bluuebunny/crossref_metadata_2025 huggingface dataset:
Failure Prediction of Coal Mine Equipment Braking System Based on Digital Twin Models
Authors: Gao Pubo , Zhao Sihai , Zheng Yi
Published: April 2024

The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach.
Estimation of Co-Seismic Surface Deformation Induced by 24 September 2019 Mirpur, Pakistan Earthquake along an Active Blind Fault Using Sentinel-1 TOPS Interferometry
Authors: Ali Muhammad , Schirinzi Gilda , Afzal Zeeshan , Budillon Alessandra , Mughal Muhammad Saleem , Hussain Sajid , Ferraioli Giampaolo
Published: April 2024

Surface deformation caused by an earthquake is very important to study for a better understanding of the development of geological structures and seismic hazards in an active tectonic area. In this study, we estimated the surface deformation due to an earthquake along an active blind fault using Sentinel-1 SAR data. On 24 September 2019, an earthquake with 5.6 Mw and 10 km depth stroke near Mirpur, Pakistan. The Mirpur area was highly affected by this earthquake with a huge collapse and the death of 34 people. This study aims to estimate the surface deformation associated with this earthquake in Mirpur and adjacent areas. The interferometric synthetic aperture radar (InSAR) technique was applied to study earthquake-induced surface motion. InSAR data consisting of nine Sentinel-1A SAR images from 11 August 2019 to 22 October 2019 was used to investigate the pre-, co- and post-seismic deformation trends. Time series investigation revealed that there was no significant deformation in the pre-seismic time. In the co-seismic time, strong displacement was observed and in post-seismic results, small displacements were seen due to 4.4 and 3.2 Mw aftershocks. Burst overlap interferometry and offset-tracking analysis were used for more sensitive measurements in the along-track direction. Comprehensive 3D displacement was mapped with the combination of LOS and along-track offset deformation. The major outcome of our results was the confirmation of the existence of a previously unpublished blind fault in Mirpur. Previously, this fault line was triggered during the 2005 earthquake and then it was activated on 24 September 2019. Additionally, we presented the co-seismically induced rockslides and some secondary faulting evidence, most of which occurred along or close to the pre-existing blind faults. The study area already faces many problems due to natural hazards where additional surface deformations, particularly because of the earthquake with activated blind fault, have increased its vulnerability.
Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks
Authors: Zhang Xinyu , Zhang Hongwei , Wang Qichao , Liu Xiaoying , Liu Shouxin , Zhang Rongchuan , Li Rongzhong , Wu Songhua
Published: April 2024

Aircraft wake vortices are serious threats to aviation safety. The Pulsed Coherent Doppler Lidar (PCDL) has been widely used in the observation of aircraft wake vortices due to its advantages of high spatial-temporal resolution and high precision. However, the post-processing algorithms require significant computing resources, which cannot achieve the real-time detection of a wake vortex (WV). This paper presents an improved Convolutional Neural Network (CNN) method for WV locating and grading based on PCDL data to avoid the influence of unstable ambient wind fields on the localization and classification results of WV. Typical WV cases are selected for analysis, and the WV locating and grading models are validated on different test sets. The consistency of the analytical algorithm and the CNN algorithm is verified. The results indicate that the improved CNN method achieves satisfactory recognition accuracy with higher efficiency and better robustness, especially in the case of strong turbulence, where the CNN method recognizes the wake vortex while the analytical method cannot. The improved CNN method is expected to be applied to optimize the current aircraft spacing criteria, which is promising in terms of aviation safety and economic benefit improvement.
CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction
Authors: Marjani Mohammad , Mahdianpari Masoud , Mohammadimanesh Fariba
Published: April 2024

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires.
Impact Analysis and Compensation Methods of Frequency Synchronization Errors in Distributed Geosynchronous Synthetic Aperture Radar
Authors: Sun Xiaoying , Chen Leping , Zhou Zhengquan , Du Huagui , Huang Xiaotao
Published: April 2024

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms.
Kinematic and Dynamic Structure of the 18 May 2020 Squall Line over South Korea
Authors: Swastiko Wishnu Agum , Tsai Chia-Lun , Kim Seung Hee , Lee GyuWon
Published: April 2024

