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A wide number of technologies are currently in store to harness the challenges posed by pandemic situations. As such diseases transmit by way of person-to-person contact or by any other means, the World Health Organization had recommended location tracking and tracing of people either infected or contacted with the patients as one of the standard operating procedures and has also outlined protocols for incident management. Government agencies use different inputs such as smartphone signals and details from the respondent to prepare the travel log of patients. Each and every event of their trace such as stay points, revisit locations and meeting points is important. More trained staffs and tools are required under the traditional system of contact tracing. At the time of the spiralling patient count, the time-bound tracing of primary and secondary contacts may not be possible, and there are chances of human errors as well. In this context, the purpose of this paper is to propose an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations.

Pandemic situations push the world into existential crises. In this context, this paper proposes an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations. By exploring the daily mobility and activities of the general public, the system identifies multiple levels of contacts with respect to an infected person and extracts semantic information by considering vital factors that can induce virus spread. It grades different geographic locations according to a measure called weightage of participation so that vulnerable locations can be easily identified. This paper gives directions on the advantages of using spatio-temporal aggregate queries for extracting general characteristics of social mobility. The system also facilitates room for the generation of various information by combing through the medical reports of the patients.

It is identified that context of movement is important; hence, the existing SemTraClus algorithm is modified by accounting for four important factors such as stay point, contact presence, stay time of primary contacts and waypoint severity. The priority level can be reconfigured according to the interest of authority. This approach reduces the overwhelming task of contact tracing. Different functionalities provided by the system are also explained. As the real data set is not available, experiments are conducted with similar data and results are shown for different types of journeys in different geographical locations. The proposed method efficiently handles computational movement and activity analysis by incorporating various relevant semantics of trajectories. The incorporation of cluster-based aggregate queries in the model do away with the computational headache of processing the entire mobility data.

As the trajectory of patients is not available, the authors have used the standard data sets for experimentation, which serve the purpose.

This paper proposes a framework infrastructure that allows the emergency response team to grab multiple information based on the tracked mobility details of a patient and facilitates room for various activities for the mitigation of pandemics such as the prediction of hotspots, identification of stay locations and suggestion of possible locations of primary and secondary contacts, creation of clusters of hotspots and identification of nearby medical assistance. The system provides an efficient way of activity analysis by computing the mobility of people and identifying features of geographical locations where people travelled. While formulating the framework, the authors have reviewed many different implementation plans and protocols and arrived at the conclusion that the core strategy followed is more or less the same. For the sake of a reference model, the Indian scenario is adopted for defining the concepts.

This paper aims to present the edge scattering dominant circuit modeling. The effect of crosstalk on gate oxide reliability (GOR), along with the mitigation using shielding technique is further studied.

An equivalent distributed Resistance Inductance Capacitance circuit of capacitively coupled interconnects of multilayer graphene nanoribbon (MLGNR) has been considered for T Simulation Program with Integrated Circuit Emphasis (TSPICE) simulations under functional and dynamic switching conditions. Complementary metal oxide semiconductor driver transistors are modeled by high performance predictive technology model that drive the distributed segment with a capacitive load of 0.001 fF, V_DD and clock frequency as 0.7 V and 0.2 GHz, respectively, at 14 nm technology node.

The results reveal that the crosstalk induced delay and noise area are dominated by the overall mean free path (MFP) (i.e. including the effect of edge roughness induced scattering), in contrary to, acoustic and optical scattering limited MFP with the temperature, width and length variations. Further, GOR, estimated in terms of average failure rate (AFR), shows that the shielding technique is an effective method to minimize the relative GOR failure rate by, 0.93e-7 and 0.7e-7, in comparison to the non-shielded case with variations in interconnect’s length and width, respectively.

Considering realistic circuit modeling for MLGNR interconnects by incorporating the edge roughness induced scattering mechanism, the outcomes exhibit more penalty in terms of crosstalk induced noise area and delay. The shielding technique is found to be an effective mitigating technique for minimizing AFR in coupled MLGNR interconnects.

