Modern agriculture faces challenges such as water wastage, unpredictable weather conditions, lack of real-time monitoring, and dependence on manual irrigation methods. To address these issues, this paper presents a smart agriculture monitoring and irrigation control system based on the ESP32 microcontroller using ESP-NOW wireless communication. The proposed system consists of one main control node and two sensor nodes placed in the agricultural field to monitor parameters such as temperature, humidity, soil moisture, rainfall, and motion. Based on predefined threshold values, the irrigation system automatically controls the water pump to reduce water wastage and improve efficiency. The collected sensor data is transmitted wirelessly to the main node using ESP-NOW and then uploaded to a cloud platform through Wi-Fi for remote monitoring using a mobile application. The system is powered by solar energy with a rechargeable Li-ion battery backup, making it suitable for remote and off-grid agricultural areas. Experimental results demonstrate reliable sensing, stable multi-node operation, efficient wireless communication, and effective automatic irrigation control, making the proposed model an affordable, scalable, and energy-efficient solution for smart agriculture.
Switches and capacitors are utilized in place of a resistor R in the switched-capacitor circuit, with a MOSFET serving as the switch. In this paper, the standard RC integrator is compared with the switch capacitor RC integrator. The design is simulated first, and solutions to the design issues are explored and implemented in 180nm technology. The analysis demonstrates that NMOS transistor performance strongly depends on the W/L ratio. As resistance decreases sharply with increasing W/L, current delivery efficiency enhances. Drain current increases substantially with larger widths due to improved channel conduction. This study analyzes the electrical behavior of an NMOS transistor for varying device widths while keeping the channel length constant. The investigation focuses on the effect of the width-to-length (W/L) ratio on drain current (ID), on-resistance (Ron), and related process parameters. The results demonstrate a strong dependence of ID and Ron on device geometry, consistent with MOSFET theory.
The software industry in India has emerged as a major driver of economic growth, export expansion, and structural transformation within the service sector. In this context, assessing firm-level efficiency is essential for understanding the sector’s contribution to economic development. This study empirically examines the technical efficiency of selected Indian software companies over the period 1992–2023 using an input-oriented Data Envelopment Analysis (DEA) model under variable returns to scale (VRS). Firm-level panel data are utilized, with total liabilities and fixed assets as inputs and net sales and profit after tax as outputs. The findings reveal substantial heterogeneity in efficiency performance across firms over time. While leading firms operate close to the efficiency frontier, several firms exhibit persistent inefficiencies, indicating significant scope for improved resource utilization and scale optimization. The results highlight the importance of managerial capability, financial structure, and efficient capital allocation in sustaining the long-term contribution of the software sector to India’s economic development.
Human-friendly analysis of chemical process industries is a nonlinear dynamic system. This paper also points out a great research issue of smart and scalable monitoring methods as the traditional statistics-based and model-driven methods are often not sufficient in discovering complex relationships with high-dimensional data due to the gradual digitalization of industry. The purpose of this paper is to review and analyses existing work on deep learning-based methods for chemical processes monitoring and fault detection. This method provides an overview of approaches based on architecture like the autoencoder, CNN, RNN, and hybrid models and their deployment in benchmark scenarios or real industrial applications. The results indicate that deep learning models enhance fault diagnosis performance via automatic feature extraction, early anomaly detection and effective modelling of temporal dependencies. Hybrid and attention-based models fast-track robustness and diagnostic capability even further. Emphasizing the practical significance of deep learning that extends beyond traditional use cases, the study mentions areas such as predictive maintenance and process safety and operational optimization. It applies for United Nations Sustainable Development Goals (SDG 9, SDG 12, SDG 7), realize any kind of industry with high efficiency and sustainability.
The rapid growth of the population has resulted in extensive land use and a notable expansion of construction activities on problematic soils. Arid and semi-arid regions in southern Algeria have witnessed significant urbanization and infrastructure development. These areas are generally characterized by unsaturated surface deposits and deep groundwater levels, allowing soils to remain unsaturated to considerable depths for long periods. Under the influence of climatic cycles and repeated wetting–drying processes, soils progressively develop their microstructure, hydraulic characteristics, and consequently their physical and mechanical properties.Recent studies have shown that soil-water retention curves (SWRC) are effective tools for characterizing the stress state of unsaturated soils, describing their hydromechanical behavior, and estimating their strength parameters. The aim of this research is to characterize these soils and examine the additional shear strength resulting from suction variations, as well as the influence of soil compaction on the collapse mechanism. Soil samples were collected from the Metlili Valley, an arid region located southwest of Ghardaïa. The findings reveal significant volumetric changes caused by suction reduction. Furthermore, an increase in suction enhances shear strength, mainly due to the rise in apparent cohesion associated with capillary effects.
