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The present study compares the use of web citations as references in leading scholarly journals in Library and Information Science (LIS) and Communication and Media Studies (CMS). A total of 20 journals (each 10 from LIS and CMS) were selected based on the publishing history and reputation published between 2008 and 2017.

The present study compares the use of web citations as references in leading scholarly journals in LIS and CMS. A PHP script was used to crawl the Uniform Resource Locators (URLs) collected from the reference list. A total of 12,251 articles were downloaded and 555,428 references were extracted. Of the 555,428 references, 102,718 web citations were checked for their accessibility.

The research findings indicated that 76.90% URLs from LIS journals and 84.32% URLs from Communication and Media Studies journals were accessible and others were rotten. The majority of errors were due to HTTP 404 error code (not found) in both the disciplines. The study also tried to retrieve the rotten URLs through Time Travel, which revived 61.76% rotten URLs in LIS journal articles and 65.46% in CMS journal articles.

This is an in-depth and comprehensive comparative study on the availability of web citations in LIS and CMS journals articles spanning a period of 10 years. The findings of the study will be helpful to authors, publishers, and editorial staff to ensure that web citations will be accessible in the future.

The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

As the influence of online reviews has changed how consumers make decisions, this study aims to understand how clout mediates service failure categories leading to “deinfluencing behaviour” through NeWom.

This paper introduces a two-step methodology for extracting “deinfluencing behaviour” from online reviews. First, a qualitative analysis using manual and text analysis software, and, second, a quantitative analysis through Exploratory Factor Analysis (EFA) and the Hayes Macro Process analysis.

The study identified 11 types of service failures grouped into five categories. Service quality was the most important factor, followed by staff attitude, food service and esthetics. Clout proved to be a valid mediator.

This research proposes a novel approach for analyzing big data serving as a blueprint for future studies. The results contribute to the academic discourse on consumer revenge while strengthening academic hospitality literature on social media dynamics.

India’s growing elderly population necessitates ensuring indoor thermal comfort because of their vulnerability to temperature-related illnesses and reduced capacity to regulate body temperature. Currently, thermal sensation (TS) assessment scales, designed for those between 20 and 60 years of age, may not accurately capture the preferences of elderly adults. To address the gap, this study aims to identify appropriate scaling methods to help the elderly understand the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE-55) seven-point TS scale clearly.

Four scaling methods – color, emoji, landscape images and regional images scales – identified from literature were analyzed using quantitative approaches. The differences between two age groups (<60 years and = 60 years) were examined using frequency distribution differences and independent sample t-test methods.

Results indicated that both younger/middle-aged (<60 years) and elderly individuals (= 60 years) were adept at identifying color and regional images scale, while emoji and landscape images scale posed challenges for the elderly. Furthermore, a tailored questionnaire instrument was developed to enhance the comprehension of TS questions for the elderly. One sample t-test results indicated that the proposed questionnaire instrument is a better fit to support ASHRAE-55 seven-point TS scale, making it particularly effective for the elderly population.

This research presents a novel, tailored questionnaire instrument that significantly enhances the elderly population’s comprehension of TS questions, thereby improving the accuracy of thermal comfort assessments and contributing to the creation of better indoor thermal environments for the elderly people.

This study aims to evaluate the suitability of Ferrock as a green construction material by analysing its engineering properties, environmental impact, economic viability and adoption challenges. It also aims to bridge knowledge gaps and provide guidance for integrating Ferrock into mainstream construction to support the decarbonisation of the built environment.

It presents a systematic and holistic review of existing literature on Ferrock, comprehensively analysing its mechanical properties, environmental and socio-economic impact and adoption challenges. The approach includes evaluating both quantitative and qualitative data to assess Ferrock’s potential in the construction sector.

Key findings highlight Ferrock’s superior mechanical properties, such as higher compressive and tensile strength, and enhanced durability compared to traditional Portland cement. Ferrock offers significant environmental benefits by capturing more CO₂ during curing than it emits, contributing to carbon sequestration and reducing energy consumption due to the absence of high-temperature processing. However, the material faces economic and technical challenges, including higher initial costs, scalability issues, lack of industry standards and variability in production quality.

This review provides a comprehensive and up-to-date analysis of Ferrock. Despite being discussed for a decade, Ferrock research has been overlooked, with existing studies often limited and published in poor-quality sources. By synthesising current research and identifying future study areas, the paper enhances understanding of Ferrock’s potential benefits and challenges. The originality lies in the holistic evaluation of Ferrock’s properties and its implications for the construction industry, offering insights that could drive collaborative research and policy support to facilitate its integration into mainstream use.

The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.

For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.

Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.

The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.

COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.

This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.