The diagonal squall line that passed through the Korean Peninsula on the 18 May 2020 was examined using wind data retrieved from multiple Doppler radar synthesis focusing on its kinematic and dynamic aspects. The low-level jet, along with warm and moist air in the lower level, served as the primary source of moisture supply during the initiation and formation process. The presence of a cold pool accompanying the squall line played a role in retaining moisture at the surface. As the squall line approached the Korean Peninsula, the convective bands in the northern segment (NS) and southern segment (SS) of the squall line exhibited distinct evolutionary patterns. The vertical wind shear in the NS area was more pronounced compared to that in the SS. The ascending inflow associated with the tilted updraft in the NS reached an altitude of 7 km, whereas it was only up to 4 km in the SS. The difference was caused by the strong descending rear flow, which obstructed the ascending inflow and let to significant updraft in the SS.
Assessing the Impact of COVID-19 on Amateur Runners’ Performance: An Analysis through Monitoring Devices
Authors: García-Arrabé María , Giménez María-José , Moriceau Juliette , Fevre Amandine , Roy Jean-Sebastien , González-de-la-Flor Ángel , de la Plaza San Frutos Marta
Published: April 2024

This retrospective study aimed to analyze the return to running of non-professional runners after experiencing asymptomatic or mild COVID-19. Participants aged 18–55 years who maintained a training load of ≥10 km/week for at least three months prior to diagnosis and utilized Garmin/Polar apps were included. From these devices, parameters such as pace, distance, total running time, cadence, and heart rate were collected at three intervals: pre-COVID, immediately post-COVID, and three months after diagnosis. The Wilcoxon signed rank test was used for analysis (significance was set at ≤0.05). Twenty-one participants (57.1% male; mean age 35.0 ± 9.8 years) were included. The results revealed a significant decrease in running duration and distance two weeks after diagnosis, without significant changes in other parameters. Three months after infection, no differences were observed compared to pre-infection data, indicating a return to the pre-disease training load. These findings underscore the transient impact of COVID-19 on training performance among non-professional runners with mild or asymptomatic symptoms, highlighting the importance of tailored strategies for resuming running after infection.
Electrochemical Impedance Spectroscopy for the Sensing of the Kinetic Parameters of Engineered Enzymes
Authors: Dusíková Adriána , Baranová Timea , Krahulec Ján , Dakošová Olívia , Híveš Ján , Naumowicz Monika , Gál Miroslav
Published: April 2024

The study presents a promising approach to enzymatic kinetics using Electrochemical Impedance Spectroscopy (EIS) to assess fundamental parameters of modified enteropeptidases. Traditional methods for determining these parameters, while effective, often lack versatility and convenience, especially under varying environmental conditions. The use of EIS provides a novel approach that overcomes these limitations. The enteropeptidase underwent genetic modification through the introduction of single amino acid modifications to assess their effect on enzyme kinetics. However, according to the one-sample t-test results, the difference between the engineered enzymes and hEKL was not statistically significant by conventional criteria. The kinetic parameters were analyzed using fluorescence spectroscopy and EIS, which was found to be an effective tool for the real-time measurement of enzyme kinetics. The results obtained through EIS were not significantly different from those obtained through traditional fluorescence spectroscopy methods (p value >> 0.05). The study validates the use of EIS for measuring enzyme kinetics and provides insight into the effects of specific amino acid changes on enteropeptidase function. These findings have potential applications in biotechnology and biochemical research, suggesting a new method for rapidly assessing enzymatic activity.
Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion
Authors: Wang Kang , Wang Aimin , Wu Long , Xie Guangjun
Published: April 2024

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.
Adaptive Cruise Control Based on Safe Deep Reinforcement Learning
Authors: Zhao Rui , Wang Kui , Che Wenbo , Li Yun , Fan Yuze , Gao Fei
Published: April 2024

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback–Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.
Failure Mode and Effects Analysis of Accessibility in Frigid Zone Campus Buildings
Authors: Qiao Wenqi , Chen Zimo , Hou Tong
Published: April 2024

