Despite significant progress in the diagnosis and treatment of cardiovascular diseases, the prevalence of hypertension continues to rise in modern conditions, while blood pressure control remains inadequate in a large number of patients. This review analyzes domestic and international publications on the use of remote technologies in the diagnosis and treatment of hypertension. The use of remote health monitoring for patients with hypertension as part of follow-up care helps improve the accessibility and quality of medical care, enables more effective disease control, and increases patient adherence to treatment. The most promising approach appears to be the use of remote blood pressure monitoring with devices that feature automatic data transmission. Remote monitoring systems allow for the timely detection of significant changes in health indicators, notification of responsible medical professionals, and, if necessary, facilitation of immediate emergency care. This ensures the rational use of human and time resources and reduces the expenditure of budget funds. An analysis of the literature reveals a significant variety of methods and technologies for implementing remote monitoring, which may influence the interpretation of clinical study results.

This study analyses the usage and consumer preference of automatic transmission cars in Coimbatore City, focusing on their suitability for urban driving conditions. The study adopts a descriptive research design using primary data collected from 151 respondents through a structured questionnaire. The research examines factors influencing the adoption of automatic transmission vehicles such as driving comfort, ease of use in traffic, maintenance aspects, and service availability. Rank Analysis is used to identify the most important factors influencing user preference toward z transmission cars. The results show that availability of service facilities and spare parts are the key factors encouraging users to adopt automatic transmission vehicles. Automatic cars are increasingly preferred due to reduced driver fatigue and greater convenience in congested city traffic. The T-test analysis indicates differences in user perceptions regarding performance, comfort, and maintenance aspects among respondent groups. The study reveals a gradual shift from manual to automatic transmission vehicles driven by urban congestion and changing lifestyle needs. It concludes that automatic transmission cars are becoming more popular in Coimbatore due to comfort, convenience, and suitability for city driving conditions.

<title>Abstract</title> We investigated the effects of the ionosphere on a very-high-frequency (VHF) signal transmitted from a polar orbiting National Oceanic and Atmospheric Administration (NOAA) weather satellite, NOAA-19, at an altitude of 861 km. NOAA-19 gathers cloud images and transmits reduced resolution images in the Automatic Picture Transmission format at 137.1 MHz. A software-defined VHF data collection system sampled the signal at Gakona, Alaska, during a high-frequency ionospheric heating experiment. A software algorithm was developed to extract the signal amplitude, frequency, and carrier phase, and the results were used to compute amplitude and phase scintillation indices as the signal traversed the artificially disturbed ionosphere. A strong response was observed in signal amplitude and phase due to the disturbances, in clear contrast to the mild disturbances experienced by global navigation satellite system signals traversing the ionosphere under similar conditions. The results demonstrate the frequency dependence of ionospheric effects on radio waves transmitted from low-Earth-orbit satellites. The study also reveals the deficiency in current ionospheric scintillation modeling in the VHF regime.

The growing interest in Automatic Transmission (AT) and Automated Manual Transmission (AMT) automobiles in India may be attributed to the growing urban congestion as well as the growing demand from consumers for more comfort when driving throughout the country. In spite of this tendency, adoption rates are rather variable across a wide range of consumer sectors and geographical locations for consumers. The purpose of this research is to investigate the primary elements that have a role in the ownership of AT/AMT cars in three of the most important cities in India: Kolkata, Bengaluru, and Pune. The objective is to investigate the ways in which demographic, economic, driving-related, and perception-based elements influence the decisions that consumers make about transmission selections. All 401 individuals who participated in the study were given a standardized questionnaire, and the results were evaluated using multiple regression analysis. According to the data, the chance of selecting an automatic transmission or automatic transmission vehicle is highly influenced by factors such as age, income, driving experience, perceived dependability, economic concerns, and comprehension of technology. On the other hand, social influence had a detrimental impact, although gender, education level, and geographical location did not have a significant association with the outcome. The findings of the study indicate that familiarity with technology, economic factors, and practical driving needs are more accurate predictors of adoption than traditional demographic markers. These findings offer valuable insights for manufacturers, marketers, and policymakers who are attempting to promote AT/AMT technologies in India's expanding automobile market.

