Efficient System Research Articles

Integration of the XY-MD02 Module in an IoT-Based Humidity and Temperature Monitoring System with Graphic Display on Nextion LCD

Abstract—Temperature and humidity monitoring is crucial in various industrial sectors such as agriculture, manufacturing and health. Integrating the latest technology, such as IoT (Internet of Things), opens up new opportunities to increase the efficiency and accuracy of monitoring systems. This research focuses on integrating the XY-MD02 module into an IoT-based monitoring system to measure and monitor humidity and temperature levels accurately. The system uses the XY-MD02 module to collect data, which is transmitted wirelessly to the server using the MQTT protocol. The collected data is processed and displayed in real-time on the Nextion LCD, providing an intuitive graphical representation of environmental conditions. Integrating the XY-MD02 module and Nextion LCD in an IoT-based monitoring system demonstrates practical and reliable humidity and temperature measurements. The average value of the measured temperature is 31.58 with a standard deviation of 0.17, indicating high accuracy in temperature measurement with low variation. Meanwhile, for humidity, the average is 62.34, with a standard deviation of 1.01. The system's compatibility with the MQTT protocol ensures smooth communication and data exchange between devices. Integrating the XY-MD02 module in an IoT-based monitoring system has proven successful in providing accurate and real-time monitoring of humidity and temperature, offering an effective solution in environmental monitoring by keeping up with the latest technological developments.

Body gesture recognition using crow search algorithm enhanced probabilistic neural network for human- computer interaction

Background Body gesture recognition has become a fundamental technique in Human-Computer Interaction (HCI). As human-machine interaction evolves, there is an increasing need for precise and efficient gesture detection systems. However, current methods face limitations such as accuracy constraints, high computational complexity, and limited adaptability. This study addresses these challenges by proposing an innovative approach to enhance the accuracy and efficiency of body gesture recognition systems. Methods The proposed system integrates advanced algorithms and techniques to improve performance. A Marker-Based Watershed Algorithm is employed for accurate image segmentation, enhancing region detection. Feature extraction uses a Convolutional Neural Network (CNN), while a Wavelet Transform-Based Pre-Processing technique improves input data quality. A unique component of this method is the application of the Crow Search Algorithm to optimize model efficiency. An Optimized Probabilistic Neural Network (PNN) is utilized for gesture classification, aiming to increase precision and computational effectiveness. Results The proposed approach achieves a gesture recognition accuracy rate of 99%. Compared to traditional methods such as Decision Trees (DT), Support Vector Machines (SVM), and Improved Neural Networks (INN), the Optimized PNN demonstrates a 2.21% improvement in overall accuracy. The implementation, carried out in Python, showcases the robustness and adaptability of the system across diverse HCI applications. Conclusions This work presents a comprehensive solution to the challenges of body gesture recognition by integrating cutting-edge algorithms. Combining the Marker-Based Watershed Algorithm, CNN-based feature extraction, and Crow Search Optimization significantly enhances the system’s accuracy and efficiency. By addressing the shortcomings of existing methods, this approach provides a more responsive, reliable, and flexible gesture recognition system, contributing to the advancement of HCI technologies. The results demonstrate the potential for improved human-computer interaction through more effective and precise gesture detection.

Analysis and Design of Constant Current/Constant Voltage Output of Array Multitransmitter Inductive Power Transfer System

ABSTRACTWireless power transfer has been widely used in unmanned aerial vehicle (UAV) charging due to its convenient and flexible noncontact charging characteristics. The working area of the uncoupler inductive power transfer (IPT) system is limited, and ZVS is easily lost in phase‐shift modulation. This paper proposes a method for constant current (CC) /constant voltage (CV) output in an array multitransmitter IPT system based on HFP modulation in a wide working area. The HFP modulation method easily realizes the current phase synchronization of multiple transmitter coils, and the size can be adjusted. At the same time, an efficient optimization model for a multitransmitter IPT system based on HFP modulation is proposed. Finally, an experimental platform for the array multitransmitter IPT system is built to verify the effectiveness of the CC/CV output realization method and the efficiency optimization model for the multitransmitter IPT system. The maximum errors in charging current and voltage are less than 2.52% and 2.1%, respectively, and the peak efficiency of the IPT system reaches 87.3%.

