TB is caused by an infection of MTB, the foremost cause of mortality attributable to a single infectious agent, resulting in numerous fatalities globally. The pathogen often resides in the human body in a dormant state. Because of how common tuberculosis is, scientists need to find new ways to treat it and make anti-tuberculosis vaccines that can handle multidrug resistance and latent TB infections. This study aims to examine the MTB protein sequence, gaining knowledge about its physicochemical properties, structure-based functional analyses, domain anticipation for specific functional predictions, 2D and 3D structures, and the PPI network. The physicochemical parameters indicated that the protein contains a greater number of negatively charged residues compared to positively charged residues in its sequence. The instability index and aliphatic index indicate that this protein is stable and has suitable thermostability. Documentation also confirms the hydrophobic nature of this protein. The protein has a sensor histidine kinase domain that helps phosphorylate a histidine residue and move its phosphate group to HssR. Consequently, this protein can function as an enzyme, engaging in interactions with ATP and protein L-histidine to produce ADP and protein N-phospho-L-histidine. Gene ontology analyses have revealed the protein interactions in cellular, molecular, and biological processes. The PPI network established an interaction network between the selected protein and 10 other proteins. The secondary structural assessment documented that the alpha helix was the most dominant structural element, followed by random coils and extended strands. Moreover, three different programs, namely AlphaFold, I-TASSER, and SWISS-MODEL, modeled the 3D structure of the protein. After studying different structures, the structural assessment study showed that the one predicted by the SWISS-MODEL program was the best one, taking into account the values for the most desired and extra-allowed areas in the plot statistics results.

This contribution presents an extended approach to utilising digital twin (DT) technology to improve sustainability and operational efficiency in assembly systems. Based on the principles of Industry 4.0, the study examines how real-time virtual replicas of manufacturing processes can drive material optimisation, in-line quality assurance and waste minimisation. In the study, two DTs were developed for an actual circular pallet assembly line consisting of three workstations. The first represents the existing assembly process with one control operation at the end of the assembly process, while the second DT includes an improved process with control operations after each workstation. To demonstrate the suitability of the improvements, various 'what-if' scenarios can be run, using the results obtained to show that quality control after each workstation not only increases system efficiency, but also significantly reduces raw material consumption and waste. The results show a reduction in raw material consumption of 8–10%, a reduction in waste generation of 18-23% and consequently energy savings of up to 10% compared to the baseline process. In both cases, the DT framework enables rapid scenario analysis and parameter tuning, thus supporting flexible decision-making while complying with the defined sustainability KPIs. The findings confirm the effectiveness of the described approach in different assembly environments, especially those with high material waste, and demonstrate its applicability to other types of discrete manufacturing. The presented approach can also be adapted for continuous production processes in other industries. Overall, the study provides empirical evidence that monitoring and simulation using the digital twin can deliver measurable environmental and operational benefits and positions DTs as strategic tools for embedding sustainability into industrial operations.

Digital infrastructure contributes significantly to global electricity consumption, with data centres, high-speed communication networks, and edge devices operating continuously to meet growing computing demands. The infrastructure is evaluated based on performance characteristics such as throughput, latency, and fault tolerance. This study proposes a novel framework integrating environmental intelligence into digital twins to enable resource-aware process control in digital infrastructure. Factors including power usage, temperature, and e-waste generation are incorporated as key components. The digital twin model uses the energy profiles of routers, switches, and computing nodes across time and usage conditions, generating real-time data to predict variations and impacts. A multi-objective optimisation engine was developed using a weighted-sum approach to balance sustainability and performance objectives, with constraints on SLA adherence and hardware availability. The objective function optimises performance and energy consumption while maintaining network performance. We designed a proof-of-concept framework that acts like a cloud-edge network. The results showed that applying the modelling resulted in a 12.6% reduction in energy consumption and a 9.8% increase in performance under typical load scenarios. The system dynamically rerouted non-critical traffic during peak grid emissions, activated low-power modes during idle periods, and recommended infrastructure upgrades based on thermal hotspot forecasts and energy impact assessments. The proposed framework demonstrates how digital twins can align operational efficiency with sustainability by embedding intelligence into real-time control mechanisms. This approach supports the broader vision of intelligent and responsible infrastructure management for next-generation computing systems.