Development of K-nearest neighbours model for diagnosing vehicle automatic transmission failures

The transition from internal combustion engines (ICE) to electric vehicles (EVs) has pushed the search for new sustainable lubricants that can withstand relevant lubrication challenges, like high torque loads and stray currents in drivetrains, which can accelerate oxidation and increase component wear, posing a critical challenge for electric powertrain components. Conventional Automatic Transmission Fluids like ATF III and ATF V have shown different wear mechanisms and coefficient of friction (CoF) trends in the presence of simulated stray currents. While many studies have focused on vegetable oils as biobased oils, waste cooking oils (WCO) offer a more sustainable alternative, yet their performance under electrified conditions is yet to be explored. In this study, four WCO samples were collected from different sources and evaluated against regular soybean oil (RSO) through structural, physico–chemical, and tribological analysis under electrified and unelectrified sliding conditions. Structural analyses using FTIR, 1H/13C NMR, GC-MS, and CMS confirmed the triglyceride integrity across all samples, with differences in fatty acid composition influencing physicochemical properties. WCO B-4, with the lowest unsaturation of 3.43 CCreated by potrace 1.16, written by Peter Selinger 2001-2019]]>C bonds/triglyceride, exhibited the highest viscosity. At the same time, WCO B-2 showed higher oxidation resistance due to high oleic acid content (66.2%) and a lower degree of unsaturation of 3.73 compared to other WCOs, which reduced reactive oxidation sites. WCO B-2 exhibited the lowest cloud and pour points, which can be attributed to the presence of low saturated fatty acids in triglyceride molecules, and is dominated by monounsaturated fatty acids. Tribological testing on aluminum–steel contacts showed that, under unelectrified conditions, WCO B-3 and B-4 resulted in reduced average coefficients of friction by 18% and 23%, respectively, and had lower average wear depth compared to RSO. Under electrified conditions, all batches of lubricants exhibited increased wear and oxidation, yet WCO B-4 maintained the lowest wear depth despite frictional instability. Additional surface characterization via high-resolution microscopy and spectroscopy techniques confirmed more severe oxidation and lower material transfer under current, underscoring the degradation risk in electrically stressed contacts.

This paper investigates global optimization methods applied to the design of a one-stage gearbox, aiming to partially automate the design using artificial intelligence. The developed software autonomously determines the gearbox parameters (number of teeth, gear width, modulus, etc.), optimizes them, and then models the assembly in Siemens NX CAD (computer-aided design). The direct connection between optimization and CAD leads to a faster designing process. The literature review reveals that the field of machine design is quite conservative, and only a few articles with some similarities to our research have been found. The paper describes gear dimensioning and the application of the Ipopt algorithm to the optimization of gear-pair parameters. Then, it addresses shaft design and bearing selection through multi-objective optimization using the NSGA-II algorithm, balancing cost, weight, and volume while meeting strength and durability constraints. The paper also describes the transfer of the optimized parameters and the creation of a CAD model. The last part is dedicated to the problems encountered, their potential solutions, and the advantages of the new approach. The proposed approach delivers a functional, optimized CAD model in about 10 min, providing a notable speed advantage over typical manual workflows.

ABSTRACTBackground:Genetic polymorphisms in the luteinizing hormone (LH) β-subunit gene have been associated with responses to controlled ovarian hyperstimulation (COH) in in vitro fertilisation (IVF) patients. Variants in rs1800447 and rs34349826 may increase androgen production, potentially impairing folliculogenesis and ovarian responseAim:The aim of this study was to investigate the clinical significance of LH β-subunit single-nucleotide polymorphism (SNP) (rs1800447 and rs34349826), levels of testosterone, sex hormone-binding globulin (SHBG) and free testosterone index in predicting COH response in polycystic ovary syndrome (PCOS) womenSettings and Design:This nested case–control study enlisted 122 women with PCOS in the Morula IVF Jakarta Clinic, Jakarta, IndonesiaMaterials and Methods:The selection of cases and controls was in the ratio of 1:2. Blood samples were taken on day 2 or 3 of menstrual cycle. Sanger sequencing was utilised to genotype the LH β-subunit genes (rs1800447 and rs34349826). Levels of testosterone and SHBG were measured to calculate the free testosterone index. Women were retrospectively grouped as hyporesponders (<8 oocytes) or normo/hyperresponders (≥8 oocytes) according to the number of retrieved oocytesStatistical Analysis Used:SPSS Software version 21.0 (IBM Corp., USA). Independent t-test or Mann–Whitney test for numerical variables and Chi-square test for categorical variables were employedResults:A unique automatic transmission (AT) variant in both rs1800447 and rs34349826 LH β-subunit was discovered, which has yet been reported previously. Notably, the proportion of heterozygous LH β genotypes (AT and AG) in both rs1800447 and rs34349826 was similar between hyporesponder and normo/hyperresponder groups (P > 0.05). All participants were normoandrogenic PCOS women, indicated by the normal level of testosterone (0.84 ± 0.35 nmol/L) and SHBG (57.87 ± 29.20 nmol/L) as well as free testosterone index (1.88 ± 1.34). No difference in testosterone levels, SHBG and free testosterone index was observed among the studied groups (P > 0.05)Conclusion:LH β gene SNPs (rs1800447 and rs34349826), testosterone level, SHBG level and free testosterone index were not significantly correlated and less effective clinical indicators for COH response in normoandrogenic PCOS women.