From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

While Six Sigma metrics have been studied by researchers in detail for normal distribution-based data, in this paper, we have attempted to study the Six Sigma metrics for two-parameter Weibull distribution that is useful in many life test data analyses.

In the theory of Six Sigma, most of the processes are assumed normal and Six Sigma metrics are determined for such a process of interest. In reliability studies non-normal distributions are more appropriate for life tests. In this paper, a theoretical procedure is developed for determining Six Sigma metrics when the underlying process follows two-parameter Weibull distribution. Numerical evaluations are also considered to study the proposed method.

In this paper, by matching the probabilities under different normal process-based sigma quality levels (SQLs), we first determined the Six Sigma specification limits (Lower and Upper Six Sigma Limits- LSSL and USSL) for the two-parameter Weibull distribution by setting different values for the shape parameter and the scaling parameter. Then, the lower SQL (LSQL) and upper SQL (USQL) values are obtained for the Weibull distribution with centered and shifted cases. We presented numerical results for Six Sigma metrics of Weibull distribution with different parameter settings. We also simulated a set of 1,000 values from this Weibull distribution for both centered and shifted cases to evaluate the Six Sigma performance metrics. It is found that the SQLs under two-parameter Weibull distribution are slightly lesser than those when the process is assumed normal.

The theoretical approach proposed for determining Six Sigma metrics for Weibull distribution is new to the Six Sigma Quality practitioners who commonly deal with normal process or normal approximation to non-normal processes. The procedure developed here is, in fact, used to first determine LSSL and USSL followed by which LSQL and USQL are obtained. This in turn has helped to compute the Six Sigma metrics such as defects per million opportunities (DPMOs) and the parts that are extremely good per million opportunities (EGPMOs) under two-parameter Weibull distribution for lower-the-better (LTB) and higher-the-better (HTB) quality characteristics. We believe that this approach is quite new to the practitioners, and it is not only useful to the practitioners but will also serve to motivate the researchers to do more work in this field of research.

The purpose of this paper is to propose radiating system by avoiding electromagnetic interference in unwanted directions and to radiate the energy in the required direction with an optimization technique.

Practically, multiple, incompatible variables require concurrent boost on a synthesis of systematic antenna assemblage. The authors have worked out the main statistic penalty function to ensure all the restrictions. Here, MBPSO (Modified Binary Particle Swarm Optimization) is developed and introduced thin planar synthesis restriction. The sigmoid function is used to update the particle position. Different analytical demonstrations have been carried out, and the exhibited methods are predominant than the algorithms.

A 20 × 10 planar antenna array is synthesized using modified BPSO. The authors have suppressed the PSLL in two principal planes and as well as in the entire f plane. Numerical results state that MBPSO outperforms the other binary BPSO, BCSO, ACO, RGA, GA optimization techniques. MBPSO achieved a −51.84 dB PSLL level, whereas BPSO achieved −48.57 dB with the same 50% thinning.

Planar array antenna formation is one of the most complex syntheses because the array gets filled with more antenna elements. The machine-like complication and implementation of such an antenna arrangement with a broad opening would be expensive. It is not easy to control the required radiation patterns shape by using a uniform feeding network. To get better flexibility for sustaining the sidelobe levelheaded along with consistent amplitude distribution. So as far as prominence has been given to the evolutionary algorithm, find an ideal solution for objective array combinational problems.

The purpose of this article is to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the analysis of variance (ANOVA) application after the preliminary test on the model specification.

A new approach is proposed to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the ANOVA application. First, we determine this relationship from the general perspective of Six Sigma methodology under the normality assumption. Then, the approach is extended to a balanced two-stage nested design with a random effects model in which a preliminary test is used to fix the main test statistic.

The features of mean-shifted and inflated (but centred) processes with the same specification limits from the perspective of Six Sigma are studied. The shift and inflation coefficients are derived for the two-stage balanced ANOVA model. We obtained good predictions for the process shift, given the inflation coefficient, which has been demonstrated using numerical results and applied to case studies. It is understood that the proposed method may be used as a tool to obtain an efficient variance estimator under mean shift.

In this work, as a new research approach, we studied the link between mean shift and inflation coefficients when the underlying null hypothesis is rejected in the ANOVA. Derivations for these coefficients are presented. The results when the null hypothesis is accepted are also studied. This needs the help of preliminary tests to decide on the model assumptions, and hence the researchers are expected to be familiar with the application of preliminary tests.

After studying the proposed approach with extensive numerical results, we have provided two practical examples that demonstrate the significance of the approach for real-time practitioners. The practitioners are expected to take additional care before deciding on the model assumptions by applying preliminary tests.

The proposed approach is original in the sense that there have been no similar approaches existing in the literature that combine Six Sigma and preliminary tests in ANOVA applications.