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Corporate Social Responsibility (CSR) is a general concept where companies voluntarily decide to adopt social, economic and environmental responsibilities for a better society and environment. Researchers in the management field have created various CSR models and theories. Moreover, enterprises are working out to strength its competitiveness performance though adopting CSR approaches. For that, decision makers in an enterprise have to target the most appropriate CSR approach that fit with the environment and the nature of their enterprise. The Analytical Hierarchy Process (AHP) has been presented through the article in order to help an organization take decision toward the most convenient CSR method according to many characteristics and criteria.

The Architecture, Engineering, and Construction (AEC) industry rely heavily on Building Information Modeling (BIM). BIM is the collection of Information and Communication Technologies (ICT), interacting policies, and procedures. BIM is a tool for managing digital project data during the life cycle of a building. Despite the many benefits and features of BIM, the Malaysian construction industry's proliferation is confronted with adoption issues. Therefore, this research study intends to find the effect of BIM adoption factors in Malaysian AEC. Quantitative data collection from construction firms is gathered. The proposed model's theoretical foundations are based on Technology, Organization Environment framework. The model is tested and validated with the Smart PLS tool. The study's findings indicate that Perceived benefits, Organizational Capabilities, and Trialability are drivers of BIM adoption. Perceived cost and Insecurity are the barriers to BIM adoption. Perceived ease of use and compatibility does not affect BIM adoption. Finally, this study performs Importance Performance Map Analysis to provide recommendations to AEC stakeholders to address the BIM adoption issues for enhancing its diffusion in Malaysia.

Over the last few decades, both developed and developing countries have an increasing trend in technology but with an enormous gap, particularly in the education sector. The e-learning following institutions can achieve benefits by evaluating their e-learning readiness through up-front analysis. Moreover, several models have been introduced to measure e-learning readiness for developed countries but these are not adequate for developing countries.  This paper introduced the e-learning readiness evaluation model for the developing country, Libya, by considering the primary education sector. Furthermore, this study examines the e-learning level of readiness in the staff of the primary school. The purpose of this study is to evaluate the e-learning readiness of staff by directing factors of e-learning readiness i.e. cultural readiness, content readiness, and technology readiness. To achieve this objective, this paper collects data through questionnaires, and respondents are 110 staff member of primary schools in Yefren, Libya. Therefore, the multivariate analysis shows that the e-learning readiness factors have e significant relationship with the adoption of e-learning because most teachers are well prepared and ready. Likewise, results indicate that technology is the most significant factor instead of other e-learning readiness factors. According to the views of staff, there should be more content development training required for primary school staff. Thus, the demographic structure is inadequate to enhance the e-learning but the staff is ready for e-learning. Consequently, this study emphasizes the significance of cultural readiness and its relationship with the adoption of e-learning in primary education sectors’ development in Yefren, Libya.

The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models.

The blockchain's immutability has allowed previously centralized operations to operate in a new way. The possibility of applications having a new architecture is given thanks to the innovative properties of the technology, which brought alternative control designs for distributed systems and allowed applications to work without the need for a central controlling point. The expansion of blockchain to other areas beyond cryptocurrencies has shown the need for applications to implement solutions to deal with corrective operations. Blockchain 3.0 applications bring new solutions for business needs. However,  as opposed to immutability, the revoking functionality is much more complex to be implemented in this type of architecture, but paramount to applications resilience, allowing faulty or invalid information to be revoked, ensuring thus that the blockchain can still be trusted. This work assesses and discusses revocation mechanisms to contribute to the technical feasibility of several applications, which require corrective operations. We present a model in the academic area, which can be replicated for other types of systems in other areas.

