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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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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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/
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Aline Luiza Brusco Pletsch, Elisete Aparecida Ferreira Stenger and Simone Sehnem
This research centres on how digital technologies are revolutionizing agriculture, affording farmers improved access to information, crop forecasts, markets and innovations, in…
Abstract
Purpose
This research centres on how digital technologies are revolutionizing agriculture, affording farmers improved access to information, crop forecasts, markets and innovations, in addition to facilitating training and other benefits. The purpose of this investigation is to examine how technologies used in the Agro 4.0 industry facilitate agricultural and livestock practices.
Design/methodology/approach
A thorough examination of the existing literature on this subject was conducted, encompassing articles published between 2013 and 2023 that have been catalogued in Scopus and the Web of Science.
Findings
The analysis of these studies reveals the growing significance of innovations such as artificial intelligence, blockchain, precision agriculture, the Internet of Things (IoT) and robotics in the transformation of agriculture and livestock farming. The implementation of these technologies is occurring across various sectors of agricultural production, including livestock production, shrimp farming, vertical farming, supply chains, irrigation, grain inspection, the dairy sector and smart farms. The impacts identified include improvements in productivity, intelligent analysis systems, operational efficiency, transparency and reliability, management per square metre, optimization, environmental sustainability, animal welfare, enhancement of food security and risk reduction.
Originality/value
Therefore, the contributions of technologies are associated with data-based decision-making, digital skills to maximize agribusiness performance, digital transformation in the field and competitiveness in the global market.
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Cleopatra Veloutsou and Estefania Ballester
The extensive brand associations research lacks organisation when it comes to the used information cues. This paper aims to systematically map and categorise the brand knowledge…
Abstract
Purpose
The extensive brand associations research lacks organisation when it comes to the used information cues. This paper aims to systematically map and categorise the brand knowledge associations’ components and develop a typology applicable to any brand.
Design/methodology/approach
Using the restaurant and hotel industries in four different European cultural clusters as contexts, this work uses well-established systematic qualitative analysis approaches to categorise, code and model pictorial content in two studies. A four-stage sampling process identified Instagram brand-posted signals (photos), 243 from 26 restaurants in Madrid, Paris and Rome for study one and 390 from 29 hotels in Moscow, Berlin and Stockholm for study two. Adhering to relevant guidelines, the manual coding procedures progressed from 246 for restaurants and 231 for hotels initially generated free information coding inductive codes to a theory-informed categorisation. Quantitative analysis complemented the qualitative analysis, revealing the information cues relative utilisation.
Findings
For both studies, the analysis produced a typology consisting of two high-level and five lower-level brand knowledge association categories, namely: (a) brand characteristics consisting of the brand as a symbol, the brand as a product and the brand as a person, and (b) brand imagery consisting of user imagery and experience imagery. The five lower-level categories comprise of sub-categories and dimensions, providing a more comprehensive understanding of the brand associations conceptual structure relevant to brands operating in any industry.
Research limitations/implications
Researchers can use this typology to holistically encapsulate brand associations or design projects aiming to deepen brand knowledge association aspects/dimensions understanding.
Practical implications
Managers can use this typology to portray brands. Some of the identified lower-level categories and/or sub-categories and dimensions are likely to need customisation to fit specific contexts.
Originality/value
The suggested categorisation offers a solid, comprehensive framework for effectively categorising and coding brand knowledge associations and proposes a new theory in the form of a typology.
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Bingzi Jin, Xiaojie Xu and Yun Zhang
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate…
Abstract
Purpose
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.
Design/methodology/approach
The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.
Findings
A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.
Originality/value
The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.