The deployment of Shared Autonomous Vehicles (SAVs) in urban areas is no longer a futuristic vision. Pilot projects are undeniable realities in various locations, and automakers research agendas are clear about this increasing autonomous trend. An ecosystem that supports the deployment of autonomous mobility is imperative, before this new type of mobility becomes a reality. Trying to understand what is absolutely essential in a city, to allow the operation of SAVs and to attract potential investors, is the aim of this research. This work started with a Systematic Literature Review (SLR), where the main concepts supporting SAVs were identified, and continued using a Topic Modeling approach, specifically Latent Dirichlet Allocation, to reach the most important topics and clusters, that were then modeled in ArchiMate into a possible ecosystem for the deployment of SAVs in urban areas. Finally, the reached model is confronted with a real case in order to establish a gap analysis between the theoretical reference model and what is already happening in Beijing. The result is an improvement of the reference model.

This paper's significance lies in investigating the multivariate spatial parameters of the service sites for gas stations according to predetermined standards and their spatial location to identify the best place for such services. The present study focuses on fueling stations in Al-Mahaweel City, located in the Babylon Province of Iraq. This case study aims to analyze the problem of inefficiency and inadequacy in serving the fuel stations inside the city. These issues have significant economic and environmental repercussions, ultimately affecting urban sustainability. For this purpose, a spatial database by Geographic Information System (GIS) is used to analyze basic services such as hospitals, schools, and others. As well as agriculture, ‎industry, and residential uses. All that is about to derive the relevant criteria ‎for the organization of the district center, which facilitates analysis and evaluation of their ‎current location. The study results indicated the need for stations within an efficient spatial. So, the specific locations for new filling stations would depend on various factors (Q=4,000 ‎square meters and W=5,000 square meters). Accordingly, this ‎article aims to meet demand based on the essential ‎urban ‎planning indicators to enhance the ease ‎of traffic, avoid road congestion, and promote ‎urban ‎sustainability.

Management of municipal solid waste (MSW) is a serious environmental challenge that many nations, particularly developing ones are currently experiencing. As a result, information about the amount and type of MSW is needed to establish an effective waste management strategy. In addition, the amount and type of MSW may change from place to place and season to season. Thus, the purpose of this study was to determine the amount of MSW recovered in the O.R. Tambo District Municipality, Eastern Cape, South African. The first objective of this study was to determine the effect of season on the amount of each waste type (aluminium cans, bottles, cardboards and plastics) recovered. The second objective was to compare the amount of each waste type recovered among the five local municipalities (Ingquza Hill, King Sabatha Dalindyebo, Mhlontlo, Nyandeni and Port St. Johns (PSJ)). The study, which focused on MSW recovered over a two-year period (September 2019 to August 2021), revealed that there were no statistically significant differences in the amount of MSW recovered among the four seasons. However, the type of waste recovered mostly varied significantly across the local municipalities. For example, PSJ had the highest recovery of bottles, while Mhlontlo had the highest recovery of other types of waste. We can conclude that solid waste recovery is crucial since it reduces the amount of waste that must be disposed of in landfills and saves more natural resources.

Disposal of solid waste is a major challenge in towns and cities due to waste being dumped at an unacceptable site. The designation of a landfill site is a challenge in Thohoyandou town. The purpose of this study was to determine a user-friendly landfill site location for Thohoyandou town. The study also intended to examine the social implications of the existing landfill site location on the communities. Data was gathered using both qualitative and quantitative methods. Data was acquired using questionnaire surveys, interviews, field survey, observations, and secondary sources. The data was analyzed using the Statistical Package for Social Scientists, and the Chi-square test. Geographical Information Systems (GIS) and Remote Sensing (RS) constitute the major methods used to determine an acceptable site for the disposal of solid waste generated in study area. The existing landfill site in the study area is not in line with environmental and social standards due to waste being dumped at an unacceptable site. The study revealed social problems such as bad smell, diseases, noise, dust and decline of standards of living in which all have emerged because of the Thohoyandou Block J landfill site. To overcome these challenges, this study incorporated six environmental parameters, including: proximity to road networks, slope, soil, land use/land cover, and built-up areas; surface water, to determine the best suitable landfill site in the study area. According to the findings of this study, out of five potential landfill sites, the site which had the highest rankings following the Analytical Hierarchy Process (AHP) was selected as the most suitable landfill site. As a result, the research recommends that Thulamela Local Municipality contemplate terminating its existing landfill site to relocate to one of the alternative acceptable sites identified by this study.