Permanent magnet synchronous motors (PMSMs) are widely used in high-performance drive systems because of their high efficiency, high torque density, and rapid dynamic response. Field-oriented control (FOC) is the dominant control strategy for such drives, but its performance depends critically on accurate rotor position and speed information. In sensorless PMSM drives, sliding mode observers (SMOs) are attractive because of their robustness and modest computational demand. However, conventional SMOs still exhibit an inherent trade-off between fast convergence and chattering suppression, while direct extraction of rotor position and speed from the estimated back-electromotive-force (back-EMF) signals remains vulnerable to noise and filtering delay. This paper presents a sensorless PMSM drive based on closed-loop FOC and an improved SMO-based estimation scheme. The PMSM is modeled in the synchronous dq reference frame, whereas the observer is constructed in the stationary reference frame from stator current dynamics and back-EMF reconstruction. To improve estimation performance, an adaptive sliding-mode injection gain is introduced to regulate the observer correction level according to the current estimation error, and a smooth switching law is employed to reduce chattering. In addition, a phase-locked loop (PLL) is incorporated to reconstruct the rotor electrical angle and speed from the estimated back-EMF components, thereby improving estimation smoothness and noise immunity. Simulation results obtained in MATLAB/Simulink under start-up, acceleration, steady-state, and load disturbance conditions demonstrate stable drive operation, accurate speed tracking, and improved estimation behavior compared with the conventional SMO. The results confirm that the proposed observer enhances convergence and reduces steady-state oscillations while preserving the simplicity and practical applicability of SMO-based sensorless control for PMSM drives.
Slurry erosion is an important wear issue in pipelines, pumps, and other industrial equipment that operate in environments where solid particles are transported along with the liquid. In this study, the slurry erosion behaviour of Super 304HCu alloy is examined under different simulated conditions, including slurry velocity, slurry concentration, and time of exposure. The experiments for these investigations are carried out in a slurry erosion test rig at various rotational speeds of 6 m/s, 9 m/s, and 12 m/s, with varying slurry concentrations of 10%, 20%, and 30%. The weight loss of the test specimens was recorded at regular intervals to determine the erosion rate. The experimental results indicate that the weight loss of the material steadily increases with increase in time of exposure, slurry velocity, and slurry concentration. At higher slurry velocities the increase in kinetic energy of the abrasive particles, result in stronger impacts on the material surface and consequently increase material removal. Similarly, the increase in slurry concentration increases the probability of the number of particles interacting with the surface, and accelerates the erosion process. SEM micrographs of the worn surfaces of the specimens revealed typical wear characteristics of erosion of a ductile material.
Topology optimization is an emerging design technique for producing extremely lightweight, highly efficient structural components without compromising structural integrity. In the current investigation, the topology of a circular mounting bracket is optimized using the integrated structural optimization capabilities of PTC Creo Parametric software. The objective is to minimize the structure's weight without compromising its strength and rigidity. From an initial weight of 1.189 kg, the design was optimized through six phases to achieve the required weights of 70%, 50%, 35%, 25%, 20%, and 17.5%. The actual weight reductions were 71.2%, 52.3%, 37.2%, 27.1%, 22%, and 19.5%. As weight decreased, relative strength increased substantially, reaching a maximum of 100.833 MPa/kg. After analyzing failure modes and strength characteristics, particularly bending failures, the 20% weight-retention design, weighing 0.261 kg (22% of the original weight), was found to be optimal. The results of the study demonstrate that it is possible to create lightweight, high-strength mounting brackets suitable for industrial and automotive applications using parametric topology optimization in PTC Creo.
