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In today’s modern world radiation is utilized in medical, industrial and military sectors. X-rays and gamma radiation are both forms of electromagnetic radiation commonly used in laboratory settings. X-rays are produced by accelerating electrons and directing them onto a target material, while the decay of radioactive isotopes emits gamma radiation. Despite the benefits of radiation, prolonged exposure can affect employees and public health. Thus, Pb (lead) shielding has been utilized as an effective mitigation strategy for mitigating the health hazards associated with radiation. Due to its large atomic number and density, Pb is effective for mitigating x-rays and gamma rays. However, even though lead provides protection from radiation it can also affect human health. This research focuses on the effect of the lead pb on employees and public health. The study proposes measures and controls to reduce lead associated health hazards. [ABSTRACT FROM AUTHOR]

The gas turbine has become an important, widely used, and reliable device in the field of energy generation, transportation, and other applications. A gas turbine, also called a combustion turbine, is a type of continuous flow internal combustion engine. The main hazard associated with gas turbines is a gas leak and the accumulation of combustible gas in a confined location, which has the potential to create an explosion or fire if ignited. Thus, this research paper focuses on reducing gas turbine fire & explosion events in order to limit the possibility of a gas leak and flammable gas buildup using advanced control measures. [ABSTRACT FROM AUTHOR]

Food waste is classified as a threat that causes a challenge in achieving the goal of sustainable development worldwide. Recently, food waste has tripled over the past 50 years due to the increase in population. Moreover, food waste is accountable for a third of all human-caused greenhouse gas emissions and it generates 8% of greenhouse gases annually. However, sustainability can be achieved when losses of resources, food waste and it is associated impacts are reduced. Food waste management systems might not yet be introduced in the offshore islands. Thus, this paper works in introducing the food waste management system to the offshore locations in UAE to prevent and minimize the volume of food waste generated. The paper provides calculations of the estimated food waste volume generated from offshore islands to determine the effective solution for food waste management. This study proposes methods that include specialized mechanisms and solutions that can help in reducing food waste and extract revenue from such practices. [ABSTRACT FROM AUTHOR]

A hybrid work environment enables employees to have a combination of working from home and the office. The intention of this paper is to investigate and provide a comparison between hybrid work and in-office work environments. As researchers indicate, WFH has a positive impact on employee productivity and well-being. In order to provide a deep understanding, an empirical survey was conducted on 95 United Emirates workers. The key findings of this survey illustrate that WFH has both positive and negative impacts on workers. The participants record positive experiences with WFH, including flexibility, increased efficiency, improved work-life balance, and better focus. However, the study revealed that some participants recorded various challenges, such as lack of social interaction, increased output expectations, and long working hours, which were obstructions to their participation in WFH. The findings from this research aid in understanding the benefits and challenges of WFH and inform organizations on the best approach to follow when instituting a hybrid work environment within their workforce. [ABSTRACT FROM AUTHOR]

Typical actions performed by order pickers in e-commerce warehouses include retrieving, scanning, grabbing, lifting, etc. making the human order-picker the spine of such warehouse operation. For an order picking system based on a Goods-to-person (GtP) strategy, the repetitive motions raise many concerns. This increased frequency of actions may lead to the development of musculoskeletal diseases in the order picker's body. Many methodologies, like human factors simulation, are available and can be utilized to assess the impact of the working environment on the human body. However, the evaluation of human factors in a simulated environment might differ from the actual case in real life since the interpretations of fatigue and discomfort can be subjective. Therefore, the objective of this paper is to validate the results obtained from the human factors simulation, by constructing a physical order picking environment and comparing the output through evaluating responses from volunteers who are subjected to similar to the simulated order picking environments and tasks. Three matrices of human factors were evaluated in this study. The study concludes that the results obtained from human factors simulation are in line with the subjective interpretation of the discomfort and fatigue experienced by the volunteers. As well as RULA ratings are similar in both cases. [ABSTRACT FROM AUTHOR]

The study aims to the impact of social media exposure and behavioural antecedents on fake news sharing behaviour among social media users in Abu Dhabi, UAE using the Theories of Planned Behaviour (TPB) and Reasoned Action (TRA) as the theoretical foundation. The quantitative research methodology using Partial Least Squares -- Structural Equation Modelling (PLS-SEM) was employed. The research targeted responses from social media users in Abu Dhabi Emirate. The study found that the background factors of individual, social and knowledge experiences define social media platform exposure of social media users in Abu Dhabi. Exposure to social media has a significant effect on social media users' attitudes and perceived norms, but not perceived behavioural control. Nonetheless, attitudes and norms have no significant effect on behavioural intention to spread fake news. Only perceived behavioural control has an impact on fake news behavioural intention and actual fake news behaviour. The model fit was satisfactory with GoF index of 0.4951, and the SRMR, d_ULS, d-G, Chi-Square, NFI fit indices were all within the acceptable range. Thus, the data fit the theory adequately. The study contributes to the existing body of knowledge by establishing that fake news behavioural intention has a significant influence on social media users' actual fake news behaviour. [ABSTRACT FROM AUTHOR]

