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Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

Performance of the structure depends on design, construction, environment, utilization and reliability aspects. Other factors can be controlled by adopting proper design and construction techniques, but the environmental factors are difficult to control. Hence, mostly in practice, the environmental factors are not considered in the analysis and design appropriately; however, their impact on the performance of the structures is significant along with the design life. It is in this light that this paper aims to perform the time-dependent performance analysis of reinforced concrete structures majorly considering environmental factors.

To achieve the intended objective, a simply supported reinforced concrete beam was designed and detailed as per the Euro Code (EC2). The time-dependent design parameters, corrosion parameters, creep and shrinkage were identified through thorough literature review. The common empirical equations were modified to consider the identified parameters, and finally, the time-dependent performance of reinforced concrete beam was performed.

Findings indicate that attention has to be paid to appropriate consideration of the environmental effect on reinforced concrete structures. In that, the time-dependent performance of reinforced concrete beam significantly decreases with time due to corrosion of reinforcement steel, creep and shrinkage.

However, the Euro code, Ethiopian code and Indian code threat the exposure condition of reinforced concrete by providing corresponding concrete cover that retards the corrosion initiation time but does not eliminate environmental effects. The results of this study clearly indicate that the capacity of reinforced concrete structure degrades with time due to corrosion and creep, whereas the action on the structure due to shrinkage increases. Therefore, appropriate remedial measures have to be taken to control the defects of structures due to the environmental factors to overcome the early failure of the structure.

Solar energy applications are limited because of its intermittent and discontinuous availability with respect to time. Hence, solar energy thermal conversion systems need integration with thermal storage units (TSUs) to use solar energy in off sunshine hours. This paper aims to perform thermal analysis of a solar air heater (SAH) integrated with a phase change material (PCM)-based TSU to supply hot air during night period.

An experimental setup with TSU as main component was prepared with SAH at its upward side, food chamber at its downward side as subcomponents. In TSU, paraffin wax was used as thermal energy storage material. Mass flow rate of air considered as an input parameter in the experiment. Two different absorber plates, namely, plane and ribbed absorber plates were used for the experimentation. Each day for a fixed mass flow of air, observations were made during charging and discharging of PCM.

Nusselt number and convection heat transfer coefficients were analytically calculated by considering flow through TSU as external flow over bank of tubes in a rectangular duct. A temperature drop of around 7-8°C during charging of PCM and temperature rise of around 4-5°C during discharging of PCM was observed from the experimental results. The average practical efficiency of TSU with ribbed absorber plate SAH during charging and discharging of PCM was 22 and 6 per cent, respectively, higher than that of TSU with plane absorber plate SAH.

There are no limitations for research on SAH integrated with TSU. Different PCM including paraffin wax, Glauber’s salt, salt hydrates and water are used for thermal storage. Only limitation is lower efficiency of SAH integrated with TSU because of lower heat transfer coefficients with air as working medium. If it can improve heat transfer coefficients of air then heat transfer rates with these units will be higher.

There are no practical limitations for research on SAH integrated with TSU. Sophisticated instrumentation is needed to measure flow rates, temperatures and pressure variations of air.

In poultry farms during night, chicks cannot survive at cold climatic conditions. Hence, hot air should be supplied to poultry farms whenever the atmospheric temperature drops. It is proposed that, in combination with TSUs, heat produced by SAH is stored in day time in the form of either sensible or latent heat and is retrieved to provide hot air in the night times. This will reduce total operating costs in poultry farms.

Conventionally, people are producing hot air by combusting coal in poultry forms. This cost around Rs. 75,000 per month for a batch of 225 to 250 chicks in a poultry form. Hot air could be produced economically during off sunshine hours from SAH integrated with TSU compared to the conventional method of coal burning. Present experimental investigations conducted to fill the literature gap in this area of research and to design a SAH integrated with TSU to produce hot air for poultry forms.