The escalating impacts of anthropogenic activities and natural disasters, aggravated by economic crises, have amplified the vulnerability of environmental and territorial systems, leading to significant urban planning and social implications. Consequently, numerous public and private buildings have been abandoned, necessitating crucial reorganizations or repurposing to prevent degradation and obsolescence due to disuse. The imperative goal is to foster resilience within territories, primarily by enhancing the adaptability of urban assets to altered conditions induced by both natural and anthropogenic disturbances, thereby increasing their inherent flexibility to offer functional responses to disruptions. This approach aims to mitigate adverse effects, expedite restoration of the status quo or augment the adaptability of structures, particularly public and strategic ones, during extraordinary phases. The proposed methodology for fortifying solidity and fostering resistance to change in the built environment involves digital cataloguing of heritage through the creation of three-dimensional models of structures. This process, known as Building Information Modelling (BIM), is predicated on a preliminary analysis of structural, architectural, and plant engineering data, which is beneficial for both ordinary and atypical management. The result is an efficient system that offers facility management opportunities for structures throughout their lifecycle and facilitates optimization of resource use. It aids in evaluating the extraordinary use of assets, examining various performance hypotheses for each scenario, understanding the time required for system setup, and determining relative economic indicators until the restoration of the preceding state.

Sea transportation plays an essential and strategic role in the mobility of passengers, goods, and services domestically, from, and abroad. A sea port, one of the sub-systems of sea transportation, is a point or node where the movement of goods and passengers using sea modes will start, end, or make significant transfers or transits in achieving an effective and efficient sea transportation system. Port efficiency and effectiveness can be seen from the productivity and ability level to finance operational activities. Port governance is critical in port management; selecting a model or form of port management also affects port efficiency and productivity. An evaluation of the performance of port services is needed to maintain a port in prime condition. Port performance assesses several indicators, such as economic, operational, and financial aspects. This study aims to map parameters used in determining port performance. The scope of the article examined contains an evaluation of port performance from an operational, financing, and sustainable perspective. The study's two research questions are listed below. The trend port indicator parameter is first. What parameters consider when evaluating public ports? This study's limitation is 200 articles that research the commercial port. In future research, it is necessary to conduct research that examines the factors or parameters of performance measurement at ports organized by the government. The study results show that the trend of port performance parameters toward sustainable port management and guide port growth for public officials and private parties to preserve port effectiveness.

The mobility of people, services and movement of goods is always considered as an essential economic-spatial factor. In addition, free movement is associated with the aim of achieving the desired goals and satisfying socio-economic, cultural and political needs in different places. This point creates different travel patterns and complications, which are usually influenced by physical, demographic, cultural and socio-economic factors that most studies have found. This research aims to identify the factors affecting the user's travel behaviour by acquiring more complete knowledge, systematic literature review (SLR), visual bibliometric analysis based on the characteristics and factors of travel behaviour and 120 selected publications in recent decades. Combining the data allowed us to select 62 publications and link them to the characteristics of travel behaviour and its factors. The results show that the complexity of travel behaviour requires a better assessment of resources and problems and predicting the impact of future trends. On the other hand, the population, the growing levels and mixing of multiculturalism and the influence of behavioural communication are increasing, and their influence should not be neglected, so the path of changing travel behaviour should be considered. This means that everyone's travel standards and assumptions need to be re-examined.

Alzheimer’s disease (AD), a progressive neurological disorder, predominantly impacts cognitive functions, manifesting as memory loss and deteriorating thinking abilities. Recognized as the primary form of dementia, this affliction subtly commences within brain cells and gradually aggravates over time. In 2023, dementia's financial burden for elderly adults aged 65 and older was projected to reach \$345 billion, encompassing health care, long-term care, and hospice services. Alarmingly, Alzheimer's disease claims one in three seniors, outnumbering combined fatalities from breast and prostate cancer. Currently, the diagnostic landscape for Alzheimer's lacks definitive tests, and diagnoses based purely on biological definitions have been observed to possess low predictive accuracy. In the presented study, a diagnostic methodology has been proposed using machine learning models that harness image features derived from brain MRI scans. Specifically, nine salient image features, grounded in color, texture, shape, and orientation, were extracted for the study. Four classifiers — Naïve-Bayes, Logistic regression, XGBoost, and AdaBoost — were employed, as the challenge presented a binary classification scenario. A grid search parameter optimization technique was employed to fine-tune model configurations, ensuring optimal predictive outcomes. Conducted experiments utilizing the Kaggle dataset, and for each model, parameters were rigorously optimized. The XGBoost classifier demonstrated superior performance, achieving a test accuracy of 92%, while Naïve Bayes, Logistic Regression, and AdaBoost registered accuracies of 63%, 70%, and 72%, respectively. Relative to contemporary methods, the proposed diagnostic approach exhibits commendable accuracy in predicting AD. If AI-based predictive diagnostics for AD are realized using the strategies delineated in this study, significant benefits may be anticipated for healthcare practitioners.

