Abstract: The object of research in the scientific article is the process of converting wind flow energy into mechanical energy. The process of converting wind energy into mechanical energy is carried out by two counter-rotating wind wheels. And into electrical energy in the generator, with the armature and inductor rotating in opposite directions. The purpose of the scientific article is to study and develop an efficient power supply system for autonomous consumers based on a wind power plant of a special design with an increased wind energy utilization coefficient. In the course of the work, the main parameters of a special-designed wind turbine with counter-rotating wind wheels were determined and the process of electric generation was modeled on the principle of counter-rotation of the armature and generator inductor; a physical model of a special-designed wind turbine based on a two-wheeled system located in the same wind flow and providing rotation of the armature and generator inductor individually from each wind wheel in the opposite direction was developed; a design documentation on the basis of which the experimental design sample was made; experimental studies of the experimental design sample of a wind turbine were conducted to determine its main parameters for the purpose of efficient power supply to autonomous consumers. Main design and technical and economic indicators: optimization of wind power plant parameters, dimensions of wind wheels, their relative location, as well as generator power depending on the expected wind speed. The degree of implementation lies in the fact that a wind generator with variable torque of a wind wheel is patented (patent for invention of the Republicof Kazakhstan No. 36903; Eurasian patent for invention No. 047230). The effectiveness of the research lies in the application of technical solutions patented by the authors in practice, which makes it possible to increase the energy efficiency of wind turbines. Scope of application of the developed wind turbine of special design in the field of alternative energy and decentralized agricultural consumers.

Abstract: Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning (iCALL) offer personalization potential but introduce critical challenges in pedagogical grounding, data privacy, and pedagogical validity. While Knowledge Graphs (KGs) and Federated Learning (FL) address these concerns individually, systematic integra-tion of all three technologies remains absent or insufficiently addressed in current re-search. This scoping review maps the FL–KG–LLM convergence landscape in educa-tional contexts. Following PRISMA-ScR guidelines, we searched six databases and screened 51 papers published between 2019 and 2025 using automated extraction. Our findings reveal a pronounced convergence deficit: no papers integrate all three domains, while 58.8% of approaches operate within isolated technological silos. Criti-cal reporting gaps emerge across the corpus, with an average “Not Reported” (NR) rate of 84.5%, particularly in privacy mechanisms (92.2%), validation metrics (90.2%), and Common European Framework of Reference for Languages (CEFR) alignment (88.2%). Domain-specific analysis reveals two distinct patterns: inter-domain gaps (disciplinary silos resulting in expected CEFR absence in single-domain papers) and intra-domain gaps (failure to report domain-critical variables, including 100% parameter NR in FL studies, 86.7% validation NR in KG studies, and 100% CEFR NR in convergence pa-pers). We identify two pillars of pedagogical grounding: a Grounding Pillar, which con-strains LLM outputs via Knowledge Graph rules, and a Validation Pillar, which con-cerns how authoritative source frameworks are mapped onto Knowledge Graph schemas. The latter remains completely unaddressed in the reviewed literature, re-vealing what we term the Integrity Gap—a systematic disconnection between techno-logical innovation and pedagogical grounding in iCALL. By framing pedagogical alignment as an upstream control and validation problem, this review offers insights relevant to the design of user-facing automated systems where trust, transparency, and human oversight are critical.

Abstract: To address the challenges of ambient light interference and slow overload recovery in transimpedance amplifiers (TIAs) for automotive Light Detection and Ranging (LiDAR) systems, this paper proposes a high-performance TIA with integrated ambient light cancellation and fast recovery capabilities. The core design includes an adaptive ambient light cancellation (ALC) loop that eliminates background currents up to 3 mA without relying on AC coupling capacitors, achieving a low-frequency cutoff frequency of 321 kHz to ensure the signal-to-noise ratio (SNR) of weak target signals. A multi-stage clamping and current transfer mechanism is employed to realize rapid overload recovery: under 100 mA heavy overload conditions, the recovery time is controlled within 8.7 ns, and the pulse broadening is limited to 2.7 ns, avoiding measurement blind zones. Implemented in a 0.18-$\mu$m SiGe BiCMOS process, the proposed TIA occupies a compact area of 0.15 mm^2, with a transimpedance gain of 80 dB$\Omega$ (10 kΩ) and a -3 dB bandwidth of 421 MHz. The input-referred noise current spectral density is 4.7 pA/√Hz, and the integrated equivalent input noise current from 1 Hz to 250 MHz is 73.6 nArms. Operating over a temperature range of -40 ℃ to 125 ℃, the TIA meets the rigorous requirements of automotive-grade applications. Performance comparisons with commercial products and state-of-the-art designs validate its superior ambient light rejection and fast recovery performance, making it suitable for direct time-of-flight (dToF) LiDAR systems in autonomous driving.

