Xuemei Li, Yuyu Sun, Yansong Shi, Yufeng Zhao and Shiwei Zhou
Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote…
Abstract
Purpose
Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.
Design/methodology/approach
This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.
Findings
Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.
Originality/value
A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.
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A combined approach of additive Holt–Winters, support vector regression, simple moving average and generalized simulated annealing with error correction and optimal parameter…
Abstract
Purpose
A combined approach of additive Holt–Winters, support vector regression, simple moving average and generalized simulated annealing with error correction and optimal parameter selection techniques emphasizing optimal smoothing period in residual adjustment is developed and proposed to predict datasets of container throughput at major ports.
Design/methodology/approach
The additive Holt–Winters model describes level, trend and seasonal patterns to provide smoothing values and residuals. In addition, the fitted additive Holt–Winters predicts a future smoothing value. Afterwards, the residual series is improved by using a simple moving average with the optimal period to provide a more obvious and steady series of the residuals. Subsequently, support vector regression formulates a nonlinear complex function with more obvious and steady residuals based on optimal parameters to describe the remaining pattern and predict a future residual value. The generalized simulated annealing searches for the optimal parameters of the proposed model. Finally, the future smoothing value and the future residual value are aggregated to be the future value.
Findings
The proposed model is applied to forecast two datasets of major ports in Thailand. The empirical results revealed that the proposed model outperforms all other models based on three accuracy measures for the test datasets. In addition, the proposed model is still superior to all other models with three metrics for the overall datasets of test datasets and additional unseen datasets as well. Consequently, the proposed model can be a useful tool for supporting decision-making on port management at major ports in Thailand.
Originality/value
The proposed model emphasizes smoothing residuals adjustment with optimal moving period based on error correction and optimal parameter selection techniques that is developed and proposed to predict datasets of container throughput at major ports in Thailand.
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Air pollution poses a significant global threat to both human health and environmental stability, acknowledged by the World Health Organization as a leading cause of…
Abstract
Air pollution poses a significant global threat to both human health and environmental stability, acknowledged by the World Health Organization as a leading cause of non-communicable diseases (NCDs) and a notable contributor to climate change. This chapter offers a comprehensive review of the impacts of air pollution on health, highlighting the complex interactions with genetic predispositions and epigenetic mechanisms. The consequences of air pollution to health are extensive, spanning respiratory diseases, cardiovascular disorders, adverse pregnancy outcomes, neurodevelopmental disorders, and heightened mortality rates. Genetic factors play a pivotal role in shaping individual responses to air pollution, influencing susceptibility to respiratory illnesses and the severity of symptoms. Additionally, epigenetic changes triggered by exposure to pollutants have been linked to respiratory health issues, cancer development and progression, and even transgenerational effects spanning multiple generations. As countries, including the UK, pursue ambitious targets for reducing emissions, ongoing research into the complex interplay of air pollution, genetics, and epigenetics is essential. By unravelling the underlying mechanisms and advancing preventive and therapeutic strategies, we can protect public health and promote sustainable environmental practices in the face of this pervasive global challenge.
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Guozhang Xu, Wanming Chen, Yongyuan Ma and Huanhuan Ma
Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the…
Abstract
Purpose
Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).
Design/methodology/approach
This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.
Findings
The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.
Originality/value
The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.
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Wenhai Tan, Yichen Zhang, Yuhao Song, Yanbo Ma, Chao Zhao and Youfeng Zhang
Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co3O4 is one of the ideal cathode materials for water zinc-ion…
Abstract
Purpose
Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co3O4 is one of the ideal cathode materials for water zinc-ion batteries due to their high theoretical capacity, simple synthesis, low cost and environmental friendliness. Many studies were concentrated on the synthesis, design and doping of cathodes, but the effect of process parameters on morphology and performance was rarely reported.
Design/methodology/approach
Herein, Co3O4 cathode material based on carbon cloth (Co3O4/CC) was prepared by different temperatures hydrothermal synthesis method. The temperatures of hydrothermal reaction are 100°C, 120°C, 130°C and 140°C, respectively. The influence of temperatures on the microstructures of the cathodes and electrochemical performance of zinc ion batteries were investigated by X-ray diffraction analysis, scanning electron microscopy, cyclic voltammetry curve, electrochemical charging and discharging behavior and electrochemical impedance spectroscopy test.
Findings
The results show that the Co3O4/CC material synthesized at 120°C has good performance. Co3O4/CC nanowire has a uniform distribution, regular surface and small size on carbon cloth. The zinc-ion battery has excellent rate performance and low reaction resistance. In the voltage range of 0.01–2.2 V, when the current density is 1 A/g, the specific capacity of the battery is 108.2 mAh/g for the first discharge and the specific capacity of the battery is 142.6 mAh/g after 60 charge and discharge cycles.
Originality/value
The study aims to investigate the effect of process parameters on the performance of zinc-ion batteries systematically and optimized applicable reaction temperature.
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Chengli Shu and Keeratinun Srimuang
Despite wide awareness of the importance of digital transformation (DT) for emerging market firms, we have limited understanding of the drivers, the process or the outcomes of DT…
Abstract
Purpose
Despite wide awareness of the importance of digital transformation (DT) for emerging market firms, we have limited understanding of the drivers, the process or the outcomes of DT in emerging market firms.