Indonesia, abundant in solar energy, poses significant potential for harnessing this renewable resource for electricity generation. This study investigates the feasibility of employing photovoltaic (PV) modules, powered by solar energy, for electric vehicle (EV) charging stations in Surakarta, Yogyakarta, Semarang, Surabaya, and Malang. Utilizing the Hybrid Optimization Model for Electric Renewable (HOMER) software, simulations were conducted to assess on-grid Solar Power Plants (PLTS) systems that leverage both PV modules and grid power. This research enhances existing studies on solar energy potential in Indonesia, emphasizing profitable renewable energy business models. Economic evaluations were conducted based on the Net Present Cost (NPC) and the Cost of Energy (COE), integral metrics for determining investment feasibility. Preliminary capital for PLTS development was estimated at Rp 5,399,387,501.00. Results indicate Semarang City as the most promising location for a PLTS system with an NPC value of Rp 23,243,190,000.00 and a COE value of Rp 1,108.11. The designed PLTS system in Semarang City is projected to generate 982.090 kWh/year of electricity, with estimated consumption at 922.467 kWh/year. This study offers novel insights into the potential of solar-powered EV charging stations in Indonesia.

Transport facilitates growth and interactions within and outside cities. Different countries follow different transport models. Increasing population, rising mobility rate and increasing trip length are responsible for increasing travel demand in India. Intention to participate in activities, demands travel, making it a derived demand. The overall purpose of this study is to examine the impact of socio-demographic factors on the mode of transport for education. The city of Pune in Maharashtra, India is chosen for the study. It is classified into clusters. Seventy-five households are selected from each cluster. For this, socio-economic classification (SEC) is used. The Ordinary Least Square Regression (OLS) model is used for analysis. ANOVA is used to test the effect of income level on the distance travelled for education. In the survey respondents have to give information on employment, education, income, age, sex and travel characteristics. The study found that for education, children generally tend to travel short distances. Children from poorer backgrounds, travel much shorter distances as opposed to children from well-to-do families. They either walk to school or use bicycles. Motorized transport either in the form of school buses or personalized vehicles such as cars or two-wheelers is the norm for children from higher income families. Therefore, their expenditure on travel for education is found to be greater. The paper brings forth issues concerning commuters, especially from a policy perspective. Challenges faced by users of non-motorized facilities such as pedestrian paths, and bicycling paths are brought forth explicitly. The paper looks beyond solutions by institutions which aim to move vehicles rather than people. Broader roads only encourage more use of personalized transport. Instead, differing modes of transport should ensure greater safety to children.

Using panel data from 30 Chinese provinces spanning 2003-2019, the relationship between trade openness and haze pollution, moderated by environmental regulation, was investigated through spatial econometric models. It was observed that the effect of trade openness on haze pollution was negative, albeit insignificant, suggesting that trade openness alone did not markedly influence haze reduction in China. Contrarily, environmental regulation, while intensifying haze pollution, displayed a significant moderating role. When combined with environmental regulations, trade openness showed potential in mitigating haze pollution, thus enhancing environmental quality. Although trade openness did not display significant regional variance in its impact on haze pollution, considerable regional disparities were found in the effects of environmental regulation on haze pollution and its moderating influence on the trade openness-haze pollution relationship.

The continual pursuit of fuel efficiency, cost-effectiveness, and desirable physical and mechanical properties of materials has steered researchers towards the latest generation of aluminum matrix composites for automotive and aerospace applications. In this context, the present study investigates the microhardness behavior of Al6061/TiC composites produced by friction stir processing. The morphological characteristics of the produced surface composites were analyzed using optical microscopy and Scanning Electron Microscopy (SEM). SEM micrographs confirmed the presence of TiC particles and their uniform distribution within the aluminum matrix. The mechanical properties of the composites were explored using a microhardness tester, revealing a distinctive feature of the Al6061/TiC composites - a 35% increase in microhardness value compared to the base Al6061 alloy. This improvement in microhardness can be attributed to enhanced interfacial bonding, obstructions in dislocation movement, and grain refinement, all contributing to Hall-Petch strengthening.