The article analyzes the main approaches to the legal regulation of the most pressing problems of artificial intelligence in telemedicine, identifies the best solutions, and develops proposals. It is concluded that a reasonable vector of development is the convergence of the studied approaches in regulating AI: in Russian legislation, increasing the responsibility and role of the manufacturer of medical devices in ensuring the safety of devices, in foreign legislation, creating rules for automatic data transmission to an authorized body that controls deviations in the behavior of artificial intelligence. It has been established that legal decisions in the field of personal data in the field of artificial intelligence in telemedicine are predominantly successful and uniform: the requirement to obtain prior informed consent to the processing of personal data, the possibility of its control and revocation, principles and rules of personal data processing, the possibility of artificial intelligence training only on anonymized data sets. At the same time, it seems correct to consolidate, together with the principle of transparency, the right to an explanation, which collectively grant the patient the right to receive accessible, understandable information about both their personal data and artificial intelligence solutions, regardless of the complexity and non–obviousness of the solution in its entirety. The study is financially supported by the Russian Science Foundation, project № 23-78-10175, https://rscf.ru/project/23-78-10175/.

Biomass production in organisms is closely linked to their growth rate, necessitating rapid, in situ, nondestructive, and accurate growth measurement. Existing imaging platforms are often limited by high cost, lack of scalability, wired connections, or insufficient automation, restricting their applicability for high-throughput growth monitoring. Here, we present SIPEREA, a scalable imaging platform built on cost-effective ESP32-CAM modules. SIPEREA comprises three graphical user interface (GUI) based applications: (1) an embedded program for the ESP32-CAM responsible for imaging, (2) an image acquisition program for automatic wireless image transmission from multiple ESP32-CAMs, and (3) an image analysis program that automatically segments organisms in the images using a deep neural network (DNN) and calculates their area. The implementation of asynchronous, sequential wireless image acquisition enables the efficient management of multiple ESP32-CAM modules. To demonstrate the usefulness of this platform, we analyzed images captured over a two-week period using four ESP32-CAM units during Lemna sp. (duckweed) cultivation to compute doubling time.

This study presents the modeling and simulation of shift control for the U340 automatic transmission using MATLAB–Simulink and Stateflow software based on a model-based design approach. The objective of this study is to build a system-level model combining physical blocks (engine, hydraulic torque converter, CR–CR planetary gearbox, powertrain – vehicle) and electro-hydraulic control blocks to evaluate the shift pattern, performance, and smoothness of the shifting process. The torque converter is modeled through dimensionless characteristics in the form of lookup tables in Simulink, while the transmission is simulated with discrete gear ratios corresponding to the closed/open states of the clutches and brakes. Simulation scenarios were performed in two cases: changing throttle opening while the vehicle was running on a flat road and fixing throttle opening but changing the simulated rolling resistance torque to simulate steep road conditions. Simulation results show that the model accurately simulates the shifting pattern of the U340 transmission; turbine shaft torque is significantly amplified in the low speed ratio range, improving acceleration capability, while pressure regulation and torque converter lock-up reduce speed fluctuations, enhancing transmission efficiency and smoothness during shifting. The proposed model can be used as an effective tool in research, teaching, and optimization of automatic transmission control strategies, as well as extended to more modern power transmission systems.