A Review of Academic and Patent Progress on Internet of Things (IoT) Technologies for Enhanced Environmental Solutions

Environmental pollution is a pressing global issue, and the Internet of Things (IoT) offers transformative potential for its management through its application in advanced real-time monitoring and analytics. However, the heterogeneous and fragmented nature of IoT technologies poses challenges to seamless integration, limiting the efficacy of these solutions in addressing environmental impacts. This paper addresses these challenges by reviewing recent developments in IoT technologies, encompassing sensor networks, computing frameworks, and application layers for enhanced pollution management. A comprehensive analysis of 74,604 academic publications and 35,000 patent documents spanning from 2008 to 2024 is conducted using a textual analysis that combines quantitative bibliometric methods along with a qualitative analysis based on both scholarly research and patent innovations. This approach allows us to identify key challenges in IoT implementation for environmental monitoring—including integration, interoperability, and scalability issues—and to highlight corresponding architectural solutions. Our findings reveal emerging technology trends that aim to overcome a few of these challenges, and we present a scalable IoT architecture as key discussions that enhances system interoperability and efficiency for pollution monitoring. This framework provides targeted solutions for specific tasks in pollution monitoring while guiding decision-makers to adopt solutions effectively.

Equilibrium Moisture Effects in Silica Gel Adsorption/Desorption

This research involves a numerical analysis focused on examining how the initial moisture content and equilibrium moisture content affect a Regular Density (RD) silica gel-packed bed, in terms of the composite mass and heat transfer dynamics during the adsorption and desorption processes. The detailed interaction between the air and the desiccant material is thoroughly represented through the development of the governing drying kinetics, mass conservation, and energy conservation equations, thereby establishing a solid mathematical foundation to clarify these essential processes. All numerical simulations are meticulously executed using the Finite Volume Element method, allowing for a detailed analysis of the mass and heat transfer phenomena within the RD silica gel-packed bed. The utilization of this advanced computational technique enables a deeper understanding of the complex interactions between the moisture content and transfer process efficiency in the desiccant system. This research culminates in the development of correlations that serve as predictive tools. An accurate estimation is facilitated by both the removal/addition of humidity from/to the air and the maximum enthalpy released/absorbed by the RD silica gel medium throughout the adsorption and desorption phases. These correlations, rooted in the equilibrium and initial moisture content of the RD silica gel medium, provide valuable insights into optimizing the performance and efficiency of desiccant systems in various operating conditions. There are intricate relationships between moisture content and transfer processes. This study contributes to advancing the understanding of desiccant systems, paving the way for more efficient and sustainable air treatment technologies.

Advancements and Challenges in Hydraulic Suspension Technologies for Half-Axle and Multi- Axle Vehicles: A Comprehensive Review

This review discusses the present developments in hydraulic pressure suspension technology, with a greater focus on half-axle structures used in multiple-axle special vehicles. Most of the critical challenges mentioned include uneven load distribution distortion of vehicle bodies and even stability on uneven surfaces. This paper evaluates some of the major technological developments such as swing-type half-axle designs and various explosion-proof mechanisms to establish how such innovations improve load management and ensure system reliability in challenging environments. The paper has importantly discussed double-pipe and single-spool explosion relief valves in association with cone relief valves and explained their success in preventing potential failures in systems. In summary, it emphasizes the substantial benefits the technologies provide in enhancing stability in vehicles, optimal distribution of loads, and less risk in operation. On the other hand, the paper gives some worthwhile directions for future research, oriented towards further increasing efficiency and safety in suspension systems of heavy-duty vehicles. Keywords— Hydraulic pressure suspension, Half-axle structures, Load distribution, Vehicle Stability

Integrated Purification Systems for the Removal of Disinfectants from Wastewater

The efficiency of integrated treatment systems for wastewater generated during the washing of disinfectant production lines was investigated. The high organic load (COD 2000 mg/L, TOC 850 mg/L) and 300 mg/L of toxic benzalkonium chloride (BAC) make wastewater an environmental hazard that requires advanced treatment. Initial tests on model BAC solutions (in concentrations corresponding to those found in wastewater), using nanofiltration and ultrafiltration membranes, resulted in up to 70% retention of BAC. To enhance purification, ion exchange and adsorption were introduced as post-membrane treatment steps. In the second part of the investigation, membrane modules characterized by the best separation properties were integrated together with macroporous cation-exchange resin and activated carbon into the purification system to treat wastewater. The research carried out showed that the purification of multicomponent wastewater is a complex task. Significantly lower BAC removal (30%) was achieved in membrane processes compared to the model solutions treatment. In integrated systems, the BAC concentration was reduced to 100 mg/L, TOC to 200 mg/L, and COD to 120 mg/L.

