Introduction

The combination of single-walled carbon nanotubes (SWCNTs) and reduced graphene oxide (rGO) in the photoresist minimizes the amount of carbon particles. While developing neuroimplants designed to restore damaged neural networks or modulate pain transmission, the key requirements are both biocompatibility and electrical conductivity.

Methods

Photolithography of the composite was performed with an ytterbium laser at a wavelength of 1035 nm with pulses of 100 ns duration, repetition rate of 30 kHz and power of ~550 mW. The photoresist used was SWCNT 0.6 mg/mL, rGO 0.6 mg/mL and SWCNT (0.3 mg/mL)/rGO (0.3 mg/mL). The final biohybrid structure contains proteins, chitosan and eosin Y. The formation of the structure was simulated by the molecular dynamics method with SEM monitoring.

The specific electrical conductivity of 15 5×5 mm layers was determined using the four-probe method. Biodegradation was estimated by the mass of the swollen sample in an isotonic sodium chloride solution and dried, with the following enzymes: lipase - 25,000 PhEur; amylase - 18,000 PhEur and protease - 1000 PhEur. In vitro biocompatibility studies were conducted with the Neuro 2A cell line with the MTT test for 72 hours.

Results

Specific conductivity: 17 mS/cm (rGO), 19 mS/cm (SWCNT) and 35 mS/cm (SWCNT/rGO). The mass loss of the SWCNT/rGO sample was 40%, the swelling increased by 20%, and the optical density (OD) of the MTT test was 0.76. Control cover glass OD=0.62. Enzymes degraded the sample in a week. Simulation and SEM confirmed laser-induced rearrangement of SWCNTs with RGO into single nanostructures with the formation of non-hexagonal carbon elements.

Conclusions

The composite developed in the process of degradation can be replaced by biological tissue during this period with the maintenance of electrical conductivity.

Funding: The work was supported by the Ministry of Education and Science of the Russian Federation (project FSMR-2024-0003).

At temperatures higher than 5 ^oC in the cooling chamber of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0^oC and 5^oC is needed to maintain food safety and qualty. This study presents model predictive control of a data-driven medium-temperature cold storage system using a subspace system identification technique. The identified linear model presents a holistic view of the whole system, with each subsystem cohesively linked together. The data-driven model was developed from synthetic data derived from a high-fidelity simulation benchmark model of a supermarket refrigeration system from Aalborg University, Denmark. The benchmark model consists of a medium-temperature closed display case, the suction manifold and the compressor rack. The data on the expansion valve, suction pressure, compressor capacity, heat transfer rate and ambient temperature were taken as inputs, while the data on the air and goods temperatures were taken as outputs based on expert knowlege. A linear model predictive controller was designed to control the temperature outputs of the identified linear model, and the outputs were compared with the proportional–integral dead band control used in the benchmark. Simulation results for 24 hours show that the model predictive controller was able to achieve an air temperature and a goods temperature within the recommended temperature range of 0 ^oC and 5 ^oC that guarantees safe storage of fresh fishes. These results imply that a reduced-order model of a commercial refrigeration system that is robust, reliable and stable can be developed and controlled to achieve the goal of food safety, thereby guaranteeing food security and reducing costs.

The transition toward sustainable energy systems has given rise to a new trend in the electrical power grid, where consumers actively participate in energy production and management—commonly referred to as prosumers. By simultaneously producing and consuming electricity, these prosumers reshape the electricity landscape by creating new complexities and possibilities for managing the energy distribution, especially with the increasing rate of renewable energy integration into the grid. This paper proposes a novel optimal economic dispatch and control method for smart power management and dispatching of renewable energy sources (RESs) in a prosumer-centric-environment-based residential microgrid. The proposed strategy integrates renewable energy sources, including solar and wind generation, within a decentralized microgrid architecture coordinated with energy storage devices. The prosumer microgrid includes a photovoltaic (PV) system, a wind turbine, a Hybrid Energy Storage System (HESS), and an Electrical Vehicle (EV), as well as electrical loads. The HESS includes Redox Flow Batteries (RFBs), Superconducting Magnetic Energy Storage (SMES), and Fuel Cells (FCs). By employing a Fuzzy-Logic-based Power Management System (PMS) optimized via the Particle Swarm Optimization (PSO) algorithm, the control method ensures efficient energy allocation and minimizes operational costs while maintaining grid-connected microgrid stability. Simulations of several market-based interaction scenarios between prosumers and the grid were conducted. The simulation results demonstrated the effectiveness of the proposed approach in achieving economic benefits while empowering end-users to take on a role in the energy ecosystem.