This study aims on a broad review of Concrete's Rheological Properties. The Concrete is a commonly used engineering material because of its exquisite mechanical interpretation, but the addition of constituent amounts has significant effects on the concrete’s fresh properties. The workability of the concrete mixture is a short-term property, but it is anticipated to affect the concrete’s long-term property.

In this review, the concrete and workability definition; concrete’s rheology models like Bingham model, thixotropy model, H-B model and modified Bingham model; obtained rheological parameters of concrete; the effect of constituent’s rheological properties, which includes cement and aggregates; and the concrete’s rheological properties such as consistency, mobility, compatibility, workability and stability were studied in detail.

Also, this review study has detailed the constituents and concrete’s rheological properties effects. Moreover, it exhibits the relationship between yield stress and plastic viscosity in concrete’s rheological behavior. Hence, several methods have been reviewed, and performance has been noted. In that, the abrasion resistance concrete has attained the maximum compressive strength of 73.6 Mpa; the thixotropy approach has gained the lowest plastic viscosity at 22 Pa.s; and the model coaxial cylinder has recorded the lowest stress rate at 8 Pa.

This paper especially describes the possible strategies to constrain improper prediction of concrete’s rheological properties that make the workability and rheological behavior prediction simpler and more accurate. From this, future guidelines can afford for prediction of concrete rheological behavior by implementing novel enhancing numerical techniques and exploring the finest process to evaluate the workability.

Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training.

The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Gated communities, residential enclaves that offer upscale housing and a variety of recreational and communal facilities within a walled area with controlled entrances, are proliferating in many of India's large metropolitan cities. In this paper, we analyze the images of place and identity that are evoked in online advertisements for gated communities in the city of Bangalore in southern India. Since the 1990s, Bangalore has become known as India's premier information technology (IT) hub and a magnet for multinational corporations and high-skill personnel. The latter include Non-Resident Indians (NRIs) who lived and worked abroad for several years and have returned to partake of new opportunities offered in the country. We explore the intersection of notions of identity, home and community in a globalised world through an examination of the graphic and textual images encoded in the advertisements of thirteen prominent developers in Bangalore whose upscale gated developments cater to NRIs. The advertisements depict high-end gated communities as places of luxury, exclusiveness, high security and convenience which also offer a range of recreational facilities for individuals and families. Additionally, those who live in the gated enclaves are portrayed as persons of distinction and class who are global and cosmopolitan in their outlook and identity.

Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.

In additive manufacturing (AM) process, the physical properties of the products made by fractal toolpaths are better as compared to those made by conventional toolpaths. Also, it is desirable to minimize the number of tool retractions. The purpose of this study is to describe three different methods to generate fractal-based computer numerical control (CNC) toolpath for area filling of a closed curve with minimum or zero tool retractions.

This work describes three different methods to generate fractal-based CNC toolpath for area filling of a closed curve with minimum or zero tool retractions. In the first method, a large fractal square is placed over the outer boundary and then rest of the unwanted curve is trimmed out. To reduce the number of retractions, ends of the trimmed toolpath are connected in such a way that overlapping within the existing toolpath is avoided. In the second method, the trimming of the fractal is similar to the first method but the ends of trimmed toolpath are connected such that the overlapping is found at the boundaries only. The toolpath in the third method is a combination of fractal and zigzag curves. This toolpath is capable of filling a given connected area in a single pass without any tool retraction and toolpath overlap within a tolerance value equal to stepover of the toolpath.

The generated toolpath has several applications in AM and constant Z-height surface finishing. Experiments have been performed to verify the toolpath by depositing material by hybrid layered manufacturing process.

Third toolpath method is suitable for the hybrid layered manufacturing process only because the toolpath overlapping tolerance may not be enough for other AM processes.

Development of a CNC toolpath for AM specifically hybrid layered manufacturing which can completely fill any arbitrary connected area in single pass while maintaining a constant stepover.