This study aims to present an integrated green transport sustainability model (GTSM) to comprehensively understand and explain the multifaceted dynamics of green transport initiatives. The purpose of the study is to evaluate gaps in understanding the interactions between socio-economic and environmental goals in green transport systems using structural equation modelling (SEM) to help in drafting sustainable transportation policy for larger acceptance and true implementation by the stakeholders. The study examines different constructs that collectively influence green transport policy effectiveness (GTPE). Ultimately, the study aims to provide a robust framework for improving the effectiveness of green transport policies and regulations.

Grounded in empirical evidence, the study utilizes SEM to demonstrate the interplay between policymaking, socio-economic factors, technological consideration and environmental outcomes in green transport. The research framework is developed based on the comprehensive review of the literatures to embrace sustainability in transportation considering stakeholders perceptions. The study navigated a GTSM under socio-economic and environmental goals for road-mapping sustainability and larger acceptance of green transportation.

It is found that technological advancements in transportation are the most significant determinants of GTPE. This implies the need to develop advancements in technologies to embrace the larger acceptance of green transport. Promotion of environmentally sustainable transportation practices, socio-economic factors and use of eco-friendly transportation modes are also found as significant predictors of GTPE, which suggested that the policies aimed at up-gradation of socio-economic standards and the use of environment friendly modes of transport can help in promoting the active involvement of stakeholders to use green transportation.

The study originally investigated critical constructs to assist in preparing sustainable transportation policy for larger acceptance and true implementation by the stakeholders. The study reciprocated its originality by presenting an integrated model related with green transport sustainability dimensions based on theoretical constructs to examine the interplay between policy effectiveness, technological advancements, socio-economic factors and environmental outcomes. The study addressed the key pillars of green transportation and originally highlighted the importance of socio-economic factors and technological advancements in advancing green transport sustainability. It is recommended that the policymaker should make investments in green transport infrastructure and should design a policy for integration of green transportation with a focus on the engagement of all stakeholders for practical implementations.

This study tries to examine the effect of artificial intelligence (AI) drivers on the willingness to adopt the human capital supply chain (HCSC) in manufacturing firms (MFs) in developing countries (DCs) including Jordan, Saudi Arabia, Bahrain, Qatar and the United Arab Emirates, which are listed in the Chambers of Industry of these countries.

The quantitative methodology with a simple random sampling method was adopted using a questionnaire survey-based approach to collect data from 233 out of 1,055 participants (human resource (HR) managers and information technology (IT) senior managers) from various MFs (private and commercial), representing a 22% response rate. Covariance-based structural equation modeling (CB-SEM) was used to analyze the raw data using Amos V.25.

The results of this study showed that there are positive and statistically significant direct association effects between the reliability of use (RoU), competitive pressures (CPs) and user confidence (UC) factors on the willingness to adopt AI in HCSC in the MFs in DCs. At the same time, there is no significant effect on a firm’s infrastructure readiness (FIRs), in addition to the indirect effect of UC in the relationship between CPs and FIRs on the willingness to adopt AI in HCSC.

Such findings of this study can provide insightful implications for stakeholders and policymakers regarding the importance of using predictive AI drivers' effect on willingness to adopt the HCSC in the MFs in DCs as emerging economies. Additionally, the managers might focus on the existence of a significant positive indirect effect of UC as a mediating factor in the relationship between FIRs and willingness to adopt AI and its applications in HCSC systems and departments.

The primary goal of this study is to determine the predictors of on-demand ridesharing intention in an emerging economy. For this purpose, the study uses the theoretical underpinnings of the theory of planned behavior (TPB).

The study surveyed 347 frequent users of ridesharing services using a set of pre-validated scales. The resulting data were analyzed using covariance-based structural equation modeling (CB-SEM).

The results of SEM analysis disclosed that the significant factors contributing to ridesharing intention are awareness of environmental consequences, subjective norm, perceived behavioral control and attitude (towards ridesharing).

This empirical research provides statistically robust insights for developing marketing strategies that attract more individuals toward ridesharing services.

