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To satisfy the electricity needs of a village in Tangi, northwest Pakistan, the present research can design and evaluate the environmental and economical aspects of an optimal hybrid photovoltaic-biogas-hydropower-battery energy sustainable system (PV-BG-HP-BESS). This framework integrates various renewable energy sources, delivering a modern, efficient approach to sustainable energy solutions. The HOMER Pro software is utilized to optimize the most economical and effective hybrid energy system. The results showed that the proposed hybrid system comprising 91.4 kWp PV modules, 19.6 kW hydropower, a 50 kW biogas generator (BG), 36 batteries, and a 60.6 kW converter was the most economical choice. This system, which used the cyclic charging (CC) method, had a cost of energy (COE) of 0.0728 $/kWh and a total net present cost (NPC) of $152,242. The suggested hybrid energy system for rural areas of Pakistan includes photovoltaic (PV), biogas (BG), hydro, and battery components to provide a dependable and sustainable power supply. This system minimizes the need for expensive fossil fuels while simultaneously minimizing environmental impact by lowering pollutants and greenhouse gas emissions. The system's annual electricity production is 294,782 kWh, with PV leads at 59.4%, BG at 6.02%, and hydro at 34.6%, ensuring uninterrupted power generation even in remote areas. The unmet load, extra electricity, and capacity shortage illustrate the reliability of the system and make it possible to address rural electrification challenges while supporting sustainable development and economic growth. Moreover, the outcomes of the proposed hybrid system dominate the previous studies in multiple objectives, including cost and sensitivity analysis, when compared.

The main objective of water purification processes is to remove harmful chemical compounds and microorganisms from water sources to produce suitable water for human consumption. Water purification plays a pivotal role in meeting the critical demand for clean drinking water across diverse industries, including medical, pharmaceutical, and chemical sectors. Its significance extends beyond industry needs, impacting the resilience and prosperity of nations. As a result, experts around the world are studying several potential technologies to increase and maintain water supplies. In regions susceptible to drought, the need for innovative water purification strategies is even more critical. This article presents a novel approach, the “Extended MULTIMOORA” method utilizing $ff^{\prime }$ -fractional linguistic term sets ( $ff^{\prime }$ -FLTSs), to address the challenge of selecting the most effective water purification process. This article introduces the concepts of arithmetic mean, geometric mean, weighted averaging, and weighted geometric operators for $ff^{\prime }$ -FLTSs and investigate its several important key features. In decision-making problems, when the weights of the decision makers are unknown, the decision-making process is more difficult, we introduce an entropy measure and a distance measure to find the weight information. Furthermore, we effectively apply the proposed technique and newly defined operators to select the best technique for purifying water at a commercial scale. At last, we establish a comparative analysis to show the validity and feasibility of the proposed techniques compared to existing methods.

The Objectives of this study is to extend the concept of q-rung linear Diophantine fuzzy sets (q-RLDFSs), followed by the Near-Earth Asteroids (NEAs) deflection detector. The q-RLDFS is more superior than linear Diophantine fuzzy sets (LDFSs) because of the qth power of reference parameters (RPs). In the present work, first we have recall the q-RLDFS and named it Non-linear Diophantine Fuzzy set (N-LDFS) further, introduce some operational rules on N-LDFS under Hamacher sum and Hamacher product. Hamacher Norms were commonly referred to as Hamacher operations, but their applications might be better expressed if they are presented with a new level of flexibility within the general parameter. Hamacher operations have not yet been applied for N-LDFS in a suitable form. Therefore we have apply the Hamacher operators for N-LDFS and develop a new area of research in decision making problems. We have apply the Hamacher operators to develop Non-linear Diophantine fuzzy aggregation operators from geometric point of view such as non-linear Diophantine fuzzy Hamacher weighted geometric (N-LDFHWG), non-linear Diophantine fuzzy Hamacher ordered weighted geometric (N-LDFHOWG) and non-linear Diophantine fuzzy Hamacher hybrid weighted geometric (N-LDFHHWG) aggregation operators. We will also establish commutativity, idempotency and monotonicity properties which are the most desirable properties for proposed operators. Ultimately, we have implement a case study regarding the decision support method to pick the best NEA deflection detector. We construct an algorithm to solve the problem of multi-attribute decision-making (MADM) problem, followed by a fun of application using the N-LDFHWG operator. A comparison between the proposed and existing methods is perform from the geometric point of view. Finally, the comparisons demonstrate the effectiveness and superiority of the proposed method.

This paper is about the effectiveness of the Cobb-Douglas (C-D) production function in industrial optimization, estimating the number of factors used in the production process of the water industry, for instance, capital and human labor. Moreover, we have modeled a nonlinear optimization problem for a local water industry using two and three factors of production. For this purpose, we have taken into account the Cobb-Douglas production function with different production factors using the Lagrange multiplier method with the ordinary least squares method. In the course of the solution, a linear function is used to calculate the cost function, and the C-D production function is used to calculate the production function. The Lagrange multiplier method with the ordinary least squares method is then used to solve the constrained optimization problem for the product of production. Furthermore, we compared the outcomes from both examples of two- and three-factor C-D production functions in order to validate the Lagrange multiplier method for the C-D production function. Moreover, the three-factor C-D production function is solved by the Lagrange multiplier method with the ordinary least squares method, which provides optimal results as compared to previous studies in literature. The validity of the proposed methodology is explained by using the products of a local production industry in Pakistan.

