bluuebunny/crossref_metadata_2025 huggingface dataset:
How Plot Spatial Morphology Drives Surface Thermal Environment: A Spatial and Temporal Analysis of Nanjing Main City
Authors: Zhao Zidong , Ye Ruhai , Wang Yingyin , Tao Yiming
Published: December 2022

Rapid urban development has changed urban substrate conditions, greatly affecting urban ecology and heating urban environment. Mitigating urban temperature rises by optimizing urban morphology is considered a promising approach; most studies ignore spatial and temporal heterogeneity. This study analyzes how plot spatial form influences urban thermal environment in the main Nanjing area from 2001, 2006, 2011, 2016, and 2021, based on geographically weighted regression models (spatio-temporal- and multi-scale). Results show that: 1. The formation of geothermal heat islands matches the direction of urban expansion, mainly due to changes in land substrate; 2. the spatio-temporal model performs best, indicating that urban morphology and surface thermal environment have obvious spatio-temporal heterogeneity; obvious scale differences exist in each index influencing the heat island effect; and 3. floor area ratio (FAR) and building density (BD) negatively and positively correlate with surface thermal conditions, with gradually increasing effect, respectively. Normalized difference vegetation index (NDVI) and distance from the nearest water body (Dis_W) negatively and positively correlate with surface thermal conditions separately; good ecological infrastructure reduces surface temperatures but shows a gradually weakening effect. Proximity to roads is associated with warmer thermal environment. This study elucidates how urban form influences surface thermal environments and suggests measures to reduce surface temperatures in the main urban Nanjing area.
The Influence of Operation Platform on the Energy Consumption of Container Handling
Authors: Li Xiaojun , Zhou Ran , Zhu Lequn
Published: December 2022

Because container terminals are facing pressure to achieve carbon neutrality in China, saving energy has become an important objective of container terminals. This paper analyzed the movement path of containers between the quay carne and the yard, and found that paths in the vertical direction existed, requiring unnecessary energy consumption. To solve the unnecessary energy consumption problem, a completely new work mode called the high platform operation mode was proposed. In this new mode, a high platform is built above the yard and container trucks drive onto the high platform. By building an energy consumption model to compare the energy consumption of the traditional and new modes, we found that the new mode is able to save 1.478 kWh of electricity compared to the traditional mode when handling one container. A terminal company in Tianjin Port was taken as an example to examine and validate the efficiency of the proposed mode. The computational results indicate that the electricity saved in 12 years would be able to cover the cost of building the high platform, meaning that the new mode could reduce container terminals’ energy consumption and accelerate the achievement of carbon neutrality.
Risk Narrative of Emergency and Disaster Management, Preparedness, and Planning (EDMPP): The Importance of the ‘Social’
Authors: Lillywhite Brielle , Wolbring Gregor
Published: December 2022

Risk perception, literacy, communication, narrative, governance, and education are important aspects of emergency and disaster management, preparedness, and planning (EDMPP) as they for example influence and direct EDMPP policies and actions. A thorough understanding of the ‘social aspects of risk is important for EDMPP, especially in relation to marginalized populations who are often overlooked. Technologies are increasingly employed for EDMPP. How these technology applications identify and engage with the ‘social’ of risk in general and the ‘social’ of risk experienced by marginalized populations is important for EDMPP. Equity, diversity, and inclusion (EDI) and similar phrases are employed as policy concepts to improve research, education, and participation in the workplace for marginalized groups such as women, Indigenous peoples, visible/racialized minorities, disabled people, and LGBTQ2S including in workplaces engaging with EDMPP which includes universities. The aim of this scoping review was to generate data that allows for a detailed understanding of the risk related discussions within the EDMPP academic literature as these discussions shape EDMPP policies and actions. The objective of this scoping review study was to map out the engagement with risk, specifically the social aspects of risk, in the EDMPP-focused academic literature with a focus on (a) EDMPP in general, (b) COVID-19, (c) EDMPP and marginalized groups, (d) EDMPP and patients, and (e) EDMPP and technologies (artificial intelligence, machine learning, machine reasoning, algorithm design approaches such as Bayesian belief networks, e-coaching, decision support systems, virtual coaching, automated decision support, e-mentoring, automated dialogue and conversational agents). Using the academic databases SCOPUS, Web of Sciences, and databases accessible under Compendex and EBSCO-HOST and performing hit count frequency searches of online and downloaded abstracts and thematic analysis of downloaded abstracts the study reveals a lack of coverage on the social aspects of risk and engagement with risk concepts such as risk perception, risk governance, risk literacy, risk communication, risk education and risk narrative especially in conjunction with marginalized groups and technologies employed in EDMPP decision support. Our findings suggest many opportunities to further the EDMPP academic inquiry by filling the gaps.
Exergy Transfer Analysis of Biomass and Microwave Based on Experimental Heating Process
Authors: Cui Longfei , Liu Chaoyue , Liu Hui , Zhao Wenke , Zhang Yaning
Published: December 2022