This research has remarkable significance as it is one of the pioneering studies that critically examine the determinants of ridesharing intention from a South Asian emerging economy. Further, the extended TPB framework proposed in this study explains 71.4% variance in ridesharing intention, which is significantly higher than existing studies, with none of them explaining more than 70% variance.

Sulphur dyes provide an inexpensive medium to dye cellulosic fibres with heavy shade depths. They offer moderate to good fastness to light and wet treatments. However, owing to the environmental hazard produced by the use of sodium sulphide, the practical implication of these dyes is steadily decreasing. Moreover, these dyes are prone to oxidation causing pronounced fading on exposure to laundering. This paper aims to present the green processing of sulphur dyes by using a biodegradable reducing agent in place of sodium sulphide to dye cotton fabrics. The study also proposes after-treatments with tannin to improve the fastness properties of the dyeings.

In this study, dyeings were produced on cotton fabric with a range of C.I. Leuco Sulphur dyes, which were reduced with sodium sulphide and glucose. Sulphur dyeings were after-treated with an environment-friendly tannin-based product (Bayprotect CL (BP)); subsequently, the after-treated samples were evaluated for colour strength, wash, light and rubbing fastness.

A novel after-treatment method was developed, which substantially improved the wash fastness of C.I. Leuco Sulphur Black 1 dyeing to ISO 105 C06/C09 washing. However, the degree of this improvement varied for the other sulphur dyes used. The surface morphology and the possible mechanisms for the improved fastness properties were also discussed.

The effect of after-treatment was significant for improving the wash fastness of sulphur black dyeings in particular, while the effect on other colours was minor. Significant improvements were observed for light and wet rub fastness for most of the dyeings, which signifies the importance of tannins as a finishing agent.

It is observed that the tannin-based product, BP, is found to provide the photoprotective effect by improving the lightfastness of the dyeings. Future research may involve the exploration of various tannins as a finishing agent to sulphur dyeings.

This novel finishing technique is found significant for improving the wash fastness of sulphur black 1 dyeings for both the reducing systems. Improvements were also observed for light and wet rub fastnesses for most of the dyeings.

Sulphur dyes are the most highly consumed colourants for cellulosic substrates owing to their reasonable cost and acceptable fastness. However, the use of noxious conventional reducing agent, sodium sulphide and impaired wash fastness against oxidative bleaching is gradually decreasing the market of these dyes. As the need for “Green” goods and services is raising public awareness, this paper aims to use a glucose-based biodegradable reducing agent in place of sodium sulphide to dye cotton fabrics with a range of commercial sulphur dyes. The study also proposes an aftertreatment method to improve the fastness properties of the dyeing.

The paper investigated the impact of a newly developed aftertreatment method on the fastness properties of dyeing. This involved the sequential application of a cationic fixing agent (Tinofix ECO) and tannin (Bayprotect CL) on the coloured fabrics and subsequent evaluation of colour strength, washing, light and rubbing fastness.

The effect of aftertreating the dyed cotton was found to significantly improve the light and wet rub fastness. The surface morphology of the dyeing remained unaffected as depicted by the absence of any finish residues.

The protective effect of the cation–tannin aftertreatments was examined with a view to providing the necessary commercial performance; however, it was established that the dry rub fastness was either reduced or remained unaffected and the wash fastness to International Organization for Standardization 105 C09 was also marginal.

This finishing technique is novel and can be found useful for manufacturing sulphur-dyed products with the improved light and wet rub fastness.

Resource-constrained assembly lines are widely found in industries that manufacture complex products. In such lines, tasks may require specific resources to be processed. Therefore, decisions on which tasks and resources will be assigned to each station must be made. When the number of available stations is fixed, the problem’s main goal becomes the minimisation of cycle time (type-II version). This paper aims to explore this variant of the problem that lacks investigation in the literature.

In this paper, the authors propose mixed-integer linear programming (MILP) models to minimise cycle time in resource-constrained assembly lines, given a limited number of stations and resources. Dedicated and alternative resource types for tasks are considered in different scenarios.