In this paper, a circular intuitionistic fuzzy set is implicated, which is the extension of a fuzzy set and an intuitionistic fuzzy set. The circular intuitionistic fuzzy set includes a radius for both membership and non-membership. After this, we describe the TODIM method for decision making, which can handle both quantitative and qualitative criteria, as well as imprecise and uncertain information. TODIM takes into account the decision-maker’s subjective preferences and attitudes towards the criteria while also considering the uncertainty and imprecision in the decision-making process. We define the algorithm of C-IF-TODIM approach for decisions making problem. The proposed method takes into account both the fuzziness and circularity of the decision-making problem, which come from the fact that the decision-maker’s preferences are subjective and that the criteria depend on each other. A case study of choosing materials for a cryogenic storage tank is used to test the proposed method, and the results are compared to those from other ways of making decisions. The results show that the proposed method makes decisions that are more accurate and reliable and that it can handle the uncertainty and complexity of choosing materials for cryogenic storage tanks well. After reviewing this paper, we can help make liquid nitrogen transportation safer and more reliable, which will cut down on the chance of accidents and financial losses. The proposed method can be applied to decision-making in other domains where circular intuitionistic fuzzy information is common.

The rapid advancement of new resources of energy has been increasing in the world day by day with traditional resources of power generation. The goals of such developments are to increase both economic and environmental benefits. In the literature, there have been a lot of hybrid systems that have come into existence as a combination of different energy resources with different configurations. In this scenario, the combination of different energy resources like photovoltaic (PV), water turbine (hydro), diesel generator (D), and battery energy storage system (BESS), each with a different configuration, is taken into account, leading to a hybrid energy management system. Furthermore, the proposed system leads to a real-world optimization problem that is solved by the use of dynamic programming. For this purpose, we have taken into account the existing day-ahead data of load, electricity, and other sources for the optimal output of the proposed hybrid system. Moreover, the objective function is the minimization of cost by organizing distributed energy resources as well as the emission reduction of toxic gases. The objective function is constrained by balancing the power between the supply and load requirements and the restrictions of each distributed energy resource. A multistage decision procedure known as Dynamic programming is used in regard to state of charge (SOC), which yields the optimal cost of the system. For this purpose, we tried to make use of conventional as well as local turbines, which lead to small hydropower plants that contribute to the proposed system as a case study in the northern areas of Pakistan in order to produce low-cost electricity. The purpose of the proposed study is to develop an optimal hybrid energy management system that generates more power at a minimum cost. Moreover, the maintenance cost is reduced to a high extent as compared to the previous hybrid system in the literature. Due to the minimum use of diesel generator, the proposed hybrid system produces optimal results in terms of clean energy provision as compared to the existent techniques in the literature.

In daily life, decision-making (DM) problem is a complicated work related to uncertainties and vagueness. To overcome these imprecisions, many fuzzy sets and theories have been presented by different scholars. Probabilistic models are the communal models proposed for the management of uncertainties. On the other hand, if these uncertainties are not probabilistic in nature, then other models such as fuzzy linguistic and fuzzy logic are developed. Here, a new approach known as the complex Pythagorean fuzzy Maclaurin symmetric mean (CPFMSM) operator is used to handle these uncertainties in DM issues. This complex Pythagorean fuzzy set (CPFS) is a modified form of the Pythagorean fuzzy set (PFS) and of the complex intuitionistic fuzzy set (CIFS). The aggregation operators have the ability to combine different sources of information. Therefore, an aggregation operator known as the MSM operator is utilized under the complex Pythagorean fuzzy (CPF) environment to extend the theory and applications of traditional MSM. For this purpose, we devised new operators known as CPFMSM and CPF dual Maclaurin symmetric mean (CPFDMSM) to aggregate CPF data. To evaluate an emergency program, the MAGDM approach is used, which is based on the newly introduced operators. Furthermore, the viability and applicability of the propounded method are certified by a detailed analysis with the other approaches researched in the past.

In this article, the considered problem of Cauchy reaction diffusion equation of fractional order is solved by using integral transform of Laplace coupled with decomposition technique due to Adomian scheme. This combination led us to a hybrid method which has been properly used to handle nonlinear and linear problems. The considered problem is used in modeling spatial effects in engineering, biology and ecology. The fractional derivative is considered in Caputo sense. The results are obtained in series form corresponding to the proposed problem of fractional order. To present the analytical procedure of the proposed method, some test examples are provided. An approximate solution of a fractional order diffusion equation were obtained. This solution was rapidly convergent to the exact solution with less computational cost. For the computation purposes, we used MATLAB.