Exergy transfer and microwave heating performances of wheat straw particles as affected by microwave power (250, 300, and 350 W), feeding load (10, 30, and 50 g), and particle size (0.058, 0.106, and 0.270 mm) were investigated and detailed in this study. The results show that when the microwave power increased from 250 to 350 W, the average heating rate increased in the range of 23.41–56.18 °C/min with the exergy transfer efficiency increased in the range of 1.10–1.89%. When the particle size increased from 0.058 to 0.270 mm, the average heating rate decreased in the range of 20.59–56.18 °C/min with the exergy transfer efficiency decreased in the range of 0.70–1.89%. When the feeding load increased from 10 to 50 g, the average heating rate increased first and then decreased in the range of 5.96–56.18 °C/min with the exergy transfer efficiency increased first and then decreased in the range of 0.07–1.89%. The highest exergy transfer efficiency was obtained at a microwave power of 300 W, feeding load of 30 g, and particle size of 0.058 mm.
Real-Time Personal Protective Equipment Compliance Detection Based on Deep Learning Algorithm
Authors: Lo Jye-Hwang , Lin Lee-Kuo , Hung Chu-Chun
Published: December 2022

The construction industry is one of the most dangerous industries in the world due to workers being vulnerable to accidents, injuries and even death. Therefore, how to effectively manage the appropriate usage of personal protective equipment (PPE) is an important research issue. In this study, deep learning is applied to the PPE inspection model to verify whether construction workers are equipped in accordance with the regulations, and this is expected to reduce the probability of related occupational disasters caused by the inappropriate use of PPE. The method is based on the YOLOv3, YOLOv4 and YOLOv7 algorithms to detect worker’s helmets and high-visibility vests from images or videos in real time. The model was trained on a new PPE dataset collected and organized by this study; the dataset contains 11,000 images and 88,725 labels. According to the test results, can achieve a 97% mean average precision (mAP) and 25 frames per second (FPS). The research result shows that the detection and counting data in this method have performed well and can be applied to the real-time PPE detection of workers at the construction job site.
Influence of Technical Parameters of the Pyrolysis Process on the Surface Area, Porosity, and Hydrophobicity of Biochar from Sunflower Husk Pellet
Authors: Wystalska Katarzyna , Kwarciak-Kozłowska Anna , Włodarczyk Renata
Published: December 2022

Biochar is a product that has been of interest to many researchers in recent years. The use and positive effect of biochar depend on its properties, which in turn result primarily from the type of substrate used for production and the technical parameters of the pyrolysis process used. From the point of view of sustainable development, agricultural raw materials, such as sunflower husks, are good materials for biochar synthesis. The research aimed to determine the effect of changing the technical parameters of the pyrolysis process (i.e., temperature, heating rate, and residence time) on the properties of biochar obtained from sunflower husk pellets. The pellets were heated to 480 °C, 530 °C, and 580 °C. The applied heating rate for 480 °C was 4.00 and 7.38 °C·min−1, for 530 °C it was 4.42 and 8.15 °C·min−1 and for 580 °C it was 4.83 and 8.92 °C·min−1. Determining these properties is important due to the use of biochar, e.g., in the processes of sorption of pollutants from the water and soil environment. The technical parameters of the pyrolysis process used allowed us to obtain hydrophilic materials with porosity in the range of 10.11% to 15.43% and a specific surface area of 0.93 m2·g−1 to 2.91 m2·g−1. The hydrophilic nature of biochar makes it possible to use them in the processes of removing inorganic pollutants and polar organic pollutants. The presence of macropores in biochar may contribute to the improvement of water management in the soil and affect the assimilation of microelements by plants. The low content of heavy metals in biochar does not pose a threat to the environment.
The COVID-19 Pandemic and Factors Influencing the Destination Choice of International Visitors to Vietnam
Authors: Wu Chihkang Kenny , Ho Minh-Thu , Le Thi Kim Trang , Nguyen Mai-Uyen
Published: December 2022

The Association of Southeast Asian Nations (ASEAN) is an attractive tourist destination with diverse and unique experiences, in which Vietnam is considered one of the most famous destinations in this region. Quality evaluations and strategies for attracting international tourists are being thoroughly researched. However, the COVID-19 pandemic has had the most significant impact on the tourism industry, which has suffered greatly. Therefore, the recovery and expansion of international tourism necessitate the employment of tourism-related businesses and service sector workers. Extensive research must be conducted to identify solutions and new directions to recover the international tourist market’s growth as quickly as possible. This study identifies the factors that influence the destination of international visitors visiting Vietnam after the COVID-19 pandemic by modifying and evaluating the scales of the theoretical model. Using the convenience sampling technique, data were collected through interviews with 208 international visitors, with 29 observed variables. Using SPSS 22.0, five factors influencing international visitors’ decisions to visit Vietnam were revealed: tourist motivation, tourist attitude, destination image, social media, and environmental quality. Finally, the authors provide policy recommendations to enhance the allure and viability of Vietnam’s tourism following the effects of the COVID-19 pandemic. This study’s outcome is intended to establish the importance of the many variables influencing the choice of destination for international visitors.
Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety
Authors: Xian Huacai , Hou Yujia , Wang Yu , Dong Shunzhong , Kou Junying , Li Zewen
Published: December 2022

