Acid lime is known for its tart flavor and is commonly used for culinary uses. Market glut during its peak production and high demand in its off season has been a key issue in the post-harvest handling and management of acid lime. Processing this fruit into a powder form for its several food uses can be solution to avoid distress sale by farmer, making a ready to use product available to the consumers. Spray drying has been a novel technique for powder production from liquid foods without affecting its nutritional properties. Experiments were carried out for production of spray dried powder from acid lime juice using Maltodextrin as additive. The parameters viz., temperature of inlet air (140-160 degrees C), concentration of maltodextrin (14-18%) and feeding rate (62.5-112.5 mL/h) were optimized with Response Surface Method. The optimized levels of temperature of inlet air, concentration of maltodextrin concentration and feeding rate were, 147.63 degrees C, 16.27% and 90.94 mL/h, respectively for desirable recovery and quality of powder. The powder prepared at optimized conditions resulted maximum recovery (18.77%), water solubility index (94.788%), vitamin C (365.937 mg/100g) and total phenols content (183.695 mg/100g). Acid lime powder produced at optimized conditions showed mixture of heterogeneous particles (150 to 6000 nm). The scanning electron microscopy analysis presented variable shape particles with smooth surface as result of encapsulation.
Chest X-ray radiography is a reasonably inexpensive and widely available diagnostic technology that can aid in identifying different illnesses like tuberculosis (TB), pneumonia, COVID-19 and many more. The demand for skilled personnel to evaluate X-ray radiographs is a challenge in many health facilities across the globe, particularly in underdeveloped regions. Machine Learning (ML) algorithms have enabled the automated diagnosis of TB from X-ray modalities. Aside from deep convolutional neural networks (DCNN) for vision applications, the Vision Transformer (ViT) network has also produced outstanding results in image classification. Motivated by the robustness of the transformer network on image processing tasks, the study proposes a transformer-based framework for early screening of TB disease. Three different vision transformer types ViT-Base16 (ViT-B16), ViT-Base32 (ViT-B32), and ViT-Large32(ViT-L32) were tested in the experiment to see how well they performed in identifying tuberculosis. When the transformer models' outcomes were contrasted with those of other CNNs, the VIT-B32 model performed admirably in the diagnostic procedure. The ViT-B32 model's attained accuracy, sensitivity, specificity, precision, F-1 score, and AUC scores of the ViT-B32 model were 96.96%, 96.89%, 97.01%, 96.72%, 96.80% and 0.97, respectively, on TB classification. The ViT-b32 model demonstrated superiority and generalizability. Because of its low cost and ease of use, the ViT-b32 model may provide an accurate diagnostic system to all TB patients for early screening.
The increasing adoption of Electric Vehicles (EVs) has the potential to significantly mitigate air pollution and carbon dioxide emissions while offering advantages such as lower operating costs, reduced noise levels, and the absence of tailpipe emissions compared to internal combustion engine vehicles. Consequently, EV technology is emerging as a key pathway toward sustainable development in the automotive sector. These benefits have stimulated market competitiveness and encouraged substantial investments by developing nations, particularly in developing economies like India. The manufacturing of electric vehicles involves the troika of electronics, mechanical, and IT-based components/tools. In this scenario, it becomes essential to have rationalised auto ancillary Small and Medium Scale enterprises (SMEs) for electric vehicles. It has been observed that ICT tools, in every kind of SME, are playing a vital role in the holistic coordination of EV-based SMEs. To gain the advantages of implementing IT (information technology) tools, researchers and practitioners need to understand and model the relevant factors responsible for their implementation. The work has identified the Critical Success Factors (CSFs) affecting EV-based SMEs and analysed their interrelationships using Interpretive Structural Modelling (ISM) and MICMAC analysis. The findings highlight the most influential factors of IT adoption in EV SMEs by studying the interrelationship between the factors. Lastly, the study attempts to suggest some implications & and prospects that the market leaders and stakeholders can follow to capture the advantages of these booming sectors for economic growth and sustainability.