Abstract: The stability and performance of drones are seriously affected by vibrations originating from propellers. These vibrations can lead to structural resonance, decreased sensor accuracy, and reduced flight safety. In this study, drone propeller vibrations are modeled and analyzed using differential equations, Laplace transform, and Fourier analysis. The propeller system is represented by single-degree-of freedom (SDOF) and multi-degree-of-freedom (MDOF) mechanical models, and the transfer function of the system is obtained using the Laplace transform. Experimental vibration data collected from the acceleration sensor(ADXL345) is analyzed using Fast Fourier Transform (FFT) using software written in the Arduino IDE application with the ESP32 development board, to determine dominant frequencies and possible resonance states.

Abstract: This paper investigates the operational performance and stability of a regional power grid with a high penetration of small hydropower plants operating under rated conditions. Grid monitoring and simulation results reveal that voltage levels frequently exceed permissible limits and exhibit significant fluctuations. The primary radial configuration of the network is the cause of often the outages of transmission lines that occur at times of peak generation. These conditions adversely affect generator operation and may contribute to equipment degradation. To enhance grid reliability and ensure stable hydropower plant operation, several mitigation measures are proposed, including the reinforcement of the transmission network through the construction of new lines to enable a ring configuration, the mandatory implementation of excitation control systems for generating units, the establishment of a real-time grid operation center, and the deployment of real-time diagnostic tools for optimized generator utilization. The proposed measures give a very handy scheme to raise voltage stability, operational reliability, and the safe inclusion of distributed hydropower generation into regional power systems.

Abstract: Scandium-doped aluminum nitride (ScAlN) is a promising replacement for undoped aluminum nitride in MEMS vibration and acoustic sensors due to its higher piezoelectric coefficients, and for RF MEMS due to its enhanced piezoelectric response and ferroelectric switching capability. However, poor process conditions often lead to degraded film performance. In this work, we optimized the growth conditions of ScAlN thin films deposited by reactive pulsed-DC magnetron sputtering system by studying the impact of N₂ flow rate, target–substrate distance, substrate temperature, and substrate bias on film stress, crystallinity, and surface morphology. Based on stress measurements, XRD rocking curves along the c-axis (002), and roughness with AOG formation probability extracted from AFM and SEM images, an optimized deposition recipe was developed that balances stress, crystallinity, and AOG density. With this optimized recipe, samples were fabricated for dielectric, ferroelectric, and piezoelectric coefficient (d33,f and d31,f) measurements. To verify scalability, d33,f, εr, and tan(δ) were measured on 100, 150, and 200 mm substrates. Dual beam laser interferometry results showed d33,f values of around 18 pm/V, εr of 18, and lowest tan(δ) of 0.4%. Cantilever-based d31,f measurements yielded a value of −6.22 pC/N. The optimized ScAlN films also exhibited remnant polarization, Pr = 130 μC/cm², and coercive field, Ec = 3.5 MV/cm.

Abstract: In 180 nm CMOS process, an enhanced dynamic bias comparator with reference-compensated offset calibration technique is implemented. In order to reduce the delay time of the comparator, an enhanced structure is used. To reduce the power consumption, a dynamic bias technique is applied to the comparator. A novel reference-compensated offset calibration technique is introduced to achieve offset calibration. Simulation results indicate that the proposed comparator achieves a delay time of 190.3 ps and an energy consumption of 324.2 fJ/comparison under operating conditions of 150 MHz and an input differential amplitude of 0.1 V. Furthermore, the application of a reference-compensated offset calibration technique facilitated a reduction in the offset voltage of the comparator from 18.1 mV to 6.3 mV.

