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Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain–cloud services being offered by existing Cloud Service providers.

Electricity supply as well as the provision of other forms of resources is one of the foundations of efficient agriculture. However, due to the reduction in the number of people living in rural settlements, there have been a large number of power lines with considerable lengths supplying small loads, hence resulting in an increase in power supply efficiency. A single-wire power transmission is an option for reducing the capital cost of power line construction by utilizing fewer conductors and fittings and lighter power transmission towers while lowering operational expenses. This paper considers the possible methods for single-wire energy transmission via the analysis of information sources such as Yandex and Google search engines; Scopus and Google Scholar scientific databases; and Cyber Leninka, eLIBRARY.ru, Elsevier, Springer, IEEE Xplore, and IGI Global electronic libraries. The conducted review revealed four alternatives: a single-wire earth return (SWER) system, a single-wire balanced line (B-Line), resonant wireless power transmission (SWPT) system, and a resonant single-wire power transmission system. The latter is of particular interest due to the lack of comprehensive and detailed information describing this technology, although it has distinct characteristics because of the peculiarities of the resonant mode of operation. The paper provides a comprehensive review of all existing published materials on the topic of “resonant systems for the transmission of electrical energy along a single wire”. The study covers the history of development and the structure of this system; describes its features, advantages, and the problems of using it; and the experience and fields of its application.

The most prominent and rapidly increasing source of electrical power generation, wind energy conversion systems (WECS), can significantly improve the situation with regard to remote communities’ power supply. The main constituting elements of a WECS are a wind turbine, a mechanical transmission system, a doubly-fed induction generator (DFIG), a rotor side converter (RSC), a common DC-link capacitor, and a grid-side converter. Vector control is center for RSC and GSC control techniques. Because of direct and quadrature components, the active and reactive power can also be controller precisely. This study tracks the maximum power point (MPP) using a maximum power point tracking (MPPT) controller strategy. The MPPT technique provides a voltage reference to control the maximum power conversion at the turbine end. The performance and efficiency of the suggested control strategy are validated by WECS simulation under fluctuating wind speed. The MATLAB/Simulink environment using simpower system toolbox is used to simulate the proposed control strategy. The results reveal the effectiveness of the proposed control strategy under fluctuating wind speed and provides good dynamic performance. The total harmonic distortions are also within the IEEE 519 standard’s permissible limits which is also an advantage of the proposed control approach.

High-quality lighting of premises in the cultivation of farm animals is an important condition for the successful development of the agro-industrial complex. Sufficient lighting with the simulation of sunrise and sunset which increases daylight hours to 15–16 h can raise the level of production by 8–15%. The most innovative, economical and efficient light source for livestock, poultry and greenhouse buildings are LED lamps allowing for a short payback period. The difference between the models developed by the authors and the traditional point method is taking into account lamp length o, lamp suspension height, location of the illuminated area under the lamp at displacement and observation angles, and luminous flux of light source, and the most important thing is body angle of the light source. This paper presents the method for calculating illumination under the LED lamp due to the large error in the values obtained by theoretical methods due to the lack of a correction factor for changing the illumination of the lamp (0.8). Prediction of crystal heating was carried out by verification calculation in the ANSYS/CFX software package. Forecasting the illumination of the fodder table was carried out by calculation in the DIALux evo software package. The main parameters of an energy-saving LED lamp (power consumption, luminous flux, color temperature and color) were selected. The consumption of electrical energy decreased by 85%; the average level of illumination increased and amounted to 123.1 lux. The developed method for calculating the illumination under the LED lamp can be considered satisfactory, the Student and Fisher criteria do not exceed tabular values and the correlation coefficient showed a close relationship. To comply with electrical safety standards in the premises, a resonant power supply system for LED lamps is offered.

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a R2 value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.

Large language models are increasingly deployed as adaptive interviewers in qualitative research and human-computer interaction, yet systematic evaluation of their interviewing behavior remains limited. We introduce a modular LLM agent for conducting semi-structured psychological interviews and present a controlled, multi-faceted evaluation protocol to assess interviewer quality across six state-of-the-art models: Claude Sonnet 4, Gemini 2.5 Pro, GPT-5 Chat, Grok 4, Qwen3-235B A22B, and DeepSeek Chat V3.1. The agent conducts adaptive interviews over 54 main questions spanning biography, family, interests, challenges, values, work, and health, deciding for each response whether a follow-up is warranted and generating tailored follow-up questions. To enable fair comparison, we standardize interview context using transcripts from ten baseline human interviews, execute all models under identical orchestration and prompts, and use a single LLM interviewee to eliminate human response variability. Expert psycholinguists evaluate interviewer behavior on five binary criteria: benevolence (empathic tone), necessity, context-awareness, openness, and justified skip (when follow-ups are unnecessary), annotating over 2900 items with high inter-rater reliability (Fleiss$$\\kappa$$0.67–0.93). We complement human judgment with efficiency metrics (latency, questioning intensity) and linguistic profiling via morpho-syntactic and psycholinguistic features on the interview text. Results reveal systematic trade-offs: Gemini 2.5 Pro has the most empathic tone, GPT-5 Chat optimizes for speed and selective precision, Grok 4 achieves exhaustive coverage at the cost of latency and occasional over-contextualization, while Claude Sonnet 4 offers balanced versatility. Linguistic markers such as person pronouns, tense, intensifiers, or syntactic complexity align meaningfully with human judgments, suggesting that stylistic choices are aligned with perceived interview quality. DeepSeek’s format instability underscores the operational importance of schema compliance. Our reusable toolkit (prompts, orchestration code, annotation rubric) provides a foundation for principled deployment of LLM interviewers in psychological experiments, enabling researchers to match model capabilities to study goals and to audit agent behavior for empathy, appropriateness, and effectiveness.

