The article is devoted to some aspects of research of hostile environment influence on the concrete durability. The mechanism of formation of chaotically diversified difficult structure is described. Chemical interactions of cement with active components of diffusion in steam space are examined. Measures for receiving conditions under which the volume of products of reaction will be equal to the volume of the reacted alkaline components of a cement stone are offered. High-quality and quantitative changes in a pore space, which are irrespective of density and the relative molecular mass of reaction products are described. Besides, movement and distribution of gaseous products of reaction in volumes of a cement stone are simulated. Reasonable integrated dependences of influence of aggressive environment on various types of concrete surfaces are given. The methods of corrosion control offered in article allow substantially to lower or absolutely to exclude adverse effect of hostile environment on concrete and reinforced concrete structures already at a stage of selection of composition of concrete mix. In this case service life of concrete and reinforced concrete structures will increase.

Double-sided trimming shear machine is used for longitudinal both–sided trimming of steel plates and simultaneous scraping of trimmed edges at specific length. Side trimming shear (STS) executes the most vital role in increasing the productivity of plate mill and due to its feature, efficiency of plate mill division is enormously increases. In general, frequency of the occurrence of breakdown in STS machine is a most challenging task when STS performs side trimming and scrap cutting machining process. Bhilai Steel Plant (Steel Authority of India at Bhilai) in India is continuously facing a problem due to breakdown of STS machine. The use of separate knife for side trimming and scrap cutting reduces the possibility of scrap jamming which is a major reason for breakdown of STS machine. Researchers and practitioners also suggest the use of arc guillotine shear for side trimming and straight guillotine shear for scrap cutting for minimizing the breakdown in STS machine. The aim of this study is to describe the problems faced by the steel industry as well as the necessary steps which should be taken for improvement of the productivity of STS machine and also it has made contribution to Bhilai Steel Plant by its growth and prosperity. The methodology of study is purely qualitative and the results point out the problems, its implications, steps taken to improve the overall productivity of Bhilai steel plant.

This study delved into the repercussions of economic instability on household food security while aiming to construct a robust framework for enhancing the resilience of the food supply chain. It thoroughly analyzed diverse factors, including government policies, economic conditions, environmental variables, and social dynamics, to gauge their reciprocal impact on food security. The research employed a meticulously chosen probability-based sample to ensure the representativeness of findings within the population, specifically focusing on residents in the Tanjung Kupang region of Johor. The study holds paramount significance as it provides novel insights for researchers, academic practitioners, and policymakers, shedding light on the far-reaching consequences of economic uncertainty on household purchasing power and its pivotal role in upholding food security. Moreover, it aspires to devise a tailored and sustainable food supply chain framework for Malaysia's unique context. By employing a quantitative approach enriched with robust statistical analysis and insights gathered through a meticulously designed questionnaire, this study sought to illuminate the intricate dynamics at play. The findings underscore the profound impact of economic instability on diminishing the income of B40 households, thereby curtailing their purchasing power. These findings align with comprehensive literature reviews from authoritative sources, underscoring factors such as inflation, soaring prices of essential commodities, and stagnant incomes contributing to the decline in income among B40 households. The implications of this research extend to policymakers, offering invaluable insights and promoting public awareness of potential challenges, along with plausible solutions.

Nomophobia is a term describing a growing fear in today’s world, the fear of being without a mobile device or beyond mobile phone contact. It is the biggest non-drug addiction of the 21st century and is mainly affected by teen-aged students. Those experiencing nomophobia may feel a sense of panic, anxiety, or distress when they are separated from their mobile phones. This work uses different statistical tools to identify the risk factor of nomophobia and machine learning techniques to propose a fresh way to measure and understand nomophobia. To create a predictive model for nomophobia, we gathered information from a broad sample (n = 357) of smartphone users and used a variety of machine learning methods. Using a questionnaire on 17 different factors and performing a statistically significant test (p<0.05) and ordinal logistic regression analysis on respondents age, level of education, CGPA, self-evaluation, per-day mobile phone usage, and use of media, we can recognize the most important features causative of nomophobia. The context of maximum phone usage is an important feature that highly affects nomophobia. About 201 respondents are at a moderate level. To develop a predictive model, decision tree (DT), random forest (RF), Gaussian Naïve Bayes (NB), and support vector machine (SVM) are utilized in this study for recognition of nomophobia addiction. Proposing an ensemble method to refine the predictive performance. From the analysis, we have found that the SVM feature selector with ensemble algorithm has classified the extent of smartphone addiction with a 57% accuracy rate. Our findings show that nomophobia tendencies can be accurately captured and predicted by machine learning approaches. The model distinguished between students who had symptoms of nomophobia and those who did not with remarkable accuracy. This study of machine learning-based methods presents a viable tool for diagnosing and treating nomophobia in students, eventually assisting in the creation of focused interventions and preventive measures.

