Food drying has an impact on the final product's quality, energy usage, and environmental sustainability, making it a crucial step in food preservation. Although crucial, traditional optimization methods usually fall short in the complex, nonlinear, and multi-objective process of food drying. Algorithms inspired by nature that simulate biological, chemical, and physical systems have become popular tools for enhancing these procedures; they perform better when faced with multiple variables and conflicting objectives. The application of nature-inspired optimization techniques, including artificial neural networks, genetic algorithms, particle swarm optimization, and the non-dominated sorting genetic algorithm II, to food drying is critically examined in this work.

The main focus of the study is on the theoretical foundations of these algorithms, their specific application in food drying, and the benefits and drawbacks of each method. Furthermore, we examine recent case studies that demonstrate the practical use of these strategies in food processing plants to enhance environmental impact, energy efficiency, and product quality. The findings imply that if these advanced optimization algorithms are included into computer-integrated production systems, the food drying industry may embrace more cost-effective and ecologically friendly procedures. The scalability of these technologies for industrial applications is also critically assessed, exposing practical challenges and providing suggestions for future research directions. This in-depth examination seeks to serve as a helpful tool for experts and academics curious in the food dying optimization, presenting insights that are board and deep, suitable for a multidisciplinary audience.

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with GPS tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system's effectiveness in detecting elevated pollutant levels in rural areas with solid-fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement.

The stamping process plays a key role in several industries, namely the automotive industry. Due to its widespread application, this sector has driven the evolution of the technologies and materials used in plastic forming. Although high-strength steels have shown excellent mechanical properties, there are drawbacks related to their formability, which are associated with the appearance of defects that compromise product quality. These materials can show unpredictable responses due to variations in their chemical composition, heat treatment, processing, and other parameters derived from their manufacturing process; thus, tests are needed to ensure that their performance is satisfactory. With the intention of predicting the appearance of these defects, the industry has resorted to numerical simulation tools to study the behavior of materials when subjected to forming processes. Simulation software is therefore a very useful instrument when knowledge about a certain material or process is limited. Therefore, the focus of this work was to study the behavior of high-yield-strength steels and the potential of numerical modeling and the advantages of applying it to the simulation of manufacturing processes to optimize the process and eliminating or preventing the formation of defects. The used Stampack software has proven to be very effective in preventing and resolving defects that arise during stamping. Through the wide range of features that Stampack offers, it was possible to recognize possible material breakage and risk factors. After the analyses, the geometry of the parts was optimized, and the stamping tests proved the veracity of the numerical results, with a 100% utilization rate of the components produced.

The intelligent control of industrial wastewater treatment processes, especially for the removal of residual chlorine ions, is crucial for ensuring environmental safety, protecting infrastructure from corrosion, and optimizing operational efficiency. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control model was developed and implemented for real-time control of a laboratory-scale activated carbon filtration unit. The primary objective was to dynamically regulate the chlorine removal process by adjusting the influent flow rate and activated carbon dosage based on predicted output parameters. The ANFIS model was trained using a dataset of 200 experimental trials that encompassed six key process variables: flow rate (m³/h), initial chlorine concentration (mg/L), system pressure (bar), pH, temperature (°C), and activated carbon dose (kg). These six parameters served as input variables, while the output control targets included residual chlorine concentration (mg/L), energy consumption (kWh), and overall system efficiency. The designed ANFIS controller actively modulated the flow rate and carbon dosage in response to changes in initial chlorine concentration, temperature, and pH, thereby ensuring optimal chlorine removal under varying conditions. Compared to a traditional Proportional–Integral–Derivative (PID) controller, the ANFIS-based system demonstrated superior performance in dynamic tracking, disturbance rejection, and steady-state regulation. Specifically, ANFIS achieved a 72.4% reduction in steady-state error, decreased settling time by 2.7 seconds, and enhanced overall prediction accuracy, as confirmed by regression values exceeding 0.96. Additionally, optimized control actions by ANFIS led to a 1.6% reduction in energy consumption compared to PID, contributing to more sustainable process operation. These results confirm the efficacy of ANFIS in handling nonlinear, multivariable industrial processes and highlight its potential for integration into full-scale intelligent supervisory control systems such as SCADA. The research demonstrates that the hybrid use of neural networks and fuzzy inference provides a flexible, data-driven solution for real-time control in industrial water treatment applications.

