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Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

Hospital solid waste may cause serious health hazards and impair the quality of life of the community through transmission of diseases and injury if not properly managed. Therefore, the purpose of this paper is to examine solid waste management practice (SWMP) in Obafemi Awolowo University Teaching Hospital Complex.

Primary data were collected for this study through interview and questionnaire administration. Systematic sampling technique was used to select 60 students, 13 staffs, 43 cleaners, 8 contractors, and a management staff for questionnaire administration. Data obtained were analyzed using frequency distribution, pictorial analysis, and factor analysis.

Findings established that solid wastes components generated in the hospital were not segregated in line with the directive of the World Health Organization. The study further established that the factors influencing SWMP accounted for 79.9 percent variance in the following proportion: available storage and collection facilities (30.94 percent), number of patients’ factors (17.86 percent), transportation factor (15.39 percent), human and material resource factors (8.33 percent), and disposal (7.36 percent).

The study therefore concludes that the effectiveness of SWMPs depends on the facilities and equipment, human resources capacity, and frequency of waste collection and disposal.

This study aims to investigate the strategic priorities of green building projects and analyze energy consumption alternatives in green residence projects using two innovative methods.

This study uses two methods, decision-making trial and evaluation laboratory (DEMATEL) to measure strategic priorities and golden-cut quantum spherical fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) to analyze energy consumption alternatives.

The study reveals that sustainability and atmosphere are the most significant factors in determining the priorities of green residence projects, whereas innovation has a limited impact on addressing environmental challenges in the building sector. The ranking of energy use alternatives shows that sustainability issues and atmosphere quality of space heating and cooking are the top priorities, whereas other factors like white goods, water heating, lighting and space cooling are ranked lower.

This paper offers a significant contribution to the understanding of green buildings by introducing innovative methodological approaches. Theoretically, it uses the DEMATEL to enhance traditional analytical frameworks, marking a novel effort in understanding green residence projects. In addition, the golden-cut quantum spherical fuzzy TOPSIS method is introduced, offering a comprehensive decision-making framework for green projects, considering factors like energy consumption and economic feasibility. This combination of methodologies provides a holistic evaluation, emphasizing sustainability in green building construction. This study reveals untapped potential for environmental sustainability and energy efficiency, enriching the existing knowledge base.

The purpose of this paper is to investigate the electrochemical treatment of textile dye wastewater in the presence of NaCl electrolyte by using aluminium electrodes.

The electrochemical treatment of textile dye wastewater was optimized using response surface methodology (RSM). RSM‐based D‐optimal design was employed to construct statistical models relating turbidity and designed effective parameters known as current density, electrolyte concentration and electrolysis time. The experimental plan consists of a three‐factor (three numerical) matrix.

The results show that the current density has significant effect on the reduction of turbidity. Besides, electrolysis time is the most influential factor on the turbidity. In order to enhance the electrochemical treatment performance, no coagulant addition or further physicochemical processes were employed.

Industrial certain textile dye wastewater in Turkey is used to determine optimal values.

The purpose of the study is to explore the bibliometric features of scientific publications in the area of domestic violence (an important global social problem) for the period 2011–2020; thus, enabling the scientific community to understand the research progress on the topic.

The data of the scientific publications were retrieved from the Scopus database. The keyword “Domestic Violence” was applied as a topic term to search articles published during the study period. The statistical analysis was conducted by using the RStudio and VOSviewer tools along with MS Excel.

A total of 11,899 scientific works were published during the study period. The annual percentage growth of publication in the area of domestic violence in the study period is 74.97%. The majority of the papers (74.72%) were published as journal articles. USA and UK have the highest numbers of scientific publications and citations. Together they account for more than half of the publications (58.38%) and citations (67 per pub). However, the highest average citation per publication has been recorded by Switzerland (34 per pub). Feder G. (UK) is the most cited author, and the Journal of Interpersonal Violence (Sage publications) has the highest number of publications, citations and source title impact ratio in the domain of domestic violence.

One can find numerous bibliometric studies in the domain of natural science, but not many studies have been conducted in the field of social sciences. In this light, the scientific community can gain from the bibliometric information regarding the scientific publications in the area of domestic violence, which is one of the dominant areas of study in social sciences.

The study will provide significant information on the trends of academic publications in the study area. It is one of the most comprehensive studies on domestic violence, which will aid the potential researchers in identifying the most prominent contributions, county-wise research distribution, author and journal productivity and other related indicators. The findings of the study will be also helpful to library authorities in reviewing and updating collection development policy.

This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

Human gait analysis is based on a significant part of the musculoskeletal, nervous and respiratory systems. Gait analysis is widely adopted to help patients increase community involvement and independent living.

This paper presents a system for the classification of abnormal human gaits using a Markerless 3D Motion Capture device. This study aims at examining and estimating the spatiotemporal and kinematic parameters obtained by 3D gait analysis in diverse groups of gait-impaired subjects and compares the parameters with that of healthy participants to interpret the gait patterns.

The classification is based on mathematical models that distinguish between normal and abnormal gait patterns depending on the deviations in the gait parameters. The difference between the gait measures of the control and each disease group was examined using 95% limits of agreement by the Bland and Altman method. The scatter plots demonstrated gait variability in Parkinsonian and ataxia gait and knee joint angle variation in hemiplegic gait when compared with those of healthy controls. To prove the validity of the Kinect camera, significant correlations were detected between Kinect- and inertial-based gait tests.