The causes of traffic crashes are complex and uncertain, among which the risky driving behaviors of drivers and the types of road sections in high-crash areas are all critical influencing factors. We used ArcGIS software to draw traffic heat maps under different thresholds to prevent the occurrence of traffic crashes accurately and effectively according to the vehicle GPS data of urban expressways in Jinan City, Shandong Province. This paper studied the relationship between risky driving behaviors (rapid acceleration, rapid deceleration, and overspeed) and road types with traffic crashes. The traffic safety evaluation model of urban expressways based on ordered logistic was established to predict the safety level of the urban expressway. The model’s accuracy was 85.71%, and the applicability was good. The research results showed that rapid deceleration was the most significant influencing factor of crashes on urban expressways. When the vehicle deceleration was less than or equal to −4 m/s2, the probability of a crash was 22.737 times greater than when the vehicle deceleration was at −2 to −2.5 m/s2; when the vehicle acceleration was greater than or equal to 3 m/s2, the probability of a crash was 19.453 times greater than when the vehicle acceleration was at 1 to 1.5 m/s2. The likelihood of a crash at a road section with a ramp opening was 8.723 times greater than that of a crash at a non-ramp opening; the crash probability of a speeding vehicle was 7.925 times greater than that of a non-speeding vehicle; the likelihood of a crash on a curve was 6.147 times greater than that on a straight. The research results can provide adequate technical support for identifying high-risk sections of expressways and active early warning of traffic crashes.
Electric Car Market Analysis Using Open Data: Sales, Volatility Assessment, and Forecasting
Authors: Pelegov Dmitry V. , Chanaron Jean-Jacques
Published: December 2022

In the context of the growing popularity of electric cars, it is important to track the sustainability of this emerging industry. This work presents the results of electric vehicle sales up to and including 2021, proposes volatility assessment and short-term forecasting using normalized monthly sales analysis, and discusses why quantitative long-term forecasting is impossible. In cases where authorities and regulators are the main driving forces of change, the electric car market does not need forecasting, but instead requires proper goal setting and timely published market data with open access.
A New Version of African Vulture Optimizer for Apparel Supply Chain Management Based on Reorder Decision-Making
Authors: Bahadoran Baghbadorani Shayan , Johari Seyed Abdolhassan , Fakhri Zahra , Khaksar Shahmirzadi Esmaeil , Navruzbek Shavkatovich Shavkatov , Lee Sangkeum
Published: December 2022

Supply chains may serve as an effective platform for the development of sustainability by encouraging responsible conduct throughout all chain members and stages. Agent technology may greatly aid in decision-making during supply chain management. Due to recent changes in the seasons, fashion trends, and the requirements of various religions, particularly with regard to the ordering procedure, the supply chain for clothing has become one of the most difficult duties in this area. Because of this, it is crucial to enhance process coordination throughout the whole clothing supply chain and develop a decision-making strategy that functions best in a fluid environment. The Unified Modeling Language (UML) is used in this work to define the relationship between agents and simulate the supply chain process. This research incorporates enhanced African vulture optimizer, a modified bio-inspired approach, and fuzzy inference theory to assist the supply chain agent in determining the appropriate replenishment quantity and reorder point to lower the inventory cost. According to test results, the suggested AAVO-based technique may be successful in determining a target demand ordering amount while reducing the overall cost of the supply chain due to a lowered convergence trend and algorithm accuracy.
Assessment and Prediction of the Water Quality Index for the Groundwater of the Ghiss-Nekkor (Al Hoceima, Northeastern Morocco)
Authors: El Yousfi Yassine , Himi Mahjoub , El Ouarghi Hossain , Aqnouy Mourad , Benyoussef Said , Gueddari Hicham , Ait Hmeid Hanane , Alitane Abdennabi , Chaibi Mohamed , Zahid Muhammad , Essahlaoui Narjisse , Hitouri Sliman , Essahlaoui Ali , Elaaraj Abdallah
Published: December 2022

