analysis of the risk assessment index system of overseas mining investment, the related parameters of the optimal model were ascertained by training the sample data of 20 countries collected in 2015 and 2016, and the investment risk of 8 test samples was evaluated. All 8 samples were correctly identified, with an error rate of 0. South Africa’s mining investment risk in 2016 was assessed using the risk evaluation model for overseas mining investment based on a support vector machine, and it was rated as grade IV (general investment risk). The results show that the model can provide a new solution for the judgment and deconstruction of the risk of overseas mining investment.
The Coupled Thermal-Structural Resonance Reliability Sensitivity Analysis of Gear-Rotor System with Random Parameters
Authors: Yu Zhenliang , Sun Zhili , Zhang Shengnan , Wang Jian
Published: December 2022

The resonance of the gear-rotor system will produce a large number of responses that do not exceed the threshold value, resulting in structural fatigue failure and transmission failure, affecting its life and reliability. It is particularly critical to consider the temperature rise under high-speed and heavy-load conditions. Therefore, the research will take the main drive gear-rotor system of a certain type of aeroengine accessory gearbox as the research object, consider the influence of the temperature field on the natural frequency of the gear-rotor system, and take the difference between the natural frequency of the gear-rotor system and the excitation frequency (gear meshing frequency) as the performance function. The PC-Kriging and adaptive design of experimental strategies are applied to the thermal-structural coupling parametric model to analyze the resonance reliability and sensitivity of the gear-rotor system. For complex mechanical mechanisms, the method has better accuracy than other surrogate models and greatly saves the time of finite element simulation in reliability analysis. The results show that the natural frequency of a gear rotor decreases with an increase in temperature, and the natural frequency of different orders varies with the change in temperature. The influence of the sensitivity of different random parameters on the resonance reliability of the gear-rotor system is obtained. Reliability research on resonance failure of high-speed and heavy-load aviation gear-rotor systems considering random parameters under a temperature rise field has important practical engineering application value and scientific research significance.
Pathways to Sustainable Development: Corporate Digital Transformation and Environmental Performance in China
Authors: Xu Pingguo , Chen Leyi , Dai Huajuan
Published: December 2022

Environmental pollution remains a serious sustainable development issue. Enterprises, as important agents of sustainable development, are receiving increasing attention regarding their efforts to protect the environment. The rapid development of the digital economy has become a new driver of corporate environmental governance and environmental performance improvements, marking a new sustainable development path. We study the environmental effects of corporate digital transformation from the perspective of environmental performance using Chinese A-share listed companies. We construct a two-step systematic GMM econometric model and find that corporate digital transformation significantly improves environmental performance. Heterogeneity analysis shows that environmental performance improvement through digital transformation is more pronounced among state-owned, large, and heavily polluting enterprises. Mechanistic analysis shows that corporate digital transformation mainly improves environmental performance by enhancing green technological innovation and corporate governance. Further analysis shows a nonlinear relationship between corporate digital transformation and environmental performance. The research not only analyzes the impact of corporate digital transformation on environmental performance from multiple dimensions but also discovers the transmission mechanism of digital transformation that affects environmental performance and verifies a possible nonlinear relationship, providing a theoretical basis and practical reference for promoting corporate digital transformation and sustainable development.
How Do Block Built Environments Affect Daily Leisure Walking among the Elderly? A Empirical Study of Gaoyou, China
Authors: Cao Yang , Wu Hao , Wang Hongbin , Qu Yawei , Zeng Yan , Mu Xiyu
Published: December 2022

Currently, the major global economies have entered an aging society. The promotion of physical activity is an important way to improve the health level of the elderly, and the study of health geography is a popular interdisciplinary research topic. This study selected 12 representative districts in Guoyu City, Jiangsu Province, to conduct a questionnaire survey on the activity range and behavior pattern of leisure walking among the elderly. We analyzed the relationship between the environment factors of different blocks and the walking activities of the elderly. The results showed that: (1) A range of 800 m from home is the most important leisure walking space for the urban elderly, and a distance between 800 and 1500 m is an important optional leisure walking space. (2) The density of open green space, commercial facilities, and public service facilities has a significant impact on the elderly’s leisure walking activities. (3) The socioeconomic background and health status of the elderly have a significant impact on their leisure walking activities. Healthy physical conditions can motivate the elderly to form good exercise habits. The government must optimize the built environment elements in a targeted living space to encourage physical activity among the elderly.
A Study on the Impact of Dynamic Visitor Demand on the Digital Transformation of Enterprises—Considerations Based on the Regional Innovation Environment and the Level of Big Data
Authors: Li Na , Liu Ziyang , Zhang Xi
Published: December 2022

To explore the impact of dynamic changes in tourist demand on the digital transformation of tourism enterprises, this paper starts with demand-side changes, environmental influences, and technical support, based on theories of supply and demand equilibrium, regional innovation environment, technology gaps, and comparative advantage, and conducts a multiple regression analysis using a two-way fixed effects model on data from China’s Shanghai, Shenzhen and New Third Board-listed tourism companies, as well as 31 provincial administrative regions. The study found that dynamic changes in tourist demand significantly impacted the digital transformation of tourism enterprises. In addition, the regional innovation environment and the level of big data also contributed positively to the relationship between tourist demand and the digital transformation of enterprises. For regions with different tourism resource endowments, there is heterogeneity in the impact of dynamic changes in tourist demand on the digital transformation of enterprises. Finally, recommendations for practitioners regarding the digital transformation of enterprises are provided based on this. We suggest that enterprises should pay attention to the changing needs of travelers. They should also consider the regional innovation environment and the level of big data as key resources to facilitate transformation. This will help accelerate digital transformation in tourism.
Assessing Digital Transformation Acceptance in Public Organizations’ Marketing
Authors: Vărzaru Anca Antoaneta
Published: December 2022