With the progress of China’s economy, there is an increasing focus on accessibility systems. Enhancements to accessibility infrastructure are being implemented in all types of structures, with a particular focus on educational institutions such as college campuses. This research was carried out to examine the campus accessibility system of Northeastern University’s Nanhu and Hunnan Campuses in Shenyang City, China, using failure mode and effects analysis (FMEA) as a methodology and using incorporating fuzzy control, which overcomes the limitations of traditional FMEA. Fuzzy-FMEA integrates the fuzzy linguistic assessment to assist the analysis process, in contrast to standard analysis which mainly relies on subjective judgment. Through calculations, it is known that ramps, barrier-free toilets, and barrier-free entrances are the items with the highest failure modes. The construction of the campus accessible environment needs to prioritize solving the problems of these facilities. The research results also found that there is a lack of research specifications for accessible environments in China’s cold regions, and universal specifications are not fully suitable. The accessibility of the new campus was improved and management was improved, reflecting the school’s increased awareness of accessible environment construction in recent years. However, there are still many common problems in the old and new campuses, proving that they are not aware of the importance and urgency of improving these problems. In addition, the construction of barrier-free facilities in the administrative office buildings where management and faculty are located on both campuses is generally in good condition. The accessible design of dormitories and canteens commonly used by students is often ignored. Moreover, the rough detailing in many buildings prevents these facilities from being used properly. These are the challenges faced by the Northeastern University in building an accessible environment.
Aging in Place in Hong Kong and Its Implications for Sustainable Development: A Qualitative Study Exploring the Needs, Beliefs, Behaviors, and Well-Being of Older Adults through Self-Determination Theory and Social Cognitive Theory
Authors: Lee Stephanie W. , Xu Jing , Wut Tai-Ming , Lau Yui-Yip , Chan Joseph H. L. , Liu Tin-Shing , Mok Louis W. Y. , Chan Jason K. Y.
Published: April 2024

With the rapidly aging population, Aging in Place (AIP) assumes an increasingly pivotal role, as it aligns with SDG 3 (Good Health and Well-being) and Environmental, Social, and Governance (ESG) principles. Despite the contributions of AIP, there is a dearth of studies investigating the corresponding needs and well-being of older adults from psychological and sociocultural perspectives. This study aimed to investigate older adults’ needs, beliefs, behaviors, well-being, as well as perceived challenges and opportunities pertaining to AIP. Adopting a qualitative design, in-depth semi-structured interviews were conducted with 41 older adults on an individual basis. Through thematic analysis, a theoretical framework with reference to Self-Determination Theory (SDT) and Social Cognitive Theory (SCT) was established. Findings reveal that the psychological needs of autonomy, competence, relatedness, and dignity are conducive to well-being and decision making. Fear of being a burden to others emerges as a dominant sociocultural construct. This research extends SDT by incorporating the need for dignity as an overarching factor intersecting with SDT needs. Fulfilling psychological needs, honoring cultural beliefs, and aligning with SDG3 and ESG principles are crucial to the successful implementation of AIP. AIP policies should not only consider older adults’ practical needs but also their psychological needs, thereby fostering an age-friendly environment through sustainable development.
Key Performance Indicators and Data Envelopment Analysis in Greek Tourism: A Strategic Planning Tool for Destinations and DMMOs
Authors: Varelas Sotirios , Tsoupros Georgios
Published: April 2024

Over the years, the tourism sector has constantly been a driving force in strengthening the Greek economy. Therefore, being capable of leveraging a tourism business’s performance can be of great importance in many aspects for destinations and destination management and marketing organizations (DMMOs). For this very purpose, this study’s methodology consists of a combined application of the key performance indicators and data envelopment analysis. The research conducted is quantitative, aiming to analyze the efficiency of the Greek hotels by region and determine the effective ones, as well as the strategic and managerial changes which should be considered by the non-effective. As a result, it shall become possible for each set of hotels to know the ideal turnover and the tourism nights spent that they should achieve, based on their current capacity in terms of beds and employees. Ultimately, this process could play a pivotal role in a region’s strategic planning, both from a resource management perspective, as well as in establishing an effective, measurable strategy that can be implemented by regional policy makers and destination managers in a real-time benchmarking process.
The Western Greece Soil Information System (WΕSIS)—A Soil Health Design Supported by the Internet of Things, Soil Databases, and Artificial Intelligence Technologies in Western Greece
Authors: Kalantzopoulos Georgios , Paraskevopoulos Panagiotis , Domalis Georgios , Liopa-Tsakalidi Aglaia , Tsesmelis Dimitrios E. , Barouchas Pantelis E.
Published: April 2024