Early social media rumor detection is essential because it prevents false information from spreading quickly and causing serious harm. Current methods frequently ignore the significance of prompt decision-making in favor of increasing classification accuracy. In this paper, we present a novel framework for early rumor detection that is based on multimodal meta-learning with adaptive decision timing. The method analyzes social media posts as an ongoing stream and learns when enough data has been observed to dynamically identify the best time to make a forecast. By integrating textual, structural, and temporal features, the proposed approach captures complex rumor propagation patterns while jointly optimizing accuracy and detection timeliness. Experimental results on benchmark datasets, including Twitter15, Twitter16, and Weibo, show that the proposed method significantly reduces detection latency, requiring substantially fewer messages for prediction, while maintaining competitive or superior performance compared to state-of-the-art baselines.
The tanning sector currently faces the urgent challenge of mitigating its environmental footprint, particularly due to the generation of chrome solid waste (SSD) during production processes. In the case study analyzed, this waste is disposed of without prior treatment, generating a significant ecological impact. This research proposes innovative solutions to reduce associated pollution and generate added value from waste. A technical and environmental assessment was conducted at the tanning stage, specifically in the splitting and sizing operations, the main sources of SSD generation. This allowed for a detailed physicochemical characterization of the leather scrape, precise quantification of generated volumes, and the assessment of specific environmental impacts. The proposed solutions represent a replicable model for the industry, combining environmental responsibility with economic profitability. This circular approach transforms an environmental problem into a sustainable business opportunity.
Tracking of the eyes and head have occupied a crucial role in human computer interaction, virtual and augmented reality, accessibility systems and cognitive studies. This paper gives a critical review on gaze estimation and head pose tracking algorithms, data sets, and performance determinants. This article compares classical and deep learning-based methods, such as convolutional neural networks, transformer models, geometric, hybrid, and multi-task learning methods. The article also tests the robustness in the external conditions like blinking, occlusion, and changes in illumination, and cross-subject individual differences. The most important publicly available datasets are compared regarding the type of input, diversity of participants, the size of data, recording distance, and environmental conditions. This article also contrasts new webcam and specialized eye-tracking systems, and, in doing so, point out trade-offs between accuracy, cost and applicability in real-time. The review also stresses on the role of domain adaptation, contrastive learning, and multi-modal inputs in generalizing across domains. In general, the article contains an attempt to present a comprehensive picture of the existing situation to researchers and practitioners to assist them in designing and choosing gaze and head pose estimation systems to be used in various applications.
This paper reaches an assessment of the performance and emission traits of a two-stroke spark-ignition engine that is run with gasoline, E20, and B20 fuel under normal and Mg-PSZ-coated piston set ups. The characterization of fuels through GC and FTIR-generated stable mixtures and effective incorporation of the oxygenated functional groups. E20 in uncoated form enhanced brake thermal efficiency (BTE) by about 4-6 % than gasoline, and lowered HC and CO by 8-12 and 10-15 % respectively. Mg-PSZ coating increased the BTE by 5-8 percent and decreased the total fuel consumption by 3-6 percent at all loads. Coated conditions resulted in a further reduction of HC and CO emissions of 10-18%. ANN modeling resulted in R2 values in excess of 0.92 and NSGA-II-TOPSIS optimization established a coated high-load case with an overall 9-12% improvement in the performance-emission which shows high level of synergy between oxygenated fuels and thermal barrier coating.
Dietary monitoring and health care will both require accurate food recognition and nutritional analysis. The current methods mainly involve convolutional networks that analyse images and word embedding that process ingredients as distinct processes with dish identification, portion size, and nutrition labelling as independent. This paper presents a multimodal transformer-based framework called as ViT-FoodNA that integrates these tasks into one end-to-end framework. It suggests using Vision Transformer (ViT), which is used to encode the visual attributes of food images, and Transformer based encoder to learn semantic relationships between inputs of ingredients. A cross-modal fusion transformer consists of visual and textual embedding to facilitate contextual interaction between the images of foods and their ingredients. Based on the result of the fused representation, the model collectively predicts the identity of the dish along with portion size and comprehensive nutritional breakdown using a shared transformer decoding strategy. In contrast to the previous convolutional neural network (CNN)-based embedding structures, the suggested architecture uses the self-attention mechanism to learn fine-grained intermodal dependencies, which enhances its resistance to visual ambiguity and missing ingredients in a list. Experiments indicate that ViT-FoodNA produces up to 12% higher Precision, Recall, F1-score, and 9.75% and 5% greater Top-1 and Top-5 accuracy and up to 32.7% and 30.4% lower MAE and RMSE than existing models. The reduction of PMAE is 6.5%, and the increase of mAP is 9.8%, which proves the great benefit of transformer-based multimodal fusion in the full analysis of diet.