Industrial information systems leveraging web technologies, ISOWT, face escalating security challenges, particularly in critical sectors like energy. Traditional qualitative assessments often fail to provide actionable, real-time insights for managing complex, dynamic threats. This paper introduces a novel security index for evaluating ISOWT in industrial organizations, integrating fuzzy logic, metric-based evaluations, fuzzy Markov chains, and multi-agent systems. The index quantifies deviations from an ideal "center of safety," enabling early risk detection and proactive mitigation. Validated through case studies on Syria’s energy sector systems—namely, the Ministry of Electricity website and Mahrukat fuel management system—the methodology achieved significant improvements, including a 45.9–58.5% increase in the security index, 56.9–60.3% reduction in page load times, and 78.3–82.4% decrease in vulnerabilities. Compared to existing methods, this approach offers superior quantitative precision, real-time monitoring, and predictive capabilities. This scalable, automated framework addresses critical gaps in ISOWT security assessment, providing a robust tool for enhancing system resilience. Its adaptability makes it applicable across diverse industrial contexts, contributing to advanced cybersecurity practices for critical infrastructure. Future work will focus on integrating advanced technologies, expanding applications to other sectors, developing adaptive fuzzy models, addressing human factors, and enhancing visualization capabilities. These advancements will further strengthen the methodology's impact and address the evolving security challenges faced by industrial organizations in an increasingly connected world.

In recent years, there has been a growing interest in the application of nano-scale thin films in various fields such as high-density storage systems, magnetic media, and micro/nanoelectromechanical systems (MEMS/NEMS). This interest is driven by advancements in the processing of metal nanomaterials. However, in many cases, the overall performance of metal thin films is compromised by mechanical weaknesses that arise during practical use. Our research focuses on investigating the deformation mechanisms and mechanical properties of gold (Au) thin films using the nanoindentation technique and molecular dynamics simulation. Our study reveals that near the indented region, elastic behaviors are observed at lower indentation velocities. As the indentation velocity increases to 15 m/s, dislocation propagation and nucleation are initiated. Furthermore, we found that the hardness of Au is significantly dependent on the indentation velocity. The lowest hardness is observed at an indentation velocity of 5 m/s, while the highest value is attained at 15 m/s. DXA analysis demonstrates that a lower number of dislocations are generated at an indentation velocity of 5 m/s, whereas a higher quantity of dislocations is evident as the indentation velocity is increased to 15 m/s. These findings indicate that there is an increase in hardness in the Au specimen, particularly at higher indentation velocities.

The filling of single-walled carbon nanotubes (SWCNTs) is a promising method for controlling the properties of SWCNTs for applications. Filling of SWCNTs with metal halides, metal chalcogenides, metals, and metallocenes is performed. Metal halides are a large group of filling substances with large work functions. Filling of the SWCNTs with metal halides leads to p-doping of the SWCNTs. Metal chalcogenides can lead to p-doping of the SWCNTs, too. Metals with low work functions result in n-doping of the SWCNTs. Filling of the SWCNTs with metallocenes with further annealing leads to variations in the doping types of the SWCNTs. The electronic properties of the filled SWCNTs are important for nanoelectronics, electrochemical energy storage, and biomedical applications. SWCNTs filled with various substances with tailored electronic properties can be applied in those fields. The electronic properties of the filled SWCNTs are influenced by the structure of the filled material. The structure of the filled materials defines their band structure. In SWCNTs with different diameters, various structures of the filled materials are obtained. This influences the electronic properties of the filled SWCNTs. Here, the structure and electronic properties of cobalt bromide-filled SWCNTs are investigated. Using high-resolution transmission electron microscopy and Raman spectroscopy, we reveal the properties of the filled SWCNTs. Novel structures of cobalt bromide-filled SWCNTs with large p-doping are observed. The obtained data are important for various uses of the filled SWCNTs.

In well-mapped environments such as estate buildings, Unmanned Ground Vehicles (UGVs) play a crucial role in applications such as mail delivery, waste collection, and security surveillance. These vehicles operate autonomously or under remote human control. For autonomous UGVs, trajectory tracking controllers are essential for the wheels to accurately follow the desired path with minimal or no tracking error. The Proportional–Integral–Derivative (PID)-based control approach is one of the most widely adopted techniques for path tracking; however, its performance can degrade due to improper parameter tuning and external disturbances. Thus, this research aims to develop an optimized PID controller for trajectory tracking of a UGV in a virtual simulation environment, UGV3DSim. This was achieved by modeling and simulating a four-wheel UGV with front wheel steering control and rear wheel speed control in MATLAB/Simulink. The physical model of the UGV, comprising the chassis, top cover, and wheels, was designed using 3D modeling software and imported into the virtual environment. Three optimized PID controllers were developed using the Single Candidate Optimizer (SCO) algorithm, the Ali Baba and the Forty Thieves (AFT) algorithm, and the Walrus Optimizer (WO). These controllers were evaluated across various trajectory scenarios: linear and circular paths, go-to-goal navigation, and infinity-shaped trajectory tracking. The performance of the developed controllers shows that the WO-based controllers generated better trajectory tracking, had minimum overshoot, and settled faster in all three scenarios.