Technological advancements in Machine learning, artificial intelligence (AI), and data science are bringing industries to the era of the fourth industrial revolution. The application of machine learning in chemical engineering is in the domains of process modeling, optimization, and predictive analysis. Traditional process modeling relies heavily on first-principal methods, which, while accurate, are computationally demanding and are non-flexible for variable process conditions. Green methanol produced through the power-to-liquid (PtL) process has gained significant popularity due to its various applications in household items, as a raw material for manufacturing valuable chemicals, and as a fuel both in blend or pure form. In today's competitive and uncertain chemical industry market, fast and accurate models are required to predict the plant output. This work aims to develop a surrogate model of the methanol production process based on the data-driven technique and using machine learning to predict energy requirements, final product purity, and methanol production rate. The effect of the sampling size and sampling technique (mainly Latin-Hypercube Sampling - LHS, Monte Carlo, and SOBOL) on the performance of the surrogate model is evaluated. A comparative analysis of different machine learning (e.g., XG-Boost, Random Forest, Decision Tree, Support Vector Regression) and Deep learning models (e.g., Artificial Neural Networks) is conducted using metrics such as coefficient of determination (R²), mean-squared error (MSE), and mean-absolute-error (MAE). Additionally, this work explores the use of these trained machine learning models in optimizing process conditions to maximize production rate, enhance product purity, and reduce energy requirements. [ABSTRACT FROM AUTHOR]

Hydrogen Sulphide Methane Reformation (HSMR) is a viable alternative for simultaneous H2S valorization and hydrogen production, offering a carbon-neutral alternative. Concurrently, the industrial revolution, driven by emerging technologies like machine learning and artificial intelligence is reshaping chemical industry as well. machine learning offers robust models for accurate process forecasting, overcoming computational demands and inflexibility. This work focuses on developing a surrogate model for the HSMR process, a promising, waste to energy, route for sustainable hydrogen production. Integrating machine learning with design of experiment techniques, the study systematically generates data using Aspen plus v11 simulation and employs established adaptive space filling sampling techniques. Models such as Linear Regression, Support Vector Regression, Random Forest Regression, Extreme Gradient Boosting, and Artificial Neural Networks was trained on the refined data set, an accuracy of up to 97% in predicting the process outputs was achieved. Furthermore, thisresearch extends to the optimization of an artificial neural network (ANN) surrogate model using evolutionary algorithms, specifically genetic algorithms and differential evolution. within the Python environment, this optimization aims to identify optimal process conditions for HSMR. A comparative analysis with the base case provides insights into the effectiveness of these evolutionary algorithms in enhancing the performance of the ANN model. This work contributes to enhancing the efficiency and competitiveness of HSMR, bridging the gap between machine learning capabilities and sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Recent advancements in supervised machine learning tools have proven their capability to act as accurate approximation surrogate models for complex the chemical production processes. In this approach, complex unit models are replaced with surrogate models built from actual chemical plant Nevertheless, real data should be handled with caution as it isn't devoid of missing points, outliers, and faulty measurement, and using them without pre-processing could lead to inaccurate prediction models. Moreover, it is well-known that ideal real data without any outliers is almost nonexistence. Hence, cleaning data from outliers is very important step in data-driven modeling development Therefore, in this study different machine learning outlier detection method are implemented and compared to clean actual plant data before they are introduced to the data-driven surrogate models. Outliers are observations that do not follow bulk pattern of the data points and are unlikely observation of data. it is worth mentioning that identifying outliers by simple inspection and visualizing data set is challenging. There are different methods that can be used to identify outliers some of these methods are based on univariate statistical methods (Interquartile Range Method) and the others are based on unsupervised machine learning methods (Local outlier Factor, Isolation Forest, and One Class Support Vector Machine) The performances of these outlier detection methods on understudy data sets, are evaluated using linear regression that is used to predict certain process variables. Results show that removing outliers using these outlier detection methods before training the surrogate models can enhance the prediction accuracy of the machine learning approximation models. [ABSTRACT FROM AUTHOR]