Water quality index (WQI) is the primary method applied to characterize water quality in the world. The current study employed the statistical analysis and multilayer perceptron (MLP) approaches for predicting groundwater quality in the Ghiss-Nekkor aquifer, northeast of Al Hoceima, Morocco. Fifty sampled groundwater were identified and analyzed for major anions and cations throughout May 2019. Several physicochemical parameters of all the samples were identified in this investigation, such as TDS, pH, EC, Na, K, Ca, Mg, HCO3, NO3, Br, SO4, and Cl. The entropy-weighted groundwater quality index (EWQI) was calculated from these parameters. The WQI procedure determined the suitability of groundwater for consumption. The WQI value varied from 90.98 to 337.28. The EC, TDS, WQI, and Cl− spatial distribution showed that EC and Cl− are associated with poor groundwater quality. A single sample (W16) represented unsuitable water for drinking purposes and offered a WQI value of 337.28, indicating poor drinking quality due to seawater intrusion, overexploitation, and harsh weather conditions. The majority of the values obtained for the parameters exceeded the recommended limit of the World Health Organization (WHO)’s guidelines for consumption. The findings show that using parameters is a straightforward method for predicting water quality indexes with sufficient and suitable precision. The MLP model shows good predictive performances in terms of the coefficient of determination R2, mean absolute error (MAE), and root-mean-square error (RMSE) with values of 0.9885, 5.8031, and 4.7211, respectively. The ANN approach was applied to develop a model that can accurately predict WQI utilizing mineralization, TH, NO3, and NO2 as inputs. The MAE for the model’s performance was calculated to be 4.72. A Bland–Altman test was used to validate that the model is suitable. Following the test, it was determined that the model is appropriate for predicting WQI, with an error of just 0.1%.
Risk Assessment of Groundwater Contamination in the Gala, Tenguel, and Siete River Basins, Ponce Enriquez Mining Area—Ecuador
Authors: Campoverde-Muñoz Paulo , Aguilar-Salas Luis , Romero-Crespo Paola , Valverde-Armas Priscila E. , Villamar-Marazita Karla , Jiménez-Oyola Samantha , Garcés-León Daniel
Published: December 2022

Groundwater is a strategic resource, which is experiencing a growing threat of contamination worldwide. This study aimed to assess the vulnerability of the groundwaters in the basins of the rivers Gala, Tenguel, and Siete in the Ponce Enriquez mining area, considering as a hypothesis that anthropogenic activities (mainly mining and agricultural) conducted in the area generate a high risk of contamination. Vulnerability to contamination was quantified using the DRASTIC and GOD methods. In addition, the risk of contamination (Rc) was calculated considering the vulnerability index (IV) and the danger index (IP). Geographic information system (GIS) environment was used for the spatial analysis and the generation of vulnerability maps. The DRASTIC method showed that the negligible-to-low vulnerability category was predominant (50.7% of the area), followed by moderate vulnerability (25.6%), and high-to-extreme vulnerability (23.7%). Regarding the GOD method, the low and medium vulnerability categories were predominant in 41.4% and 27.5% of the area, respectively. Both methods agree that the center area, where the mining activities are located, has a vulnerability ranging from negligible to moderate. The IP was low in the eastern zone (58.7 km2) and ranged from moderate (426.2 km2) to high in the central and western zones (371.1 km2), where mining activities predominate. The RC revealed that 20% of the study area corresponded to a high contamination index; from which, 87% comes from agricultural activities, 9% from mining activities, and 4% due to shrimp farms and urban areas. This is the first study that evaluates the vulnerability to the contamination of groundwater in a mining area of Ecuador. The results of this research can serve as a baseline for future research, since the methodology used provides information on the priority areas for the protection of aquifers, considering the current land use in the study area.
Research on Spatial Difference, Distribution Dynamics and Influencing Factors of Urban Water-Use Efficiency in the Yellow River Basin
Authors: Ji Zhiheng , Yu Wei
Published: December 2022

This study creatively uses the Dagum Gini coefficient, Kernel density estimation, and Markov chain to measure the spatial difference and distribution dynamics of urban water-use efficiency in the Yellow River Basin from 2008 to 2018 accurately and also analyzes its formation mechanism by using the Spatial Durbin Model. The results show that the hypervariable density and the intraregional differences constitute the main source of regional differences in the whole basin; the dynamic evolution characteristics of the urban water-use efficiency distribution in different reaches are different. The spatial factors have a non-negligible impact; the urbanization process and population density spatial spillover effects are negative in the state of spatial interaction; the spillover effect of upgrading the industrial structure is positive; the direct and spillover effects of openness are both positive; and the direct effect of the water-use structure is positive. In order to improve the urban water-use efficiency in the Yellow River Basin, it is necessary to comprehensively promote new urbanization, upgrade industrial structures, promote energy conservation and emission reduction, construct business environments, and establish an inter-regional coordination mechanism.
One Century of Pasture Dynamics in a Hilly Area of Eastern Europe, as Revealed by the Land-Use Change Approach
Authors: Văculișteanu Georgiana , Doru Silviu Costel , Necula Nicușor , Niculiță Mihai , Mărgărint Mihai Ciprian
Published: December 2022

Land use is paramount to sustainable development, and in the past, important changes happened under the influence of various factors. Revealing these changes in a meaningful manner, not just as total statistics but also as fluxes and at a spatial level, allows us to detect and associate them with the factors involved. We show a study case in Iași County, Romania, using a raster approach to change detection for a land-use-type database that extends to the 1920s. The database was created from topographic, remote sensing, and field data collected between 1920 and 2006, with five intervals between 1960, 1980, 1990, and 2000, starting from CORINE Land Cover data. These periods mark the socio-political and natural changes in the study area. The change detection results are well matched with the identified drivers and their spatial distribution. The fluctuations between land-use types provide a good way to create drivers’ associations. Our analysis can be easily applied to any other concerned areas and could be used as base references for any legislative intention to determine land-use-type changes because it can be learned from past conversions with regard to failures or examples of good practice.
A Proposed Metrics Based on Sustainable Development Goals (SDGs) for Public Self-Service Machines
Authors: Zhu Liang , Husin Mohd Heikal
Published: December 2022

