Electric vehicle (EV) charging stations are becoming increasingly widespread, but their front-end rectifiers often degrade grid power quality by introducing high-input current harmonics, a low power factor, and voltage distortion. Although conventional diode bridge rectifiers (DBRs) are simple and low-cost, they typically exhibit total harmonic distortion (THD) exceeding 25% and power factors below 0.80. To address these issues, active power factor correction (PFC) techniques have been employed in the literature; however, they increase system complexity, cost, and control algorithm sophistication. Thus, this paper proposes a linear integrated transformer (ILT)-based DBR, which is designed to improve power quality in EV charging stations without relying on active control mechanisms. The proposed configuration integrates a linear transformer, passive filter network, and diode bridge to achieve both voltage step-down with galvanic isolation and harmonic mitigation in a single structure. This system offers improved voltage regulation, flux balancing, filter resonance, and reduced current distortion. The proposed system is validated using MATLAB/Simulink R2021a. The results demonstrate that it achieves a THD of 4.32%, complying with IEEE 519 harmonic standards. In addition, the input power factor improves to 0.981. The system also reduces the DC output voltage ripple from 6.8% to 1.2%, enhances voltage regulation by 9.1%, and increases overall efficiency to 96.3%. These findings establish the proposed ILT-DBR as an affordable, robust, and compact solution for next-generation EV charging infrastructure, specifically designed to meet the needs of smart grid deployment and integration in Tier-2 and Tier-3 cities, where simplicity and power quality compliance are priorities.

Introduction:

Aflatoxin M1 (AFM1) is a carcinogenic mycotoxin commonly found in dairy products from livestock consuming contaminated feed. Its thermal stability renders pasteurization ineffective, necessitating alternative detoxification strategies. This study evaluated the AFM1-binding capacity of exopolysaccharides (EPS) from kefir grains using two extraction methods.

Methods:

EPS were produced by fermenting semi-skimmed milk with 10% kefir grains. After grain removal and casein elimination, EPS were extracted via (1) cold ethanol precipitation and (2) hot aqueous extraction followed by ethanol precipitation. EPS were dialyzed (14 kDa), lyophilized, and analyzed for sugar and protein content. AFM1 retention (1 μg/L) by 1% EPS was assessed in phosphate buffer (pH 6.8, 30°C) over 3 hours with hourly sampling, ultrafiltration (30 kDa), and HPLC-fluorescence quantification.

Results:

Cold extraction yielded higher solids (9,400 mg) with balanced sugar-to-protein ratios, while hot extraction reduced protein content by 69% and increased sugar concentration eight-fold. Cold-extracted EPS maintained >80% AFM1 retention throughout the assay period. Hot-extracted EPS exhibited lower, time-dependent retention. Method-dependent differences were statistically significant (p < 0.05).

Conclusions:

Extraction methodology significantly affects EPS composition and AFM1-binding efficiency. Cold-extracted EPS, retaining protein components, demonstrated superior AFM1 sequestration, suggesting protein–mycotoxin interactions are crucial for binding. These findings highlight the potential of kefir EPS as a natural mycotoxin mitigation strategy in dairy processing, warranting further investigation for industrial applications.

This work presents the design and validation of an Adaptive Fuzzy PID (PIDFA) controller for real-time stabilization of nonlinear and underactuated systems, using the inverted pendulum as a benchmark. Conventional PID controllers, while widely used, lack robustness in dynamic environments due to their fixed parameters and reliance on precise models. The proposed PIDFA architecture embeds a fuzzy inference mechanism that continuously adjusts the PID gains based on an instantaneous system error and its derivative, eliminating the need for offline tuning and improving performance under uncertainty.

The control design integrates fuzzification, rule-based gain scheduling, and defuzzification. Separate fuzzy systems regulate the proportional, integral, and derivative components, enabling real-time gain adaptation. A set of 49 linguistic rules per gain ensures interpretable and efficient control logic. Simulations conducted in MATLAB/Simulink evaluate the controller under three scenarios: stabilization from initial deviation, step disturbance rejection, and structural parameter variation.

Results confirm that the PIDFA achieves fast stabilization (settling time <1.2 s), low overshoot (<5%), and robust performance without saturation or chattering. The controller adapts to parameter shifts and external disturbances without manual reconfiguration, demonstrating strong real-time applicability. This study supports the use of fuzzy adaptive controllers in managing uncertain and nonlinear systems and outlines a methodology transferable to broader domains such as robotics and power systems.