Cardiovascular disease (CVD) remains a major global health issue, closely associated with the excessive intake of saturated fatty acids (SFAs). High SFA consumption contributes to elevated cholesterol levels and vascular obstruction, emphasizing the need for healthier alternatives in food products. One promising strategy is the development of functional shortening with reduced SFA content. This study aimed to optimize the ratio of Glycerol Monostearate (GMS) and Sorbitan Monostearate (SMS) to improve the lipid profile of low-saturated fat shortening using integrated lipidomic and chemometric approaches. The experiment involved two concentrations of GMS:SMS (5% and 10%) with six formulations each: 100:0, 80:20, 60:40, 40:60, 20:80, and 0:100, prepared in triplicate. Lipid profiling was conducted using lipidomic analysis, along with standard assessments such as free fatty acid (FFA) content, peroxide value (PV), and fatty acid composition via Gas Chromatography–Mass Spectrometry (GC-MS). Multivariate analyses, including Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), were employed to interpret the complex dataset. Among the formulations, three under the 5% GMS:SMS group (100:0, 80:20, and 60:40) showed the best oxidative stability based on FFA and PV. Notably, the 5% GMS:SMS (80:20) formulation demonstrated the most favorable lipidomic profile, marked by a higher number of unsaturated lipid species (39 in ESI+) and reduced SFA content (12 in ESI+). These findings highlight the potential of GMS and SMS as structuring agents in designing low-saturated shortening products with improved lipid profiles, offering a viable strategy for the development of heart-healthier fat-based food products.

Precise temperature regulation in nonlinear and highly dynamic systems—such as the Continuous Stirred-Tank Heater (CSTH)—poses significant control challenges due to internal nonlinearities, time-varying dynamics, and external disturbances. Traditional linear controllers often fall short in addressing these issues effectively. To overcome these limitations, this paper proposes a novel Fuzzy Logic Control (FLC) strategy tailored for CSTH temperature regulation. The proposed controller features a hybrid structure that combines a Fuzzy Fractional-Order Proportional-Integral (FOPI) controller cascaded with a Fractional-Order Proportional-Derivative (FOPD) compensator, forming a Fuzzy FOPI-FOPD control scheme. This configuration harnesses the strengths of fuzzy logic for handling uncertainty and nonlinearity. To optimize the controller's parameters, various metaheuristic algorithms are employed, with a primary focus on the Teaching-Learning-Based Optimization (TLBO) technique. The Integral Time Absolute Error (ITAE) is used as the performance index to implememt the optimization process, ensuring effective minimization of tracking error over time. Extensive simulation studies are conducted under different operating conditions, including setpoint variations and external disturbances, to validate the effectiveness of the proposed method. Comparative analysis reveals that the Fuzzy FOPI-FOPD controller outperforms conventional control strategies in terms of transient response, steady-state accuracy, and robustness. Specifically, the controller exhibits reduced overshoot, faster settling time, and improved disturbance rejection. These results highlight the controller's potential for practical deployment in industrial thermal processes. Overall, the proposed optimization-based fuzzy fractional-order control framework offers a highly effective and flexible solution for complex nonlinear process control applications.

This research presents a neural network-based predictive model to evaluate the operational performance of a cabinet-type solar dryer utilized for dehydrating Plantago major leaves under natural climatic conditions. While solar drying is known for its sustainability and energy efficiency, its performance is highly influenced by complex, nonlinear factors such as solar irradiance, internal chamber temperature, and ambient humidity. To overcome the constraints of conventional statistical modeling, a multilayer feedforward artificial neural network (ANN) was constructed using MATLAB’s Neural Network Toolbox. The architecture consisted of an input layer with three neurons, two hidden layers each containing ten neurons, and a single output neuron. Input parameters included solar radiation (W/m²), drying chamber temperature (°C), and relative humidity (%), while the output was defined as drying efficiency, calculated from weight loss and initial/final moisture content. Experimental data were gathered during the summer season under varying irradiance levels (650–900 W/m²) and ambient temperatures (30–42°C), yielding 120 samples. The dataset was normalized and partitioned into training (70%), validation (15%), and testing (15%) subsets. The network was trained using the Levenberg–Marquardt optimization algorithm to minimize the mean squared error. The final model achieved a strong predictive correlation (R² = 0.97) and low error (MSE < 0.0015), demonstrating a 22–25% improvement over traditional models. This methodology enables real-time monitoring and supports the integration of intelligent control systems in solar drying processes, enhancing both efficiency and consistency.