Forecasting the efficiency of agile projects using machine learning

The aim of the research is to address current challenges in the development of informa-tion systems and the integration of artificial intelligence technologies to optimize Agile meth-odologies. The study focuses on modeling and forecasting the efficiency of Agile processes, taking into account changing requirements over time and random deviations. Forecasting is understood as the process of estimating the future efficiency of Agile processes based on the analysis of time series and machine learning models. Efficiency forecasting is considered as time series forecasting, where parameters adapt depending on the system and data, allowing the model to generalize and take into account various factors affecting project outcomes. The study examines the selection of the optimal model for forecasting Agile process effi-ciency by comparing machine learning methods such as Support Vector Machines (SVM), Random Forest, and neural networks (CNN, LSTM). Comparative analysis showed that neu-ral networks (LSTM, Bidirectional LSTM, CNN) are more effective for predicting project suc-cess, demonstrating high accuracy and lower errors. The practical value of the research lies in identifying LSTM and other neural network architectures as effective tools for predicting project success. This can serve as a guideline for implementing effective management systems in real-world conditions. For example, the application of LSTM in large IT companies for forecasting the success of Agile projects has significantly improved planning accuracy and reduced risks. The research confirms that the integration of machine learning models, particularly LSTM and CNN, significantly improves the accuracy of forecasting and managing Agile pro-jects. The application of these technologies can greatly enhance project management effi-ciency in information systems.

AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents

In recent years, the interplay between AI agents and blockchain has enabled secure and scalable collaboration among multi-agent systems, promoting unprecedented levels of autonomy and interoperability. AI agents play a vital role in facilitating complex decision making and improving operational efficiency in blockchain systems. This collaborative synergy is particularly evident in how multi-agent systems collectively tackle complex tasks to ensure seamless integration within these frameworks. While significant efforts have been made to integrate AI agents and blockchain, most studies overlook the broader potential of AI agents in addressing challenges such as interoperability, scalability, and privacy issues. In this paper, we bridge these gaps by illustrating the interplay between AI agents and blockchain. Specifically, we explore how AI agents enhance decentralized systems and examine blockchain’s role in enabling secure and scalable collaboration. Furthermore, we categorize practical applications across domains, such as Web3, decentralized finance (DeFi), asset management, and autonomous systems, providing practical insights and real-world use cases. Additionally, we identify key research challenges, including the complexities of multi-agent coordination, interoperability across diverse systems, and privacy maintenance in decentralized frameworks. Finally, we offer future directions in terms of governance, sovereignty, computation, and interpretability to promote a secure and responsible ecosystem.

Enhancing Ad Hoc Network Security using Palm Vein Biometric Features

This study proposes an innovative approach to securing ad hoc networks through palm vein biometric authentication, addressing critical security vulnerabilities in decentralized wireless communications. The research introduces an Adaptive Fusion Biometric Key Generation (AFBKG) framework that seamlessly integrates palm vein biometric features with state-of-the-art cryptographic protocols. The methodology implements a comprehensive six-stage process, incorporating Near-Infrared (NIR) imaging at 850 nm wavelength, advanced image preprocessing techniques, and deep learning-based feature extraction using a fine-tuned Convolutional Neural Network (CNN), culminating in a robust 512-dimensional feature vector. A rigorous performance evaluation was conducted, which demonstrated exceptional results, achieving 98% authentication accuracy with a 0.1% False Acceptance Rate (FAR) and 95% spoofing resistance. The AFBKG algorithm significantly outperforms traditional security methods, demonstrating 95% authentication strength and 92% resistance to Man-in-the-Middle (MITM) attacks while maintaining minimal key management complexity (15%). The system's superior scalability (90%) and computational efficiency (10% overhead) compared to conventional biometric approaches are noteworthy. These findings establish palm vein biometric authentication as a cutting-edge solution for enhancing ad hoc network security, offering substantial improvements over traditional password-based systems and alternative biometric methods.

