Uzbekistan's rural regions experience continuing issues in energy access because of poor grid networks and variable renewable sources. The solution is small-scale wind turbines and energy storage. But the wind speeds and load demand are variable and thus intelligent control systems are needed for them to perform at their best.
This paper is an attempt to design an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to control a small wind power system with a battery storage unit. The controller will be intelligent to control the flow of power between the wind turbine, battery, and local loads. A MATLAB/Simulink model is created to simulate the reaction of the system to various wind and load conditions.
The results of the simulation prove that the ANFIS controller is better in stabilizing the voltage, reducing power fluctuations, and optimizing the battery charge–discharge cycle compared to other conventional PI and standalone fuzzy controllers. Environmental variability is effectively responded to by the system, making it more reliable and energy-efficient.
ANFIS control and wind–battery microgrid integration provide a feasible and expandable off-grid electrification solution to remote areas. This strategy promotes the renewable energy ambitions of Uzbekistan and offers an example of smart microgrid implementation in other resource-limited rural areas. The next steps would be in practical application and hardware verification.

With the growing integration of machine learning techniques into geophysical research, their application to ionospheric modeling specifically in predicting Total Electron Content (TEC) using GNSS data, has gained significant traction. While various studies have explored this approach across different global regions, the Philippine sector remains largely unexamined despite its scientific relevance. Situated in the low-latitude ionospheric region, the Philippines experiences complex phenomena such as the Equatorial Ionization Anomaly, making it an ideal candidate for focused TEC modeling. This study presents a machine learning-based approach to predict regional ionospheric TEC using GNSS data from the PIMO receiver station (14.6°N, 121.1°E), covering the period from 2010 to 2020. Three ML algorithms—Random Forest, Support Vector Machines, and Gradient Boosting—are used to develop predictive models using features such as temporal parameters and space weather indices: average interplanetary magnetic field magnitude, Bz-component, solar wind proton density, plasma speed, flow pressure, Kp-index, Dst-index, F10.7 solar flux, AE-index, and the Lyman-alpha index. Model performance are evaluated and compared across the three algorithms, with further analysis conducted on feature importance and dimensionality reduction using Principal Component Analysis. Preliminary expectations suggest that all models will yield predictions closely aligned with observed TEC values, with the F10.7 solar flux and Lyman-alpha indices emerging as the most influential predictors. This work lays the foundation for more comprehensive TEC modeling in the Philippine region by enabling future expansion to include data from additional local GNSS stations, ultimately enhancing the spatial resolution and robustness of regional ionospheric models.

The operation of walking-type agricultural tractors (WTATs) emits exhaust gases, which may pose health risks to operators from extended exposure. Comprehending the dispersion characteristics of these emissions is crucial for formulating effective safety protocols. This study evaluated the near-field spatial distribution of carbon monoxide (CO) emissions from a WTAT through experimental measurements and computational fluid dynamics (CFD) simulations, considering different throttle settings, relative wind speeds, and wind angles of attack. The findings indicated that throttle settings had a significant impact on the concentration of CO emissions produced by the engine. A reduction of approximately 86% in CO concentration was observed within 20 cm from the exhaust outlet. At elevated relative wind speeds, the CO plume exhibited a tendency to disperse in alignment with the airflow direction, directing emissions toward the far-left side of the operator while maintaining a low risk of exposure, irrespective of forward speed. Ambient wind angles ranging from -15° to -30° were identified as critical, as emissions were directed toward the operator's body, thereby increasing potential exposure. The CFD model demonstrated strong agreement with experimental data, particularly at reduced forward speeds, and effectively identified critical exposure zones. This study advocates for the utilization of personal protective equipment, including safety masks, during field operations to reduce health risks linked to inhalation of exhaust gases.

In recent years, the number of consumers of three-wheeled vehicles has increased because of their practicality. However, along with the rise in demand comes a growing safety concern, particularly regarding vehicle lateral stability during maneuvers. This highlights the need to understand the factors that influence stability.

This research aims to develop a validated model that evaluates the effects of the banking angle on three-wheeled e-bike lateral stability. Using simulation, the model is designed to determine critical banking angles that lead to skidding during cornering. The static model was validated by comparing the weight of the actual and simulated e-bike model. Then, the dynamic model was validated by comparing the acceleration when undergoing steady-state cornering.