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.
Can the Circular Economy Demonstration Policy Enhance the Green Innovation Level? A Quasi-Natural Experiment from China
Authors: Wu Yongchao , Wei Xinyuan , Liu Yanan , Li Huilong
Published: April 2024

Although both circular economy and green innovation (GI) have received increasing research attention, little is known about their relationship. Based on quasi-natural experimental analysis, this study explored the impact and mechanism of the National Circular Economy Demonstration City (NCEDC) policy on GI in China and adopted a difference-in-difference (DID) model using the data of 265 cities in China from 2004 to 2018. The results show that the NCEDC policy has significantly improved the cities’ GI level, which can be achieved through industrial structure upgrading and innovation R&D investment effect, and has a stronger performance in eastern cities and lower administrative level cities. Our findings not only present novel evidence on the relationship between the circular economy and GI, but also offer valuable insights for advancing policy pilot initiatives in the field of environmental management.
SustainableTransformation of Undergraduate Engineering Education in China through Sino-Australian Cooperation: A Case Study on Electro-Mechanical System
Authors: Guo Yufei , Long John M. , Huynh Van Thanh
Published: April 2024

China’s undergraduate engineering education is facing two significant challenges: passive learning and limited cross-cultural communication. In response, active learning methods such as project-based learning (PBL) and Sino-foreign cooperative education emerge as promising solutions. However, despite their potential, PBL’s application remains constrained, and many Sino-foreign cooperative programs persist with traditional passive learning approaches. How to adeptly integrate the two, namely realizing active learning in Sino-foreign cooperative education, is the primary objective of this study. To this end, a sustainable strategy for cross-cultural transfer and the adaptation of active learning is proposed. Initially, a PBL-based active learning pedagogy is tailored for a course within a Sino-Australian cooperative undergraduate program. This approach integrates hands-on activities, teamwork, and the flipped classroom method. Moreover, to ensure implementation quality, an assessment method reliant on continuous student survey feedback is suggested. Subsequently, through the statistical analysis of students’ survey data as well as final grades, both quantitative and qualitative results affirm the efficacy of the proposed strategy.
Experimental and Analytical Study on Recycled Aggregate RC Columns: Short and Slender Loaded Axially and Eccentrically
Authors: Najar Bakhtyar Nassih , Rasheed Mereen Hassan Fahmi , Taha Bahman Omar
Published: April 2024

To protect the environment and preserve natural resources, it is crucial to use recycled aggregate (RA) in construction. The recycled coarse aggregate reinforced concrete columns with the addition of steel fiber evaluated under concentric and eccentric loadings for short and slender columns were examined experimentally and analytically in this research. Twenty-four column specimens were built for this study to examine the impact of steel fiber, recycled aggregate, slenderness, and eccentricity on the behavior of reinforced concrete columns. This research examined the failure mode, maximum load-carrying capacity, strain in the concrete, strain in the reinforcement, and ductility. Based on the results, it can be concluded that employing recycled concrete aggregate is a potential approach to meet design codes. The addition of 1% steel fiber effectively prevents concrete from crushing and spalling. Steel fiber, however, improved the columns’ ductility and strength. The results showed the maximum load-carrying capacity of the specimens and the results of using ACI-318 code equations agreed very well. Furthermore, a model is proposed for columns with both natural and recycled aggregate and which accounts for the eccentricity and slenderness to forecast the load-carrying capacity. The outcomes demonstrated that the design principles were met well. Plots of load–moment interaction diagrams for short and slender columns made with the ACI-318 method are compared to the findings of the experiments.
A Case Study in Natural Fibre Material (Luffa Sponge) Development Using E2-Material-Driven Design
Authors: Li Chao , Wang Luzhen , Zhang Le
Published: April 2024