This study focuses on the compound (Na₀.₅Bi₀.₅)ₓMg₁₋ₓ[(Ti₀.₈Zr₀.₂)₀.₉(Nb₂⁄₃Zn₁/₃)₀.₁]O₃ (BNTZZN-x%Mg), which was synthesized using the molten salt method for x = 0.00, 0.02, 0.06, and 0.08. The resulting samples were calcined at 900 °C for 4 hours and subsequently sintered at 1150 °C. The measured densities were found to be close to the theoretical values. X-ray diffraction (XRD) analysis confirmed the formation of a pure perovskite phase. Scanning electron microscopy (SEM) images revealed a slight variation in grain size with increasing Mg content. Overall, the material exhibited relatively high purity and density. Dielectric properties were measured on the single-phase ceramic, and their evolution was studied over a temperature range of 27 °C to 227 °C across various frequencies.
The inception of cognitive radio networks (CRNs) institutes both possibilities and complications in spectrum access and security. Blockchain technology has made an appearance as a possible solution to address these issues, providing improved transparency, security and effectiveness in spectrum management. Researchers propose several security methods to mitigate these attacks, each with drawbacks. Most of these methods have higher complexity, while others cannot be used to focus on training time and accuracy. To overcome these issues while maintaining higher security and Quality of Service (QoS), a novel blockchain method called, eXtreme Gradient Boost Quadratic Balloon Cryptographic Block Compression (XGBQ-BCBC) based secured routing in CRN is proposed. The method initially collects multiple secondary user and primary user from different cognitive radio controllers and creates blocks for the storage of these sets in blockchain network. Here, the eXtreme Gradient Boost Quadratic Node Classifier is employed where the weak classifier results are combined using Quadratic Classifier for classifying and authenticating the nodes as normal nodes or attacks based on the residual energy and relative trustworthiness factor and stored in the blockchain. At this point, the data packets collected from the normal nodes are hashed by using Balloon Cryptographic Matyas–Meyer–Oseas (MMO) Block compression function. Next, the hashed results are sent to the normal nodes by authenticating the normal nodes. If normal node’s authentication verification is passed, then the hashed results are said to the receiver nodes, therefore ensuring robust data transmission in the CRN. The simulation results of the proposed method demonstrate 27%, 32% data confidentiality and data integrity in the presence of MUs, when compared to other methods like Channel Availability Probability and multi-objective optimization. As a result the security of cognitive radio blockchain network is demonstrated to be improved in a significant manner.
Visually impaired people (VIP) need assistive technology for smooth and confident navigation on pavements along the roads. VIPs find it hard to follow the speech generated through a text-to-audio converter due to the presence of advertisement text. Automating the process of audio conversion from the signboards by integrating the capabilities of emerging AI models in a fraction of a second will be useful for their confident navigation. In this context, SignSpeak, an AI-based assistive utility, is designed to convert signboard text to speech using visual inputs, which may be captured through smart sensors or canes. The proposed system follows a three-step (detection-extraction-conversion) pipeline. Initially, a fine-tuned lightweight YOLOv11s model is used to generate a bounded box for the relevant text region, which is input to Gemini model, a multimodal large language model for text extraction. Lastly, the extracted text is converted into audio using the Google Text-to-Speech tool. We fine-tuned the YOLOv11s model on 200 signboard images and tested its performance on an additional curated dataset of 42 real-world signboard images. Experimental findings indicate that the proposed integrated pipeline achieved superior performance compared to the solitary usage of the Gemini model for text extraction followed by its audio conversion.
Algeria is classified as a semi-arid to arid zone due to the importance of evapotranspiration compared to precipitation. This study presents a summary of experimental results on an unsaturated soil in the Biskra region classified as an arid zone. The behavior of soils in these regions is somewhat unique and evolving. The soils in the Biskra region are distinguished by their geomorphological position and origin, as well as their water regime. They share particular characteristics such as unstable structure often in honeycomb shape, heterogeneity, large porosities, and good surface aeration. It should be noted that the rise of groundwater has continued to shape the urban landscape of the Biskra Valley. The mode of accumulation of soil deposits seems to be mainly responsible for the formation of an open and metastable structure, which is susceptible to collapse. The objective of understanding the effect of lineaments is to be able to better characterize the structure of the soil in this region The study's primary goal is to locate regions in Biskra region that are prone to soil collapse by combining Remote Sensing and geographical information system (GIS) methods. To create thematic maps such as geology, geomorphology, lineament and lineament density, drainage, drainage density, and slope maps, the researchers employed FCC Image of Landsat TM 30 m resolution data and topographic maps. Several geomorphic units were identified through this process.
