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1 – 10 of 149Asmae El Jaouhari, Jabir Arif, Ashutosh Samadhiya, Anil Kumar and Jose Arturo Garza-Reyes
Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To…
Abstract
Purpose
Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC).
Design/methodology/approach
The study presents a conceptual environmental framework – based on 29 factors constituting four stakeholders' rectifications – that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework.
Findings
The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth.
Originality/value
This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness and increased variability.
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This study aims to investigate how institutional and organisational factors affect case management of patients with mental disorders by GPs in Italy and Spain. The paper…
Abstract
Purpose
This study aims to investigate how institutional and organisational factors affect case management of patients with mental disorders by GPs in Italy and Spain. The paper highlights the importance of improving the effectiveness of primary care to ensure easy access to mental health services, which is crucial in responding to the increasing incidence of mental disorders and preventing negative outcomes.
Design/methodology/approach
This article details a qualitative research study that examines the management of patients with mental disorders by general practitioners (GPs) in Italy and Spain, using cross-national comparison and in-depth interviews with GPs as research methods.
Findings
The study revealed that Italian self-employed GPs have more scheduling autonomy than Spanish Health Centre GPs. Both face high work pressure and resource scarcity, highlighting the need for targeted training. The COVID-19 pandemic led to a rise in phone consultations.
Originality/value
This study provides novel insights into mental health management by examining the case management of patients with mental disorders by GPs in Italy and Spain, with a focus on the impact of institutional and organisational factors. The cross-national comparison and in-depth interviews enhance the originality of the study, offering a nuanced understanding of the constraints faced by GPs in their work context. Furthermore, the comparison of the similar primary care frameworks of Italy and Spain may offer insight into their evolution.
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Md. Mehrab Hossain, Shakil Ahmed, S.M. Asif Anam, Irmatova Aziza Baxramovna, Tamanna Islam Meem, Md. Habibur Rahman Sobuz and Iffat Haq
Construction safety is a crucial aspect that has far-reaching impacts on economic development. But safety monitoring is often reliant on labor-based observations, which can be…
Abstract
Purpose
Construction safety is a crucial aspect that has far-reaching impacts on economic development. But safety monitoring is often reliant on labor-based observations, which can be prone to errors and result in numerous fatalities annually. This study aims to address this issue by proposing a cloud-building information modeling (BIM)-based framework to provide real-time safety monitoring on construction sites to enhance safety practices and reduce fatalities.
Design/methodology/approach
This system integrates an automated safety tracking mobile app to detect hazardous locations on construction sites, a cloud-based BIM system for visualization of worker tracking on a virtual construction site and a Web interface to visualize and monitor site safety.
Findings
The study’s results indicate that implementing a comprehensive automated safety monitoring approach is feasible and suitable for general indoor construction site environments. Furthermore, the assessment of an advanced safety monitoring system has been successfully implemented, indicating its potential effectiveness in enhancing safety practices in construction sites.
Practical implications
By using this system, the construction industry can prevent accidents and fatalities, promote the adoption of new technologies and methods with minimal effort and cost and improve safety outcomes and productivity. This system can reduce workers’ compensation claims, insurance costs and legal penalties, benefiting all stakeholders involved.
Originality/value
To the best of the authors’ knowledge, this study represents the first attempt in Bangladesh to develop a mobile app-based technological solution aimed at reforming construction safety culture by using BIM technology. This has the potential to change the construction sector’s attitude toward accepting new technologies and cultures through its convenient choice of equipment.
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Indrila Goswami Varma, Bhawana Chanana, Rambabu Lavuri and Jaspreet Kaur
The unprecedented pandemic of COVID-19 is not a typical crisis. This crisis has irrevocably altered human behavior, most notably consumption behavior. The uncertainty caused due…
Abstract
Purpose
The unprecedented pandemic of COVID-19 is not a typical crisis. This crisis has irrevocably altered human behavior, most notably consumption behavior. The uncertainty caused due to economic insecurity and fears of death have resulted in a paradigm shift away from consumer materialism and toward consumer spiritualism. The present study examines the effect of various dimensions of “spirituality” on consumers’ conspicuous consumption of fashion. The study employs a descriptive empirical research design to determine the impact of multiple dimensions of spirituality on the conspicuous consumption of Generation Z in India. These dimensions include General spirituality belief, Global personal spirituality and reincarnation spirituality. Additionally, the moderating effect of dispositional positive emotion on the relationships mentioned above has been investigated.
Design/methodology/approach
The data were accumulated through purposive sampling from 517 Generation Z consumers and analyzed using structural equation modeling.
Findings
Reincarnation, general personal and global personal spirituality had a direct positive impact on conspicuous consumption of fashion. Dispositional positive emotion had a positive moderation effect between the reincarnation, general personal and global personal spirituality and conspicuous consumption.
Originality/value
The study will assist fashion brands and retailers in better understanding consumer behavior and associated opportunities and threats post COVID-19. For merchants and business owners in emerging countries, this study will help them to apply new techniques for keeping customers. It is useful to evaluate a shopper’s views towards spirituality, disposition and conspicuous consumption.
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Ali Rashidi, George Lukic Woon, Miyami Dasandara, Mohsen Bazghaleh and Pooria Pasbakhsh
The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers…
Abstract
Purpose
The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.