The drying process in indirect solar dryers, which is strongly affected by rapidly changing ambient conditions, represents a highly nonlinear and dynamic system. Accurate modeling is essential for performance evaluation, process optimization, and reliable prediction of drying chamber temperature, which is crucial for ensuring efficient moisture removal while maintaining the nutritional and sensory quality of dried products. In this study, a fuzzy logic-based modeling approach using the Mamdani inference system was developed to predict the drying chamber temperature under a wide range of operating conditions. Experimental measurements were performed with solar radiation varying from 400 to 950 W/m² and the ambient temperature ranging from 20 to 50 °C, covering both static and dynamic system responses. The fuzzy model inputs consisted of solar radiation and ambient temperature, represented by five triangular membership functions (“very low,” “low,” “medium,” “high,” and “very high”) for solar radiation, and three triangular membership functions (“cold,” “warm,” and “hot”) for ambient temperature. The output variable (drying chamber temperature) was modeled with five triangular membership functions (T1–T5). The Mamdani system employed 15 “if–then” rules, and the centroid method was used for defuzzification. Model validation was conducted across the full range of operating conditions, showing strong agreement between predicted and experimental data. For instance, at 700 W/m² and 46 °C, the predicted temperature was 50.9 °C versus a measured temperature of 51.0 °C, while at 750 W/m² and 50 °C, the prediction (52.0 °C) closely matched the experimental value (51.8 °C). Statistical evaluation yielded RMSE = 0.38 °C, MAE = 0.29 °C, and R² = 0.997, confirming high accuracy and robustness. These results demonstrate that Mamdani fuzzy logic can effectively model the thermal behavior of solar dryers under diverse climatic conditions, and they provide a solid basis for developing real-time intelligent control strategies to optimize energy efficiency and product quality.

This paper deals with the design of an optimal storage management system to enhance microgrid frequency stability and control in the presence of Renewable Energy Sources (RESs) including a solar PV generator and wind farm. In a classical power system, a Load Frequency Control (LFC) loop is applied to cope with frequency deviations by acting on the selected conventional power generation unit such as thermal or nuclear power plants. On the other hand, in the case of small or islanded areas, this option is not applicable due to the lack of power generation units with such inertia. In this context, implementing a storage system seems to be a good solution to handle dynamic frequency deviation in microgrids, which is the main contribution of this paper. The main idea was to create a centralized multi-storage system that can support frequency control using a smart power management strategy using nature-inspired optimization algorithms. A hybrid energy storage system was employed including an electrical vehicle, redox flow batteries, super conducting magnetic energy storage and fuel cells. Each storage unit was controlled using an optimal Fuzzy-PIDN controller. A recently developed optimization algorithm named Mountain Gazelle Optimizer (MGO) was used to find the best controller parameters, aiming to improve the storage units' control and management to support the microgrid frequency control in case of load disturbances or climatic changes. The proposed control strategy ensures efficient energy sharing and enhances dynamic microgrid stability. Several scenarios were performed to demonstrate the validity of the proposed method. Firstly, the proposed strategy was simulated in presence of static and dynamic load changes, and then the study was extended to study the impact of climatic changes on green power generation sources such as wind speed variation and solar shading to reduce the maximum frequency deviation and find a robust solution to avoid power outages and load shedding.

In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of quantum control methodologies—grounded in established principles of quantum mechanics—into the automation of thermal processes in power plant operations. Specifically, it investigates a hybrid quantum-fuzzy control system for managing steam heating processes, a critical subsystem in thermal power generation. Unlike conventional control strategies that often struggle with nonlinearity, time delays, and parameter uncertainty, the proposed method incorporates quantum-inspired optimization algorithms to enhance adaptability and robustness. The quantum component, based on recognized models of coherent control and quantum interference, is utilized to refine the inference mechanisms within the fuzzy logic framework, allowing more precise handling of state transitions in multivariable environments. A simulation model was constructed using validated physical parameters of a pilot-scale steam heating unit, and the methodology was tested against baseline scenarios with conventional PID control. Experimental protocols and statistical analysis confirmed measurable improvements: up to 25 % reduction in fuel usage under specific operational conditions, with an average of 1 to 2 % improvement in energy efficiency. The results suggest that quantum-enhanced intelligent control offers a feasible pathway for bridging the gap between quantum theoretical models and macroscopic thermal systems, contributing to the development of more energy-resilient industrial automation solutions.

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to enhance the control of temperature and flow rates in industrial extraction columns. Unlike classical fuzzy controllers that rely solely on expert-defined rules, our approach utilizes a quantum-inspired optimization algorithm to adaptively refine fuzzy rule weights based on performance feedback. While the physical quantum effects at macroscopic industrial scales are negligible, the proposed method emulates the computational advantages of quantum systems, such as parallel rule evaluation and probabilistic amplitude processing, in a classical environment. A MATLAB/Simulink-based simulation model of the extraction column was developed to validate the approach. Experimental tests were conducted under controlled conditions using synthetic data and varying operational parameters to measure improvements in control performance. The hybrid controller achieved a 0.7 % reduction in phenol consumption and reduced temperature deviations by 2.2 % compared to a baseline fuzzy controller. Energy savings ranged from 1 % to 2 % depending on operating scenarios. These results were supported by repeated simulations and statistical analyses across multiple testing cycles. The proposed system demonstrates the potential of quantum-inspired fuzzy control to enhance process efficiency, reduce energy use, and improve product quality in complex chemical extraction applications.