With the growing environmental issues, the World Commission on Environment and Development (WCED) has developed a strategy for sustainable development that will enable the present generation to meet its needs for development without harming the environment for future generations. By introducing a 24-h self-service via Public Self-Service Machines, several issues can be alleviated, such as the congestion in the traditional service environment, to support specific services outside of the traditional office hours, and to allow citizens to feel that the government is committed to providing convenience and mindful public services. However, few existing studies have examined sustainability as a fundamental attribute of these Public Self-Service Machines. This lack of focus would result in Public Self-Service Machines not being sustainable in the long run. Therefore, further research on how to design and measure sustainable development attributes for Public Self-Service Machines is necessary to address this issue effectively. In this paper, the sustainable development attributes of Public Self-Service Machines based on the 17 Sustainable Development Goals (SDGs) are firstly presented. Then, a questionnaire survey was conducted to collect and analyze the views of industry experts to propose 13 metrics to measure the sustainable development attributes of Public Self-Service Machines.
Analysis of the Underlying Causes of Waste Generation in Indonesia’s Construction Industry
Authors: Fitriani Heni , Ajayi Saheed , Kim Sunkuk
Published: December 2022

The construction industry produces enormous volumes of construction waste that have a negative influence on the environment. This study examines the underlying causes of waste generation in the Indonesian construction industry based on professional perspectives. This study used a questionnaire as a research instrument and examined the data with reliability and exploratory factor analysis. Based on the analysis, there are eight underlying causes that contribute to the generation of construction waste in the Indonesian construction sector. The five most significant underlying causes of waste generation that obstruct sustainable practices include waste-inducing site and human resource management approaches, inadequate collaboration and support among stakeholders, equipment management, material logistics management, and poor working environments. As a result, it is crucial for Indonesian experts to pay attention to the factors that contribute to waste generation in order to lessen its negative effects on the environment and promote sustainable practices.
Study on the Identification and Classification of Key Influencing Factors of Debris-Flow-Prone Areas in Liaoning Province Based on Self-organizing Clustering and Sensitivity Analysis
Authors: Wang Fei , Cao Yongqiang , Fan Shuaibang , Zhang Ruoning
Published: December 2022

Due to the characteristics of sudden occurrence, fast disaster speed, and severe damage, debris-flow disasters can easily result in the loss of human lives and cause serious damage to property and social infrastructure. In this study, taking the debris-flow events in Liaoning province from 1960 to 2020 as the study period, the natural geographical characteristics and key influencing factors of the debris-flow-prone areas were explored utilizing the self-organization mapping clustering method and nonlinear global sensitivity analysis. The main conclusions were as follows: (1) The key influencing factors of debris flow are the content of clay, sand, and silt; the first type of debris flow is sensitive to the fluctuation in slope and elevation; the second and third types of debris flows are more significantly affected by changes in land use and geomorphology; the third type of debris flow is weakly sensitive the NDVI value, vegetation type, slope direction, and soil type; (2) The first type of debris flow was widely distributed, mainly located in most of the hills of eastern and the southwest part of Liaoning province; the focus of the second type of debris flow focus was further in Xiuyan County and scattered in the hills of northeast Liaoning province; the third type of debris flow was mainly distributed on the peninsula of Liaodong and the southwest of Liaoning province. (3) When the clay content is 12–27%, sand content is 49–70%, silt content is 18–29%, the elevation is 0–500 m, the slope is 0°–30°, and the land use is at the junction of arable land, medium cover grassland, and forested land, etc., debris flow disasters are very likely to occur.
Impact of Introversion-Extraversion Personality Traits on Knowledge-Sharing Intention in Online Health Communities: A Multi-Group Analysis
Authors: Lv Mian , Sun Yongbo , Shi Binbin
Published: December 2022

This paper investigates the influence of introversion-extraversion personality traits on the knowledge-sharing intention of online health communities (OHCs) using personality trait theory and social capital theory. This study investigates two types of users in OHCs—doctors and patients—and compares and analyses the knowledge-sharing paths of these two types of users. The results show that extraversion personality, interaction, and reciprocity positively influence the physicians’ and patients’ knowledge-sharing intention; for both types of users, interaction partially mediates between extraversion personality and knowledge-sharing intention, and reciprocity partially mediates between interaction and knowledge-sharing intention. Comparative analyses show that the physicians’ introversion-extraversion personality traits have stronger positive effects on interaction, and interaction has stronger positive effects on trust and reciprocity than patients, the physicians’ trust and reciprocity have stronger positive effects on knowledge-sharing intention than patients, and the physicians’ introversion personality traits have stronger positive effects on knowledge-sharing intentions than patients. This study enriches the theory of user knowledge-sharing in OHCs while advancing the managers’ understanding of what motivates users’ knowledge-sharing intention.
Artificial Intelligence Model for Risk Management in Healthcare Institutions: Towards Sustainable Development
Authors: Darwiesh Abdelaziz , El-Baz A. H. , Abualkishik Abedallah Zaid , Elhoseny Mohamed
Published: December 2022