This study investigates the impact of two drying methods — solar cabinet drying and open-air sun drying — on the retention of water-soluble vitamins (C, B₂, B₃, B₆, B₉) and flavonoids in Plantago major L. leaves. The goal is to determine which method ensures better preservation of nutraceuticals for functional food applications. Fresh Plantago leaves were divided and dried under two conditions: a solar cabinet dryer and natural open-air conditions. Both external and internal leaf zones were analyzed separately using HPLC-DAD at wavelengths of 250, 254, and 276 nm to quantify vitamin and flavonoid content. The solar dryer significantly outperformed open-air drying in preserving bioactive compounds. External tissues from solar-dried samples retained the highest level of vitamin C (971.9 mAU·s), while vitamin B₆ was best preserved in solar-dried internal tissues (512.4 mAU·s). Flavonoids such as rutin and degidrokvertsetin showed more pronounced peaks in solar-dried leaves, with degidrokvertsetin reaching 631.5 mAU·s — far higher than open-air equivalents (~437.7 mAU·s). The controlled temperature and enclosed design of the solar dryer minimized oxidative and photochemical losses compared to unregulated open-air drying. Solar cabinet drying is a superior method for retaining heat-sensitive vitamins and antioxidant flavonoids in Plantago major leaves. Its controlled microclimate leads to better quality and stability of nutraceuticals, making it more suitable for producing functional food ingredients than traditional open-air drying.

The challenge of food adulteration has always been a major concern and thus there is a need to formulate quick, sensitive, and un-destructive methods to detect food adulteration. Authors provide the design and performance analysis of a Smart Optical Sensing platform driven by Arrow-type Silica Photonic Crystal Fiber (PCF) that is designed to specifically detect adulterants in food products. The COMSOL Multiphysics numerical simulation environment is used to increase the interaction between light and matter. The intended PCF structure has a pitch size of 1.5 µm, diameter of the air holes to be 0.75 µm and core displacement parameters to be asymmetrical (dx = 0.5 µm, dy = 0.25 µm) that leads to a robust field of evanescent confinements and proves to have high optical sensing qualities. The simulation outcomes also show a large absorbance difference (0.8512 a.u.) between non-adulterated and adulterated ones and the greatest decrease in transmittance amounting to 30% exists in the case of the adulterated specimens. Fluorescence spectral displacement of 40 nm to 75 nm and Raman based at 200 to 800 cm^-1 allowed distinguishing between chemicals at the level of a molecule, and identification of adulterants which include artificial food dyes, heavy metals, and chemical preservatives. A combination of NIR spectroscopy with COMSOL-based modelling will define a sound system of non-destructive, fast, and scalable food safety tests.

Milk is one of the most valuable products in terms of nutritional value and balanced composition. About a third of the entire herd of cows gets sick with mastitis, while milk from such cows must be disposed of due to the presence of pathogenic microorganisms and their toxins. Intensification and robotization of various areas of the agro-industrial complex requires modern systems for assessing the quality of raw materials and food products. One of the possible options for detecting mastitis milk is the use of sensor technologies. A method is proposed for detecting milk from cows with mastitis by analyzing the gas phase above milk samples using chemical sensors, including the subclinical course of the disease with a pathogenic microorganism content in milk of no more than 1000 CFU / ml, and differentiating such samples from milk with a high level of coliform bacteria and a general level of contamination, as well as with non-vital forms of pathogenic microorganisms. The work used piezoelectric quartz sensors with polycomposite coatings to identify informative sensor output data related to the presence of pathogenic microorganisms and their toxins, milk samples were analyzed in parallel using microbiological and molecular genetic methods. The work demonstrated approaches to determining Staphylococcus, Streptococcus, Klebsiella spp. in milk from mastitis cows at a level of 1000-10000 CFU/ml with high sensitivity due to variation in the preparation of milk samples before measurement. Thus, the use of gas sensors will allow obtaining information on the microbiological safety of milk within three hours.

Introduction

Household electricity consumption is a significant part of total energy consumption. Trends in the spread of remote work and distance learning only strengthen this contribution. Modern smart grid technologies allow for detailed analysis of the consumption patterns of each individual household. However, the task of analyzing the time series in aggregate, comparing, and classifying households is not easy, since each such series is unique. Modelling the stability of consumption using time series of readings is the main subject of this presentation. We present our method for monitoring the stability of residential electricity consumption.

