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The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC).

Task scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall.

The proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding P_rc (Precision), R_ca (Recall), F ms (f measure), Acr (Accuracy)and C_t (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for P_rc, R_ca, F_ms and A_cr, takes the lowest time (C_t) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to R_t (Response Time), P_t (Process Time), A_wt (Average Waiting Time), A_tat (Average Turnaround Time), L_cy (Latency) and T_p (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for R_t, P_t, A_wt, A_tat and L_cy and also provides the highest values for T_p. So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud.

The proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.

To improve the wear resistance of the sliding boot, the wear-resistant Fe-21 Wt.% Cr-5 Wt.% B alloy is prepared, and the wear mechanism is studied under dry sliding condition.

The anti-wear Fe-21 Wt.% Cr-5 Wt.% B alloy is prepared by powder metallurgy technique. The tribological behavior of Fe-Cr-B alloy sliding against ASTM 1045 steel pin is studied at 30-60 N and 0.03-0.12 m/s using a reciprocating pin-on-disk tribometer under dry sliding condition. Meanwhile, the ASTM 5140 and 3316 steel are studied as compared samples.

The friction coefficients of tested specimens increase with the increasing normal load. However, this effect is the opposite in case of different sliding speeds. The specific wear rates increase as the sliding speed and normal load increase. The Fe-Cr-B alloy shows the best tribological properties under the dry sliding condition and the wear mechanism is mainly ploughing.

This wear-resistant Fe-21 Wt.% Cr-5 Wt.% B alloy can replace the traditional materials to process the sliding shoes and improve the service life of coal mining machine.

The purpose of this paper is to investigate how employer brand experience (EBE) impacts organizational citizenship behavior (OCB). In addition, it aims to identify the mediating role of employee engagement (EE) in relationship between EBE and OCB.

To test the research hypotheses, a web questionnaire was developed and data were collected from 426 respondents working in the Indian banking sector. Hypotheses were tested using structural equational modeling.

EBE was positively related to OCB. The predicted mediating role of EE in the relationship between EBE and OCB was also supported.

The study is confined to the banking sector only, which limits the generalization of the findings.

The results imply that firms should leverage on various dimensions of employer brand (EB) i.e. compensation, work–life balance, working environment, training and corporate social responsibility to enhance EE and OCB.

The research is among the very few to confirm the role of EBE vis-à-vis current employees especially in a collectivist society like India. The study also confirmed the mediating role of EE between EBE and OCB which have not been studied previously.

This study aims at improving combustion process to reduce emissions. Emissions such as carbon monoxide, particulate matter and unburnt hydrocarbons are a result of incomplete combustion. These emissions have useful energy but cannot be reclaimed. Hence, to enhance combustion, effect of biofuel blending on diesel combustion was investigated.

Essential oils have been found easier for blending with diesel because of simple molecular structure compared to vegetable oils. Lavender oil is an essential oil which has not yet been studied by blending with diesel. The major constituents of lavender oil are linalyl acetate (cetane number improver) and linalool (nitrogen oxides reduction). A single-cylinder, four-stroke diesel engine was run by blending diesel with lavender oil (Lavandula angustifolia oil [LAO]) in varying proportions, 5%, 10% and 15% by volume.

Higher heat release rate (HRR) was observed using lavender oil blends compared to pure diesel. Compared to diesel, an increase in brake-specific fuel consumption using blends was observed. LAO15 has the lowest CO emissions at all loading conditions, 29.3% less at 100% load compared to diesel. LAO5 and LAO15 have 6.9% less HC emissions at 100% load condition compared to diesel. LAO15 has only 1.3% higher NO_x emissions compared to diesel at 100% load condition. LAO5 has the lowest smoke content at all loading conditions.

Lavender oil was used directly without any processing. Tested on single-cylinder engine.

To the best of the author’s knowledge, currently, there is no published work on lavender oil–diesel combination. Lavender oil can provide a simple renewable solution for diesel additives with potential up to 15% blending.

In an era of global competition, firms need to collaborate for long-term benefits. Researchers have investigated the linkages between supply chain collaboration (SCC), customer satisfaction and loyalty. However, little attention has been paid to these linkages in the home electronics sector. This study attempts to investigate the impacts of SCC on firms' competitive advantage, customer satisfaction and customer loyalty in the home electronics sector of Vietnam.

Besides aggregation of literature review, the authors conducted an experimental study with a sample of 300 customers who bought household electronic appliances in the first six months of 2021 in Hanoi city, Vietnam. In this study, structural equation modelling (SEM) was used to analyse the hypotheses.

The findings indicate that SCC has a positive impact on competitive advantage, increasing customer satisfaction and loyalty in the home electronics sector. Evidence also revealed that competitive advantage can be enhanced through information sharing, decision synchronisation and incentive alignment.

This study can be applied to foster a more effective collaboration approach amongst supply chain members in the household electronic appliances sector, which, in turn, will increase competitiveness, customer satisfaction and loyalty.

The current study aimed to examine the moderating role of perceived supervisor support in the nexus between employee empowerment and organizational commitment in the Ghanaian hospitality industry.

A quantitative research design was adopted, and data were collected from 274 frontline workers from 4-star and 5-star hotels at two different waves within a 7-month interval. The data received were analyzed using descriptive and inferential statistics with the aid of Statistical Package for Social Sciences (SPSS V. 23.0) and SmartPLS (V.4.0), respectively.

As hypothesized in the study, employee empowerment was significantly related to organizational commitment. Furthermore, the results revealed that perceived supervisor support moderated the nexus between employee empowerment and affective and continuance commitment but did not moderate the nexus between employee empowerment and normative commitment.

Arguably, support from supervisors has been theoretically identified as a key construct in enhancing subordinates' commitment to an organization. However, less is known in the literature about the moderating role of perceived supervisory support in the nexus between employee empowerment and organizational commitment, especially in the Ghanaian hospitality industry.

The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India.

The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India.

The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India.

The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue.

Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.

Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.

The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.

This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

This article is based on a study that was commissioned by the Youth Justice Board for England and Wales. We report on the learning profiles and education needs of a cohort of young offenders who were recruited for the study. The research was a national cross‐sectional survey of 301 young offenders who were resident in custodial settings or attending youth offending teams in the community. The young people were assessed using the WASI and the WORD measures to obtain psychometric information (IQ scores and reading/reading comprehension ages). One in five (20%) young people met the ICD‐10 criteria for mental retardation (IQ<70), while problems with reading (52%) and reading comprehension (61%) were common. Verbal IQ scores were found to be significantly lower than performance IQ scores, particularly in male offenders. It is clear from these results that a large proportion of juvenile offenders have a learning disability, as characterised by an IQ<70 and significantly low reading and reading comprehension ages. The underlying aetiology of this association is less clear and may be a consequence of both an increased prevalence of neurocognitive deficits and the impact of poor schooling. There is some evidence that developmental pathways may be different for boys compared with girls.