ABSTRACT Information on road gradient and vehicle mass is critical for the performance of the automatic shift system. This study presents a real‐time identification method for estimating vehicle mass and road gradient using an extended Kalman filter (EKF) enhanced with a mass pre‐estimation step. As a sensorless approach, it offers considerable practical advantages for heavy‐duty trucks equipped with automatic transmissions. First, a Kalman state equation is formulated based on the longitudinal vehicle dynamics model. Subsequently, an EKF‐based estimation algorithm is designed to identify both vehicle mass and road gradient. To accelerate convergence in real‐time identification, driving data collected during clutch disengagement in the gear‐shifting phase are utilized for mass pre‐estimation prior to the EKF process. This pre‐estimated mass value then serves as the initial condition for the EKF, significantly reducing the convergence time. The efficacy of the proposed method is demonstrated through truck experiments. Results show that both road gradient and vehicle mass converge to near the true values within 2 s. With mass pre‐estimation, the convergence time is reduced by approximately 60% compared to the standard EKF initialized with a random mass value. Moreover, the low RMSE confirms that the identification accuracy is sufficiently high for automatic shifting strategy applications. In conclusion, the mass pre‐estimation‐based extended Kalman filter offers an effective and rapid solution for estimating truck mass and road gradient.

This research presents a comprehensive investigation of electromagnetic gearing systems for automotive powertrain applications, evaluating their potential to replace conventional mechanical automatic transmissions. Three fundamental electromagnetic architectures - Variable Volume Approach (VVA), Fixed Volume Approach (FVA or separate drive of individually magnetized ring), and Fixed Volume Approach Pole-based (FVAP) - are analyzed through five distinct design configurations employing harmonic and geometric sequencing. A rigorous analytical framework is developed to assess electromagnetic coupling dynamics, evaluating critical parameters including energy density, volumetric constraints, magnetic flux distribution, and torque transmission characteristics. Performance assessment using Gray Relational Analysis (GRA) reveals the FVAP configuration as superior (GRA grade: 0.95), demonstrating optimal torque density (3.01 MNm/m³ FEM) and specific torque (320-344 Nm/kg). Comparative analysis shows electromagnetic systems achieve 20–22 % higher specific torque than optimal range of mechanical planetary gears about (267–282 Nm/kg), though their volumetric torque density remains below conventional systems (3–5 MNm/m³). The study identifies key performance trade-offs: VVAH (0.72) and VVAG (0.68) deliver competitive torque but exhibit elevated flux leakage, while FVAG (0.58) and FVAH (0.64) show balanced but limited performance. The research highlights electromagnetic gearing as a transformative technology for weight-sensitive applications, offering inherent advantages including contactless operation, adaptive torque control, and reduced maintenance. Findings suggest these systems are particularly suitable for electric and hybrid vehicle architectures where power density and efficiency are critical. Future development pathways focus on advanced magnetic materials and superconducting implementations to bridge the volumetric performance gap. This work provides a foundational framework for next-generation transmission systems, establishing quantitative performance benchmarks and design guidelines for electromagnetic gearing in automotive applications.

The problem lies in the difficulty of locking the pulley retainer on automatic motorcycle transmissions without using a special Y-shaped key. The purpose of the study is to obtain a special pulley retainer lock product on automatic motorcycle transmissions that can be adjusted the distance between the two retaining pins through adjusting bolts with easy and effective locking steps. The study method includes the design of the pulley retainer lock component formation, the selection of stainless steel pulley retainer lock materials, the production of pulley retainer lock components through flat and curved forging, turning the handle, forming a Y-shaped lever pair plate, drilling 2 holes at the ends of the lever, drilling the Y-shaped auxiliary tool opening and closing pins, and drilling the Y-shaped auxiliary tool opening distance adjustment bolts, assembling the components that form the pulley retainer lock, and testing the use of the pulley retainer lock. The production results are in the form of a special pulley archery key with a total length of 300 mm, the maximum opening distance on the axis of 2 pens is 110 mm, the length of 2 pen tips is 7 mm with a total pen length of 30 mm, and one diameter is 9 mm with a total pen length of 30 mm, the adjusting bolt for the opening angle of the pulley retaining key measures M6x20 mm, and the diameter of the Y-shaped tool rivet pen used is 6 mm.