The results validate the static and dynamic models as the solved percentage difference between the actual and simulated setup was limited to 19.628%. They also show that the skid index increases as the bank angle increases and the e-bike starts to skid beyond a banking angle of 6 degrees. It was found that the e-bike understeers at low bank angles due to the increased centrifugal force. By contrast, the e-bike oversteers at high bank angles due to the influence of gravitational force.

Drivers and passengers should be aware that driving at high speeds during cornering, especially on roads with high bank angles, could lead to accidents. To avoid accidents, drivers should consider driving at speeds less than 12 km/h and on inclines less than 6°.

In response to the escalating global fuel crisis, this study investigates the potential of using chlorophyll from the microalga Chlorella vulgaris as a natural, abundant, and sustainable photosensitizer for Dye-Sensitized Solar Cells (DSSCs), aiming to provide an environmentally benign alternative to conventional ruthenium-based dyes. The study investigates chlorophyll extracted from the highly abundant microalga Chlorella vulgaris as a green, cost-effective alternative to the rare and expensive ruthenium-based dyes traditionally used. The study began with a rigorous comparison of extraction solvents, determining that acetone yields a significantly higher concentration of chlorophyll (28.76 µg/L) than methanol, establishing it as the superior medium for pigment harvesting. When this chlorophyll extract was integrated as a photosensitizer in a TiO₂-based solar cell, it achieved a power conversion efficiency of 0.0115%. While modest, this represents a more than 2,000-fold performance increase over the dye-free control cell, unequivocally demonstrating the pigment's photoelectric activity. However, the results reveal a critical limitation: the inherent molecular structure of chlorophyll, while perfected for photosynthesis, inhibits robust electronic binding to the TiO₂ semiconductor surface, leading to inefficient charge injection and low overall performance. This study provides a crucial insight for the field, concluding that while Chlorella vulgaris is an excellent and sustainable source, future success for chlorophyll-based DSSCs will depend on molecular engineering strategies to enhance the crucial dye–semiconductor interface.

Slaughterhouses release a significant amount of wastewater containing varying concentration of organic matter, nutrients, and other pollutants that necessitate robust treatment solutions. In this paper, a multi-stage lab-scale plant that consists of a pre-treatment stage, two trains with either a Static Granular Bed Reactor (SGBR) and Expanded Granular Sludge-bed Bioreactor (EGSB) coupled individually to membrane bioreactors (MBRs), were used. This configuration was designed to systematically evaluate and compare the efficacy of each anaerobic bioreactor type within an integrated treatment train. The lab-scale plant was operated for 77 days, a duration selected to ensure process stability and collect sufficient data for statistical analysis, and quality performance parameters were investigated using capability indices (Cp, Cpk, Pp, and Ppk). The secondary experimental data was analysed through QI Macros (SPC software for Microsoft Excel) to provide a rigorous, statistical evaluation of process control and performance. The lab-scale plant designed with the three stages, i.e. bio-physical pre-treatment stage – SGBR or EGSB units – MBRs, indicated a significant potential and capability of the overall process to treat PSW, whereby treatment stages had Pp = Ppk = 1.00 with 99.73% of each treatment stage outputs being within specifications. This high degree of statistical capability demonstrates exceptional process control and a low probability of producing non-conforming effluent. Furthermore, all stages performed with Cpk equal to 0.99, 1.06, 0.83, 0.86, and 0.79, respectively. The key findings of the paper reveal the treatment efficiency, Operational Stability, and Statistical Performance; which is a focused interpretation of the Capability Indices (Cp, Cpk) for each reactor, explicitly stating which system showed better performance.

The increasing demand for sustainable and cleaner fuels has encouraged the exploration of alcohol–gasoline blends as alternatives for spark-ignition (SI) engines. This study investigates the performance and emission characteristics of a single-cylinder, four-stroke SI engine fueled with ternary blends of n-butanol, ethanol, and n-propanol mixed with gasoline. A single-cylinder, four-stroke SI engine was tested using the Varying Speed Test procedure based on PNS 396–397:2024. The tests were carried out by gradually decreasing the engine’s shaft rotation speed in 100-RPM intervals (from 2900 rpm to 3800 rpm) from its rated speed, while keeping the carburetor setting constant across all fuel blends. Five fuels were used in total, G100 (100% gasoline), BE15P0 (7.5% n-butanol, 7.5% ethanol), BE10P5 (5% n-butanol, 5% ethanol, 5% n-propanol), BE5P10 (2.5% n-butanol, 2.5% ethanol, 10% n-propanol), and BE0P15 (15% n-propanol), with each blend containing 15% alcohol by volume. This study measured engine brake power (BP), brake thermal efficiency (BTE), and brake-specific fuel consumption (BSFC), as well as emissions of CO and NO_x. Results showed that among all the blends, BE0P15 stood out by recording the highest average BTE and BP, the lowest BSFC, and the lowest emissions of both CO and NOₓ. These results suggest that fuel blends with higher amounts of n-propanol can improve combustion efficiency while also reducing harmful emissions. Based on the findings, n-propanol-rich blends have potential as cleaner and more efficient alternatives to pure gasoline in spark-ignition engines, especially under varying speed conditions.