Design/methodology/approach
We conducted a qualitative study on 24 case companies in Thailand and embraced thematic analysis to generate our research findings.
Findings
The framework shows that the DT process in emerging market firms proceeds over three stages—market-opportunity sensing, digital technology acquisition and leading DT—which are driven by technological dynamism, business ties and institutional support. Once DT is successfully implemented, emerging market firms can improve their operational efficiency, customer relationship management, business model innovation and human resources management.
Originality/value
This study thus contributes to the DT literature by offering a three-stage model of DT and identifying important antecedents and consequences of DT, which together specify how emerging market firms transform themselves digitally.
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Lakshmi Devaraj, Thaarini S., Athish R.R. and Vallimanalan Ashokan
This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It…
Abstract
Purpose
This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.
Design/methodology/approach
This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.
Findings
This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.
Originality/value
This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.
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Xumei Lin, Peng Wang, Shiyuan Wang and Jiahui Shen
The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion…
Abstract
Purpose
The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.
Design/methodology/approach
A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.
Findings
The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.
Practical implications
Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.
Originality/value
The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.
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The fishing cat's unique hunting strategies, including ambush, detection, diving and trapping, inspired the development of a novel metaheuristic optimization algorithm named the…
Abstract
Purpose
The fishing cat's unique hunting strategies, including ambush, detection, diving and trapping, inspired the development of a novel metaheuristic optimization algorithm named the Fishing Cat Optimizer (FCO). The purpose of this paper is to introduce FCO, offering a fresh perspective on metaheuristic optimization and demonstrating its potential for solving complex problems.
Design/methodology/approach
The FCO algorithm structures the optimization process into four distinct phases. Each phase incorporates a tailored search strategy to enrich the diversity of the search population and attain an optimal balance between extensive global exploration and focused local exploitation.
Findings
To assess the efficacy of the FCO algorithm, we conducted a comparative analysis with state-of-the-art algorithms, including COA, WOA, HHO, SMA, DO and ARO, using a test suite comprising 75 benchmark functions. The findings indicate that the FCO algorithm achieved optimal results on 88% of the test functions, whereas the SMA algorithm, which ranked second, excelled on only 21% of the functions. Furthermore, FCO secured an average ranking of 1.2 across the four benchmark sets of CEC2005, CEC2017, CEC2019 and CEC2022, demonstrating its superior convergence capability and robustness compared to other comparable algorithms.
Research limitations/implications
Although the FCO algorithm performs excellently in solving single-objective optimization problems and constrained optimization problems, it also has some shortcomings and defects. First, the structure of the FCO algorithm is relatively complex and there are many parameters. The value of parameters has a certain impact on solving optimization problems. Second, the computational complexity of the FCO algorithm is relatively high. When solving high-dimensional optimization problems, it takes more time than algorithms such as GWO and WOA. Third, although the FCO algorithm performs excellently in solving multimodal functions, it rarely obtains the theoretical optimal solution when solving combinatorial optimization problems.
Practical implications
The FCO algorithm is applied to the solution process of five common engineering design optimization problems.
Originality/value
This paper innovatively proposes the FCO algorithm, which mimics the unique hunting mechanisms of fishing cats, including strategies such as lurking, perceiving, rapid diving and precise trapping. These mechanisms are abstracted into four closely connected iterative stages, corresponding to extensive and in-depth exploration, multi-dimensional fine detection, rapid and precise developmental search and localized refinement and contraction search. This enables efficient global optimization and local fine-tuning in complex environments, significantly enhancing the algorithm's adaptability and search efficiency.
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Yueyong Wang, Tianjiao Liu, Dan Luo, Zunling Du, Liang Yao and Yimin Zhang
This paper aims to investigate the influence of various laser texture parameters (diameter of pit, depth of pit and area density) on the tribological and tribo-vibration…
Abstract
Purpose
This paper aims to investigate the influence of various laser texture parameters (diameter of pit, depth of pit and area density) on the tribological and tribo-vibration characteristics of tapered roller bearings (TRBs) under full oil lubricate conditions.
Design/methodology/approach
The laser surface texture parameters include: the diameter of pit (D: 60 µm, 100 µm, 200 µm), the depth of pit (H: 5 µm, 10 µm, 20 µm) and the area density (S: 6%, 12%, 24%). The outer raceway used laser marking device to prepare many regular pits. The tribological and tribo-vibration characteristics of pitting laser textured TRBs under full oil lubrication were studied by using the MMX-1A universal wear tester machine and vibration testing equipment. Through experiment and analysis, the effects of raceway pitting textures on tribological and tribo-vibration noise performance of TRBs were summarized.
Findings
When pit-textured TRBs operate under full oil, compared with the non-textured bearings, the average coefficient of friction and wear amount are significantly reduced. When D = 100 µm, H = 10 µm, S = 12%, average coefficient of friction = 0.00195 and wear amount = 0.12 mg, they are all at their minimum values. Compared to the same condition of non-textured groups, the coefficient of friction decreases by 66.6%, and the wear amount decreases by 79.3%. The energy from time-frequency and power spectrum analyses is mainly concentrated at high frequencies, with the signal power of pitting textured groups being lower than non-textured when the Y-direction is around 3600 Hz.
Originality/value
The experimental work can provide a reference for the investigation on the pitting textured TRBs.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0357/