Besides, past modelling decisions and assumptions are questioned. The authors discuss how they were leading to suboptimal solutions and offer a rectification.

The proposed models and data set fulfil more practical concerns by taking into account characteristics found in real-world assembly lines.

The proposed MILP models are applied to an existing data set, results are compared against a constraint programming model, and new optimal solutions are obtained. Moreover, a data set extension is proposed due to the simplicity of the current one and instances up to 70 tasks are optimally solved.

This paper aims to study the flow and heat transfer inside a wavy enclosure filled with Cu-water nanofluid under magnetic field effect by parallel implemented meshfree approach.

The simulation has been carried out for a two-dimensional model with steady, laminar and incompressible flow of the nanofluid filled inside wavy enclosure in which one of the walls is sinusoidal such that the amplitude (A = 0.15) and number of undulations (n = 2) are fixed. A uniform magnetic field B₀ has been applied at an inclination angle γ. The governing equations for the transport phenomena have been solved numerically by implementing element-free Galerkin method (EFGM) with the sequential as well as parallel approach. The effect of various parameters, namely, nanoparticle volume fraction (φ), Rayleigh number (Ra), Hartmann number (Ha) and magnetic field inclination angle (γ) has been studied on the natural convection flow of nanofluid.

The results are obtained in terms of average Nusselt number calculated at the cold wavy wall, streamlines and isotherms. It has been observed that the increasing value of Rayleigh number results in increased heat transfer rate while the Hartmann number retards the fluid motion. On the other hand, the magnetic field inclination angle gives rise to the heat transfer rate up to its critical value. Above this value, the heat transfer rate starts to decrease.

The implementation of the magnetic field and its inclination has provided very interesting results on heat and fluid flow which can be used in the drug delivery where nanofluids are used in many physiological problems. Another important novelty of the paper is that meshfree method (EFGM) has been used here because the domain is irregular. The results have been found to be very satisfactory. In addition, parallelization of the scheme (which has not been implemented earlier in such problems) improves the computational efficiency.

The study aims to analyse the role of digitisation in accounting in enhancing the overall effectiveness of accounting functions. To achieve this, the study provides empirical evidence from the stakeholder’s perspective of digitisation of accounting, auditing, reporting and regulatory compliance procedures.

The study has applied a quantitative approach to identify the thoughts of auditors, accountants and academicians on the impact of digitalised accounting applications on accounting functions. The data was collected by administering an empirical study and a sample of 482 professionals from the accounting, auditing and academic sectors. To analyse and interpret data descriptive statistics, structured equation modelling and mediation analysis has been used.

The finding of the study signifies the relevance of digitalised accounting applications in accounting functions and reveals that there is a significant impact of digitalisation on accounting, auditing, reporting and regulatory compliance aspects of accounting functions. The outcome of the study explores that a digitalised accounting system reduces possible errors and improves the accuracy and transparency of the system.

The study highlighted the importance of developing new methods and techniques that can be used in practice. This indirectly advocates the inclusion of such concepts in accounting curricula to emphasise the need to understand the challenges and opportunities created by digitisation. Furthermore, the study will become a motivation to scholars who intend to explore different areas through which new technologies can be adopted to transform traditional accounting systems.

The contributions of the current study have implications that the adoption of digitised accounting enhances economic efficiency through a reduction in accounting costs, and enhanced accuracy that leads to the elimination of penalties and litigations for non-compliance with regulatory authorities. This indirectly impacts positively on the financial health of the business organisations and economies at large. This implication becomes greater evidential support to the organisations which are yet to plan the adoption and implementation of digital tools in their organisation for accounting functions.

Digitalisation is a relevant part of the accounting function to improve efficiency and accuracy. Since accounting and auditing practitioners struggle to control the accuracy and efficiency of transactions. Furthermore, the outcome of the study assists organisations in gaining real-time access to financial data, transforms workflows and empowers management to make timely informed sound decisions, optimise resource allocation, efficient regulatory compliance and so on.