Article
Engineering, Marine
H. Diaz, A. P. Teixeira, C. Guedes Soares
Summary: A Monte Carlo simulation procedure is developed to select the optimum location of wind farms by combining major decision criteria and subjective judgments from decision-makers. The method utilizes Monte Carlo simulation, conventional Analytic Hierarchy Process, and Fuzzy Analytic Hierarchy Process. It is applied to offshore wind farms in Spain to rank the most suitable turbine positioning locations.
Article
Computer Science, Information Systems
Ya-guang Guo, Qian Yin, Yixiong Wang, Jun Xu, Leqi Zhu
Summary: Based on the connotation and structure of government service resources, this study used data from 2019 to 2021 to calculate the efficiency of government service resource allocation in different periods in L city. By adding the government cloud platform and cloud computing resources and applying the data envelopment analysis method, the study analyzed and discussed the patterns and evolutionary trends of government service resource allocation efficiency. The results show that the overall efficiency level of government service resource allocation in L city is not high, and the relative difference in allocation efficiency is a common phenomenon in regional development.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2023)
Article
Management
Saber Mehdizadeh, Alireza Amirteimoori, Vincent Charles, Mohammad Hassan Behzadi, Sohrab Kordrostami
Summary: The article introduces a two-stage network DEA model with stochastic data to address real-world scenarios involving stochastic behavior. The model is formulated based on probability distribution properties, and discussions on the relationship between the two stages at different confidence levels and aspiration levels are provided. The proposed approach is applied to a real case involving 16 commercial banks in China to demonstrate its applicability.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Environmental Sciences
Yanjie Lu, Yisu Ge, Guodao Zhang, Abdulkareem Abdulwahab, Anas A. Salameh, H. Elhosiny Ali, Binh Nguyen Le
Summary: A significant amount of solid waste in landfills worldwide comes from construction and demolition projects. Recycling construction waste can reduce landfill waste and the need for energy and resources. Using a management hierarchy that includes rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycling, and disposal is an effective approach to waste reduction.
Article
Energy & Fuels
Hena Oak
Summary: This paper examines the energy efficiency, feedstock usage, and ownership impact on urea plants in India, and estimates the potential for energy savings. The results indicate that privately-owned plants using natural gas as feedstock have the highest energy efficiency scores, while government-owned plants using non-natural gas feedstock have the lowest scores.
Article
Green & Sustainable Science & Technology
Boris Prevolsek, Maja Borlinic Gacnik, Crtomir Rozman
Summary: This paper examines the efficiency of tourist farms in Slovenia using a combination of non-parametric programming-Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP). The results indicate potential improvements in efficiency and additional criteria for the tourist farms analyzed. The AHP model provides a more accurate ranking within the group of farms assessed as efficient by DEA.
Article
Engineering, Environmental
Qiusheng Song, Peng Jiang, Song Zheng
Summary: This paper proposes a safety assessment method for chemical plant production process that comprehensively considers the safety influencing elements of human factors. The method combines analytic hierarchy process and cloud model for evaluation, and simulation results show that it is reliable, practical, and scientific.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Review
Biotechnology & Applied Microbiology
Dawei Ni, Ziwei Chen, Yuqing Tian, Wei Xu, Wenli Zhang, Byung-Gee Kim, Wanmeng Mu
Summary: Sucrose, a widely spread disaccharide, has been used in daily human life for centuries. It is an abundant and cheap sweetener that plays an essential role in our diet and the food industry. However, overconsumption of sucrose has been found to be related to various diseases. As a result, researchers have been exploring methods to convert sucrose into high value chemicals. Compared to chemical methods, biotechnological approaches using enzymes are more environmentally friendly. This review discusses enzymatic catalysis and whole-cell fermentation of sucrose for the production of valuable chemicals, as well as multienzyme cascade catalysis and metabolic engineering strategies.
BIOTECHNOLOGY ADVANCES
(2022)
Article
Chemistry, Applied
Yuji Kaiya, Ryo Tamura, Koji Tsuda
Summary: This paper discusses the applications of kinetic models in chemical processes and the importance of complete profiling. It proposes the use of entropic sampling to approximate complete profiling and analyzes the relationship between failure rate and process parameters through a specific case study.