To unleash the emotional potential of natural fibre materials in sustainable development and utilisation, this paper presents a material-driven design method with emotional and ecological indicators (E2-MDD). The method offers product-level solutions for the sustainable development of natural materials. The method involves several steps, such as screening the main material quality, capturing the user emotion vision, deconstructing the E2 vision pattern, and deducting the product design concept. The method was tested on luffa sponge samples, seen as one kind of traditional fibre resource, which resulted in four differentiated schemes, which were evaluated using the E2-MDD ring radar column score chart. The study identified three key emotional qualities for natural fibre materials: associativity, uniqueness, and biophilicity. The results show that product concepts closer to the natural material’s original form scored higher, while the inclusion of non-natural materials had a negative impact on the evaluations. This study also found that E2-MDD could strengthen the emotional and ecological connection between people and products, further indicating that material and design can establish a link between environmental friendliness and emotional experience. Lastly, the paper suggests future development areas for the E2-MDD method, including focusing on users, ecology, and business.
Research on the Spatiotemporal Characteristics of the Coupling Coordination Relationship of the Energy–Food–Water System in the Xinjiang Subregion
Authors: Gao Jing , Xu Jian
Published: April 2024

In the Xinjiang region, the sustainable management of water resources, energy, and food is crucial for regional development. This study establishes a coupling evaluation index for energy–food–water (EFW) systems from the perspectives of supply, consumption, and efficiency. Using an integrated EFM-CDD-RDD-CCDM approach, an assessment of the coupling and coordination levels of the EFW systems in 14 cities within Xinjiang was conducted for the period of 2004 to 2020. Additionally, the method of obstacle degree identification was utilized to determine the main barriers affecting the EFW systems. Key findings included the following. (1) In terms of individual system coordination indices, the water resource systems exhibited overall higher coordination (ranging from 0.30 to 0.72) with comparatively minor spatial variability, while the energy (from 0.18 to 0.81) and food (from 0.12 to 0.83) systems showed greater temporal and spatial fluctuations. From 2004 to 2020, improvements were observed in the coordination of food and water resource systems, whereas a decline was noted in the coordination of the energy subsystem. (2) Prior to 2011, the coupling of food–water and energy–food systems showed an upward trend, whereas the energy–water coupling decreased annually by 2.62%, further highlighting the tensions between energy development and water resource constraints in Xinjiang. (3) The comprehensive coupling coordination index of the Xinjiang EFW systems ranged between 0.59 and 0.80; between 2004 and 2020, there was an oscillatory increase. From 2004 to 2016, the coupling and coordination degree across the municipalities generally improved, with the regions on the western side and southern slope of the Tianshan Mountains, the Altai Mountains, and the northwestern edge of the Junggar Basin exhibiting the highest levels, followed by the three prefectures in southern Xinjiang. (4) The EFW obstacle degree posed by the food systems in Xinjiang and its divisions showed a decreasing trend from 2004 to 2020, with the energy system identified as the main factor affecting the coupling and coordination degrees of the EFW systems (increasing by 44% to 52%). Therefore, it is imperative to accelerate the energy transition and optimization in the lead energy development and production areas of Xinjiang. This research provides a scientific basis for Xinjiang’s sustainable development strategies and highlights potential directions for the future optimization of resource management.
Deformation Mechanism of Underlying Sandy Soil Induced by Subway Traffic Vibrations
Authors: Sun Jinbo , He Dingding , Sun Ke , Gong Liang
Published: April 2024

The safety of the geotechnical environment around subways is vitally important for their sustainability. Ground settlement is a very common threat to the safe operation of subways and is related to subway traffic vibrations. Taking Nanjing subway line 10 as an example, field tests, discrete element simulations, and a grey relational analysis are carried out to study the deformation mechanism of the underlying sandy soil induced by vibrations. The results show that the vibration load of Nanjing subway Line 10 is mainly concentrated in the vertical direction. The particles’ coordination number below the subway increases under the vibration load, while the coordination number on the side of the tunnel decreases, which may cause a shear dilatancy effect. Among the five microscopic indexes, the local porosity and the coordination number are closely related to the deformation. Although the deformation of the underlying sandy soil is quite small under the subway traffic load, it will accumulate under frequent loading and then lead to urban engineering geological disasters.
Can the Top Management Team’s Environmental Attention Promote Corporate Green Innovation?
Authors: Wang Ying , Liu Yiyang
Published: April 2024