It is well known that the Dead Weight Testers (DWTs) or Pressure Balances (PBs) are among the finest instruments for accurate high-pressure measurement, with low uncertainties in the pressure range of a few KPa to a few GPa. During the measurement process, either the piston or the cylinder of DWTs is made to rotate either manually or through a motor to minimise the frictional forces and to distribute the lubricating fluid in the crevice of the piston and cylinder (P-C) assembly. The majority of industrial DWTs are not equipped with a motor, and hence, the weights on the piston are rotated manually, which possibly contributes to the additional tangential forces. Also, by using manual rotation, the constant rpm of the piston is very difficult to maintain. To address these issues, the authors have proposed a novel design of a mechanical instrument designated as a dead weight rotator (DWR), which is capable of rotating the weights at constant rpm and can be accommodated in any type of DWT. The design is targeted for similar to 30 rpm rotation of the piston and for a dead weight of up to 100kg. In addition, simulation studies based on finite element analysis are carried out on the developed design to check the effects of the operation of DWR on the dead weights. The simulation results, including stress, strain, deformation analysis, and various design aspects of the DWR, along with their limitations and future scope, are discussed in detail. Ongoing work focuses on the fabrication and experimental design, which will be reported in due course.
The emergence of new technologies and the development of CPSS has resulted in large scale benefits across several industries including Automobile (Automotive), Manufacturing (Manufacturing), Production Units (Production Facilities), Smart Grid Systems (Smart Grids), and Industrial Control Systems (ICS). Over the course of the past decade, the introduction of Industry 4.0 has significantly changed how products are developed, developed and distributed; the primary effect has been the addition of AI, IoT and automation technologies into the development lifecycle. Industry 5.0 advances human-machine communication, and has introduced new technologies like COBOTS and X IoT. Industries can benefit from CPSS whether or not they plan to fully implement CPSS into their business model and can expect to see benefits including a reduction of costs associated with logistics and production cycles, increased throughput and significant increases in margins, efficiencies over Industry 4.0, a more human-oriented model for doing business, resilience, sustainability and environmental focus which was lacking in Industry 4.0. However, due to the openness of CPSS and their ability to connect wirelessly, they are subject to cyber intrusion risks, which can compromise the security of a business. To protect the integrity of a business, CPSS designers must be familiar with the security and privacy issues and the techniques available to mitigate them. In addition, the diversity of components that make up Cyber Physical Systems (CPS) (sensors, actuators, wireless chips, embedded systems, etc.) creates a fundamental challenge for the security of CPS. The purpose of this article is to understand how Cyber Physical Social Systems (CPSS) are used and affect companies in the environment of Industry 5.0 through their transformation of companies' manufacturing and production processes. Explaining the full understanding of CPSS processes requires utilizing selected papers from 2019 onwards using various levels of detail on different Applications and Problems faced in Industry 4.0 with CPSS solutions, which can currently be a source of some ambiguity as well.
Ongoing research on wearable rehabilitation robots explores challenges related to actuation and control in human-centric environments. To achieve precise force control and ensure smooth operation of rehabilitation devices, new actuation mechanisms and advanced control methods are continually being analyzed. The task becomes more challenging when the focus is on the human ankle joint. Conventional actuation methods are being replaced by hybrid actuation methods like series elastic actuators to take advantage of both passive and active elements. Selecting parameters for passive elements and choosing control methods remain challenging due to their behavior and limited operational range. This work has been done to investigate the effect of the passive element spring in series elastic actuators and improve their applicability in the rehabilitation domain. Response of the series elastic actuator for torque tracking when subjected to various standard input signals is thoroughly analysed employing conventional Proportional Integral Derivative (PID) control and Robust Integral of Sign of Error (RISE) based advanced control. The emphasis is on understanding how these controls perform when applied to active orthosis for rehabilitation purposes using RMSE and mean error metrics. The PID control stabilizes much more quickly within a time frame of similar to 0.02 seconds, whereas RISE control for the same input stabilises, taking similar to 0.48 seconds to start tracking with minimal error. RISE excels for ankle reference inputs, with extremely low RMSE and Mean Error, especially at higher stiffness values. Results obtained from the analysis will aid orthotic designers in designing robotic ankle foot orthosis and implementing control methodology for ankle rehabilitation robots.