This study presents the modeling, simulation, and experimental validation of a fuzzy-logic-based intelligent control system applied to a cabinet-type solar dryer for drying Plantago major leaves under real climatic conditions in Tashkent (Uzbekistan) during the summer season. Traditional on/off and PID controllers often fail to maintain optimal drying conditions due to nonlinearities and fluctuations in solar radiation and ambient temperature. To address these limitations, a fuzzy inference system (FIS) was developed in MATLAB/Simulink using two input variables—internal air temperature and relative humidity—and one output variable—fan speed. The fuzzy system employed seven linguistic rules with triangular membership functions, allowing for smooth and adaptive real-time control. The experimental setup included a cabinet solar dryer loaded with 1,5 kg of Plantago leaves. During field trials, the solar irradiance ranged from 650 to 900 W/m² and ambient temperatures from 32 °C to 42 °C. The fuzzy controller’s performance was benchmarked against a conventional PID controller. The results showed that the fuzzy system reduced the total drying time by 22%, improved energy efficiency by 18%, and ensured a better moisture uniformity (±4%) across trays. Moreover, the post-drying phytochemical analysis confirmed better preservation of heat-sensitive bioactive compounds. The fuzzy controller maintained the drying air temperature within the optimal 45–50 °C range, even under fluctuating external conditions. This study demonstrates that fuzzy logic provides an effective solution for adaptive solar drying in hot continental climates. Future work will focus on integrating IoT-based remote monitoring and hybrid control optimization to enhance automation and scalability.

The accelerated growth of the global economy has contributed to the shift towards sustainable energy sources, amplifying the need for biomethanol production from biomass-derived syngas. This study evaluated an extensive kinetic model incorporating equilibrium restrictions for biomentanol synthesis utilizing a fibrous Cu/Zn/Al/Zr catalyst. A modified Langmuir–Hinshelwood–Hougen–Watson (LHHW) model was employed to better capture the equilibrium effects of CO hydrogenation, CO₂ hydrogenation, and reverse water gas shift (RWGS) for precise predictions. Parameters evaluated included temperature (200-300°C), pressure (50-100 bar), H₂/CO ratio (1.5–2.5), and H₂/CO₂ (2.5–3.5) molar feed and space velocity (100-1000 h⁻¹). LHHW rate constants were calculated from pilot scale experimental data, and the model bioethanol yield was compared against ground truth values, yielding a deviation of 7.23%. The LHHW was solved with the ODE15s solver in MATLAB to address the stiff equations generated by the LHHW model. Sensitivity analysis evaluated by the finite difference technique assessesed the uncertainty of biomethanol yield, highlighting that pressure had the most influence. From this, an 11.70% yield increase was seen when pressure was raised from 65 to 75 bar. ChemCAD8 was employed for process flowsheeting, enabling quantification of the biomethanol yield, and analyzed with the coefficient of determination (R²), root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The model attained an R² of 0.9756, an MSE of 0.2453, an RMSE of 0.4953, and an MAE of 0.035. A plug‐flow reactor simulation reached 0.402 kg methanol per kg biomass with 87.3 % CO₂ conversion, compared to experimental values of 0.379 kg/kg and 81.4 %. The simulation carried a ±4.23 % uncertainty at 95 % confidence. These results were generated at optimal points spanning 246 °C, 78 bar, 379 h^-1, H₂/CO=2.3, and H₂/CO₂=3. The refined kinetic model accurately predicted biomethanol performance; however, for effective scale-up application, heat management of the catalyst should be practiced.

Abstract. Water scarcity and environmental pollution remain among the most pressing global challenges of the 21st century, particularly in industrial regions. This study proposes an artificial neural network (ANN)-based predictive model for the intelligent control of water hardness (H) and total dissolved solids (TDS) mass concentration in the industrial wastewater treatment process using ion-exchange resins. Experimental data obtained from a pilot-scale purification system treating wastewater from the Kungrad Soda Plant in Uzbekistan were used to train and validate the model. The ANN was developed in MATLAB using a feedforward backpropagation algorithm, with H (in milligrams of calcium carbonate per litre, mg/L) and TDS (in mg/L) as input quantities and the servo valve opening degree (SerK) as the output quantity. The predictive model was trained on 80 experimental datasets and achieved high accuracy, with a mean squared error (MSE) of 9.72 × 10⁻⁴ and a regression coefficient R = 0.987, indicating a strong correlation between predicted and measured values. The trained ANN accurately modelled the nonlinear interdependence between influent water quality parameters and process control actions. For example, at input values of H = 2.0 mg/L and TDS = 20.0 mg/L, the model predicted a valve opening degree of 12.5%, which closely matched the empirical value. Similarly, when H = 3.43 mg/L and TDS = 35.5 mg/L, the model correctly predicted a minimised valve opening of 4.16%, confirming its predictive reliability across a broad operational range. These results demonstrate that the proposed ANN-based model can serve as an effective and reliable tool for real-time control and optimisation of wastewater treatment processes. Its ability to generalise from experimental data makes it particularly well-suited for dynamic and uncertain industrial environments, supporting smarter, data-driven decision-making in water resource management.