ORGANIC PROCESS RESEARCH & DEVELOPMENT
(2022)
Article
Computer Science, Artificial Intelligence
Zhou Jiang, Zhenwu Wei
Summary: Grassland resources play a crucial role in various aspects of ecosystems, and evaluating these resources is essential for sustainable development. This paper constructs an evaluation system by improving neural networks and using a comprehensive index method to assess different levels of resource and environmental carrying capacity, forming a complete set of evaluation criteria.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jin Fang, Fariborz Y. Partovi
Summary: This paper introduces a technology-based model for identifying various criteria in a decision-making situation, which was validated through analysis of reviews of hotels and restaurants.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zhou-Jing Wang
Summary: This study develops a modeling method for triangular fuzzy multiplicative preference relations and proposes an index for measuring the consistency of TFMPRs, as well as two logarithmic least square models. A comparative analysis is conducted through a numerical example to clarify the effectiveness and advantages of the proposed method, and the practicality of the proposed triangular FAHP approach is demonstrated.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Nursing
Jian Zhou, Wantai Dang, Zongting Luo, Xinxin Fan, Hui Shi, Na Deng, Guizhi Xiong
Summary: This study aimed to determine the relationship between the demand for telenursing and the chronic illness resources available to patients with T2DM, as well as the factors that affect this requirement. The telenursing needs of patients with T2DM primarily focus on basic nursing needs for the disease, and chronic disease resources have an impact on patients' telenursing needs.
JOURNAL OF CLINICAL NURSING
(2023)
Article
Economics
Marwa Hasni, Safa Bhar Layeb, Najla Omrane Aissaoui, Aymen Mannai
Summary: This study combines DEA and GRA to evaluate healthcare organizations at a macro level, offering some suggestions for resource allocation.
MANAGERIAL AND DECISION ECONOMICS
(2022)
Review
Chemistry, Multidisciplinary
Abhilash, Vaidyanathan Swetha, Pratima Meshram
Summary: Graphene has revolutionized the field of nanotechnology with its tremendous properties, and the chemical synthesis route offers scalable and high-volume production. Utilizing alternative low-cost carbon resources reduces dependency on traditional resources, lowers costs, and provides a solution for waste biomass management.
Article
Thermodynamics
Di Cong, Lingling Liang, Shaoxing Jing, Yongming Han, Zhiqiang Geng, Chong Chu
Summary: This study proposes an energy supply efficiency evaluation model for integrated energy systems based on SBM-DEA and Monte Carlo to achieve energy optimization and carbon tax reduction effectively. It has a high degree of discrimination and can obtain better effective decision-making units, leading to the successful achievement of energy optimization and carbon tax reduction in integrated energy systems.
Article
Agricultural Economics & Policy
Zhiqiang Geng, Lingling Liang, Yongming Han, Guangcan Tao, Chong Chu
Summary: This paper proposes a novel risk early warning modelling method based on the LSTM neural network and AHP-SP, which shows higher accuracy in predicting the development trend of food safety risk compared to traditional methods. The method can provide decision-making basis for relevant departments to formulate targeted risk prevention and control measures.
BRITISH FOOD JOURNAL
(2022)
Article
Automation & Control Systems
Zhiqiang Geng, Xiaoyan Duan, Yongming Han, Fenfen Liu, Wei Xu
Summary: Sparse principal component analysis (SPCA) is widely used in fault detection for complex chemical processes. However, it has limitations in data processing, fixed models, and single fault classification in time-varying processes. Therefore, an adaptive SPCA algorithm fused with improved variation mode decomposition (ASPCA-IVMD) is proposed for fault detection in chemical processes.
Article
Automation & Control Systems
Zhiqiang Geng, Zhiwei Chen, Qingchao Meng, Yongming Han
Summary: In this article, a novel Gated Convolutional neural network-based Transformer (GCT) is proposed for dynamic soft sensor modeling of industrial processes. The GCT encodes short-term patterns, filters important features adaptively, models the correlation between moments using multihead attention mechanism, and obtains prediction results through a linear neural network layer. Experimental results show that the proposed method achieves state-of-the-art performance in the dynamic soft sensor modeling of industrial processes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Yongming Han, Wang Qi, Ning Ding, Zhiqiang Geng
Summary: This article presents a fault diagnosis method based on short-time wavelet entropy integrating LSTM and SVM to extract and process fault information in MMC system, achieving accurate and robust fault diagnosis of multiple fault types.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Zhiqiang Geng, Xiaoyan Duan, Jiatong Li, Chong Chu, Yongming Han
Summary: Food safety has a significant impact on the world economy and global health, and improving the accuracy of risk prediction and prevention is crucial for sustainable development. This paper proposes a food safety risk prediction model based on an improved random forest method and the Monte Carlo algorithm to enhance prediction accuracy and ensure personnel safety. The model outperforms existing techniques and provides decision-making assistance for preventing and controlling food risk events.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Yongming Han, Jintao Liu, Fenfen Liu, Zhiqiang Geng
Summary: This paper proposes an intelligent moving window based sparse principal component analysis integrating case-based reasoning method for fault diagnosis in the drilling process of the petrochemical industry. Experimental results demonstrate that the method effectively reduces risks and costs.