This paper proposes an artificial intelligence model to manage risks in healthcare institutions. This model uses a trendy data source, social media, and employs users’ interactions to identify and assess potential risks. It employs natural language processing techniques to analyze the tweets of users and produce vivid insights into the types of risk and their magnitude. In addition, some big data analysis techniques, such as classification, are utilized to reduce the dimensionality of the data and manage the data effectively. The produced insights will help healthcare managers to make the best decisions for their institutions and patients, which can lead to a more sustainable environment. In addition, we build a mathematical model for the proposed model, and some closed-form relations for risk analysis, identification and assessment are derived. Moreover, a case study on the CVS institute of healthcare in the USA, and our subsequent findings, indicate that a quartile of patients’ tweets refer to risks in CVS services, such as operational, financial and technological risks, and the magnitude of these risks vary between high risk (19%), medium risk (80.4%) and low risk (0.6%). Further, several performance measures and a complexity analysis are given to show the validity of the proposed model.
The Influence of Cross-Section Shape on Failure of Rock Surrounding the Main Tunnel in a Water-Sealed Cavern
Authors: Cong Yu , Liu Heyi , Wang Xiaoshan , Guo Defu , Han Liliang , Zhao Yi , Zou Lilei
Published: December 2022

The influence of cross-section shape on rock stability was investigated by designing a similar model test and numerical simulation using particle flow code (PFC). The test results showed that the left- and right-hand sides of the entrance are subjected to tension, mainly forming vertical cracks or oblique cracks with a large dip angle. The vicinity of the entrance is subjected to the shear effect and the overall failure of the model is brittle in the similar test. Mesoscopic fractures mainly appear as tensile fractures, and a small number of shear fractures are found in the vicinity of the entrance. A long narrow coalescent-fracture zone is separately formed at the left- and right-hand sides of the entrance when approaching peak load in PFC test. Stress concentration occurs at the end of the long axis of the elliptic cross-section. The stress is high at the arch foot and spandrel of a horseshoe-shaped cross-section and a coalescent fracture zone formed at the arch foot on the right-hand side caused the tunnel to fail. The ovoid-shaped and vertical-wall-arched cross-sections are under significant tension, owing to the force chains distributed along the tunnel wall, show a large included angle with the tunnel wall. From the perspective of bearing capacity, a circle is the best section. From the perspective of failure mode, the horseshoe-shaped section is more suitable for use in corresponding practical engineering.
Matrix Equation’s Reflexive and Anti-Reflexive Solutions over Quaternions
Authors: Liu Xin , Wen Kaiqi , Zhang Yang
Published: December 2022

We consider when the quaternion matrix equation AXB+CXD=E has a reflexive (or anti-reflexive) solution with respect to a given generalized reflection matrix. We adopt a real representation method to derive the solutions when it is solvable. Moreover, we obtain the explicit expressions of the least-squares reflexive (or anti-reflexive) solutions.
A Review on Non-Newtonian Nanofluid Applications for Convection in Cavities under Magnetic Field
Authors: Selimefendigil Fatih , Şenol Gürel , Öztop Hakan F. , Abu-Hamdeh Nidal H.
Published: December 2022

This review is about non-Newtonian nanofluid applications for convection in cavities under a magnetic field. Convection in cavities is an important topic in thermal energy system, and diverse applications exist in processes such as drying, chemical processing, electronic cooling, air conditioning, removal of contaminates, power generation and many others. Some problems occur in symmetrical phenomena, while they can be applicable to applied mathematics, physics and thermal engineering systems. First, brief information about nanofluids and non-Newtonian fluids is given. Then, non-Newtonian nanofluids and aspects of rheology of non-Newtonian fluids are presented. The thermal conductivity/viscosity of nanofluids and hybrid nanofluids are discussed. Applications of non-Newtonian nanofluids with magnetohydrodynamic effects are given. Different applications of various vented cavities are discussed under combined effects of using nanofluid and magnetic field for Newtonian and non-Newtonian nanofluids. The gap in the present literature and future trends are discussed. The results summarized here will be beneficial for efficient design and thermal optimization of vented cavity systems used in diverse energy system applications.
MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data
Authors: Martins Jorge Emanuel , D’Alimonte Davide , Simões Joana , Sousa Sara , Esteves Eduardo , Rosa Nuno , Correia Maria José , Simões Mário , Barros Marlene
Published: December 2022

