The rapid expansion of Dubai and the United Arab United infrastructure in the Middle East over the past twenty years has resulted in a large accumulation of concrete waste in landfills and dumping sites. The subsequent hasty increase in the construction and demolition of the infrastructure has outstripped effective waste management facilities, posing a serious natural challenge. This concrete waste greatly endangers human health, property, and biological systems when buried. To find effective solutions to this issue, different waste management methods were researched and compared to find the most effective. This paper aims to differentiate the most appropriate waste management strategies and determine their impact on the environment. These methods included reducing the waste, reusing those wastes that can be reused, recycling, and disposal in landfills and government dumping sites. This paper opens the door for future studies to determine the impact indicators and specific waste management methods that substantially influence the environment and the cost of damage. In addition, a Life Cycle Cost Assessment (LCA) may be carried out to supplement this research and determine the financial benefits of the various specific waste management options and the cost of damages. The study predominantly assesses the different construction and demolition waste management strategies. It primarily adds valuable knowledge concerning these waste recycling management techniques and their environmental effect. This study also suggests directions for future exploration, such as identifying key impact indicators and management strategies that have a significant impact on the environment. Alternatively, LCA can complement the results by assessing the financial benefits of different physical waste management options. To sum up, the exponential growth of infrastructure in Dubai has led to a considerable challenge regarding concrete waste. The LCA-based comparison between landfill and recycling methods highlights the urgency of adopting sustainable waste management practices. This study not only broadens the understanding of damage cost models but also provides a comprehensive assessment of practical waste management options, contributing to informed decision-making to minimize environmental impact.

This paper presents the engineering basis for a document, Residential Design and Construction Guidelines (hereafter, Guidelines). The Guidelines is developed by the engineers of Build Change and its partners based on their technical reconnaissance after the 2013 earthquake in Bohol on October 15, 2013, and Typhoon Haiyan on November 8, 2013. The Guidelines are centered on the observations of typical residential construction and damage in these areas and the requirements of the 2010 National Structural Code of the Philippines. The Guidelines provide illustrated and simple instructions on residential construction, making them accessible to homeowners and local builders. The Guidelines are designed for disaster-resistant, permanent low-rise housing construction, endorsed by the Department of Public Works and Highways of the Philippines (in March 2016). Increasing understanding and application of the Guidelines in residential construction in earthquake- and typhoon-prone areas will increase the structural safety of houses and the country's resiliency.

Indian retail industry held the second rank in A.T. Kearney's Global retail development index in 2021 and ranked 16th FDI confidence index. The retail industry provides ample job opportunities and contributes to the economic progress of the nation. After the advent of new innovation there are new technologies which have emerged in business and it also substantially changed consumer behavior. Digital communication allows the user to transfer data from one end to another using digital technologies like e-mail, phone calls, video conferencing, and several instant message applications. Digital communication allows retailers to send personalized business information to their loyal customers and the customers respond to those messages, which in turn generate business for unorganized retailers. Digital communication not only improves the business of unorganized retailers but also the business image, Geographical spread, profitability, new customer acquisition, customer retention, and the like. Digital communication will transform the traditional unorganized retailer into an offline-to-online commerce model. Based on theoretical review this study identified constructs and proposed major determinants which influence technology adoption and its continuance among the unorganized retailers. This study assesses the digital communication technology usage in supply chain management among unorganized retailers with the constructs like experience, effortlessness, efficiency, enrichment, trust, security, digital infrastructure, satisfaction, and continuous intention to use. The results show that digital communication technology's effort, experience, and efficiency influence satisfaction. However, enrichment failed to influence satisfaction. Further, the results show that satisfaction and security influence continuous intention to use. However, trust and digital infrastructure failed to influence. Therefore, digital communication technology usage in supply chain management among unorganized retailers is substantially influenced by constructs like effort, experience, efficiency, satisfaction, and security.