The Project Portfolio Selection is a complex process that involves many factors and considerations since the project is proposed until the project portfolio is finally selected. Project Portfolio Management is the iterative process of assessment, selection and implementation of projects, and the most important part of that is to select projects for portfolio where the or-ganization is required to identify and prioritize projects with the most objective alignment to its objectives. Since the selection of a suitable projects is very important, it is necessary to develop mathematical models to lead the organization towards the final goal. To achieve this goal, these models should reflect the organization's position, goals and priorities as much as possible. In this paper, we propose a multi - objective model for selecting the project portfolio that maximizes efficiency, quality while minimizes the risk involved in project execution.

Humanitarian assistance by foreign organizations in general and foreign military forces in particular, is typically provided in a non-neutral political environment. Local politics that range from national pride, through strained relations with the country offering military logistic support, to blatant aversion to the population in need, affect the ability to provide effective humanitarian aid. The current paper presents the use of mathematical modeling and robustness approach when the government of the affected area declines offers of aid from international organizations because of political constraints. The multi-objective model seeks to minimize unsatisfied demands and total costs of the government and suppliers. To explore the effects of various parameters and show managerial insights that can guide DMs under a variety of conditions, the sensitivity analysis of the experiments are presented.

In the past, due to the use of two-dimensional design, the probability of error in large projects increased. Over time, the need for a digital 3D model has increased in the lifetime of refineries, petrochemicals and power plants. Today, the editable digital 3D model is the basic needs of industrial plants. The Abadan Oil Refinery Co., due to its need for inspections and possible changes in the refinery, has also needed an editable 3D Digital Plant during the past few years. In this paper, with the development of a new methodology, the non-editable Navisworks® model of phase 1 and 3 of Abadan Oil Refinery was transformed into an editable Digital 3D model in AVEVA PDMS. In this methodology, the PDMS programming language called Programming Macro Language (PML) as well as .net code was used. Finally, using the Navisworks® model, these two phases, which consisted of 38 Areas, 2185 equipment, 17966 pipes, 7046 instruments, and 2617 structures were converted into an editable PDMS model for nine months.

During the past few years, there have been an increasing competition among auto producers. Customers have experienced emissions misrepresentation activities from well-known auto producers such as Volkswagen and Mitsubishi, and have increased their expectations, accordingly. This paper designs a questionnaire to measure factors influencing on customer satisfaction in Likert scale, and distributes it among 210 randomly selected people who purchased a product from an Iranian Auto maker named SAIPA. The questionnaire was validated by some experts and it was also verified using Cronbach alpha (α = 0.83). Using structural equation modeling with Varimax rotation, the study has detected four groups of factors including car specifications and options, before sales services, after sales services and payment policy, which influence the most on customer satisfaction.

This paper is aimed at reviewing present state of the art (1998-March 2019) on the impact of Enterprise Resource Planning (ERP) implementation in Small and Medium Enterprises (SMEs). It includes critical success factors and critical failure factors. The technique depends on writing survey for optional information gathering. It characterizes the expressions that are utilized to get explore papers from databases and advanced studies on the ERP implementation in SMEs. It additionally incorporates the consideration and prohibition criteria to im-prove nature of papers. At that point methodical audit is made on the accessible papers to research the effect of ERP usage in SMEs. Critical factors are identified for success and failure of ERP implementations and actual impact of the same on SMEs (add actual success and failure factors here besides impact). The research found in this paper has limitations in terms of the period of which research papers have been reviewed. An implicit limitation is that it does not consider an empirical study except focusing on the state of the art found in the re-search area. However, its insights will have potential benefits and the directions for future work. This paper contributes to the research on the impact of ERP implementation on SMEs either positively or negatively. It discovers critical success factors, critical failure factors and impact through secondary data collection method. The insights will help SMEs and stake-holders of SMEs and ERP service providers learn about the reasons for failure or success and take necessary course of action.

There have been many unexpected natural disasters such as earthquake, flood, etc. in developing countries, which have created catastrophic incidents and we need to do appropriate planning for relief to reduce the possible casualties. Such actions normally face different challenges such as damages on transportation infrastructures including roads, bridges, etc. One of the primary actions for such crises management is associated with facility location for relief distribution centers. This paper presents a multi-objective mathematical problem and applies it for a real-world case study in northern region of Iran. The study uses Lp metric to handle different objectives and fuzzy programming is used to cope with uncertainty. The preliminary results indicate that the proposed study of this paper has been able to provide efficient results.