With the energy transition underway, there is a consensus effort from both worldwide governments and industry, to facilitate the integration of more renewable energy into power grids. However, the main challenge with integrating renewables like wind and solar, is the intermittent nature of these sources that result in inefficiencies and a lack of reliability. One possible energy system that can allow for an effective integration with intermittent sources is a polygeneration energy system (PES). The concept of a PES has been proposed in academic literature in recent times, where a typical pathway is the use of excess power from a fossil power plant being directed towards the production of valuable liquid fuels, in addition to electricity to the grid. In this study, the concept of a polygeneration system has been modified and extended to include renewable energy generation from wind, and a chemical production pathway of methanol to act as a form of long duration storage for times when the wind generation is much higher for any load demand and therefore increasing the flexibility. The approach taken in this model development departs from previous polygeneration modeling studies, by using a network constrained Unit Commitment (UC) model that is well established in the domain of power systems engineering, to optimally schedule the power planning and power flow. First, the design and operation of a power generation planning model is developed to showcase how the power system responds to the intermittency of wind in the form of wind scenarios. Mixed-Integer Linear Programming (MILP) models are then developed for the chemical production of methanol and integrated with the power planning model as a multi-scale (design and operation) model of a renewable polygeneration energy system (RPES) with chemical storage. To showcase the design and operation of the proposed RPES, the model is first solved in a deterministic manner. The total RPES system cost, based on real world wind power data and load demand data, was found to be USD 2317.93 million. The chemical production block had a cost of USD 138.51 million when integrated as a part of the RPES and the power generation planning block had a cost of USD 2179.42 million. The integrated model resulted in costs for the chemical production block that were much lower than the stand-alone plant while the RPES model also showed how excess intermittent wind power could be used for driving the chemical production. A key contribution to this work is also the implementation of machine learning methods, like K-Means clustering to help with the model's solution tractability and representation of a full year's hourly wind data and load demand. The MILP models have been developed using the General Algebraic Modeling System (GAMS) software and solved using state of the art optimization solvers BARON and CPLEX. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing attention on patient safety due to the high rate of medical errors. In order to improve patient safety, systems approaches have been adapted to help identify patient safety risks within the scope of risk management. In this study, a review of the literature on systems approaches is carried out in patient safety applications. The findings proved that systems approach provided valuable insights to comprehensively identify and mitigate patient safety risks. Further, the study provides the opportunities and challenges to implement the systems approaches in patient safety context. The paper presents valuable outcomes for healthcare quality and process improvement managers. [ABSTRACT FROM AUTHOR]

Patient safety is the first and foremost goal, as well as it is a critical issue in the healthcare sector. To ensure achieving this goal, there is a continuous need for quality improvement. Six Sigma is known as one of the most effective improvement technologies widely adopted in healthcare systems that aims to reduce the variation and eliminate the defects. In this study, a systematic review of the literature is carried out to investigate the outcomes of implementing a Six Sigma tool in patient safety applications. The findings proved that applying Six Sigma in healthcare is valuable and useful in solving many raised issues, reduce medication errors, and increase patient safety. This paper provides opportunities, challenges, and techniques to investigate the relationship between Six Sigma and patient safety. The paper presents valuable outcomes for healthcare quality and process improvement managers. [ABSTRACT FROM AUTHOR]

Patient safety is considered as one of the crucial aspects of the healthcare sector that required improvement. Earlier studies show the high rate of harm to patients and financial losses. In order to improve patient safety, various tools and methods were adopted from other safety-critical industries. While Prospective Hazard Analysis (PHA) tools has proven its effectiveness to enhance the safety and quality of systems and processes in others, now they also gained increased attention in healthcare. One example is the Hazard and Operability (HAZOP) technique to systematically and comprehensively identify hazards and deviations of a system from the intended design in the healthcare sector. The aim of this study is to review HAZOP and identify the potential contribution and challenges of this tool in the healthcare field, in particular patient safety context. [ABSTRACT FROM AUTHOR]

Medical errors are recognized as a major challenge to healthcare worldwide. To prevent medical errors, Failure Mode and Effect Analysis (FMEA) has been adapted from other safety-critical industries to healthcare. Although the FMEA is still underutilized in healthcare, it has begun to be exploited through various applications in the context of patient safety in recent years; therefore, its extensive results should be communicated to encourage its practice. To explore this, we have reviewed the literature on FMEA in patient safety context published since 2015 using the PubMed index. After reviewing twenty papers regarding the selection criteria, we found that FMEA can be utilized in healthcare to contribute towards proactive risk identification, medical error reduction, overall patient safety, and quality improvement. Despite its benefits, earlier studies also highlighted that safety culture should support the use of FMEA along with the required resources and training for healthcare providers. Further, FMEA can be integrated with other tools, such as incident reporting, to provide more comprehensive risk picture within the scope of risk management. [ABSTRACT FROM AUTHOR]