Control of a Doubly Fed Induction Generator for Variable Speed Wind Energy Conversion Systems using Fuzzy Controllers optimized with a Genetic Algorithm

This paper presents a comprehensive study of a wind turbine system operating under variable wind conditions, utilizing a Doubly Fed Induction Generator (DFIG) connected to the grid. The DFIG is controlled via a rotor-side transducer, allowing for independent regulation of the conductors to manage both active and reactive power flows effectively. The control strategy focuses on generating reference voltages for the rotor to ensure that active and reactive power align with the desired targets, optimizing the tracking of the maximum power point to maximize electrical output. The research analyzes the system's dynamic performance under fluctuating wind conditions, emphasizing control strategies for managing active and reactive energy. A notable innovation is the integration of fuzzy logic and genetic algorithm into the control strategy for the wind turbine's switching mechanism, which enhances system performance and efficiency. Simulation results demonstrate that this approach provides higher efficiency, improved performance, and greater stability compared to the traditional Proportional-Integral (PI) controllers. Advanced artificial intelligence methods, such as fuzzy genetic algorithm control, were employed and the proposed system's effectiveness was validated with Matlab/Simulink simulations.

Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation

Super Resolution (SR) images contain more useful information than Low Resolution (LR) images. Generally, SR images are highly preferred over LR images in the medical field due to their high quality. Generally, SR images are affected by many factors, like blur, noise, and decimation. Therefore, the present study proposes Deep Feature Blend Attention Mechanism for generating SR images with optimal outcomes. In this study, a Deep Learning (DL) based encoder-decoder is used to extract the detailed information from the LR image, assist in noise removal, and improve image quality. Using Gaussian blur, an LR image is produced, which is given as input to the encoder-decoder. The Attention mechanism with feature blending is performed to produce the reconstructed SR image. The low and high frequency component issues from feature map are improved using the attention mechanism. Also, feature blend attention mechanism is implied to reconstruct SR images from LR images. The present study utilizes the feature blend technique, which helps select the optimal features and blending to attain the optimal feature set. The study is performed with and without the feature blend technique in attention mechanism to expose the impact of the feature blend technique, and it is used to avoid overfitting issues and produce optimal results. The performance of the proposed system is assessed by using PSNR and SSIM to show the system's efficiency. The proposed system is compared with other state-of-the-art studies to expose the efficacy of the present system.

Advances and challenges in hybrid photovoltaic-thermoelectric systems for renewable energy

Integrating thermoelectric generators (TEGs) with photovoltaic (PV) devices presents an effective strategy to enhance the power generation of PV cells, thus substantially contributing to the widespread adoption of solar energy. By harnessing both photon and heat energy from sunlight, this integration maximizes energy capture and improves overall system efficiency, thereby advancing the feasibility and scalability of solar energy generation. This article provides a timely review of the advances and challenges in hybrid photovoltaic-thermoelectric generator (PV-TEG) technology, covering fundamentals, the impact of thermal, contact, and load resistance on performance, various integration options (such as hybrid PV-TEG systems with spectral splitters, phase change materials, and thermal systems), thermal management, feasibility, economic and environmental aspects, and long-term efficiency improvements. Following a detailed analysis and review of extensive progress, PV-TEG systems demonstrate higher efficiency across diverse environmental conditions compared to standalone PV devices. Finally, we address constraints, propose potential remedies, and point out future directions in the field.

Intelligence Based Controlling Models for Effective Power Tracking and Voltage Enhancement in Grid-PV Systems

The underlying research work is focused on enhancement in the efficiency and voltage gain for solar PV systems with the help of designing a novel regulating framework that includes advanced converter topologies integrated with intelligent control techniques. Optimization in extracting energy from the solar panel at different climatic conditions, voltage gain with minimum losses, and enhancing overall system efficiency and power quality are major tasks to be undertaken. The proposed control architecture presents a new Fractional Order Proportional-Integral-Derivative Control (FOPTC) technique and its adaptation mechanism for correct MPPT under dynamic variations of meteorological conditions. Consequently, it offers improved energy harvesting because it is able to identify the global maximum power point with higher speed and precision than traditional control techniques that apply hybrid approaches. The improved topological structure will ensure a substantial rise in voltage gain and efficiency with reduced voltage and current stresses upon the circuit components. In addition, the idea of a Neuro Feed Quadratic Controller (NFQC) is introduced to generate the regulating pulses for switching components of the converter for optimizing the voltage conversion process. Simulation and analytical studies confirm the higher efficiency, improved voltage gain, and reduced total harmonic distortion in the proposed framework over conventional systems. Doi: 10.28991/ESJ-2025-09-01-015 Full Text: PDF