The study aimed to determine the levels of heavy metals in some selected plant samples near the Wadafiea Dumpsite in Khartoum North, Sudan, and compare the variations between dry and rainy seasons. Except for Sudanese sorghum, Conocarpus lancifolius, and Leptadenia arborea, zinc contents in all plant samples during the dry season were higher than WHO/FAO guideline value (5mg/kg). In the rainy season, Cd concentrations were generally lower than in the dry season due to rainfall dilution. According to the findings, an open landfill of solid waste could have a severe impact on the quality of plants in the research area and surrounding farms, perhaps causing future concerns for human health and the environment due to pollution.

In the context of environmental monitoring studies, the complex dynamics of environmental systems, constrained by the distribution, intensity and interaction of multiple sources, limits the ability to detect pollution phenomena and to identify their sources. The deployment of multidisciplinary, multilevel and multi-factorial strategies supports the identification of the links between the pollutants’ sources and targets. Our new biomonitoring strategy, based on the integration of remote (satellite) and proximal (drone) sensing monitoring data with field data (bio/chemical analyses) and focused on the use of cyanobacteria as bioindicators of pollution, was implemented and was validated through its application on a test-bed area, i.e., Lake Avernus (Campania Region, Southern Italy). A long-term analysis of multispectral remote sensing observations centred on the Lake Avernus area highlighted the periodicity and seasonality of cyanobacterial bloom events over the time interval 2019-2021. However, a sudden change of characteristics, observable through remotely sensed data, was evidenced during the first and major lockdown related to the COVID-19 pandemics, in year 2020. This sudden change depended on the drastic modification of human habits and a reduction in pollutant emissions, as widely reported by the scientific literature. During the same lockdown period, the opportunity to collect samples in the field allowed to identify an unusual progression of Microcystis' bloom, whose dynamics is triggered by the existing anthropogenic sources and the evolution of environmental parameters, that can stimulate the blooming events. This work shows and demonstrates how pollution attribution can be achieved using remote sensing of cyanobacteria, which are excellent bioindicators due to their sensitivity to multiple stressors and rapid response to habitat changes throughout the event.

In the quest to achieve Sustainable Development Goals (SDGs), health economics and the financing of health expenditures emerge as pivotal elements. This literature-based exploration delves into the intricate nexus between health financing and sustainable development. Interpretations of pertinent data tables suggest that the financing level of health services at the household level is typically below the global average, indicating a prominent gap in health financing development. Specifically, Turkey's stance on health financing is evaluated against global benchmarks, highlighting its unique challenges and opportunities. This research underscores the intrinsic relationship between health financing and sustainable development, emphasizing the imperative for ongoing evaluation and enhancement in this domain to foster sustainable progression. Notably, the study refrains from employing statistical methodologies, relying solely on literature assessments and data table interpretations. Health financing, pivotal to sustainable development, invariably demands continual advancements and research.

The integration of diesel and biodiesel, particularly biodiesel derived from water hyacinth, as a combined fuel source has recently emerged as a promising area of study, with a particular focus on the effects of nanoparticle additives. Notably, the reduction of emissions achieved by introducing iron oxide nanoparticles (Fe₃O₄) to biodiesel has been substantiated. However, the potential impact of blending nanoparticles with the diesel and biodiesel mix on the performance characteristics of a diesel engine has yet to be sufficiently explored. This research undertook performance and emission assessments employing diverse fuel samples in a single-cylinder diesel engine. The thermal brake efficiency metrics for the 50 ppm and 100 ppm iron oxide nanoparticle blends surpassed those of the D80B20 and D60B40 biofuel blends, exhibiting increases of 3.5% and 4.85% for D80B20N50 and D80B20N100, and 6.2% and 7.4% for D80B20N50 and D80B20N100, respectively, in comparison to neat diesel. The carbon monoxide emission levels of the biofuel blends with iron oxide were less than that of neat diesel, with the most significant reduction detected in the D60B40N100 blend. Furthermore, the nitrogen oxide emissions for all nanoparticle blends were lower than those for neat diesel, attributable to a shortened ignition delay and minimized fuel usage during combustion, subsequently leading to a reduction in nitrogen oxide emissions.