Enterprise green innovation (GI) is the micro foundation for coping with the challenges of the ecological environment and achieving sustainable development. The top management team’s environmental attention (TMTEA) is crucial for guiding environmental strategy and resource investment and promoting enterprise transformation towards sustainable development and GI. Drawing on an attention-based view (ABV) and lifecycle theory, this study analyzes data from 1722 listed companies in China (2010–2021) to examine TMTEA’s impact on corporate GI and its regulatory mechanisms. The results show the following: (1) TMTEA promotes enterprise GI, particularly in the growth and decline stages. (2) Government environmental attention negatively moderates TMTEA’s influence on corporate GI. (3) Compensation and equity incentives positively moderate the TMTEA–GI relationship. These insights enrich executive attention and GI literature, aiding decision-makers and enterprises in formulating effective GI strategies. Limitations include reliance on Chinese-listed company data, potentially limiting generalizability, and the need for qualitative research to deepen understanding of management processes and governance mechanisms.
Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage
Authors: Cai Yufeng , Zeng Xuelan , Li Weichi , He Song , Feng Zedong , Tan Zihang
Published: April 2024

With pronounced differences in emission factors among vehicle types and marked spatiotemporal heterogeneity of vehicle fleet composition, extrapolating fleet composition from insufficient sample hour periods and road segments will introduce significant uncertainty in calculating regional daily road traffic emissions. We proposed a framework to manage uncertainty in urban road traffic emissions associated with vehicle fleet composition from the perspective of spatiotemporal sampling coverage. Initially, the respective relationships of the temporal and spatial sampling coverages of fleet composition with the resulting regional daily road traffic emission uncertainties were determined, using the core area of a typical small and medium-sized city in China with the widely-used International Vehicle Emissions (IVE) model as example. Subsequently, function models were developed to explore the determination of the spatiotemporal sampling coverage of fleet composition. These results of emission uncertainties and function models implied that gases with larger emission factor discrepancies between vehicle types, such as NOx, required greater spatiotemporal sampling coverage than gases with smaller discrepancies, such as CO2, under the same uncertainties target. Therefore, sampling efforts should be prioritized for gases with larger emission factor discrepancies. Additionally, increasing sampling coverage in one dimension (either spatial or temporal) can reduce the minimum required coverage in the other dimension. To further reduce uncertainty, enhancing both spatial and temporal sampling coverage of the fleet composition is more effective than enhancing one type of coverage alone. The framework and results proposed in this work can reduce the uncertainty of emissions calculations caused by insufficient sampling coverage and contribute to more accurate transport emission reduction policy formulation.
Differential Subordination and Superordination Using an Integral Operator for Certain Subclasses of p-Valent Functions
Authors: Almutairi Norah Saud , Shahen Awatef , Darwish Hanan
Published: April 2024

This work presents a novel investigation that utilizes the integral operator Ip,λn in the field of geometric function theory, with a specific focus on sandwich theorems. We obtained findings about the differential subordination and superordination of a novel formula for a generalized integral operator. Additionally, certain sandwich theorems were discovered.
Applying Integrative Systems Methodology: The Case of Health Care Organizations
Authors: Schwaninger Markus , Klocker Johann
Published: April 2024

