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This study aims to explore the potential of virtual positioning systems (VPSs) in revolutionising library access. It examines the benefits and challenges associated with implementing VPSs.

The study takes a comprehensive approach by analysing library users’ current challenges in accessing physical resources and services within traditional library settings. It analyses the benefits of VPSs in enhancing library access, considering factors such as improved navigation, accessibility for personalised recommendations, virtual tours and interactive experiences. The study also examines the implications of implementing VPSs regarding library resource management, staff training and infrastructure requirements.

The findings reveal that VPS has the potential to address various challenges faced by library users, such as limited availability of resources, inconvenient locations and inadequate access for individuals with special needs. VPS offers improved navigation, enhanced accessibility, personalised recommendations, virtual tours and interactive experiences. Implementing VPSs requires robust technological infrastructure, user adoption, privacy considerations and system maintenance. Libraries must invest in hardware, network infrastructure, staff training and data protection measures.

This study contributes to the ongoing discourse surrounding the transformation of libraries and the assimilation of emergent technologies. It highlights the potential of VPSs in revolutionising library access. By embracing the latent potential of VPSs, libraries can transcend physical boundaries, enhance user experiences and ensure seamless access to a wealth of resources in a digitised world.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

The purpose of this paper is to identify faults in distribution systems which are unavoidable because of adverse weather conditions and unexpected accidents. Hence, quick fault location is vital for continuous power supply. However, most fault location methods depend on the stored database for locating fault. The database is created by simulation, which is time consuming. Therefore, in this work, a comprehensive fault location method to detect faulty section and fault distance from one-ended bus using limited simulated data is proposed.

The work uses voltage sag data measured at a primary substation. Support vector machine estimates the data which are not simulated. The possible faulty section is determined using matching approach and fault distance using mathematical analysis.

This work proposed a ranking analysis for multiple possible faulty sections, and the fault distance is calculated using Euclidean distance approach.

The research work uses Malaysian distribution system as it represents a practical distribution system with multiple branches and limited measurement at primary substation. The work requires only metering devices to identify fault which is cost effective. In addition, the distribution system is simulated using real-time PSCAD by which the capability of proposed method can be fully tested.

The paper presents a new method for fault analysis. It reduces simulation time and storage space of database. The work identifies faulty section and ranks the prior faulty section. It also identifies fault distance using a mathematical approach.

This study examines potential causal interactions between a dominant economy and its trading partners, with the view of verifying surmised economic contagion effects traditionally presumed to emanate from dominant economies toward trading partners.

The study used the Toda–Yamamoto Wald test approach to bi-variate causality analysis.

This study verified the existence of the economic contagion phenomenon; Estimated empirical evidence failed to fully support the presumption that such contagion effects mostly emanates from dominant economies toward trading partners, all things being equal. For instance, although this study found significant economic contagion effects emanating from the US economy toward the Chinese economy, the authors also detected six different uni-directional causal interactions with the direction of causality emanating from trading partners toward the US economy.

The uniqueness of this study stems not from its verification of the economic contagion phenomenon using equity market-related economic uncertainty as the potential contagion. This study fills a gap in the present literature by focusing on the happenings in the equity market as the potential candidate of the economic contagion phenomenon between a dominant economy and its key trading partners.

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

Ad hoc mobile networks are commonplace in every aspect of our everyday life. They become essential in many industries and have uses in logistics, science and the military. However, because they operate mostly in open spaces, they are exposed to a variety of dangers. The purpose of this study is to introduce a novel method for detecting the MAC layer misbehavior.

The proposed novel approach is based on exponential smoothing for throughput prediction to address this MAC layer misbehavior. The real and expected throughput are processed using an exponential smoothing algorithm to identify this attack, and if these metrics exhibit a trending pattern, an alarm is then sent.

The effect of the IEEE 802.11 MAC layer misbehavior on throughput was examined using the NS-2 network simulator, as well as the approval of our novel strategy. The authors have found that a smoothing factor value that is near to 0 provides a very accurate throughput forecast that takes into consideration the recent history of the updated values of the real value. As for the smoothing factor values that are near to 1, they are used to identify MAC layer misbehavior.