CVBEM is a numerical method of solving boundary value problems that satisfy Laplace's Equation in two dimensions. Three key parameters that impact the computational error and functionality of CVBEM are the basis function, the positions of the modeling nodes, and the coefficient determination methodology. To demonstrate the importance of these parameters, a case study of 2D ideal fluid flow into a 90-degree bend and over a semicircular hump was conducted comparing models using original CVBEM, complex log, complex pole, and digamma function variants basis functions, using two different NPAs, NPA1 and NPA2, and using collocation and least squares methods to determine coefficients. Results indicate that the combination of the original CVBEM basis function, NPA2, and least squares results in an approximation with the least computational error. Moreover, least squares appear to enable stability in both NPAs regarding reduction of computational error due to taking advantage of all boundary data and more stable condition number growth. By exploring the interaction of the three main CVBEM parameters, this paper clarifies the unique impact they have on the modelling process and explicitly identifies a fourth parameter, collocation point placement, as being impactful on computational error.

In addressing the challenge of obstacle scattering inversion amidst intricate noise conditions, a model predicated on convolutional neural networks (CNN) has been proposed, demonstrating high precision. Five distinct noise scenarios, encompassing Gaussian white noise, uniform distribution noise, Poisson distribution noise, Laplace noise, and impulse noise, were evaluated. Far-field data paired with the Fourier coefficients of obstacle boundary curves were employed as network input and output, respectively. Through the convolutional processes inherent to the CNN, salient features within the far-field data related to obstacles were adeptly identified. Concurrently, the statistical characteristics of the noise were assimilated, and its perturbing effects were diminished, thus facilitating the inversion of obstacle shape parameters. The intrinsic capacity of CNNs to intuitively learn and differentiate salient features from data eradicates the necessity for external intervention or manually designed feature extractors. This adaptability confers upon CNNs a significant edge in tackling obstacle scattering inversion challenges, particularly in light of fluctuating data distributions and feature variability. Numerical experiments have substantiated that the aforementioned CNN model excels in addressing scattering inversion complications within multifaceted noise conditions, consistently delivering solutions with remarkable precision.

This study explores the performance of the Photovoltaic/Thermal system using nanofluid with a novel collector design. Experiments were carried out on the University of Technology- Iraq campus. An experiment was carried out using two photovoltaic modules, one connected to 120 protrusions arranged eight columns by 15 rows (for comparison) and the other not. Nanofluid was used to cool solar panels with flow rates of 1.5 and 3.5 l/min. The nanofluid contains nano-Al2O3 at 1%, 2%, and 3% concentrations in water. As the flow rate of water used as a cooling fluid increased, the surface temperature of the cell decreased. The cell temperature is reduced by 22.3% when Al2O3/water is added at a volumetric concentration of 3%. An increase in the electrical and thermal efficiency of PV/T systems was also recorded by 12% and 18.4%, respectively, at a concentration of 3%.

Using an entropy generation analysis, heat exchangers can be designed with optimal efficiency. This study delves into the irreversibility of forced convection heat transfer and friction flow around a horizontal cylinder, revealing that pressure drops induce entropy generation that varies in accordance with Reynolds numbers. The investigation encompasses four groups of Re_D, covering ranges of 0.4_D<4, 4_D<40, 40_D<4000, and 4000_D<40000. The study aims to elucidate the relationship between the entropy generation number (Ns), Re_D, the irreversibility distribution ratio (∅), the optimal Reynolds number (Re_D,opt), and the Bejan number (Be), particularly where entropy generation has a minimal effect. Additionally, it seeks to determine the relationship between the duty parameter and Re_D,opt across all Re_D ranges. The findings highlight the optimum design point for forced convection around a horizontal cylinder. At this point, the entropy generation number reaches its minimum value when Ns=1 and the ratio Re_D/Re_D,opt=1, marking the optimal point for irreversibility or entropy generation. At this juncture, the irreversibility distribution ratio ∅ equals 0.5, and the optimal Bejan number stands at 0.667.