Methods
As a measure of stability, we use the auto-similarity coefficient defined as the geometric mean of pairwise correlations between fragments (windows) of the corresponding time series. The method was introduced in our previous work. Here, we test the applicability of this approach to a real-world data set.

Results
This study found that one week is an appropriate window size for studying the stability of consumption. And also the capabilities of the method are demonstrated for real data of selected Swedish households. The method also reveals seasonal differences; for example, with a high stability of the pattern in the winter months, the same household has low stability in the summer vacation period. Cases with both a high degree of stability and low stability indicators are considered.

Conclusion

The proposed method can be applied to the analysis of the stability of electricity consumption and thus enriches the arsenal of mathematical modeling methods.

Abstract:This paper presents a new methodology for the diagnosis of induction machine faults. A modern and efficient diagnosis technique should be non-destructive. The proposed technique is based on the vibratory behavior analysis.

Introduction:Vibration analysis is a very interesting and recent technique because any change in the mechanical or electrical conditions of the machine affects its dynamic conditions and thus its vibration behavior.

Methodology:The virtual works method is applied locally for the magnetic force computation. To calculate the dynamic response of the stator to the magnetic force stresses, the dynamic equation must be solved. The vibratory response (acceleration) of the stator is equal to a linear combination of associated mode shapes.

Numerical calculation codes based on finite elements method are developed under Matlab environment to diagnosing rotor bar breakage, short circuit and other faults. These codes were applied in healthy and faulty cases of the machine.

The Fast Fourier Transform was investigated. So, we compared between the frequency spectra of the accelerations in the faulty and healthy cases, to predict the defect.

Results:The obtained results prove that vibration analysis technique has the advantage of allowing the detection and the localization of defects. More details and results proving the interest of this technique for the monitoring of the induction machines will be presented in the full paper.

Conclusion:This study has given more understanding of the dependent roles of vibration analysis in predicting and diagnosing machine faults. So, we have proved the interest of this technique for the monitoring of the electrical machines.

The development of advanced current control strategies for six-phase induction machines (IMs) has been driven by the need to take full advantage of their inherent efficiency, fault tolerance, and reliability. In that sense, proportional—resonant (PR) controllers, known for their current tracking accuracy in a stationary reference frame, are particularly well-suited to multiphase drive applications. This work presents a sensitivity and robustness analysis of a PR current controller designed for six-phase IM drives. The PR is evaluated through simulation under four rigorous test scenarios. Two tests assess current tracking accuracy and dynamic performance at low and high speeds, including full-speed reversals between ±500 r/min and ±1000 r/min. The remaining tests introduce ±50% uncertainties in stator resistance and magnetizing inductance to evaluate robustness against parameter mismatches. Across all scenarios, the controller achieves total harmonic distortion (THD) below 0.35% and root mean square error (RMSE) under 0.02 A in both the (α-β) and the (x-y) plane. Even under abrupt speed reversals, current stability is restored in approximately 2.5 seconds, demonstrating robust dynamic behavior. Compared with more complex model-based approaches, the proposed PR control scheme achieves comparable tracking accuracy and robustness while offering simpler implementation and lower computational demand. These results validate the feasibility of PR-based control for high-performance multiphase drives operating under variable conditions.

Distributed optical fiber sensors based on stimulated Brillouin scattering (SBS) allow temperature and strain measurements at multiple points along a fiber. Conventional interrogation methods based on a pulsed pump fail to reach a cm-scale spatial resolution due to the acoustic response time of the silica fiber. Instead, the Brillouin Optical Frequency Domain Analysis (BOFDA) technique achieves a finer resolution by pre-activating the acoustic wave involved in the scattering process. Unfortunately, BOFDA also suffers from artifacts caused by the “secondary interaction” between the pump and the sidebands of the acoustic wave. These artifacts introduce systematic errors in the estimate of the Brillouin frequency shift. To correct these distortions, earlier proposed methods based on either iterative numerical compensation or high-pass filtering have some drawbacks, such as a long processing time or a degradation of the SNR. Here, we propose a method based on the direct measurement of the secondary interaction, achieved through the injection of a double-sideband-modulated pump with a suppressed carrier. Under these conditions, the SBS interaction between the sidebands of the pump and those of the acoustic wave induces a frequency-doubling modulation in the probe intensity. By acquiring such modulation, one can estimate the secondary interaction signal and subtract it from the original BOFDA signal to mitigate systematic errors. Theoretical and experimental results validate the proposed technique.