Compliant mechanisms, which derive motion through the elastic deformation of flexible members, offer distinct advantages over traditional mechanisms, including reduced part count, enhanced reliability, and smooth, frictionless operation. However, their fatigue life is a critical design concern due to the continuous cyclic stresses endured during operation. The fatigue performance of these mechanisms is strongly influenced by a combination of material properties, geometrical design, and loading conditions. Among these, geometrical parameters—particularly the radius of curvature—play a crucial role in determining stress concentration and overall durability.This study focuses on analyzing the impact of the radius of curvature on the fatigue life of compliant mechanisms using Finite Element Analysis (FEA). Four different conFigureurations of square and circular motion stages with varying curvature radii were evaluated under constant loading conditions. The analysis considered key parameters such as damage factor, safety factor, and fatigue life cycles. Results indicate that increasing the radius of curvature significantly improves fatigue resistance by distributing stress more evenly and reducing critical stress concentrations.The findings suggest that appropriate geometric optimization, especially in curved beam regions, can substantially enhance the life span and safety of compliant mechanisms without compromising their function. These insights are particularly beneficial for the design of mechanisms in applications requiring high precision and longevity, such as biomedical devices, micro-positioning systems, and additive manufacturing equipment. This research contributes valuable design guidelines for enhancing the fatigue performance and operational reliability of compliant mechanisms.
Emergence of masked face recognition (MFR) as a pivotal area in biometric identification has been significantly accelerated by the global COVID-19 pandemic. In response, the research community has developed a variety of innovative techniques to address recognition and detection under occlusion, with a growing emphasis on Generative Adversarial Networks (GANs) for masked face restoration and inpainting. We examined three interconnected sub-domains: Masked Face Recognition (MFR), Face Mask Detection, and Face Unmasking (FU), each addressing unique aspects of the problem from identifying individuals with partially or fully covered faces to reconstructing occluded facial regions for improved accuracy. The core focus of this paper is on the role of GANs in overcoming occlusion by synthesizing realistic facial textures in the masked regions, thereby restoring the identity cues. Beyond technical developments, the paper analyzes the limitations and open research problems, such as maintaining identity consistency in restored images, handling diverse mask types and occlusion levels, and ensuring generalizability across different demographic groups and environments. By integrating insights from recent advances and identifying existing research gaps, this survey aims to serve as a comprehensive reference for academics and practitioners engaged in the development of robust, privacy-aware, and ethically responsible masked face recognition systems enhanced by GANs.
Sustainable procurement (SP) has emerged as a strategic mechanism for integrating environmental, social, and economic considerations into organizational decision-making. This study aims to synthesize and analyze the intellectual and thematic development of SP research through a systematic literature review (SLR) combined with bibliometric analysis. Using the PRISMA framework, 4,425 Scopus-indexed publications were initially identified, from which 138 peer-reviewed journal articles met the inclusion criteria for in-depth analysis. The findings reveal a significant increase in research attention since 2012, reflecting SP’s evolution from a compliance-based policy tool to a strategic capability that drives innovation and sustainability performance. Five major thematic clusters were identified: (1) sustainability in public procurement, (2) the role of SP in the circular economy, (3) sustainable construction, (4) the role of corporate social responsibility (CSR), and (5) SP in higher education. Theoretically, the study draws on institutional theory, stakeholder theory, and the resource-based view to explain how external pressures, stakeholder expectations, and organizational capabilities jointly influence SP adoption and implementation. The results underscore the importance of robust institutional frameworks, leadership, and capability-building in advancing sustainable procurement. Finally, the study highlights future research opportunities in digital transformation, cross-sectoral collaboration, and the measurement of SP’s contribution to environmental, social, and governance (ESG) outcomes, thereby offering actionable insights for policymakers, practitioners, and academics.