Agriculture activities are completely dependent upon energy production worldwide. This research presents sensorless speed control of a three-phase induction motor aided with an extended Kalman filter (EKF). Although a proportional integral (PI) controller can ensure tracking of the rotor speed, a considerable magnitude of ripples is present in the torque generated by a motor. Adding a simple derivative to have a proportional integral derivative (PID) action can cause a further increase in ripple magnitude, as it allows the addition of high-frequency noise in the system. Therefore, a fractional-order-based PID control is presented. The proposed control scheme is applied in a closed loop with the system, and simulation results are compared with the PID controller. It is evident from the results that the fractional order control not only ensures 20 times faster tracking, but ripple magnitude in torque was also reduced by a factor of 50% compared to that while using PID and ensures the effectiveness of the proposed strategy.

Biogas production from waste materials has emerged as a promising avenue for sustainable energy generation, offering a dual benefit of waste management and renewable energy production. The selection and preparation of waste feedstocks, including agricultural residues, food waste, animal manure, and municipal solid wastes, are important for this process, while the microbial communities are majorly responsible for bioconversions. This review explores the role of complex microbial communities and their functions responsible for the anaerobic digestion of wastes. It covers the crucial physiological processes including hydrolysis, acidogenesis, acetogenesis, and methanogenesis, elucidating the microbial activities and metabolic pathways involved in the prospects of improving the efficiency of biogas production. This article further discusses the influence of recent progress in molecular techniques, including genomics, metagenomics, meta-transcriptomics, and stable isotope probing. These advancements have greatly improved our understanding of microbial communities and their capabilities of biogas production from waste materials. The integration of these techniques with process monitoring and control strategies has been elaborated to offer possibilities for optimizing biogas production and ensuring process stability. Microbial additives, co-digestion of diverse feedstocks, and process optimization through microbial community engineering have been discussed as effective approaches to enhance the efficiency of biogas production. This review also outlines the emerging trends and future prospects in microbial-based biogas production, including the utilization of synthetic biology tools for engineering novel microbial strains and consortia, harnessing microbiomes from extreme environments, and integrating biogas production with other biotechnological processes. While there are several reviews regarding the technical aspects of biogas production, this article stands out by offering up-to-date insights and recommendations for leveraging the potential of microbial communities, and their physiological roles for efficient biogas production. These insights emphasize the pivotal role of microbes in enhancing biogas production, ultimately contributing to the advancement of a sustainable and carbon-neutral future.

Drying food stuffs and other materials belongs to one of the most commonly used feedstock processing techniques, featuring rather high energy consumption. The major disadvantage of conventional electric convective-type household dryers is substantial thermal energy emission into the environment with a wet exhaust, worked-out drying agent. Among other principal disadvantages common to all dryers of this type, the following have to be mentioned: spatial inhomogeneity of heating a product under processing and that of drying agent distribution due to its temperature reduction and relative humidity growth as it moves upwards. A block diagram and a breadboard model of a convective-type thermoelectric dryer employing a thermoelectric heat pump have been designed. In our approach, a product is treated with the help of a drying agent (normally, heated air) with partial exhaust-air recirculation and heat recovery. Laboratory studies of the drying process have been carried out using apple fruits as a test material in order to evaluate the power consumed for evaporation of 1 kg of water in the newly developed convective-type thermoelectric drying unit. Physical parameters of apple fruits before and after drying both in the thermoelectric drying unit and in a conventional series-produced convective-type domestic dryer have been reported. The energy efficiency of the newly designed drying unit has been compared with that of some series-produced samples. It has been found out that, unlike conventional convective-type dryers, the breadboard model of the developed thermoelectric drying unit features a smoother product drying process owing to the presence of side air channels and more effective drying agent path organization in the processing chamber. This conclusion was supported by the results of the carried out tests. Application of thermoelectric heat pumps with the function of the exhaust drying agent heat recovery will make it possible to reduce the drying agent heater installed capacity and the power consumed by the newly designed convective-type thermoelectric drying unit by up to 20% in the course of the drying process, compared to series-produced household convective-type dryers.