Systems thinking has a lot to offer to the field of strategizing. Calls for a greater use of the Systems Approaches to management are increasingly being advanced in the world of organizations, public and private. Yet, practical implementation of such postulates is lagging. This article aims to demonstrate the potential of systems thinking in shaping strategy in management and organization. It builds on a framework known as Integrative Systems Methodology, a circular set of established components of strategic processes: all elements of the methodology are covered, from finding purpose and goals, modeling, and diagnosing to the design of strategies and change. Along this path, complementary methodologies are combined to cope with high environmental complexity. In the present case, these are the systems methodologies of Organizational Cybernetics (namely Viable System Model) and System Dynamics. The layout of the treatise is conceptual in nature. Nevertheless, long-term empirical evidence from a set of real-world organizations-one center and several peripheral healthcare entities-is presented to make the methodology tangible: theoretically grounded and experientially corroborated. The case study we are introducing is from health care, concretely: an oncological care system. Structural patterns are elicited, which are isomorphic, applicable to any health care system. From a conceptual-theoretical stance, crucial factors conducive to continuous organizational learning are elaborated. Professionals can benefit from acquiring new ways of coping with the ever-increasing complexity.
Heterologous Exchanges of Glycoprotein and Non-Virion Protein in Novirhabdoviruses: Assessment of Virulence in Yellow Perch (Perca flavescens) and Rainbow Trout (Oncorhynchus mykiss)
Authors: Vakharia Vikram N. , Ammayappan Arun , Yusuff Shamila , Tesfaye Tarin M. , Kurath Gael
Published: April 2024

Infectious hematopoietic necrosis virus (IHNV) and viral hemorrhagic septicemia virus (VHSV) are rhabdoviruses in two different species belonging to the Novirhabdovirus genus. IHNV has a narrow host range restricted to trout and salmon species, and viruses in the M genogroup of IHNV have high virulence in rainbow trout (Oncorhynchus mykiss). In contrast, the VHSV genotype IVb that invaded the Great Lakes in the United States has a broad host range, with high virulence in yellow perch (Perca flavescens), but not in rainbow trout. By using reverse-genetic systems of IHNV-M and VHSV-IVb strains, we generated six IHNV:VHSV chimeric viruses in which the glycoprotein (G), non-virion-protein (NV), or both G and NV genes of IHNV-M were replaced with the analogous genes from VHSV-IVb, and vice versa. These chimeric viruses were used to challenge groups of rainbow trout and yellow perch. The parental recombinants rIHNV-M and rVHSV-IVb were highly virulent in rainbow trout and yellow perch, respectively. Parental rIHNV-M was avirulent in yellow perch, and chimeric rIHNV carrying G, NV, or G and NV genes from VHSV-IVb remained low in virulence in yellow perch. Similarly, the parental rVHSV-IVb exhibited low virulence in rainbow trout, and chimeric rVHSV with substituted G, NV, or G and NV genes from IHNV-M remained avirulent in rainbow trout. Thus, the G and NV genes of either virus were not sufficient to confer high host-specific virulence when exchanged into a heterologous species genome. Some exchanges of G and/or NV genes caused a loss of host-specific virulence, providing insights into possible roles in viral virulence or fitness, and interactions between viral proteins.
Epidemiological Features of Human Norovirus Genotypes before and after COVID-19 Countermeasures in Osaka, Japan
Authors: Shirai Tatsuya , Phadungsombat Juthamas , Ushikai Yumi , Yoshikaie Kunihito , Shioda Tatsuo , Sakon Naomi
Published: April 2024

We investigated the molecular epidemiology of human norovirus (HuNoV) in all age groups using samples from April 2019 to March 2023, before and after the COVID-19 countermeasures were implemented. GII.2[P16] and GII.4[P31], the prevalent strains in Japan before COVID-19 countermeasures, remained prevalent during the COVID-19 pandemic, except from April to November 2020; in 2021, the prevalence of GII.2[P16] increased among children. Furthermore, there was an increase in the prevalence of GII.4[P16] after December 2022. Phylogenetic analysis of GII.P31 RdRp showed that some strains detected in 2022 belonged to a different cluster of other strains obtained during the present study period, suggesting that HuNoV strains will evolve differently even if they have the same type of RdRp. An analysis of the amino acid sequence of VP1 showed that some antigenic sites of GII.4[P16] were different from those of GII.4[P31]. The present study showed high infectivity of HuNoV despite the COVID-19 countermeasures and revealed changes in the prevalent genotypes and mutations of each genotype. In the future, we will investigate whether GII.4[P16] becomes more prevalent, providing new insights by comparing the new data with those analyzed in the present study.
The Three-Dimensional Wave-Induced Current Field: An Analytical Model
Authors: Gic-Grusza Gabriela
Published: April 2024

