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1 – 10 of 843Shuai Zhan and Zhilan Wan
The credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers…
Abstract
Purpose
The credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.
Design/methodology/approach
Using the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.
Findings
The results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.
Originality/value
Credit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.
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Sadia Samar Ali, Shahbaz Khan, Nosheen Fatma, Cenap Ozel and Aftab Hussain
Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this…
Abstract
Purpose
Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.
Design/methodology/approach
The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.
Findings
This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.
Research limitations/implications
This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.
Practical implications
By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.
Originality/value
This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.
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Hei-Chia Wang, Army Justitia and Ching-Wen Wang
The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests'…
Abstract
Purpose
The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.
Design/methodology/approach
We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.
Findings
Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.
Research limitation/implications
This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.
Originality/value
This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.
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The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.
Abstract
Purpose
The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.
Design/methodology/approach
This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.
Findings
Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.
Originality/value
The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.
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Hoda Sabry Sabry Othman, Salwa H. El-Sabbagh and Galal A. Nawwar
This study aims to investigate the behavior of the green biomass-derived copper (lignin/silica/fatty acids) complex, copper lignin/silica/fatty acids (Cu-LSF) complex, when…
Abstract
Purpose
This study aims to investigate the behavior of the green biomass-derived copper (lignin/silica/fatty acids) complex, copper lignin/silica/fatty acids (Cu-LSF) complex, when incorporated into the nonpolar ethylene propylene diene (EPDFM) rubber matrix, focusing on its reinforcing and antioxidant effect on the resulting EPDM composites.
Design/methodology/approach
The structure of the prepared EPDM composites was confirmed by Fourier-transform infrared spectroscopy, and the dispersion of the additive fillers and antioxidants in the EPDM matrix was investigated using scanning electron microscopy. Also, the rheometric characteristics, mechanical properties, swelling behavior and thermal gravimetric analysis of all the prepared EPDM composites were explored as well.
Findings
Results revealed that the Cu-LSF complex dispersed well in the nonpolar EPDM rubber matrix, in thepresence of coupling system, with enhanced Cu-LSF-rubber interactions and increased cross-linking density, which reflected on the improved rheological and mechanical properties of the resulting EPDM composites. From the various investigations performed in the current study, the authors can suggest 7–11 phr is the optimal effective concentration of Cu-LSF complex loading. Interestingly, EPDM composites containing Cu-LSF complex showed better antiaging performance, thermal stability and fluid resistance, when compared with those containing the commercial antioxidants (2,2,4-trimethyl-1,2-dihydroquinoline and N-isopropyl-N’-phenyl-p-phenylenediamine). These findings are in good agreement with our previous study on polar nitrile butadiene rubber.
Originality/value
The current study suggests the green biomass-derived Cu-LSF complex to be a promising low-cost and environmentally safe alternative filler and antioxidant to the hazardous commercial ones.
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Payman Sahbah Ahmed, Ava A.K. Mohammed and Fakhir Aziz Rasul Rozhbiany
The purpose of this study is to get benefits from manufacturing harmful wastes is by using them as a reinforcement with epoxy matrix composite materials to improve the damping…
Abstract
Purpose
The purpose of this study is to get benefits from manufacturing harmful wastes is by using them as a reinforcement with epoxy matrix composite materials to improve the damping characteristics in applications such as machine bases, rockets, satellites, missiles, navigation equipment and aircraft as large structures, and electronics as such small structures. Vibration causes damaging strains in these components.
Design/methodology/approach
By adding machining chips with weight percentages of 5, 10, 15 and 20 Wt.%, with three different chip lengths added for each percentage (0.6, 0.8 and 1.18 mm), the three-point bending and damping characteristics tests are utilized to examine how manufacturing waste impacts the mechanical properties. Following that, the optimal lengths and the chip-to-epoxy ratio are determined. The chip dispersion and homogeneity are assessed using a field emission scanning electron microscope.
Findings
Waste copper alloys can be used to enhance the vibration-dampening properties of epoxy resin. The interface and bonding between the resin and the chip are crucial for enhancing the damping capabilities of epoxy. Controlling the flexural modulus by altering the chip size and quantity can change the damping characteristics because the two variables are inversely related. The critical chip size is 0.8 mm, below which smaller chips cannot evenly transfer, and disperse the vibration force to the epoxy matrix and larger chips may shatter and fracture.
Originality/value
The main source of problems in machine tools, aircraft and vehicle manufacturing is vibrations generated in the structures. These components suffer harmful strains due to vibration. Damping can be added to these structures to get over these problems. The distribution of energy stored as a result of oscillatory mobility is known as damping. To optimize the serving lifetime of a dynamic suit, this is one of the most important design elements. The use of composites in construction is a modern method of improving a structure's damping capacity. Additionally, it has been demonstrated that composites offer better stiffness, strength, fatigue resistance and corrosion resistance. This research aims to reduce the vibration effect by using copper alloy wastes as dampers.
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Geming Zhang, Lin Yang and Wenxiang Jiang
The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is…
Abstract
Purpose
The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.
Design/methodology/approach
The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.
Findings
The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.
Originality/value
The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.
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Italo Cesidio Fantozzi, Sebastiano Di Luozzo and Massimiliano Maria Schiraldi
The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM)…
Abstract
Purpose
The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.
Design/methodology/approach
The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.
Findings
As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.
Research limitations/implications
The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.
Practical implications
The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.
Originality/value
The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.
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Cemalettin Akdoğan, Tolga Özer and Yüksel Oğuz
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of…
Abstract
Purpose
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).
Design/methodology/approach
Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.
Findings
In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.
Originality/value
An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.
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