Acinetobacter baumannii, an opportunistic human pathogen, targets critically ill patients as its primary target. A. baumannii, which was previously considered benign, is now recognized as a global hazard in the healthcare context. Its propensity to acquire multidrug, extensive drug, and even pan-drug resistance phenotypes at previously unanticipated rates is the primary reason for this. The objective of this investigation was to characterize the physicochemical properties, functional annotation, structure anticipation with active site determination, and antigenicity of the selected uncharacterized protein of A. baumannii. The protein's acidic nature was indicated by its putative pI (4.43), while its hydrophilicity was indicated by its GRAVY (-0.404). The functional annotation indicated that the protein was involved in catalytic reactions that led to the synthesis of L-asparaginyl-tRNA (Asn) and L-glutaminyl-tRNA (Gln). The protein was linked to both molecular functions and biological processes, as evidenced by the GO analyses. The protein's involvement with ten additional proteins was also demonstrated by the PPI network. The structural analyses demonstrated that nearly 50% of the residues were involved in the formation of an alpha-helix, while nearly 46.15% were involved in the formation of a random coil. The Swiss Model was found to be the most appropriate model for the uncharacterized protein in the 3D structural comparison. Furthermore, we quantified the active site's volume (4235.245 ų) and surface area (896.977 Ų). The protein was identified as non-antigenic. In order to verify the results of this investigation, an experimental investigation should be implemented.

Solar dryers are recognized as sustainable and energy-efficient technologies for dehydrating agricultural products under environmentally friendly conditions. These systems utilize solar radiation as a renewable energy source to reduce the moisture content of produce while preserving its nutritional and microbiological quality. However, the performance of solar dryers is significantly affected by environmental variables such as ambient temperature, solar irradiance, and airflow rate, which fluctuate dynamically during the drying process. Conventional control strategies, such as Proportional–Integral (PI) controllers, often exhibit limitations in such nonlinear and time-variant systems due to their slower response and limited adaptability.

This study proposes an intelligent control approach based on Artificial Neural Networks (ANNs) to enhance the accuracy and responsiveness of temperature regulation within a solar drying chamber. A mathematical model of the drying process was developed and implemented using MATLAB R2014a and the Simulink simulation environment. The ANN-based predictive controller was benchmarked against a traditional PI controller through a series of comparative simulations. The results indicated that the ANN-based system achieved a settling time of 160 seconds, representing a 36% improvement over the 250-second response time observed with the PI controller. Moreover, the ANN controller maintained temperature stability within ±1.2°C, demonstrating superior precision and robustness.

The findings suggest that neural network-based intelligent regulation significantly improves the dynamic performance of solar drying systems, enabling the real-time optimization of drying conditions. This method holds considerable promise for automating and industrializing solar drying technologies, with potential benefits in energy savings and product quality enhancement.

This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The system was represented as a cascade of several continuous stirred-tank reactors (CSTRs), and experimental data confirmed a fermentation cycle of approximately 10 days. During this period, biomass concentration reached 6.8 g/L and ethanol levels exceeded 42 mmol/L. Substrate concentration (S) declined from 120 to 5 g/L, demonstrating effective conversion. The model was linearized around an operating point and reformulated into a 12-state space system with input variables: temperature (set at 20–22 °C) and pH (maintained within 4.2–4.5). These inputs were controlled using fuzzy logic control (FLC) and model predictive control (MPC). Simulation results indicated that the FLC controller reduced temperature deviation to ±0.3 °C and minimized pH fluctuation below ±0.05. The MPC strategy improved substrate consumption efficiency by 8.5% and decreased fermentation time by 12 hours under optimized input profiles. The combined FLC–MPC control scheme demonstrated superior robustness, smooth trajectory tracking, and adaptability to biological variability compared to traditional methods. The developed framework supports intelligent brewery automation and provides a scalable foundation for further integration of digital fermentation technologies.