This study aims to show the state of industrial diversification in manufacturing in Ecuador and to establish clusters that enable to differentiate the state between cantons that share similar industrial characteristics. The three-digit categorization of the International Standard Industrial Classification (ISIC) is used, including a total of 136 sectors in 221 cantons at the national level in 2019. In addition, the Shannon and Simpson indices were calculated as a diversification measure of the manufacturing industry, explicitly allowing us to quantify both the breadth and dominance structure of industrial activities across cantons. These indices, combined with Gross Value Added, schooling and the Environmental Promotion Tax, support the application of a multivariate K-means clustering approach. The method is particularly applicable for distinguishing territorial patterns of industrial structure, as it enables the identification of groups of cantons with similar diversification profiles and economic characteristics. The novelty of this study lies in integrating ecological-based diversity measures with clustering techniques to characterise manufacturing heterogeneity at a sub-national level, an approach not previously applied in the Ecuadorian context. This methodological framework is used to detect the optimal number of clusters, in order to increase the heterogeneity between groups and improve the analyses for each of them. Once the homogeneous clusters were established, the results showed that the cantons of Guayaquil and Quito have a supremacy over the rest of the regions, in terms of diversification of their manufacturing industry. Finally, more than 70% of the cantons do not show significant industrial diversification according to the estimated indices.
Corporations are increasingly exposed to water related challenges, including scarcity, pollution, and rising water costs. This study investigates the relationship between water costs and firm performance in food and beverages sector where water is a critical production input. Using panel data from 2014 to 2023 of 40 firms the analysis applies Generalized Method of Moments (GMM) to address profitability persistence and endogeneity. The key variable-water charges as a ratio to cost of production-captures firm's exposure to water related expenses. Results reveal a significant and positive association between water costs and Return on Assets (ROA), suggesting that firms currently translate water cost to their scale advantages. The study offers novel firm-level evidence from an emerging economy, demonstrating that for resource cost to act as a driver of profitability as well as efficiency, policymakers should consider integrative ecological service valuation and stricter disclosure norms to promote water efficiency and sustainable industrial growth.
This paper focuses on enhancing the on-chip inductor's performance by improving the inductance (L), Quality factor (Q-factor), and Self-Resonant Frequency (SRF), which are crucial in the design of high-performance Radio Frequency Integrated Circuits (RFICs) and Monolithic Microwave Integrated Circuits (MMICs). This work proposes a new approach by incorporating a geometry variation of the traditional on-chip inductor using the Hilbert fractal geometry with a Split Ring Array (SRA) configuration. This approach leverages the superior attributes of Hilbert-based fractals in designing on-chip inductors. Simulation results have shown an inductance of 1.249 nH, a Q-factor of 13, and an SRF of 55 GHz, marking three times higher Figure of Merit (FoM) compared with conventional on-chip inductor designs, illustrating the potential that this geometry offers to push forward the performance benchmarks in integrated circuit applications. These results demonstrate the feasibility of fractal geometries to achieve superior performance metrics. The proposed design will become highly prospective for future RFIC and MMIC applications in terms of high-efficiency inductors.