Digital transformation has a substantial impact on the activities of public organizations. The way public organizations conduct marketing has also changed due to digital transformation. This paper evaluates how digital transformation influences public organizations’ marketing activities in their employees’ perception and examines the changes triggered by digital technology in public marketing. The research results are based on the literature review empirical studies based on a survey conducted among 425 employees of public organizations in Romania in the marketing field. Using the technology acceptance model and structural equation modeling to analyze and interpret the data, the paper demonstrates that digital technologies have a substantial impact on marketing, in the perception of technology users, in helping to build relationships with the public, and in increasing citizens’ trust in public organizations. Digital technologies, by their significant characteristics (innovativeness, social influence, accessibility, and rapidity), have a significant impact on all public marketing activities. However, they are substantially transforming quantitative marketing research activities due to the digitization of financial accounting and managerial information systems, as well as interactive and transparent communication and public relations activities. Public organization managers must explain the benefits of digital marketing to employees from the internal branding perspective as well as the effectiveness of public marketing activities.
Empirical Analysis of Sustainable Trade Effects of FTAs Based on Augmented Gravity Model: A Case Study of China
Authors: Qiu Bangchu , Tian Gang , Wang Daoming
Published: December 2022

China has developed many strategies to promote sustainable trade, including accelerating the signing of free-trade agreements (FTAs). However, there is a lack of studies examining the impact of FTAs on the sustainable growth of foreign trade from a holistic perspective. This paper applies an augmented gravity model to verify the trade creation and diversion effects of China’s FTAs that are currently in effect. A panel data analysis over the period of 1995–2019 is conducted on 34 countries and regions, including China, and Poisson pseudo-maximum likelihood (PPML) is employed to test three different fixed-effects models. The results show that the trade effects differ across FTAs, with nine FTAs generally making positive contributions to China’s trade and social welfare growth, and the remaining five FTAs exhibiting varying degrees of trade diversion or contraction effects. Empirically, it is clear that participation in FTAs has contributed to the sustainable trade of China, but China still needs to take prudent action to pursue further trade liberalization in order to counter the potential threat of trade diversion.
Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks
Authors: Špiler Marko , Matejić Tijana , Knežević Snežana , Milašinović Marko , Mitrović Aleksandra , Arsić Vesna Bogojević , Obradović Tijana , Simonović Dragoljub , Despotović Vukašin , Milojević Stefan , Adamović Miljan , Resimić Milan , Milošević Predrag
Published: December 2022

In this paper we demonstrate a new conceptual framework in the application of multilayer perceptron (MLP) artificial neural networks (ANNs) to bankruptcy risk prediction using different time-delay neural network (TDNN) models to assess Altman’s EM Z″-score risk zones of firms for a sample of 100 companies operating in the hotel industry in the Republic of Serbia. Hence, the accuracies of 9580 forecasting ANNs trained for the period 2016 to 2021 are analyzed, and the impact of various input parameters of different ANN models on their forecasting accuracy is investigated, including Altman’s bankruptcy risk indicators, market and internal nonfinancial indicators, the lengths of the learning periods of the ANNs and of their input parameters, and the K-means clusters of risk zones. Based on this research, 11 stability indicators (SIs) for the years under analysis are formulated, which represent the generalization capabilities of ANN models, i.e., differences in the generalization errors between the preceding period and the year for which zone assessment is given; these are seen as a consequence of structural changes at the industry level that occurred during the relevant year. SIs are validated through comparison with the relative strength index (RSI) for descriptive indicators of Altman’s model, and high correlation is found. Special focus is placed on the identification of the stability in 2020 in order to assess the impact of the COVID-19 crisis during that year. It is established that despite the fact that the development of bankruptcy risk in the hotel industry in the Republic of Serbia is a highly volatile process, the largest changes in the analyzed period occurred in 2020, i.e., the potential applications of ANNs for forecasting zones in 2020 are limited.
Efficient Construction Waste Management: A Solution through Industrial Revolution (IR) 4.0 Evaluated by AHP
Authors: Musarat Muhammad Ali , Alaloul Wesam Salah , Hameed Nasir , R Dhinaharan , Qureshi Abdul Hannan , Wahab Mohamed Mubarak Abdul
Published: December 2022

Construction waste management is a global concern not only because it impacts the financial efficiency of construction projects, but also because of its negative influence on the environment. The construction industry is a major contributor to environmental pollution due to its carbon footprint and greenhouse gas emissions. Thus, effective construction waste management solutions are required for sustainable development by preventing material waste. Hence, this study employed the Analytical Hierarchy Process to prioritize the possible construction waste, factors leading to its generation and the most effective IR 4.0 solution. A questionnaire was constructed, and after refinement, it was then distributed among the engineers, contractors, professors, and other industry professionals. The results from the analysis provide us with the list of factors ranked on their comparative weightage and score. The wastage of cement due to moisture is found to be the highest ranked potential waste. Moreover, changes in orders by the client are termed as the most highly ranked cause of rework and material wastage. Similarly, the Industrial Building Systems are the best solution for efficient construction waste management that Industrial Revolution 4.0 can provide. The results of this study can help to enhance project control by providing information on possible construction wastes and the factors that lead to their generation.