Landfill site selection for sustainable solid waste management using multiple-criteria decision-making. Case study: Al-Balqa governorate in Jordan.

Landfill site selection is an essential aspect of sustainable solid waste management as it ensures that the waste generated by a community or region is disposed of in an environmentally friendly and safe manner. The approach for selecting landfill sites seeks to choose locations that provide the least risk to the environment and public health while still satisfying the demands of the local community. This research aims to use a multi-criteria assessment to determine a landfill location in Al-Balqa Governorate in Jordan to ensure that the chosen site meets the needs of all stakeholders while minimizing the negative impact on the environment and the community. This research developed a hierarchy structure to make landfill site selection decisions, which involves identifying parameters such as distance to surface water, land cover, distance from urban and rural areas, distance to roads, slope, and soil permeability. A rating system was used to evaluate each criterion, and weights were assigned to reflect their relative importance. An overlay weighting technique was then used to assess site suitability based on expert opinions from related fields. In this studied area, about 204,283 m2 is required to address 25 years of municipal solid waste volume, whereas this technique identified around 79,210,000 m2 of potential landfill sites. Overall, the research highlights the importance of using a multi-criteria assessment approach for landfill site selection to ensure that the chosen site meets the needs of all stakeholders while minimizing negative impacts on the environment and public health. The study provides insights that can be useful for decision-makers involved in sustainable solid waste management in Jordan and other similar regions. -How to Find the suitable landfills to achieve sustainable development.-What aspects and criteria were comsidered in choosing the landfill site.

Application of condition-based maintenance for electrical generators based on statistical control charts.

Condition-based maintenance involves activities that are conducted based on the equipment's performance. Continuous monitoring of equipment will ensure that it will be maintained according to a relevant activity plan. This paper proposes a maintenance framework to analyze the application of statistical control charts for condition-based maintenance of electrical generators. The proposed framework consists of four components that collaboratively determine a performance threshold for a given piece of electrical equipment. Based on the slow progression and dynamics of mechanical failures, Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process. The analysis is based on detecting the dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. With the help of experimental methodology, failures in the performance modes and defined modes are measured. Then empirical analysis reveals how control charts respond to failure detection. The results show that X-bar consistently demonstrates failure detection capability, while R charts sometimes fail when data deviates from normality. Moreover, heat monitoring surpassed vibration and noise in failure detection, where temperature control charts successfully identified failure. The overall results support the significant role of statistical charts in decision-making regarding condition-based maintenance for electrical equipment like generators.•Application of statistical control charts for condition-based maintenance of electrical generators.•Detecting dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts.•Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process.

The Economy of Motion for Laparoscopic Ball Clamping Surgery: A Feedback Educational Tool.

The Ball Clamping module of the Laparoscopic Surgery Training Box involves the transfer of beads across the training board using laparoscopic tools. Fundamentals of Laparoscopic Surgery (FLS) requires practitioners to move their hands at as short a distance as possible to perform the functions in the shortest amount of time. This study introduces a feedback tool that presents to the student, after attempting their exam, the right direction (step by step) of obtaining the optimal pathway for minimizing distance traveled in the Ball Clamping Module of the Laparoscopic Surgery Training Box. The shortest distance tour for the ball clamping task is determined using the Traveling Salesman Model (TSM). A sensitivity analysis is conducted to assess the model's applicability to different types and settings of trainer boxes.•Find the best sequence of points resulting in the shortest distance tour for the ball clamping task.•The effects of adding or removing columns from the box cannot be intuitively predicted.

The effect of customer satisfaction on parcel delivery operations using autonomous vehicles: An agent-based simulation study.

The quality of Third-Party Logistics (3PL) services represented by delivery time decides the outcome of customer satisfaction. The result of this satisfaction judges the type of Word of Mouth (WoM) that, if positive, plays a vital role in attracting non-customers who are willing in 3PL services to join as customers. In this paper, we investigate the effect of an essential factor represented by Word of Mouth on the number of customers in 3PL companies. Therefore, an agent-based model for parcel delivery is developed to investigate the impact of social factors such as WoM and other operational factors, including vehicle number and speed, on customer number and satisfaction, average service time, and vehicle utilization. As a methodology, state charts of Vehicle, Customer, Hub agents are developed to mimic the messaging protocols between these agents under the WoM concept. A case study based in 3PL in Jordan is used as a test bench of the developed model. A sensitivity analysis study is conducted to test the developed model's performance, including different levels of influential model parameters such as targeting non-customers parameters by Loyal/Unhappy customers. Key results reveal that the best scenario is achieved when the WoM value equals 10, the vehicle number equals 30, and the vehicle speed equals 60 km/h. These model parameters result in higher customer numbers of 873, vehicle utilization equals 63%, and customer satisfaction equals 99%. Video of our proposed model showing it in action can be found at: https://www.youtube.com/watch?v=3rR4l130-QU.

A new framework for warehouse assessment using a Genetic-Algorithm driven analytic network process.

A novel way of integrating the genetic algorithm (GA) and the analytic network process (ANP) is presented in this paper in order to develop a new warehouse assessment scheme, which is developed through various stages. First, we define the main criteria that influence a warehouse performance. The proposed algorithm that integrates the GA with the ANP is then utilized to determine the relative importance values of the defined criteria and sub-criteria by considering the interrelationships among them, and assign strength values for such interrelationships. Such an algorithm is also employed to linguistically present the relative importance and the strength of the interrelationships in a way that can circumvent the use of pairwise comparisons. Finally, the audit checklist that consists of questions related to the criteria is integrated with the proposed algorithm for the development of the warehouse assessment scheme. Validated on 45 warehouses, the proposed scheme has been shown to be able to identify the warehouse competitive advantages and the areas where more improvements can be achieved.

Estimation of the probable outbreak size of novel coronavirus (COVID-19) in social gathering events and industrial activities.

BACKGROUND: The reproduction number (R0) is vital in epidemiology to estimate the number of infected people and trace close contacts. R0 values vary depending on social activity and type of gathering events that induce infection transmissibility and its pathophysiology dependence. OBJECTIVES: In this study, we estimated the probable outbreak size of COVID-19 clusters mathematically using a simple model that can predict the number of COVID-19 cases as a function of time. METHODS: We proposed a mathematical model to estimate the R0 of COVID-19 in an outbreak occurring in both local and international clusters in light of published data. Different types of clusters (religious, wedding, and industrial activity) were selected based on reported events in different countries between February and April 2020. RESULTS: The highest R0 values were found in wedding party events (5), followed by religious gathering events (2.5), while the lowest value was found in the industrial cluster (2). In return, this will enable us to assess the trend of coronavirus spread by comparing the model results and observed patterns. CONCLUSIONS: This study provides predictive COVID-19 transmission patterns in different cluster types based on different R0 values. This model offers a contact-tracing task with the predicted number of cases, to decision-makers; this would help them in epidemiological investigations by knowing when to stop.