The paper presents a simulation study on the sensitivity performance of CMOS-MEMS pressure sensors utilizing the Split channel MOSFET structures. These pressure sensors have been designed as split circular curved and split square curved channel MOSFETs integrated on corresponding silicon based circular and square diaphragms. These pressure sensors operate using the piezoresistive effect of MOSFET as a transduction mechanism, referred to as piezo-MOS. The research has been carried out using n-MOS, p-MOS, and CMOS (integrated p-MOS and n-MOS) current mirror circuits. Each circuit has been individually integrated with both diaphragm geometries. The proposed pressure sensors were redesigned using SCL 180 nm CMOS technology, with a 100 & micro;m channel width and 10 & micro;m channel length. The piezo-MOS structure has been split into four segments, resulting in a current mirror integrated split curved channel MOSFET pressure sensor. The mechanical properties of the proposed pressure sensors have been studied using the finite element analysis solver, COMSOL Multiphysics software. The circuit characteristics have been evaluated using Tanner T-Spice. The external pressure ranging from 0 kPa to 450 kPa has been applied to the pressure sensors. The sensitivities of n-MOS, p-MOS, and CMOS (integrated p-MOS and n-MOS) current mirrors, for Split circular curved channel pressure sensor, have been obtained as 122.051, 0.249, and 377.611 mV/MPa, respectively. Similarly, for the Split square curved channel, it has been obtained as 138.821, 0.401, and 492.250 mV/MPa, respectively, for the corresponding readout circuits. The sensitivity variation over different temperature ranges has also been studied and compared for the proposed pressure sensors. Enhanced sensitivity and compatibility with CMOS technology could make the proposed pressure sensors suitable for the next-generation pressure sensing devices.
Coffee provides a comforting experience that varies among individuals, making fuzzy logic a suitable method for evaluating perceived comfort. This study aimed to assess the comfort experienced after the intake of Robusta coffee processed through different postharvest methods (wet, semi-wet, and hybrid), using Kansei Engineering and sensory preference analysis. Thirty panellists (13 women, 17 men), aged 17-65 years and regular coffee consumers, participated in the study. Descriptive analysis was conducted using XLStat, and comfort modelling was performed using a Mamdani-type Fuzzy Inference System via Python Scikit-Fuzzy. The results showed that: (1) significant heart rate changes occurred after the intake of hybrid-and semi-wet-processed coffee, while significant blood pressure changes were found only after the intake of hybrid-processed coffee; (2) postharvest processing affected Kansei physiological responses, with heart rate and oxygen saturation as key indicators of comfort; (3) increased heart rate and decreased oxygen saturation were associated with reduced comfort; (4) the intake of hybrid-processed coffee resulted in the highest comfort and sensory preference; and (5) aroma showed the strongest association with perceived comfort. These findings suggest that Kansei physiological responses can be applied as a reliable tool for evaluating comfort in coffee intake. Coffee that induces positive physiological responses may enhance emotional well-being, providing valuable input for product development aligned with consumers' affective needs.
Prior EEG research has primarily focused on N-back cognitive task data, utilizing established techniques such as timefrequency spectrum and wavelet-based methods for feature extraction. However, Principal Component Analysis (PCA), despite its utility in boosting classifier performance, falls short in capturing nonlinear feature relationships. This study proposes a novel approach that integrates Multivariate Empirical Mode Decomposition (MEMD) with Independent Component Analysis (ICA) to enhance signal processing and feature extraction. Multivariate empirical mode decomposition generates analytic functions from EEG data by decomposing the multichannel EEG into Intrinsic Mode Functions (IMFs), from which diverse features are extracted. ICA then further reduces dimensionality, leveraging higher-order statistics to pinpoint critical features. The resulting significant, independent features are used to train and test various machine learning models, with the k-nearest neighbors algorithm emerging as the most successful, achieving a remarkable 95.27% classification accuracy. This approach enhances feature extraction and classification of cognitive task-related EEG data.