Motorcycles are the most widely used vehicles in Indonesia due to their practicality, especially those with automatic transmissions. However, the increasing number of motorcycles, particularly in East Java Province, has contributed significantly to air pollution through exhaust gas emissions. This study aims to analyze the effect of injector hole variation and fuel octane rating (RON) on exhaust emissions and in 110cc gasoline motorcycles. The method involves laboratory testing with injector hole variations (4, 6, 8, 10) and the use of Shell RON 92 and RON 95 fuels. The observed variables include exhaust emissions (CO and HC) and specific fuel consumption, with engine temperature controlled at 80–85°C and fuel pressure at 43 psi. The results show that the configuration of a 6-hole injector with RON 95 produces the lowest emission values, with CO at 0.02% and HC at 84 ppm, meeting the Indonesian Ministry of Environment and Forestry’s emission standards (CO ≤ 3%, HC ≤ 1000 ppm), while also achieving the most efficient fuel consumption across all RPM ranges.

In modern industrial systems, diagnosing faults in the rolling bearings of high-speed rotating machinery remains a considerable challenge due to the scarcity of reliable fault samples and the inherent complexity of the diagnostic task. To address these limitations, this study proposes an intelligent fault diagnosis method that integrates a generative adversarial network (GAN) with a convolutional block attention mechanism (CBAM). First, after systematically evaluating several loss functions, a GAN based on the Wasserstein distance loss function was adopted to generate high-quality synthetic vibration samples, effectively augmenting the training dataset. Subsequently, a convolutional block attention mechanism-based convolutional neural network (CBAM-CNN) was developed. By adaptively emphasizing salient features through channel and spatial attention modules, the CBAM-CNN improves feature extraction and recognition performance under limited-sample conditions. To validate the proposed method, an experimental platform for a two-speed automatic mechanical transmission (2AMT) of an electric vehicle was developed, and diagnostic experiments were conducted on high-speed rolling bearings. The results indicate that, under extremely severe conditions, CBAM-CNN achieves a diagnostic accuracy of 96.64% for rolling element pitting defects using only 10% of authentic samples. For composite faults, the model maintains an average accuracy above 97%, demonstrating strong generalization capability. These findings provide solid theoretical support and practical engineering guidance for rolling bearing fault diagnosis under few-shot conditions.

With the advancement of mechanical, electronic and driver assistance technologies, the automatic transmissions have been playing a significant role in today's vehicles. In this paper, a systematic review of advanced automatic transmissions is shown Their operating principles and basic components are briefly explained, as well as the major designs existing in today’s automotive market. The paper compares the base technology torque-converter transmissions with new systems like Continuously Variable Transmissions (CVT) and Dual-Clutch Transmissions (DCT), matching differences in terms of efficiency, performance as well as maintainability. The paper also describes improvements in the driving comfort and fuel economy by means of electronic control units (ECUs) as well as sensor-triggered shift strategies. This study examines recent technological advances in automatic transmissions and their actual applications; identifies specific ways of improving vehicle performance through the development of parchment technologies; presenting a clear professional view on exactly how said parchment technology contributes to achieving better automotive performances by ATs, as well as explains our expectations for the next generation transmission technology, including AI-based g control and connect with HEV systems.