In this article, we introduce a new algorithm-based scheme titled asymptotic homotopy perturbation method (AHPM) for simulation purposes of non-linear and linear differential equations of non-integer and integer orders. AHPM is extended for numerical treatment to the approximate solution of one of the important fractional-order two-dimensional Helmholtz equations and some of its cases . For probation and illustrative purposes, we have compared the AHPM solutions to the solutions from another existing method as well as the exact solutions of the considered problems. Moreover, it is observed that the symmetry or asymmetry of the solution of considered problems is invariant under the homotopy definition. Error estimates for solutions are also provided. The approximate solutions of AHPM are tabulated and plotted, which indicates that AHPM is effective and explicit.

This paper presents some new exact solutions corresponding to unsteady fractional Jeffrey fluid produced by a flat plate between two side walls perpendicular to the plate. The fractional calculus approach in the governing equations is used. The exact solutions are established by means of the Fourier sine transform and discrete Laplace transform. The series solution of velocity field and the associated shear stress in terms of Fox H-functions, satisfying all imposed initial and boundary conditions, have been obtained.

Nature-inspired optimization techniques are useful tools in electrical engineering problems to minimize or maximize an objective function. In this paper, we use the firefly algorithm to improve the optimal solution for the problem of directional overcurrent relays (DOCRs). It is a complex and highly nonlinear constrained optimization problem. In this problem, we have two types of design variables, which are variables for plug settings (PSs) and the time dial settings (TDSs) for each relay in the circuit. The objective function is to minimize the total operating time of all the basic relays to avoid unnecessary delays. We have considered four models in this paper which are IEEE (3-bus, 4-bus, 6-bus, and 8-bus) models. From the numerical results, it is obvious that the firefly algorithm with certain parameter settings performs better than the other state-of-the-art algorithms.

This paper presents the solution of directional overcurrent relay (DOCR) problems using Simulated Annealing based Symbiotic Organism Search (SASOS). The objective function of the problem is to minimize the sum of the operating times of all primary relays. The DOCR problem is nonlinear and highly constrained with two types of decision variables, namely, the time dial settings (TDS) and plug setting (PS). In this paper, three models of the problem are considered, the IEEE 3-bus, 4-bus, and 6-bus, respectively. We have applied SASOS to solve the problem and the obtained results are compared with other algorithms available in the literature.

The fractional order Susceptible-Infected-Recovered (SIR) epidemic model of childhood disease is considered. Laplace–Adomian Decomposition Method is used to compute an approximate solution of the system of nonlinear fractional differential equations. We obtain the solutions of fractional differential equations in the form of infinite series. The series solution of the proposed model converges rapidly to its exact value. The obtained results are compared with the classical case.

Abstract Introduction Invasive aspergillosis is a serious complication in immunocompromised patients. It is an opportunistic disease, which predominantly occurs in the lungs, although dissemination to virtually any organ is possible. Invasive aspergillosis in an immunocompetent patient with extension to the mediastinum has rarely been reported. Here, we present the case of a patient with no apparent immunodeficiency state, who presented with Aspergillus endocarditis and fulminant invasive aspergillosis with extensive involvement of the mediastinal structures, which ultimately was responsible for her death. To the best of our knowledge, this is the first reported case in the literature on fulminant invasive mediastinal aspergillosis with extension to the pulmonary vasculature and concomitant Aspergillus endocarditis in an apparently immunocompetent patient without pre-existing lung disease. Case presentation Our patient was a previously healthy 47-year-old Asian woman, who presented to our emergency room with severe progressive shortness of breath of one month’s duration, associated with orthopnea and unstable vital signs. Conclusion Invasive aspergillosis has been described in the presence of pulmonary disease, such as chronic obstructive pulmonary disorder, and one case has been reported in a patient without preexistent disease, but none of these have been fatal. Our case is therefore the first reported case of its kind. Our case shows that fulminant aspergillosis can occur in an immunocompetent host and can be fatal. We conclude that invasive aspergillosis should not be excluded from the differential diagnosis on the basis of immunocompetency.

The current study set out to assess and create long-term solutions for improving environmental performance concerning water use, wastewater production, and treatment at Syntan plant (glass-lined vessel unit) and application laboratory (small-scale leather retanning. Based on evaluations and analyses, best available techniques including water gauging, pressurized vessel washing, dedication of vessels to similar production, reuse techniques, developing commercial grade intermediate products from wash water, managing cooling water and developing reuse methods of reverse osmosis reject water were applied to reduce water consumption and effluent generation in process and non-process activities. Furthermore, the reduced effluent was subjected to treat using electrochemical processes, i.e., electrocoagulation and electro-Fenton, before it was drained to outside environment. As a result of the applications, 0%-100% change was measured in various process and non-process activities, whereas, 12.8%-100% reduction was measured in effluent. Soft cooling water consumption was reduced by 46.7%. The results of treated effluent parameters were compared and found the final removal efficiencies of total dissolved solids (51.4%), total suspended solids (99.2%), chemical oxygen demand (98.5%) and electric conductivity (67.7%). It is concluded that this study can be considered as a successful model to increased water efficiency in chemical industries, Furthermore, it could serve as a building block for the incorporation of cleaner and sustainable production approach into national agenda and to overcome stern issues of high-water and energy consumption and effluent management in different industries. [ABSTRACT FROM AUTHOR]

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region. [ABSTRACT FROM AUTHOR]