How do access and spatial dependency shape metro passenger flows

Spatial imbalances in metro ridership significantly reduce the overall efficiency of metro system. Understanding the factors that contribute to metro ridership is essential for developing targeted strategies to improve ridership equity and overall system performance. This study introduces novel spatial dependency indices based on spatial weight matrices and land-use function complementarity to explore how access and inter-station spatial dependency affect metro ridership, focusing on station-level boardings and alightings, as well as station-to-station flows. Using the data from the Xi'an Metro, the findings indicate that access to employment and residence from metro stations considerably enhances station-level boardings and alightings. Walking access emerges as a critical factor, especially in the context of station-to-station travel. Furthermore, the analysis reveals a complementarity feature within the metro system, where increases in boardings (alightings) at one station leads to a higher demand at others. Stations that serve areas with complementary land-use functions tend to attract more travel between them. These findings emphasize the critical role of access and spatial dependency in enhancing transit planning and system efficiency.

Eccentric Wear Mechanism and Centralizer Layout Design in 3D Curved Wellbores

In deep oil and gas wells, sucker rod strings (SRS) frequently experience breakage and eccentric wear problems. To address this engineering challenge, this study establishes a new coupled three-dimensional (3D) mechanical-mathematical model for sucker rod strings in 3D curved wellbores. The model comprehensively considers well trajectory, rod string structure, and external excitation, analysing the influences of elastic force, inertial force, and friction force on the sucker rod micro-elements. The formulated differential equations are discretised using the central difference method to obtain the configuration of each point on SRS and the 3D distribution of stress and strain, thereby determining the eccentric wear points between the rod and tube. A numerical solution program was developed and successfully applied in the Daqing oilfield. Results from two case studies demonstrate significant improvements: for A1# well, the system efficiency increased from 16% to 20%, while for A2# well, the pump efficiency improved from 39.8% to 58.9% and system efficiency from 33.4% to 35%. The model overcomes previous limitations by considering rod torque, 3D curved tubing spatial coordinates, tubing non-anchoring effects, and forced buckling influence, providing a theoretical basis for dynamic calculations of sucker rod pumping systems in 3D curved wells.

Numerical analysis of the effect of baffle length and arrangement on the thermal performance of solar air channels

Improving heat transfer in solar air ducts is crucial to increasing the efficiency of solar heating systems. The addition of baffles is an effective technique for optimizing this heat transfer. This work presents an in-depth analysis of the effect of baffle length and arrangement in an air channel with rectangular baffles to direct and disrupt airflow, thereby increasing the rate of heat transfer. Computational fluid dynamics was employed for the calculations. The Finite volume method was used to discretize the governing equations, and the SIMPLE algorithm was used to the pressure-velocity coupling. A standard k−ε turbulence model based on Reynolds number ranges of 8000≤Re≤30000 has been used to numerically study flow properties and heat transfer. The results show that adapting baffle length plays a crucial role in controlling flow velocity, which directly influences temperature distribution in the system. In addition, the study reveals that periodic arrangement of baffles (Case C) increases maximum velocity values and optimizes flow by increasing fluid acceleration and dynamic pressure. For a Reynolds number of 30,000, periodic arrangements achieve the highest value for TPF, i.e. 3.267. This indicates that periodic baffles create optimized recirculation zones, improving fluid mixing and heat transfer without excessively increasing flow resistance.

Towards sustainable ships: Advancing energy efficiency of HVAC systems onboard through digital twin

Towards sustainable ships: Advancing energy efficiency of HVAC systems onboard through digital twin

Comparing the efficiency of urban bus systems in Brazil, France, Spain and United States using social, financial, and environmental indicators

Comparing the efficiency of urban bus systems in Brazil, France, Spain and United States using social, financial, and environmental indicators

Optimizing the efficiency of photovoltaic-thermoelectric systems equipped with hybrid nanofluid channels: Environmental and economic considerations

Optimizing the efficiency of photovoltaic-thermoelectric systems equipped with hybrid nanofluid channels: Environmental and economic considerations