Abstract: This paper presents an all-digital fractional-N phase-locked loop (ADPLL) operating in the 2.86-3.2 GHz range, optimized for IoT and high-frequency RF transceiver applications demanding stringent phase noise performance, fast settling time, and high integration capability. The key innovation lies in the introduction of a bandpass delta-sigma time-to-digital converter (BPDSTDC) that achieves high-resolution phase detection, an extended detection range of ± 2π, and superior noise-shaping characteristics, completely eliminating the complex calibration procedures typically required in conventional TDC designs. The proposed architecture synergistically combines the BPDSTDC with digital down-conversion blocks to extract phase error at baseband, a divider chain integrated with phase interpolators achieving 1/4 fractional resolution to suppress in-band quantization noise, and a wide-bandwidth digital loop filter (>1 MHz) ensuring fast dynamic response and robust stability. The bandpass delta-sigma modulator is implemented with compact resonator structures and a flash quantizer, achieving an optimal balance among resolution, power consumption, and silicon area. The incorporation of highly linear phase interpolators extends fractional frequency synthesis capability without requiring complex digital-to-time converters (DTCs), significantly reducing design complexity and calibration overhead. Fabricated in a 180-nm CMOS technology, the proposed chip demonstrates robust measured performance. The band-pass delta-sigma TDC achieves a low integrated rms timing noise of 183 fs within a 1-MHz bandwidth. Leveraging this low TDC noise, the complete ADPLL exhibits a measured in-band phase noise of -120 dBc/Hz at a 1-MHz offset for a 3.2-GHz output frequency while operating with a loop bandwidth exceeding 1 MHz. This corresponds to a normalized phase noise of -216 dBc/Hz. The system operates from a 1.8-V supply and consumes 10 mW, achieving competitive performance compared with prior noise-shaping TDC-based all-digital PLLs.

Abstract: This work addresses the qualification of photovoltaic micromodules intended for energy-harvesting applications in Internet of Things systems and proposes a novel qualification method based on highresolution electronic signature masks obtained through Time-Domain Reflectometry. The method applies frequency-to-time conversion of measured S11 parameters acquired using a Vector Network Analyzer under laboratory conditions. Accelerated reliability testing is employed as a sample preparation stage to validate the proposed qualification approach and to contextualize micromodules within a framework beyond existing regulatory standards, which currently exclude PV devices below 5 Wp. The qualification method based on pass/fail TDR signature masks demonstrated in this study has the potential to become a powerful tool for quality inspection of photovoltaic micromodules by assessing deterioration over time. The research introduces TDR technology—traditionally associated with high-frequency telecommunications—into the photovoltaic contex for research and development, manufacturing inspection, and certification purposes.

Abstract: Cable theft is a major infrastructure security issue in South Africa, especially in Gauteng municipalities, where poverty is spatially concentrated to the extent that it has given rise to large-scale theft of electricity distribution infrastructure components. This essay focuses on the complexity of the reasons behind cable theft, including unemployment-related desperation, organized crime syndicates, governmental corruption, and international commodity markets. The study involved 19,919 reported cases from around the country and 5,914 cases from Gauteng since April 2019. It is found that cable theft is not limited to property crime, but also poses a threat to basic service delivery in the electricity, water, health, and education systems. The study reveals that effective mitigation should involve combined interventions using advanced technology (intrusion detection systems, smart meters, and mechanical barriers), enhanced law enforcement and prosecution, community involvement, and socioeconomic development measures. Evidence-based policies, such as special detection offices, scrapyard control, dedicated prosecution, and legitimacy programs that integrate infrastructure security and affordability approaches, are proposed as critical elements of sustainable reduction strategies. This study concludes that the solution to cable theft is closely connected to the constitutional obligation of South Africa to progressively fulfil socioeconomic rights; thus, municipal, provincial, and national governments must act in collaboration to address this issue.

Abstract: This study presents a unified state-space model of an electric vehicle (EV) powertrain that explicitly captures the dynamic coupling between electrochemical battery behaviour, energy depletion, and electromechanical drivetrain dynamics. A Thevenin battery model, augmented with a State-of-Charge (SOC) state, is algebraically combined with a Permanent Magnet Synchronous Motor (PMSM) drivetrain to form a single fourth-order linear timeinvariant system, eliminating algebraic loops and enabling system-level eigenstructure analysis. Beyond subsystem integration, the proposed formulation reveals how battery internal dynamics introduce additional slow modes that reshape the damping and transient response of the drivetrain, even in open-loop operation. Eigenvalue analysis and time-domain simulations demonstrate that battery parameters and energy dynamics directly influence motor current overshoot, voltage sag, and long-term energy behaviour during load disturbances and regenerative operation. The results show that battery dynamics are not passive energy elements but active contributors to EV powertrain behaviour, highlighting the necessity of unified modelling for accurate transient analysis and for future motor control and battery management system co-design.

Abstract: This paper presents a cloud-edge digital twin framework designed to enhance battery lifecycle management within electric vehicles, contributing to sustainable transportation and advanced battery system engineering. The architecture integrates a static state-of-health (SOH) model trained offline with a dynamically retrained state-of-charge (SOC) model updated periodically via cloud-based machine learning. Using a public NASA battery dataset, the system employs random forest, light gradient boosting, and deep neural networks to achieve SOH estimation errors below 1.8% RMSE and SOC errors under 0.81% RMSE while maintaining inference times under one second—compatible with onboard BMS deployment. The retrainable SOC model adapts to aging effects, ensuring continued accuracy as battery capacity degrades. This adaptive digital twin supports predictive maintenance, real-time health monitoring, and optimized battery utilization, aligning with smart manufacturing and sustainable energy system goals by extending operational life and improving reliability in EV applications.