During a pandemic like COVID-19, the country adopted a lockdown to minizine the spread of the pandemic which disrupted the global supply chain and supply chain resilience failed to minimize disruption. In this regard, this study aims to explore the resilience system among countries during a global disruption and the impact of supply chain disruption on supply chain resilience through misinformation/fake news, panic buying behavior, and inflation factors. For this purpose, the analytical study has been selected to predict the impact of supply chain disruption on supply chain resilience through misinformation/fake news, panic buying behavior, and inflation and propose a solution accordingly. The data from 89 countries are collected on various factors for the year 2020 and mediating analysis is selected to test the hypothesis through regression and correlation. They illustrate that there is a 46% correlation and 21% dependency between supply chain disruption and supply chain resilience through misinformation/fake news, panic buying behavior, supply chain disruption & inflation. The criterion validity of convergence validity and Cronbach of homogeneity test are applied. It has been found that panic buying behavior & supply chain disruption has a 71% strong correlation as compared to other factors and the reliability is 69% which is highly reliable and acceptable. In the end, it is concluded that strong coordination among countries will minimize global supply chain disruption through supply chain resilience for continuing supply chain activities and supply chain organizations & mass media organizations coordination with each other for minimizing misinformation/fake news, panic buying behavior supply chain disruption, and inflation factors to improve supply chain resilience by using artificial intelligence technology.

One of the most hazardous phenomena in forests is wildfire or bush fire and early detection of massive damage prevention is vital. Employing Unmanned Aerial Vehicles (UAV) as a visual and extinguisher tool in order to prevent this tragedy which brings fatal effects on humans and wildlife has high importance. Additionally, using aerial imagery could assist firefighters to recognize fire intensity and localize and route the fire in the forest which shrinks down casualties of firefighters. All these benefits and more is just possible by employing cheap UAVs. The proposed research uses nature-inspired image processing techniques in order to segment and classify fire in color and thermal images. Multiple nature-inspired and traditional computer vision techniques, including Chicken Swarm Algorithm (CSA) intensity adjustment (contrast enhancement), Denoising Convolutional Neural Network (DnCNN), Local Phase Quantization (LPQ) feature extraction, Bees Image Segmentation, Biogeography-Based Optimization (BBO) feature selection, Firefly Algorithm (FA) classification and more are employed to achieve high classification and segmentation accuracy. The system evaluates nine performance metrics including, F-Score, Accuracy, and Jaccard for the segmentation stage and four performance metrics for the classification stage. All experiments are conducted on the two most recent UAV fire datasets of FLAME (2021) and DeepFire (2022). Additionally, fire intensity, fire direction, and fire geometrical calculation are calculated which assists firefighters even more. As smoke shows the location of the fire, a smoke detection workflow is proposed, too. Proposed system Compared with traditional and novel methods for segmentation and classification leading to satisfactory and promising results for almost all metrics. The trained model of this system could be used in most of the current rescue UAVs in real-time applications. For the FLAME dataset (color data), segmentation precision is 95.57 % and classification accuracy is 91.33 %. Also, For the DeepFire dataset segmentation precision is 91.74 % and classification accuracy is 96.88 %.

A growing number of companies treat "sustainability" as an important objective in their strategy. Sustainable and green suppliers increase efficiency by reducing costs and waste in industries. Sustainable and green supplier selection is a critically important factor in moving forward along the path of sustainability. A fuzzy R-method is proposed in this paper based on ranking the fuzzy numbers of alternative suppliers and criteria. Triangular fuzzy membership function and different fuzzy scales are presented to demonstrate and validate the proposed method. Fuzzy composite scores are generated, and these scores are converted into crisp forms to evaluate the alternative suppliers. The novelty of the proposed method is that it is simple, can deal with qualitative and quantitative criteria of supplier selection, and requires less computational effort for evaluating and ranking the green and sustainable suppliers in fuzzy group decision-making situations. The weights of the criteria and alternative suppliers are generated using a simple equation and hence there is no need to apply different methods for weights generation and ranking. Furthermore, this method does not require normalization of the data. Two realistic group decision-making problems of green and sustainable supplier selection to test the method. Sensitivity analysis for the proposed method is also conducted to check the consistency of the proposed method to different weights of the criteria. The proposed method is effective, robust, and competitive to the existing multi-criteria decision making (MCDM) methods of sustainable supplier selection.