Natural gas is a vital component of the world's energy supply. Therefore, optimizing natural gas processes is important to facilitate more efficient and less costly operations. Optimization which relies exclusively on simulation is impractical, due to the enormous computational cost of complex simulations. Alternatively, surrogate models can be constructed from and used in lieu of actual simulation models. The proposed work aims to develop a black-box surrogate-based optimization model that represents the natural gas treatment process which consist of separation, sweetening and dehydration units. A combination of simulation, experimental design, data analytics and mathematical programming was utilized. The problem size was reduced by using a regression model of the system's behavior. The regression model was based on the simulation and experimental design. This simplified model was then used in a mathematical programming model minimizing a cost function. The methodology is suitable for synthesis of largescale complex problems with fewer degrees of freedom. Linear regression along with polynomial feature transformation machine learning methods were used to generate the surrogate model. A case study was developed to investigate the impact of increasing H2S composition in the feed gas. It was observed that the feed pressure had the highest influence among the parameters. [ABSTRACT FROM AUTHOR]

The power companies try to reduce the power generation cost to satisfy the costumers and increase the profit. For this purpose, various acts including energy management strategies, power loss reduction plans, efficiency increasing of the gird components, etc. have been done in the past. Currently, the integration of natural gas and electricity networks for simultaneous operation is one of the most effective approach. In the integrated networks, the natural gas and electricity are managed simultaneously, making it more beneficial for both suppliers and customers. In this paper, the supplied and demanded natural gas and electricity are managed simultaneously to reach more benefits from both technical and economic perspectives. The studied integrated network includes power plants, gas supplier, gas storage, water electrolyzer, fuel cell units, wind energy and hydrogen vehicles. A comprehensive investigation is carried out to optimal energy management in a modern integrated energy systems, the hydrogen vehicles, as a new transportation vehicle, is included in the system. Both natural gas and electricity demands are supplied optimally using the proposed optimization algorithm. The demand response programs are considered as the flexible loads to increase the system profits. The optimal operation problem is modeled as a mixed integer linear programming problem and is optimized using GAMS programming software. The proposed methodology is simulated in various scenarios and its robustness and effectiveness are discussed. [ABSTRACT FROM AUTHOR]

Pulp and paper mills utilizing the Kraft pulping method demand large amount of water for the manufacturing processes. The purpose of this study is to evaluate opportunities of minimizing fresh water intake within pulp and paper processes by recycle and regeneration of wastewater streams. The compounds of concern are chlorinated salts which have a negative impact on environment and process equipments. Superstructure optimization is applied to synthesize Reverse Osmosis Network (RON) in order to regenerate wastewater streams with reduced salt concentration at minimum cost and to satisfy process water demand. A case study is presented to evaluate potential benefits of RON on minimizing wastewater consumption. [ABSTRACT FROM AUTHOR]

The 'Hydrogen Economy' is a proposed system where hydrogen is produced from carbon dioxide free energy sources and is used as an alternative transportation fuel. Application of hydrogen on-board fuel cell vehicles (FCVs) can significantly decrease harmful air pollutants and greenhouse gases emissions. There must be significant transition of infrastructure in order to achieve the hydrogen economy with investment required in both production and distribution infrastructure. This research is focused on the projected demands for infrastructure transition of hydrogen economy in Ontario, Canada. Three potential hydrogen demand and distribution system development scenarios are examined to estimate hydrogen FCVs market penetration, as well as the associated hydrogen production and distribution. Demand of transportation hydrogen is estimated based on various types of hydrogen FCVs. Finally, an estimate of hydrogen demand from FCVs in Ontario and the resulting cost of delivered hydrogen are investigated. [ABSTRACT FROM AUTHOR]

This paper describes the development of new process simulation modules and associated teaching materials that is integrated within the capstone design course. The process simulation package HYSYS is used to develop these modules. The main advantage of using a process simulator in promoting the systems approach is its powerful interactive feature that allows students to specify and modify, in real-time, physical properties such as pressure and temperature, chemical properties, and other relevant process parameters of a system being studied. Subsequently, the students can observe the resulting changes not only on the individual unit operations but more importantly, on the overall system behavior as well as the operating economics, which really are the actual aspects emphasized by the systems approach. Likewise, by using a process simulator, the students can reproduce controlled 'misbehavior' of the system and in so doing, be able to study, understand, and appreciate the effects and impacts of the various process parameters on each and every component of the system. The new modules and teaching material proved to actively engage students' participation and have been integrated horizontally through time so that the principles each set of modules stresses on are easily mastered from week to week. The modules were developed in such a way that they proceed from simple to complex applications. The modules have been integrated vertically so that they can reinforce the subject matter learned by the students in the lecture component of the capstone design course. Vertical integration has been achieved by developing modules that are directly related to the topics the students are currently taking in a particular week. [ABSTRACT FROM AUTHOR]