Abstract: Substrates have become essential enabling materials for creating lightweight electronic components, particularly supporting advanced telecommunication technologies. This progress is driven by continuous advancements in novel substrate materials and cut-ting-edge fabrication techniques, pushing the limits of high-frequency device design. This paper explores both the challenges and breakthroughs in 5G mmWave substrate technology, focusing on recent developments in materials, device fabrication and integration methods that enhance performance and providing an in-depth analysis on the importance of mmWave technology. This paper highlights the key concerns in substrates design to researchers and academicians accelerates invention and commercialization of substrate designs in areas such as antenna engineering and integrated circuit technologies as well as addressing key issues like scalability and thermal impact in flexible substrates. Since matters related to material losses and substrates’ fabrication constraints are increasingly severe at high frequencies, mmWave substrates are highly needed to be look at, therefore this paper details the particular issues related to mmWave propagation and manufacturing design processes for high-frequency devices. Aims at optimizing antenna and system reliability by employing advanced design and materials as well as outlines the existing gaps that need a clarification to augment 5G mmWave infrastructure and services.

Abstract: The most severe premalignant lesion of glandular epithelium of the cervix is adenocarcinoma in situ (AIS). In most cases it is associated with persistent Human papillomavirus (HPV) infection and most often occurs in women in the fourth decade of life. In most high-income countries, primary screening has shifted to HPV testing, while cytology is used for patient triage. Even with current robust screening protocols, their sensitivity for glandular lesions remains limited. Diagnosis of AIS obtained by biopsy, brushing or curettage is confirmed by excisional methods and pathohistological verification. Therapy depends on the patient’s lifestyle and reproductive age. In our case, we present nulliparous patient with persistent ASC-US, HPV infection with alpha-7 types (without HPV 16 and 18 types), and AIS which was diagnosed after conization, follow up and two biopsies with curettage of cervical canal. Our case report highlights limitations in detection of glandular lesions and need for caution in patients with persistent and seemingly low-grade cytological abnormalities, notably in young patients with high-risk HPV types.

Abstract: This article investigates the qualification of photovoltaic micromodules intended for energy harvesting in Internet-of-Things systems, with emphasis on the degradation mechanisms induced by accelerated environmental aging typical of tropical conditions. The study employs multiple complementary characterization techniques—including pulsed I–V measurements, electroluminescence imaging, and impedance spectroscopy—combined with multivariate statistical analysis to support decision-making regarding acceptance criteria, fault prediction models, maintenance scheduling, and failure mode clustering.In addition, dimensionality-reduction methods are explored to extract the most relevant indicators from empirical datasets and to improve interpretability when dealing with highly correlated or redundant variables. The work addresses a regulatory gap affecting photovoltaic devices below 5 Wp, a class of modules whose deployment is rapidly expanding in remote, autonomous, and low-power IoT applications, yet remains largely unsupported by existing reliability and certification standards.

Abstract: This paper presents the implementation of an early stage fault detection and health monitoring system for electric motors and their drive units. The study focuses on developing a cost-effective system capable of identifying abnormal behavior in both drive electronics and mechanical components before a major failure occurs. The proposed design integrates multiple sensing parameters such as vibration, acoustic signals, and electrical quantities including voltage and current. These inputs are processed using data-driven techniques to assess motor condition and identify fault patterns. A microcontroller-based platform is used for real-time monitoring and signal processing, providing early warnings through an intuitive serial interface. Experimental observations confirm that this approach can effectively detect drive faults, motor imbalance, and bearing wear at an early stage, reducing downtime and maintenance costs. This work demonstrates a practical and scalable method to enhance the reliability and operational safety of motor-driven systems, contributing to improved industrial efficiency and predictive maintenance strategies.