According to the authors’ modest knowledge, this new scheme has not been proposed in the state of the art for the detection of greedy behavior in mobile ad hoc networks.

The purpose of this paper is to investigate the linkages among equity exchange traded funds (ETF) returns and transmission of volatilities of the USA, Europe and key emerging countries’ stock markets. Standard & Poor’s 500 (spy) and iShares Europe are used to represent the USA and European stock markets, the emerging market part of the data set consists of daily returns of equity ETF representing broad equity market indices of the BRIC countries (Brazil, Russia, India and China); the mist countries (Mexico, Indonesia, South Korea and Turkey) and South Africa and covers the period of February 3, 2012-February 28, 2014.

The paper utilizes multi-variate auto-regressive moving-averages (MARMA) methodology to study equity market returns and spillovers. Second, generalized auto-regressive conditional heteroskadasticity (GARCH) modeling is employed to model volatility persistence and transmissions.

The findings include the existence of significant co-movement of returns among all country ETFs; however, despite increasing interdependencies among the global stock markets there are still very good opportunities for diversification. For example, USA and Europe based investors may do well to ignore opportunities in each other’s markets but can realize diversification benefits by investing in ETFs representing China, South Africa and Turkey. As far as volatilities are concerned, the findings indicate that no ETF volatility is transmitted from the sample countries to USA, Brazil, China and South African stock markets. Also, US market volatility is transmitted to India, Russia, Mexico and Turkey while European volatility spills over to Mexico and South Korea. The presence of spillovers among stock markets’ return series and persistence of volatilities are useful to investors interested in diversifying their portfolios and to traders/fund managers who are interested in maximizing returns.

The implications include: first, investors should not only rely on current domestic news to guide their investment decisions, but also take into consideration international news for there are substantial spillovers. Second, given that volatilities can proxy for risk, there are lessons for both individual and institutional investors in terms of further examining pricing securities, hedging and other trading strategies as well as framing regulatory policies. Third, investors should be able to ride the financial cycle by following closely monetary policies of the FED and European Central Bank and resulting credit expansion or contraction since research indicates (and as corroborated in this study) equity prices are linked to VIX which is also correlated with capital flows and credit expansion and interest rates. Limitations include: first, the investigation could be expanded to include individual countries in Europe instead of using one Europe-wide ETF. As ETFs for other emerging markets become available it is also possible to include additional countries. Second, ETFs may not be the best vehicles for diversification.

Methodology (MARMA and GARCH) is widely used for analyzing financial data. The use of BRIC and MIST countries and the interaction among them may be novel. Spillovers among emerging financial markets is a fairly new area. Typically, the authors see studies of spillovers from the developed countries to the developing ones. The data period is important since it covers both credit expansion and contraction (or the start of it) by the FED and is current.

This paper aims to provide a bibliometric review and visualisation analysis of the literature on Sustainable Stock Indices (SSI) between January 2001 and March 2022. The purpose of performing this bibliometric analysis is to empirically report the trend, intellectual structure, knowledge development directions and identify prospective research topics in the area of SSI.

A total of 222 publications were selected after evaluating, identifying and synthesising the extensive publications using the Preferred Reporting Items for the Systematic Reviews and Meta-Analyses (PRISMA) approach. The articles were extracted from the databases of SCOPUS, Web of Science and Google Scholar. The study uses VOSviewer and RStudio software to answer four research questions.

The results signify that there has been a considerable increase in the level of research considering SSI. Further, the study shows that SSI is among the top five trending keywords in the research related to finance and environment. Most papers considered as a sample for this study are based on Dow Jones Sustainable Indices. Noteworthy, very few economies are participating in this research domain, and the significant contribution is from the developed countries.

The present review paper may assist the researchers in identifying the trending research topics in this domain. It may serve as a roadmap for several further studies in the area.

This study is unique in terms of reviewing the literature based on SSI. Further, it provides a holistic view of the current trend, global position and research hotspots of SSI, which has important implications for future research.

The paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.

The paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.

The paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.

The study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues.

This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm.

The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis.

This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.