Article
Thermodynamics
Jintao Liu, Liangchao Chen, Wei Xu, Mingfei Feng, Yongming Han, Tao Xia, Zhiqiang Geng
Summary: This paper proposes a novel production prediction model using an attention mechanism and gated recurrent unit, which improves the accuracy and stability of gasoline production. By processing the collected data and analyzing the correlations, the performance of the model is further optimized.
Article
Automation & Control Systems
Hao Wu, Yongming Han, Qunxiong Zhu, Zhiqiang Geng
Summary: A novel feature-disentangled AE (FDAE) integrating Resnet is proposed to improve the low robustness and weak generalization of existing deep AE for soft sensor modeling. The FDAE can obtain disentangled multisource features through a trend-periodic LSTM and a dynamic self-attention CNN, and the Resnet is used to establish the relationship between these features and outputs. Experimental results demonstrate that the FDAE-Resnet outperforms other state-of-the-art methods in melt index modeling, reducing the root mean square error by 26.2% and the mean absolute percentage error by 38.2% on average in changed working conditions.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Hao Wu, Zhichao Zhang, Xiaoyong Li, Kai Shang, Yongming Han, Zhiqiang Geng, Tingrui Pan
Summary: In recent years, HAR with wearable devices has been widely used for everyday life tracking and healthcare monitoring. This study proposes a HAR system based on a pedal wearable device, which overcomes the challenges of weak sensor durability and difficulty capturing dynamic features of traditional devices. The system utilizes a novel DST-LSTM method and obtains pedal musculoskeletal response data to classify activity statuses using multi-head graph attention networks and a spatial gate.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Automation & Control Systems
Zhiqiang Geng, Chunjing Shi, Yongming Han
Summary: This research proposes a deep learning method that combines deep convolutional generative adversarial networks (DCGAN) and a seam carving algorithm to address the issue of small sample defect detection. The method is applied to the defect detection of water walls in an actual thermal power generation plant, achieving a detection accuracy of 98.43%, surpassing other methods and demonstrating the best generalization ability.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Zhiqiang Geng, Xintian Wang, Yuangang Jiang, Yongming Han, Bo Ma, Chong Chu
Summary: This paper proposes an improved adaptive particle swarm optimization algorithm (IAPSO) for optimizing the long short-term memory (LSTM) neural network (IAPSO-LSTM) to develop a food safety risk early warning model. The proposed IAPSO algorithm is compared with traditional PSO, classic PSO, adaptive PSO, and deterministic and adaptive PSO based on five benchmark functions, showing the best convergence speed and precision. The risk value of food safety detection data is obtained using the analytic hierarchy process, and the IAPSO algorithm is used to optimize the LSTM's hyperparameters. The model is evaluated using composite seasoning detection data, demonstrating superior performance and the ability to effectively warn potential food safety risks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Yongming Han, Yue Wang, Yuan Cao, Zhiqiang Geng, Qunxiong Zhu
Summary: This article proposes a novel binary particle swarm-wrapped feature selection optimization framework (BPSWO), which can improve the intrusion detection accuracy of machine learning methods. The proposed method is examined on the public power system from Oak Ridge National Laboratory, USA and the IEEE 57-bus system. Experimental results show that the BPSWO can achieve the state-of-the-art in the detection accuracy, proving the effectiveness and stability of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Hao Wu, Yongming Han, Min Liu, Zhiqiang Geng
Summary: In this article, a novel robust low-rank clustering contrastive learning (LrCCL-T) method is proposed to learn intrinsic and invariant feature representations from process data. The LrCCL-T integrates Lr prior and adaptive CCL to enhance the learned feature representations. The transformer is used to build the soft sensor model and extract dynamic temporal relationship between the learned features and outputs. Experimental results on industrial datasets demonstrate the effectiveness and robustness of the proposed method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Zhen Zhang, Yongming Han, Bo Ma, Min Liu, Zhiqiang Geng
Summary: This article proposes a novel temporal chain network (TCNet) for long-term series forecasting (LTSF). By constructing a one-way chain graph neural network (GNN) and introducing an intuitive attention mechanism, the TCNet avoids the issue of temporal information loss in transformer-based LTSF methods. Experimental results demonstrate that the TCNet outperforms current baselines on multiple benchmark datasets. Moreover, the article suggests that the bottleneck of transformer-based LTSF methods mainly stems from the complex architecture of the decoder.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.