Manufacturing facilities are systems that require adequate designing, maintenance and reservations for improvement in the future. Layouts need to be effectively designed to reduce operating to the minimum. Computer simulation is a process of investigating and analyzing the behavior of production processes for effective decision-making using computers to generate solutions that will positively impact short, and long-term planning of the Plants and save costs of real-life implementation. This study investigated a 500Kg capacity shea nut processing plant using FlexSim©. The findings from the initial model were not effective and experienced bottlenecks in workstations (Roaster and Milling) sections, poor cycle and lead times coupled with manual labor, Plant efficiency was 35.7%. However, the Improvement Layout Model was able to address these bottlenecks, the results showed the Plant efficiency increased to 83.3%, shorter lead and cycle times, improved machine utilization, and throughput capacity of the Plant. The results were an indication of conformance to the layout design developed to aid in enhancing the traditional shea nut processing that is largely dominated by traditional processing practices.

The trend toward sustainable city development is associated with intelligent transportation systems (ITS). Automation, efficiency, safety, security, and cost-effectiveness are critical factors in establishing each aspect of a smart city. Real-time data obtained from ITS play an essential role in improving the level of service of road segments, enhancing road safety, and supporting road users with road circumstances information. Travel time information is applicable in travel time maps, decision makings for traffic congestion, dynamic pricing of the network, emergency relief services, traffic flow monitoring, traffic jams management, and air quality analysis. Travel time on a road segment highly depends on geometrical specifications, environmental and weather conditions, traffic flow, and driving behavior. Due to specific driving behavior and road conditions, the above parameters are not essentially applicable in another region. The present research uses the data collected from loop detectors and License Plate Recognition (LPR) systems to develop a Bureau of Public Roads (BPR) model for Iran’s freeway network (Tehran-Qom Freeway). Because of the large amount of data, the SQL server program was used for creating and organizing the database and the BPR model was calibrated using SPSS statistical software. The results of the BPR model were evaluated with an ANOVA test, indicating that the derived model can estimate the travel time at freeway sections with a %5.2 error for the volume-to-capacity ratio (V/C) of less than 0.8.

The ready-to-wear sector is expanding quickly and already accounts for a significant contribution to Ethiopia's export revenue. Due to its lower labor and investment costs, availability of resources, government assistance, etc. than other sectors, this industry attracts young entrepreneurs from around the nation as investment destinations. However, while investing in the leather industry, there are frequently many more aspects to consider. Again, there are numerous leather investment opportunities in the leathers sector, including handbags, jackets, belts, shoes, gloves, etc. Different sorts of leather items necessitate various levels of capital investment, operator skill sets, and resource requirements, among many other things. Again, based on their capacity for managing businesses, available capital, business location, and other factors, not all investors are in the same position. To assist investors in making decisions on the opening of a new leather industry, this paper presents a FAHP methodology for choosing the best leather item among various possibilities in a fuzzy environment. The methods enable the decision-maker to incorporate language expressions of expert judgment into the evaluation process. Using the suggested process, the ideal leather item is chosen for the launching of a leather industry in Ethiopia under the current circumstances.

The world is increasingly witnessing food insecurity and deteriorating individual and global livelihoods. This study sought to assess the effectiveness of contract farming in the form of command farming in achieving sustainability of maize supply chain in Zimbabwe. A descriptive approach was adopted where a sample of 35 agricultural experts were randomly selected to respond to questionnaires. The researchers also relied on interviews with farmers, the results of which were triangulated with quantitative data to improve the dependability of results. The study shows a positive and significant relationship between command farming and sustainability of the maize supply chain in Mazowe district of Zimbabwe. The study, therefore, recommends the government to invest more in command agriculture to improve productivity and sustainability in the maize sector. It further recommends investment in joint contract farming with agricultural firms and government to improve farming business to sustain the country’s agricultural sector.