Abstract: The paper states that biogas plants are of particular importance in the development of renewable energy sources, and their efficiency is largely determined by the accuracy and reliability of parameter measurements during the production process. Sensors that determine temperature, pressure, pH, humidity, methane (CH₄) and hydrogen sulfide (H₂S) concentrations, gas flow, and oxidation-reduction potential (ORP) form the basis of the monitoring system. However, during operation, they are affected by nonlinear dependence, noise, drift, and errors that reduce the reliability of measurements. To solve this problem, mathematical modeling and sensor optimization methods are proposed. The study proposes a mathematical model that describes the correlations between the physicochemical characteristics of the environment and the output signals of the sensors. Based on this model, an analysis of the sensitivity of the measurement channels was carried out, critical areas where accuracy is significantly reduced were identified, and methods for compensating for errors were proposed. To improve the reliability of the results, intelligent data processing was used, including artificial neural networks, which allow adaptive adjustment of output data and calibration in real-time monitoring mode. The proposed approach improves measurement accuracy and the stability of the sensor system to external influences, which is also of practical importance for monitoring and controlling biogas plants. A mathematical model was proposed that takes into account the physicochemical dependence on environmental parameters (temperature, pressure, pH, Ch₄ and H₂S concentrations, humidity, gas flow, and redox potential) and sensor response. Based on this, a sensitivity analysis of the measurements was performed to identify areas of maximum error. Intelligent data processing using artificial neural networks was used to compensate for systematic errors and sensor drifts, which allowed for real-time calibration and correction of sensor readings.

Abstract: Multipath interference and non-stationary channel dynamics severely degrade Wi-Fi CSI-based vital sign monitoring. This paper introduces Deep Wavelet Scattering Networks (DWSN), integrating multi-resolution wavelet scattering transforms with deep convolutional separation layers and path signature normalization. Extending the wavelet-domain decoupling framework, DWSN achieves translation/deformation invariance through second-order scattering coefficients while learning non linear separation boundaries. Rigorous theoretical analysis derives scattering stability bounds under Lipschitz-continuous multipath perturbations (O(ϵlog(1/ϵ))), establishing >32 dB cross-talk attenuation. Extensive experiments on 200 synthetic CSI traces (3–12 Rayleigh paths, SNR: 0–20 dB) demonstrate 67% CTR improvement over EMD, 58% MAEreduction (0.7 BrPM RR, 1.6 BPM HR at SNR=5dB), and 2.3× robustness to HR/RR transitions vs. baseline wavelet MRA. Real-time ESP32 deployment achieves 68 ms latency via tensorized scattering operators. No human subjects were involved; all validation uses synthetic physiological models.

Abstract: The exploration of probabilistic computing has recently gained momentum as a promising par-adigm to overcome the limitations of deterministic CMOS logic. In this paper, we present an FPGA-based digital emulator of probabilistic bits (p-bits) and stochastic logic circuits introducing three key innovations. First, we implement p-bits with a sigmoidal activation function, enabling faithful emulation of spin-inspired probabilistic logic while preserving the statistical character-istics of physical p-bit devices. Second, we eliminate the need for the sequencer—commonly re-quired in weighted p-bit architectures to sequentially activate each unit and ensure stability—by demonstrating, through quantitative metrics that our fully parallel design achieves stable and reproducible behavior. Third, we propose a reusable and parameterized hardware library of elementary probabilistic components, implemented as modular Verilog HDL blocks that provide a robust foundation for the construction of more complex stochastic systems, including Boltzmann machines, probabilistic SAT solvers, stochastic optimization architectures, and binary neural networks. With the proposed approach, we designed and emulated on Altera/Intel FPGAs using the Quartus Prime Integrated Development Environment (IDE) a wide set of probabilistic logic gates and probabilistic digital systems up to finite state machines (FSMs), achieving excellent results in terms of both accuracy and stability of the outcomes. The proposed FPGA-based archi-tecture thus serves both as a research instrument for investigating probabilistic computation and as a practical platform for scalable hardware accelerators in emerging stochastic applications.

Abstract: To enhance the anti-jamming performance and operational reliability of drones, this paper presents the design, fabrication, and measurement of a novel polarization-reconfigurable metasurface antenna that meets these demands. The design process is guided systematically by characteristic mode analysis, in which the modal significance coefficient is used as a key tool to predict resonant frequencies and optimize bandwidth. A major innovation lies in the mechanical rotation mechanism, which enables the antenna to switch between left-hand circular polarization, linear polarization, and right-hand circular polarization, thereby avoiding losses associated with active electronic components. The antenna features a compact geometry of 0.49λ × 0.49λ and delivers strong performance across all polarization states, impedance bandwidth exceeds 29.9%, average gain ranges from 5.1 to 6.0 dBi, and high polarization purity is achieved, with an axial ratio bandwidth >10% in circular polarization modes and cross-polarization discrimination >23 dB in the linear polarization state. Simulated and measured results are in good agreement, confirming the effectiveness and robustness of the proposed design for modern 5G/6G terminals.