Passive design solutions play a pivotal role in fostering sustainable practices within traditional architecture, as they empower historical urban designs to harmoniously engage with their surroundings and weather conditions, particularly in hot regions such as the United Arab Emirates. This research followed a qualitative approach to propose modifications for the thermal conditions and comfort in the modern contemporary urban districts based on the positive strategies from old traditional ones in a Hot-Arid Climate - Ajman-United Arab Emirates (UAE) as a case study using ENVI-met software-microscale three-dimensional software model for simulating complex urban environments. Moreover, this study made an evaluation and comparison of the outdoor air temperature and thermal comfort between the traditional and modern urban districts to highlight the passive design solutions that increase the thermal effectiveness in the traditional urban fabrics, as some of these passive design solutions can be used to modify the thermal conditions in the modern ones. Additionally, the research output revealed that the traditional urban design has valuable, sustainable strategies, as there was a decrease in the maximum reading for the air temperature for the traditional Ajman heritage district compared to the modern district on the 21^st of August - as a reference day- and that improved the thermal comfort in the outdoor open spaces too. In conclusion, the study results confirmed that the thermal conditions in the existing modern districts could be improved using passive design solutions such as shading devices and greenery. Finally, this research is expected to be a phase amongst different phases that can benefit urban designers and architects to adopt strategies from traditional and vernacular urban projects and merge them with contemporary modern urban design.

In pursuit of achieving developed country status by 2041, Bangladesh is committed to comprehensive socioeconomic development—a goal intrinsically tied to the critical task of securing a reliable, uninterrupted energy supply while optimizing utilization of available energy sources. This study used 1980–2018 annual data to examine the implications of energy transition and causal relationships among economic growth, renewable energy, and natural gas consumption in Bangladesh. A rigorous two-step process investigated the causal correlations among variables. The autoregressive distributive lag (ARDL) model was used to scrutinize long-term relationships, while a vector error correction (VEC) model was used to ascertain the directionality of these causal relationships. The outcomes of the bound tests conclusively revealed the presence of a long-run equilibrium relationship among the variables. Causality analyses indicated a unidirectional causal relationship from renewable energy consumption to economic growth in the long run and from natural gas consumption to economic growth in the short run. A bidirectional causal relationship was found between natural gas and renewable energy consumption in the long run. These findings underscore the potential of energy conservation strategies to catalyze economic growth and suggest an avenue for Bangladesh to achieve its ambitious socioeconomic development goals.

The electrification of naval propulsion systems is increasingly investigated as a promising avenue to reduce CO₂ emissions. This study explores the application of electric propulsion in diverse waterborne transport sectors, ranging from commercial and industrial cargo ships to naval vessels, passenger cruise liners, ferries, and small recreational boats. In these systems, propellers are powered by large electric motors, which are progressively transitioning to induction or synchronous multiphase solutions. A crucial component of these systems is the Battery Storage System (BSS), which is integrated with an energy storage management system to create a grid that powers the electric motors. The BSS is integral to the vessel's operational autonomy, providing consistent energy for continuous operation. A Hybrid Energy Storage System (HESS) composed of two or more battery packs with varying characteristics may be deployed to prevent battery oversizing. This system comprises cells with different technologies, specifically interconnected through distinctive Battery Management Systems (BMSs) and converters. This paper delves into the key challenges and optimization of HESS modular solutions, outlining the energy storage requirements and management strategies necessary for diverse vessel working cycles. Simulation results are presented to demonstrate the system's ability to supply a realistic 10-hour load cycle, even when starting from State of Charges (SOCs) unbalanced by over 30%. These findings illuminate the potential of HESS solutions in maintaining effective and sustainable electric propulsion in naval transport systems.

In the quest to design a robust model for microwave heating systems with symmetrical octagonal tube cavities (MWHSO), a fuzzy-based approach, specifically the Takagi Sugeno Fuzzy Model, was explored to capture the dynamics of the heating process. To achieve this, the mathematical model was adaptively adjusted according to varying input conditions through the utilization of fuzzy logic. Input data were sourced from two magnetrons, with the system outputs derived from measurements acquired from five temperature sensors placed on the heated object. For performance evaluation, the Root Mean Square Error (RMSE) was employed. A comparison was drawn with the autoregressive model with exogenous variable (ARX), a traditional approach wherein the system's mathematical model remains static. Simulation studies were conducted, treating every probe measurement across all dataset validations as distinct cases. It was found that the T-S Fuzzy model surpassed the ARX40 in performance in 33 of the total cases, accounting for 92.49%. The most notable performance of the fuzzy-based approach was observed at a 180-Watt power input, recording an average RMSE of 0.00574 across the five sensors. In contrast, the ARX-based model registered an RMSE of 0.01256. These findings suggest that the fuzzy-based modeling approach presents a compelling alternative for representing the dynamic heating processes within MWHSO.