Many scientific publications that affect machine learning have set the basis for pattern recognition and symmetry. In this paper, we revisit the concept of “Mind-life continuity” published by the authors, testing the symmetry between cognitive and electrophoretic strata. We opted for machine learning to analyze and understand the total protein profile of neurotypical subjects acquired by capillary electrophoresis. Capillary electrophoresis permits a cost-wise solution but lacks modern proteomic techniques’ discriminative and quantification power. To compensate for this problem, we developed tools for better data visualization and exploration in this work. These tools permitted us to examine better the total protein profile of 92 young adults, from 19 to 25 years old, healthy university students at the University of Lisbon, with no serious, uncontrolled, or chronic diseases affecting the nervous system. As a result, we created a graphical user interface toolbox named MODeLING.Vis, which showed specific expected protein profiles present in saliva in our neurotypical sample. The developed toolbox permitted data exploration and hypothesis testing of the biomolecular data. In conclusion, this analysis offered the data mining of the acquired neuroproteomics data in the molecular weight range from 9.1 to 30 kDa. This molecular weight range, obtained by pattern recognition of our dataset, is characteristic of the small neuroimmune molecules and neuropeptides. Consequently, MODeLING.Vis offers a machine-learning solution for probing into the neurocognitive response.
Approximation Algorithm for X-ray Imaging Optimization of High-Absorption Ratio Materials
Authors: Liu Yanxiu , Li Ye , Jiang Sheng , Ye Xin , Liu Guoyi
Published: December 2022

In the application of X-ray industrial flaw detection, the exposure parameters directly affect the image quality. The voltage of the tube is the most important factor, which is difficult to be accurately calculated. Especially in the detection of a workpiece composed of both high absorption coefficient and low absorption coefficient materials, the improper symmetric balance of the tube voltage would lead to an overexposure or underexposure phenomenon. In this paper, based on the X-ray absorption model, combined with the performance of the X-ray imaging detector, and taking the optimal symmetry and contrast as the model constraint condition, the key factors of high absorption ratio material imaging are decomposed. Through expansion and iteration, the calculation process is simplified, the optimal imaging convergence surface is found, and then the optimal energy input conditions of high absorptivity materials are obtained and symmetrically balanced. As a result, this paper solves the problem of fast selection and symmetric factor chosen of the optimal tube voltage when imaging materials with high absorption ratios. It reduces the subsequent complications of the X-ray image enhancement process and obtains a better image quality. Through experimental simulation and measurement verification, the error between the theoretical calculation results and the measured data was better than 5%.
A Gain Scheduling Design Method of the Aero-Engine Fuel Servo Constant Pressure Valve with High Accuracy and Fast Response Ability
Authors: Zhao Wenshuai , Wang Xi , Jiang Zhen , Long Yifu
Published: December 2022

Constant pressure valve, which has an axially symmetric structure, is an essential component to supply the servo reference pressure for aero-engine hydraulic mechanical control systems, and its performance directly impacts the performance of the control system. This paper constructs a closed-loop disturbance rejection system of the constant pressure valve based on the linear incremental description method, in which the controlled object is the controlled pressure, and the stabilization controller is constructed by the closed-loop feedback motion valve. Meanwhile, the linear models of the closed-loop system are calculated. Subsequently, based on the bode frequency domain characteristic analysis, the accurate influences of the stabilization control gain on the dynamic performance and stability of the system are given. On this basis, a gain scheduling design method of the system is proposed, and the geometry design and implementation method of the inlet orifice is proposed to complete the design work. The simulation results show that under bad conditions, which include the 1 MPa strong step disturbance of the inlet pressure and the step disturbance of the variable outlet flow area, the steady-state working range of the controlled pressure is 1.5 ± 0.01 MPa, the steady-state error is not more than 0.7%, and the regulation time is not more than 0.006 s.
Estimation of the Confidence Interval for the Ratio of the Coefficients of Variation of Two Weibull Distributions and Its Application to Wind Speed Data
Authors: La-ongkaew Manussaya , Niwitpong Sa-Aat , Niwitpong Suparat
Published: December 2022

The Weibull distribution, one of the most significant distributions with applications in numerous fields, is associated with numerous distributions such as generalized gamma distribution, exponential distribution, and Rayleigh distribution, which are asymmetric. Nevertheless, it shares a close relationship with a normal distribution where a process of transformation allows them to become symmetric. The Weibull distribution is commonly used to study the failure of components and phenomena. It has been applied to a variety of scenarios, including failure time, claims amount, unemployment duration, survival time, and especially wind speed data. A suitable area for installing a wind turbine requires a wind speed that is both sufficiently high and consistent, and so comparing the variation in wind speed in two areas is eminently desirable. In this paper, methods to estimate the confidence interval for the ratio of the coefficients of variation of two Weibull distributions are proposed and applied to compare the variation in wind speed in two areas. The methods are the generalized confidence interval (GCI), the method of variance estimates recovery (MOVER), and Bayesian methods based on the gamma and uniform priors. The Bayesian methods comprise the equal-tailed confidence interval and the highest posterior density (HPD) interval. The effectiveness of the methods was evaluated in terms of their coverage probabilities and expected lengths and also empirically applied to wind speed datasets from two different areas in Thailand. The results indicate that the HPD interval based on the uniform prior outperformed the others in most of the scenarios tested and so it is suggested for estimating the confidence interval for the ratio of the coefficients of variation of two Weibull distributions.
Universal Nuclear Equation of State Introducing the Hypothetical X17 Boson
Authors: Veselský Martin , Petousis Vlasios , Leja Jozef , Navarro Laura
Published: December 2022