The challenge of electricity distribution’s upgrade to incorporate new technologies is big, and electric utilities are mandated to work diligently on this agenda, thus making investments to ensure that current networks maintain their electricity supply commitments secure and reliable in face of disruptions and adverse environmental conditions from a variety of sources. The paper presents a new model based on energy homeostasis for power control and energy management using tariffs differentiation as incentive, considered by ENEL, the largest electric utility in Chile. The model optimizes grid-tied distributed generation (DG) systems with energy storage, in line with the utility’s green energy program, part of its Smart Grid Transformation, aimed at installing grid-tied DG systems with solar generation and energy storage in Santiago, Chile. Results present different tariff options, system’s capacity and energy storage alternatives, in order to compare proposed strategies with the actual case, where no green energy is present. The results show the advantage of the proposed tariffs scheme and power-energy management model based on different scenarios, providing a good and safe option for installing DG solutions to the grid.

Falls are a serious concern among the elderly due to being a major cause of harm to their physical and mental health. Despite their potential for harm, they can be prevented with proper care and monitoring. As such, the motivation for this research is to implement an algorithmic solution to the problem of falls that leverages the benefits of Machine Learning to detect falls in the elderly. There are various studies on fall detection that works on one dataset: wearable, environmental, or vision. Such an approach is biased against low fall detection and has a high false alarm rate. According to the literature, using two datasets can result in high accuracy and lower false alarms. The purpose of this study is to contribute to the field of Machine Learning and Fall Detection by investigating the optimal ways to apply common machine and deep learning algorithms trained on multimodal fall data. In addition, it has proposed a multimodal approach by training two separate classifiers using both Machine and Deep Learning and combining them into an overall system using sensor fusion in the form of a majority voting approach. Each trained model outputs an array comprising three percentage numbers, the average of the numbers in the same class from both arrays is then computed, and the highest percentage is the classification result. The working system achieved results were 97% accurate, with the highest being achieved by the Convolutional Neural Network algorithm. These results were higher than other state-of-the-art research conducted in the field.

The article is devoted to the problem of designing recycling in sustainable logistics systems in the field of processing wastewater treatment waste - municipal sewage sludge (MSS) in the focus of information technology management . The essence of the developed and proposed wastewater treatment project is based on its integration into the city's public utilities system, which will be a single basis for a closed-type logistics system that is managed on the basis of the introduction of a unified information system. This approach to the design of sustainable logistics systems ensures the formation of a closed material and informational logistics flow according to the "supply of raw materials - production - product distribution". This allows solving two problems: ensuring the creation of a closed logistics system that is managed by a single information base and developing a new type of fertilizer. The solutions proposed in this project in the implementation of the MSS processing project are complex and allow implementing all areas of the sustainable development strategy in the aspect of implementing modern information systems. The design technology makes it possible to completely close the logistics cycle for the supply and processing of MSS into finished products in the form of organic fertilizers

In our research, we provide a statistical syntax parsing method experimented on Kannada texts, which is an official language of Karnataka, India. The dataset is downloaded from TDIL website. Using the Cocke-Younger-Kasami (CYK) parsing technique, we generated Kannada Treebank dataset from 1000 annotated sentences in the first stage. The Treebank generated in this stage contains 1000 syntactically structured sentences and it is used as input to train the syntax parser model in the second stage. We have adopted Probabilistic Context Free Grammar (PCFG) while training the parser model and extracting the Chmosky Normal Form (CNF) grammar from a Treebank dataset. The developed syntax parser model is tested on 150 raw Kannada sentences. It outputs with the most likely parse tree for each sentence and this is verified with golden Treebank. The syntax parser model generated 74.2% precision, 79.4% recall, and 75.3% F1-score respectively. The similar technique may be adopted for other low resource languages.

This study aimed to identify the impact of strategic vigilance (SV)on the useful application of electronic management in the National Railway Transport Company (SNTR), and to achieve the objectives of the study; a survey was designed and distributed to a simple random sample of 200 individuals from the National Railway Transport Company (SNTR) ), From which 180 questionnaires were retrieved, The results obtained indicate Spanish the respondents' perception of both strategic vigilance with its dimensions (competitive vigilance and technological vigilance, commercial vigilance and environmental vigilance) and electronic management in the researched company, The findings also revealed positive effect of strategic vigilance in the practical application of electronic management in the company National Rail Transport (SNTR).