The green transition to reduce reliance on fossil fuels and minimize global warming is driving an increasing demand for NdFeB permanent magnets. These magnets play a crucial role in electric motors, which power vehicle propulsion, as well as in magnetic brakes, bearings, and other components. Sintered NdFeB magnets bring significant benefits to electric vehicles. Their superior magnetic properties enable electric motors to achieve a high torque-to-weight ratio, enhancing vehicle performance and acceleration. Moreover, their compact design facilitates the creation of smaller, lighter motors, contributing to more streamlined and efficient vehicle designs. These magnets also excel in heat resistance, making them ideal for the challenging conditions within electric vehicle motors. Their ability to operate effectively at relatively high temperatures ensures consistent motor efficiency and reduces the likelihood of component failures. Relatively rapid transformation from internal combustion engine vehicles to electric vehicles also requires a steep change in the demand for and supply of the raw materials used in the production process. Overall, NdFeB magnets are inevitable in modern technology and are found in numerous products and systems we encounter daily.However, NdFeB magnets are prone to degradation in harsh environments due to the low corrosion resistance of both iron and neodymium. Consequently, magnets require corrosion protection since their magnetic properties would be jeopardized due to the degradation caused by the corrosion process. Commonly used commercial coatings are Ni-Cu-Ni-based.The present study investigated the effect of temperature of automotive transmission fluid (ATF) on the corrosion of Ni-Cu-Ni coated neodymium-iron-boron permanent magnets. ATFs are complex mixtures containing various additives designed for the automatic transmissions performance. The compounds react with each other to form a stable lubricant surface. Hydrocarbons in mineral oil themselves do not cause corrosion, but the presence of water content, sulphur molecules and acid compounds can cause a chemical attack on the surface of sintered NdFeB magnets and their coatings. Therefore, additives can be chemically aggressive, potentially exacerbating the corrosion of NdFeB magnets. In particular, sulphur compounds, which are inherently contained in the purified mineral oil and originate from the ATF compositions, are highly corrosive to the metal of electric circuits and NdFeB magnets. After exposing materials to higher temperatures (ranging from 80 to 140 degrees Celsius) for extended periods, the changes in the composition of ATF were analyzed using different methods of organic analysis (nuclear magnetic resonance and liquid chromatography with mass spectrometry). On the other hand, the changes in microstructure, morphology and chemical composition of the Ni-Cu-Ni coatings were investigated using scanning electron microscopy with energy-dispersive X-ray spectroscopy. As reference materials, copper and nickel metals were studied.The results show that the temperature of automatic transmission fluid has a decisive effect on the protective ability of Ni-Cu-Ni coating deposited on NdFeB magnets.Acknowledgements: This study was part of the project financed by the Gremo i-Motion programme (No. 12816). The financial support by the Slovenian Research and Innovation Agency (ARIS) is also acknowledged (core programme funding P2-0393).

To address the issues of frequent shifting, significant shift shock, and prolonged power interruption in pure electric vehicles equipped with a two-speed automatic mechanical transmission under dynamic load conditions, this paper proposes an adaptive shift control strategy based on load state recognition. By establishing a Recursive Least Squares-Multi Feedforward (RLS-MFF) estimation algorithm, the characteristics of vehicle load variation are identified in real time. A dynamic load-speed coupling model is constructed to achieve feedforward-feedback composite compensation for target rotational speed. A multi-constraint shift decision mechanism is designed to suppress shift failures caused by abrupt load changes, optimize the control strategy, and improve shift success rates under varying loads. Simulations based on Simulink and bench experiments for two-speed automatic mechanical transmission are conducted. The results show that the load mass estimation error was reduced from ±5.7% with traditional methods to ±1.16%, significantly improving dynamic load identification accuracy. Gear shift frequency was reduced by up to 17.6%, effectively suppressing the phenomenon of frequent shifting and resolving the issue of frequent gear changes under dynamic load conditions. This provides theoretical foundation and engineering insights for enhancing the control performance of multi-speed transmissions in pure electric vehicles.

This study presents the development of an innovative educational tool designed to enhance the comprehension of automatic transmission systems. The teaching aid features a 2004 Toyota Corolla automatic gearbox integrated with a variable-speed electric motor, which is connected to the torque converter through a system of pulleys. A gear position shifter is paired with a programmed Arduino-based display panel that includes a gear position indicator and an LCD screen linked to a magnetic sensor on the output shaft. This setup provides real-time feedback on rotational speed, enabling students to analyze the relationship between engine speed, torque converter dynamics, and output performance. The project is aligned with core Mechanical Engineering courses such as Automotive Systems Engineering and Transmission System Design, fulfilling key learning objectives like understanding planetary gear mechanisms and torque converter operations. Its primary aim is to provide students with a hands-on learning platform that replicates the functional behavior of an actual automatic transmission while allowing direct comparison with manual transmission systems. By using the electric motor to emulate engine input power, learners can visualize and investigate how variations in engine speed influence torque converter behavior. Overall, this project offers a practical, interactive, and effective resource for both educators and students, bridging theoretical knowledge and real-world automotive applications.