Within the scope of the Symmetry journal special issue on: “The Nuclear Physics of Neutron Stars”, we complemented the nuclear equation of state (EoS) with a hypothetical 17 MeV boson and observed that only instances with an admixture of 30%–40% satisfy all of the constraints. The successful EoS resulted in a radius of around 13 km for a neutron star with mass MNS≈1.4M⊙ and in a maximum mass of around MNS≈2.5M⊙. The value of the radius is in agreement with the recent measurement by NICER. The maximum mass is also in agreement with the mass of the remnant of the gravitational wave event GW190814. Thus, it appears that these EoSs satisfy all of the existing experimental constraints and can be considered as universal nuclear equations of state.
L(2, 1)-Labeling Halin Graphs with Maximum Degree Eight
Authors: Qiu Haizhen , Che Yushi , Wang Yiqiao
Published: December 2022

Suppose that T is a plane tree without vertices of degree 2 and with at least one vertex of at least degree 3, and C is the cycle obtained by connecting the leaves of T in a cyclic order. Set G=T∪C, which is called a Halin graph. A k-L(2,1)-labeling of a graph G=(V,E) is a mapping f:V(G)→{0,1,…,k} such that, for any x1,x2∈V(G), it holds that |f(x1)−f(x2)|≥2 if x1x2∈E(G), and |f(x1)−f(x2)|≥1 if the distance between x1 and x2 is 2 in G. The L(2,1)-labeling number, denoted λ(G), of G is the least k for which G is k-L(2,1)-labelable. In this paper, we prove that every Halin graph G with Δ=8 has λ(G)≤10. This improves a known result, which states that every Halin graph G with Δ≥9 satisfies λ(G)≤Δ+2. This result, together with some known results, shows that every Halin graph G satisfies λ(G)≤Δ+6.
Grey Wolf Optimizer-Based Optimal Controller Tuning Method for Unstable Cascade Processes with Time Delay
Authors: Dogruer Tufan
Published: December 2022

This paper presents a controller design technique for cascade control systems based on the grey wolf optimization (GWO) algorithm. In the proposed control scheme, the proportional-integral-proportional-derivative (PI-PD) controller structure is used to control both the open-loop unstable process in the primary loop and the stable process in the secondary loop. To determine the optimal controller parameters, a new optimization algorithm is used in which the Euclidean distance function is used as a multi-objective function. A symmetry property of the Euclidean distance function is that its distance does not depend on the starting point and destination. The multi-objective function is designed according to system time response specifications such as settling time, overshoot, and steady-state error. Thus, the optimization algorithm allows the simultaneous determination of all controller parameters according to the desired output response. Three simulation studies are presented in the paper and the results are compared with studies using various methods based on internal model control, a modified Smith predictor, and a genetic algorithm. The simulation results reveal that the proposed method improves the performances of the systems in the control of cascade processes.
A Note on Higher Order Degenerate Changhee–Genocchi Numbers and Polynomials of the Second Kind
Authors: Liu Liwei , Wuyungaowa
Published: December 2022

In this paper, we consider the higher order degenerate Changhee–Genocchi polynomials of the second kind by using generating functions and the Riordan matrix methods. At the same time, we give some properties of the higher order degenerate Changhee–Genocchi polynomials of the second kind. In addition, we establish some new equalities involving the higher order degenerate Changhee–Genocchi polynomials of the second kind, the generalized Bell polynomials, higher order Changhee polynomials, the higher order degenerate Daehee polynomials of the second kind, Lah numbers and Stirling numbers, etc.
Analysis of a Productive Topological Index Correlated to Fullerenes’ Physical Properties
Authors: Sheikh Umber , Tahir Qurrat ul Ain , Anjam Yasir Nadeem , Pincak Richard
Published: December 2022

Fullerene is a cage-like carbon allotrope admitting a vast range of applications. Some of the important fullerenes are C54,C58,C60,C70,C74,C76,C78,C80C82,C84,C86,C90. The physical properties of fullerenes can be exhibited using the degree-based topological indices. The sum based geometric arithmetic index is significant in this manner. The sum based atomic bond connectivity, Randic, first and second Zagreb indices are well known topological indices. We have determined the regression relation between each of these indices and the sum based geometric arithmetic index. Moreover, the correlation coefficient is also calculated. Correlation is a symmetric relation, as it provides association between two variables. On the basis of regression analysis and correlation coefficient, it was found that each of this index is strongly related to the sum based geometric arithmetic index. Moreover, we have computed the regression relations concerning the physical properties depending on the sum based geometric arithmetic index. The physical properties include binding energies, Ramsauer-Townsend minima, shape resonances and heat of formation of fullerene molecules. It was concluded that the sum based GA index is the best in presenting the heat of the formation of molecules.