The various techniques used for gait assessments are often high in price and have existing limitations like the hindrance of components. The results suggest that the Kinect-based gait assessment techniques can be used as a low-cost, less-intrusive alternative to expensive infrastructure gait lab tests in the clinical environment.

In the present study, cotton fabrics are dyed with extracts obtained from the rinds of the pomegranate fruit, Punica (P.) granatum. The fabrics are then assessed for antimicrobial, dyeability and fastness properties. The colour strength (K/S) of the dyed fabric is assessed through a dyeability test. The finished fabrics are qualitatively assessed for antibacterial activity by the disc diffusion and parallel streak methods (AATCC 147) and quantitatively by the percentage reduction test (AATCC 100) against the test organisms, Escherichia coli and Staphylococcus aureus. The fabrics are also assessed for fastness properties, such as wash, light and rubbing fastness per AATCC standards. The fabrics are then finally subjected to wash durability tests. The dyeability results show that the fabrics dyed with P. granatum rind extracts have a high K/S value when compared with the untreated control fabric. The natural extract dyed fabric has prominent antimicrobial activity which is evidenced by a clear zone of bacterial inhibition in qualitative tests and bacterial reduction in quantitative tests whereas the control fabrics have no antibacterial activity. Wash durability testing shows that the antimicrobial activity of the fabrics is durable up to 10 wash cycles.

The volatile nature of foreign portfolio flows, especially flows into debt market, has large implications on financial and macroeconomic stability in recipient countries. It is necessary to identify the main drivers of portfolio investments in bond market of developing economies to design effective policies to enhance resilience of the economy and help in managing capital flow volatility. The determinants of foreign portfolio investment to Indian equity market have been examined in literature, but flows to bond market remain unexplored. Thus, the purpose of this paper is to identify the possible determinants of foreign portfolio flows to Indian bond market both in the short and in the long run.

This study carries out a time series analysis by deploying autoregressive distributed lag (ARDL) approach to cointegration of monthly data of the period from January 2002 to December 2016 for the Indian economy. A mix of pull and push factors has been analysed in this study. Domestic growth, domestic stock market performance, interest rate differential, exchange rate, volatility in exchange rate, stock market returns in other emerging economies, foreign output growth and dummy variables to trace the external developments such as global financial crisis and unconventional monetary policies of advanced economies have been used as explanatory variables.

The dominant pull factor such as interest rate differential explains the dynamics of flows in Indian bond market. The relationship between capital movements and interest rate differentials is the most accepted paradigm in international finance (Haynes, 1988). Among other domestic factors are stock market performance, volatility in exchange rates and domestic growth rates which are found to be significant drivers of foreign portfolio bond flows to India. The study also confirmed that global conditions could induce a fast outflow of capital from India.

The study concludes that both domestic factors and external factors are equally important in determining the foreign portfolio investments in the Indian debt market.

The empirical analysis conducted in this study suggests that direct and indirect measures can be taken to increase and stabilise foreign investments in the Indian bond market. Direct policy measures refer to those tools which are under the ambit of policymakers. Indirect measures comprise those tools that are not under the direct control of the fiscal and monetary authorities but require coordinated efforts of the government and private sector. In this context, strengthening of not only financial and economic but also administrative institutions will be necessary. Creditworthiness and policy credibility should be improved to address erratic foreign portfolio investment in debt market of India.

This study is an original research study. This study adds to the existing literature and is expected to guide policymakers on the specific aspect of the management of capital flows as it gets affected by changes in monetary and fiscal policies.

The purpose of this paper is to investigate the condition of insulation in high-voltage equipments using partial discharge (PD) measurements. It proposes the methods to eliminate several noises like white noise, random noise and discrete spectral interferences which severely pollutes the PD signals. The study aims to remove these noises from the PD signal effectively by preserving the signal features.

This paper employs fast Fourier transform, discrete wavelet transform and translational invariant wavelet transform (TIWT) for denoising the PD signals. The simulated damped exponential pulse and damped oscillatory pulse with low- and high-level noises and a measured PD signal are considered for this analysis. The conventional wavelet denoising approach is also improved by estimating the automated global optimum threshold value using genetic algorithm (GA). The statistical parameters are evaluated and compared. Among these methods, GA-based TIWT approach provides robustness and reduces computational complexity.

This paper provides effective condition monitoring of power apparatus using GA-based TIWT approach. This method provides the low value of mean square error, pulse amplitude distortion and also high reduction in noise level due to its robustness and reduced computational complexity. It suggests that this approach works well for both signals immersed in noise as well as for noise immersed in signals.

Because of the chosen PD signals, the research results may lack for multiple discharges. Therefore, researchers are encouraged to test the proposed propositions further.

The paper includes implication for the development of online testing for equipment analysis and diagnostics during normal operating condition. Corrective actions can be planned and implemented, resulting in reduced unscheduled downtime.

This PD-based analysis often present well in advance of insulation failure, asset managers can monitor it over time and make informed strategic decisions regarding the repair or replacement of the equipment. These predictive diagnostics help society to prioritize investments before an unexpected outage occurs.

This paper provides an enhanced study of condition monitoring of HV power apparatus by which life time of insulation can be increased by taking preventive measures.