Access to clean, reliable energy remains a persistent challenge in underserved communities, especially during periods of limited solar availability. This project introduces a hybrid solar charging station designed to power micro-mobility devices and portable electronics while supporting real-world STEM education. The initiative, jointly developed by University of Texas at Tyler and Houston City College, aims to enhance energy access and knowledge empowerment in the Greater Houston area. The system integrates a 150-250 W solar photovoltaic panel, a battery storage set, a 30-100 W hydrogen fuel cell, and a solar charger controller, coordinated through a customized energy management system. A distributed array of sensors collects performance data, which is transmitted via an IoT-enabled platform to a cloud-based dashboard for real-time monitoring, remote control, and analytics. This dual-energy production design ensures continuous power availability by using solar energy as the primary source during daylight to charge the batteries, which in turn supply power to both the electrical loads and the hydrogen generator. The fuel cell uses hydrogen that serves as a backup energy source during periods of low or no sunlight. In addition to its energy function, the station serves as a multidisciplinary educational tool. Students engage in hands-on learning across renewable energy technologies, embedded control systems, and data-driven system analysis. The platform also lays the groundwork for implementing more advanced control strategies, including predictive and adaptive methods that incorporate elements of machine learning. This system provides a model for sustainable energy deployment and workforce development in regions with limited energy access.

This theoretical study, conducted using Density Functional Theory (DFT) with the Vienna Ab Initio Simulation Package (VASP), investigates the electronic and optical properties of a diketopyrrolopyrrole (DPP)-based material, focusing on its potential applications in semiconductor sensors and photodetectors. DPP is an organic semiconductor material that shows great promise for use in various optoelectronic devices.

The results reveal that DPP exhibits a direct band gap of 2.30 eV, which is crucial for its ability to absorb and emit light effectively. The Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) energy levels are clearly defined at –4.47 eV and –2.17 eV, respectively, confirming the material’s semiconducting nature and making it suitable for electronic applications requiring controlled charge transport.

The calculated optical absorption spectrum shows a significant peak centered at 538 nm in the visible range, which corresponds to the HOMO–LUMO transition, making it effective for light detection in the visible spectrum. Additionally, significant absorption is observed in the ultraviolet (UV) region (below 400 nm), which further extends the material’s potential applications in UV-sensitive devices.

These optical and electronic properties make DPP a promising material for photodetectors, where efficient light absorption and signal generation are needed. Its tunable electronic structure also makes it suitable for semiconductor sensors, which can be used to detect a wide range of chemical or environmental factors. This study emphasizes the great potential of DPP for advancing high-performance optoelectronic devices and sensing technologies, with applications ranging from solar energy conversion to environmental monitoring, offering new possibilities for future technologies.

The study focuses on the determination of the conditions of the divergence of the variance of the measurement for antenna array able to perform measurements of direction of electromagnetic waves.

The payload of the study is a cross array of antennas, able to perform measurements of direction through array beamforming and Angle of Arrival (AOA) technology.

In particular, starting from the modeling of satellite kinematics in terms of position and attitude combined with its relative position with respect to an emitter of electromagnetic waves located on the surface of the Earth, the study gives the mathematical fundamentals to determine potential cases that lead to the divergence in the variance of the estimation of the position of the emitter of signal.

The numerical predictions, conducted through the evaluation of the metrics of Cramér-Rao Lower Bound (CRLB), are on the angles of Azimuth, Elevation, and broadside through the generation of errors in the attitude with Monte Carlo simulations.

Recent advanements on the miniaturization of the electronics make these studies of particular interest for a new set of technological demonstrators equipped with payloads composed of antenna array. Applications of interest are on Earth-scanning missions with exemplary cases of search and rescue or spectrum monitoring of jamming in E1/L1 band for GNSS.