Cross-functional organizational projects are frequently challenged by executional uncertainty, complex interdepartmental dependencies, and constrained availability of role-specific human resources. This study presents a discrete-event, agent-based simulation framework designed to assess the effectiveness of three workflow control strategies—Static, Reactive, and Predictive—in managing task execution within resource-limited organizational environments. The simulation model emulates a structured procurement process comprising ten interdependent tasks executed by five functional roles: Engineering, Procurement, Finance, Legal, and Compliance. Each task is characterized by variable execution durations, precedence constraints, and department-specific resource requirements. Agents represent staff roles with finite availability, and tasks are processed in a non-preemptive, first-come-first-served manner. Control strategies are implemented as dynamic scheduling policies: the Static strategy follows a predetermined execution sequence; the Reactive strategy adapts in response to observed task delays or resource conflicts; and the Predictive strategy utilizes short-term historical performance data to forecast task durations and proactively adjust the schedule. The experimental design spans multiple configurations, varying control strategy type, task duration variability (low, medium, high), resource availability (full, moderate, limited), and project load (single vs. concurrent execution). Each configuration is simulated 100 times using Monte Carlo techniques to capture performance distributions. Key performance indicators include total project duration, average task delay, agent utilization rates, and rescheduling frequency. Simulation results demonstrate that the Predictive control strategy consistently outperforms its counterparts, particularly in high-variability, low-resource scenarios. It achieves this by anticipating future delays and smoothing resource demand, thereby minimizing downstream bottlenecks and task queue congestion. Analysis further reveals that highly central, resource-constrained roles—most notably in Finance and Legal—are primary contributors to systemic delay propagation. The proposed simulation framework offers an extensible methodology for analyzing general workflow processes in organizational systems and provides data-driven insights for optimizing project executions.

The post-harvest mechanization of makapuno processing is key to improve efficiency and quality of the product. This study aimed to design, and evaluate an electric motor-driven makapuno shredder to enhance post-harvest processing. The makapuno shredder consists of a 186 W electric motor (1725 rpm), a V-belt and pulley transmission system, the shredding blade, and the framing system. A Completely Randomized Design (CRD) was conducted to evaluate the performance of the VMAC -1 makapuno variety under three shredding treatments: 35 rpm, 144 rpm, and through the manual shredder. The shredding capacity (kg/hr), shred quality (% per class), and the total weight of shreds (g per 5 min) were the primary performance parameters.

Results revealed significant differences among treatments. The shredding capacities were 12.17 kg/hr, 13.82 kg/hr and 10.34 kg/hr for 35 rpm, 144 rpm and the manual shredder, respectively. Additionally, the total weight of shreds after 5 minutes was 1,151.27 g (144 rpm), 1,014.03 g (35 rpm), and 861.20 g (manual), which confirms that higher rpm increases the output rate. However, in the shred quality analysis, it was revealed that 144 rpm resulted in over-shredding, leading to high proportion of finer shreds and reduce shred quality. This also resulted in 77.12% of shreds in Class C (low quality) and did not yield in Class A (high quality). Conversely, the 35 rpm speed produced highest percentage of Class B shreds with 52.70% that offers balanced optimization of the shredder. Although, manual shredder produces high-quality shreds, the 35 rpm setting provides better balance between efficiency and quality. Further improvement of the design of the shredding blade is recommended to match the quality of the manual shredder. Hence, the findings suggest the need to provide an optimized mechanized processing that contributes to sustainable and scalable makapuno production.

Grid impedance variation has the possibility of leading to voltage oscillation and control instability, which poses a serious problem to electric vehicle (EV) charger design. In response to this problem, this paper proposes a robust control approach that is capable of dealing with grid impedance variation and system uncertainties. The proposed dual-loop control strategy is composed of an outer-loop proportional–integral (PI) controller and an inner-loop robust state feedback controller with integral action. The benefits of control are maximized according to linear matrix inequality (LMI) techniques. This paper aims to address the effects of grid impedance variation by including the uncertainty model considering the potential varying parameters in the control design process. Additionally, the uncertainty model considers sixteen possible sets, which are described by variations in the four most important parameters: grid impedance, grid resistance, filter impedance, and filter resistance. Compared to PI control, this LMI-based robust control method provides significantly improved disturbance rejection, faster transient response, and greater resilience to parameter variations, ensuring a more reliable and efficient operation of the EV charger. This proposed single-stage AC/DC EV charger topology also offers higher efficiency, reduced component count, and lower cost compared to conventional double-stage designs, making it a more compact, reliable, and economically attractive solution for modern electric vehicle charging systems. The simulation results confirm that the designed control method maintains excellent charging performance under different grid impedance conditions with a unity power factor.