Wave-induced currents play a critical role in coastal dynamics, influencing sediment transport and shaping bottom topography. Traditionally, long- and cross-shore currents in coastal zones were analyzed independently, often with two-dimensional models for longshore currents and undertow being used. The introduction of quasi-three-dimensional models marked a significant advancement toward a more holistic understanding. Despite recent proposals for fully three-dimensional models, none have achieved widespread acceptance, primarily due to challenges in accurately capturing depth-dependent radiation stress. This paper presents an innovative analytical model advocating for comprehensive three-dimensional approaches in coastal hydrodynamics. The model, based on novel simplification rules, refines relationships governing turbulent stress tensors and provides valuable insights into wave-induced stresses. It offers analytical solutions for both homogeneous and general coastal zones, laying the foundation for future advancements in numerical modeling techniques.
An Investigation of Contaminant Transport and Retention from Storage Zone in Meandering Channels
Authors: Jung Sung Hyun , Park Inhwan , Shin Jaehyun
Published: April 2024

Contaminant trapping by recirculation zones occurring at the apex of natural meandering channels induces a long tail in the contaminant cloud, thereby complicating the prediction of mixing behaviors. Thus, the understanding of the interaction between solute trapping and recirculating flow is important for responding to and mitigating water pollution accidents. In this research, the EFDC model was employed to reproduce three-dimensional flow structures of recirculating flow at the channel apex and investigate the influence on contaminant mixing. To investigate the contaminant transport characteristics from the storage zone in meandering channels, simulations were conducted using various discharge values to assess the impact of storage zone development on the concentration–time curves. The analysis of the relationship between the storage zone size and mixing behaviors indicates that an increase in discharge could result in a shorter tail and larger longitudinal dispersion even with the larger storage zone size. On the other hand, the enlarged recirculation zone size contributes to reducing transverse dispersion, evidenced by flatter dosage curves under lower flow rate conditions. These findings suggest that the increase in longitudinal dispersion with a larger flow rate is primarily caused by the reduction in transverse dispersion resulting from the formation of the recirculation zone.
Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction
Authors: Wang Xueni , Chang Jianbo , Jin Hua , Zhao Zhongfeng , Zhu Xueping , Cai Wenjun
Published: April 2024

Accurate medium- and long-term runoff prediction models play crucial guiding roles in regional water resources planning and management. However, due to the significant variation in and limited amount of annual runoff sequence samples, it is difficult for the conventional machine learning models to capture its features, resulting in inadequate prediction accuracy. In response to the difficulties in leveraging the advantages of machine learning models and limited prediction accuracy in annual runoff forecasting, firstly, the variational mode decomposition (VMD) method is adopted to decompose the annual runoff series into multiple intrinsic mode function (IMF) components and residual sequences, and the spectral clustering (SC) algorithm is applied to classify and reconstruct each IMF. Secondly, an annual runoff prediction model based on the adaptive particle swarm optimization–long short-term memory network (APSO-LSTM) model is constructed. Finally, with the basis of the APSO-LSTM model, the decomposed and clustered IMFs are predicted separately, and the predicted results are integrated to obtain the ultimate annual runoff forecast results. By decomposing and clustering the annual runoff series, the non-stationarity and complexity of the series have been reduced effectively, and the endpoint effect of modal decomposition has been effectively suppressed. Ultimately, the expected improvement in the prediction accuracy of the annual runoff series based on machine learning models is achieved. Four hydrological stations along the upper reaches of the Fen River in Shanxi Province, China, are studied utilizing the method proposed in this paper, and the results are compared with those obtained from other methods. The results show that the method proposed in this article is significantly superior to other methods. Compared with the APSO-LSTM model and the APSO-LSTM model based on processed annual runoff sequences by single VMD or Wavelet Packet Decomposition (WPD), the method proposed in this paper reduces the RMSE by 40.95–80.28%, 25.26–57.04%, and 15.49–40.14%, and the MAE by 24.46–80.53%, 16.50–59.30%, and 16.58–41.80%, in annual runoff prediction, respectively. The research has important reference significance for annual runoff prediction and hydrological prediction in areas with data scarcity.