Design/methodology/approach
The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.
Findings
This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.
Originality/value
The proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.
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Hana Begić, Mario Galić and Uroš Klanšek
Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with…
Abstract
Purpose
Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.
Design/methodology/approach
The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.
Findings
The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.
Originality/value
The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.
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Divya Shree M. and Srinivasa Rao Inabathini
This paper aims to present the simulation, fabrication and testing of a novel ultra-wide band (UWB) band-pass filters (BPFs) with better transmission and rejection characteristics…
Abstract
Purpose
This paper aims to present the simulation, fabrication and testing of a novel ultra-wide band (UWB) band-pass filters (BPFs) with better transmission and rejection characteristics on a low-loss Taconic substrate and analyze using the coupled theory of resonators for UWB range covering L, S, C and X bands for radars, global positioning system (GPS) and satellite communication applications.
Design/methodology/approach
The filter is designed with a bent coupled transmission line on the top copper layer. Defected ground structures (DGSs) like complementary split ring resonators (CSRRs), V-shaped resonators, rectangular slots and quad circle slots (positioned inwards and outwards) are etched in the ground layer of the filter. The circular orientation of V-shaped resonators adds compactness when linearly placed. By arranging the quad circle slots outwards and inwards at the corner and core of the ground plane, respectively, two filters (Filters I and II) are designed, fabricated and measured. These two filters feature a quasi-elliptic response with transmission zeros (TZs) on either side of the bandpass response, making it highly selective and reflection poles (RPs), resulting in a low-loss filter response. The transmission line model and coupled line theory are implemented to analyze the proposed filters.
Findings
Two filters by placing the quad circle slots outwards (Filter I) and inwards (Filter II) were designed, fabricated and tested. The fabricated model (Filter I) provides transmission with a maximum insertion loss of 2.65 dB from 1.5 GHz to 9.2 GHz. Four TZs and five RPs are observed in the frequency response. The lower and upper stopband band width (BW) of the measured Filter I are 1.2 GHz and 5.5 GHz of upper stopband BW with rejection level greater than 10 dB, respectively. Filter II (inward quad circle slots) operates from 1.4 GHz to 9.05 GHz with 1.65 dB maximum insertion loss inside the passband with four TZs and four RPs, which, in turn, enhances the filter characteristics in terms of selectivity, flatness and stopband. Moreover, 1 GHz BW of lower and upper stopbands are observed. Thus, the fabricated filters (Filters I and II) are therefore evaluated, and the outcomes show good agreement with the electromagnetic simulation response.
Research limitations/implications
The limitation of this work is the back radiation caused by DGS, which can be eradicated by placing the filter in the cavity and retaining its performance.
Practical implications
The proposed UWB BPFs with novel resonators find their role in the UWB range covering L, S, C and X bands for radars, GPS and satellite communication applications.
Originality/value
To the best of the authors’ knowledge, for the first time, the authors develop a compact UWB BPFs (Filters I and II) with BW greater than 7.5 GHz by combining reformed coupled lines and DGS resonators (CSRRs, V-shaped resonators [modified hairpin resonators], rectangular slots and quad circle slots [inwards and outwards]) for radars, GPS and satellite communication applications.
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Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems…
Abstract
Purpose
Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.
Design/methodology/approach
The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.
Findings
The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.
Originality/value
The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.
Details
Keywords
Aicha Gasmi, Marc Heran, Noureddine Elboughdiri, Lioua Kolsi, Djamel Ghernaout, Ahmed Hannachi and Alain Grasmick
The main purpose of this study resides essentially in the development of a new tool to quantify the biomass in the bioreactor operating under steady state conditions.
Abstract
Purpose
The main purpose of this study resides essentially in the development of a new tool to quantify the biomass in the bioreactor operating under steady state conditions.
Design/methodology/approach
Modeling is the most relevant tool for understanding the functioning of some complex processes such as biological wastewater treatment. A steady state model equation of activated sludge model 1 (ASM1) was developed, especially for autotrophic biomass (XBA) and for oxygen uptake rate (OUR). Furthermore, a respirometric measurement, under steady state and endogenous conditions, was used as a new tool for quantifying the viable biomass concentration in the bioreactor.
Findings
The developed steady state equations simplified the sensitivity analysis and allowed the autotrophic biomass (XBA) quantification. Indeed, the XBA concentration was approximately 212 mg COD/L and 454 mgCOD/L for SRT, equal to 20 and 40 d, respectively. Under the steady state condition, monitoring of endogenous OUR permitted biomass quantification in the bioreactor. Comparing XBA obtained by the steady state equation and respirometric tool indicated a percentage deviation of about 3 to 13%. Modeling bioreactor using GPS-X showed an excellent agreement between simulation and experimental measurements concerning the XBA evolution.
Originality/value
These results confirmed the importance of respirometric measurements as a simple and available tool for quantifying biomass.
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This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms;…
Abstract
Purpose
This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion.
Design/methodology/approach
ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation.
Findings
The collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second.
Originality/value
Furthermore, while ITLC and OAF require the use of GPS transponders and GPS, speed sensors and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors.
Details