In this study, a novel analytical reagent based on arsenazo III immobilized on a polymer matrix composed of polyacrylonitrile, polyethylene polyamine, and dichloroethane (PPD) was developed and applied for the spectrophotometric determination of Pb(II) ions. The proposed method demonstrated standard analytical and metrological characteristics for the highly accurate determination of Pb(II) ions, as evidenced by a relative standard deviation (Sr) of 0.06 and a minimum detectable concentration (C-min) of 0.24 mu g/L. The accuracy and reproducibility of the method were evaluated using Student's t-test and Fisher's F-test. Comparison of the experimental and tabulated values (experiment = 1.09; t(table) = 2.83, F-experiment = 2.52; F-table = 4.47) confirmed the reliability of the proposed analytical procedure. At pH 5.8 in an acetate buffer medium, the Pb(II)-arsenazo III complex immobilized on the PPD matrix exhibited a maximum analytical signal at a wavelength of 640 nm. The measurement results followed the Beer-Lambert-Bouguer law, showing a linear relationship in the Pb(II) concentration range of 0.04-2.4 mu g/mL. The method is simple, rapid, cost-effective, and allows direct ion determination in water samples with high sensitivity and selectivity.
The present evaluation is to characterise and assess the selected novel vermicompost-based Plant Growth-Promoting (PGP) bacteria on pea crops. Vermicompost tea was prepared using the production assembly of two plastic sieve containers and a storage bin. Among the five bacterial isolates from vermicompost tea, a single prominent isolate was found optimistic for many of the PGPR traits like nitrogen fixation, indole acetic acid production, phosphate solubilization, plant defence enzymes, anti-fungal activity and siderophore secretion. The bacterization of pea seeds with vermicompost tea and N3 bacterial culture indicated an increase in germination, vigour index and fresh seed weight, when compared to the control. The isolate showed significant growth and exhibited a variety of plant growth- promoting traits on pea crops, making it a suitable inoculant. The selected bacterium showed antifungal activity against Fusarium oxysporium and prominent expression of beta-1,3glucanase, phenylalanine ammonia-lyase, peroxidase and chitinase defense proteins. The pot study of the pea plants treated with the vermicompost and N3 isolate on the 5th, 10th, and 15th day showed the optimum root length, shoot length, and dry biomass weight with reference to the control. The isolate was identified as Pseudomonas plecoglossicida [Yank1] derived from the phenotypic and genotypic characteristics. The Yank1 strain received accession number MF153408 to deposition in GenBank. Thus, the current study proposes the beneficial effects of Pseudomonas plecoglossicida isolated from vermicompost tea for the first time as PGPR on pea plant through collective action mode.
Air quality deterioration has been a major concern due to the increased road transportation and other urban activities to fulfill the requirements of the growing population in the cities. A relative difference between the impact of stationary sources and road traffic on urban air quality was rarely addressed. Further, dispersion extent of traffic emissions during the regulated road vehicular density and movement was underexplored. The study quantitatively assessed the influence of regulated road traffic emissions on atmospheric particle pollution using the episodic COVID-19 traffic restrictions in Lucknow, India. Concurrent VKT emissions and near-road measurements of PM10 were evaluated for 9 sampling sites covering residential, commercial, and industrial areas in the city. An incremental trend is observed in ambient PM10 by 1.9 and 1.7 times with the sequentially increased load of vehicular emission from the complete lockdown to partial lockdown and the following partial lockdown to unlock phases of COVID-19, respectively, in the city. USEPA AERMOD-model predictions concerning traffic emission alone provided the impact of air dispersion ranging from 0.2 km to 1.5 km distance from the city roads to nearby surrounding areas. Predictions and observations of PM10 differed maximally during the complete lockdown, and by the unlock phase of COVID-19, the difference was reduced, which indicates the significant influence of stationary and non-stationary sources in the city. Further, the variations between the predictions and observations of PM10 for residential, commercial, and industrial sites in the city give a comprehensive understanding of emission source distribution impact under urban land use settings in Lucknow.
The study provides empirical evidence on whether social media factors influence the counterfeit purchase intentions of consumers or not. The present study assumes that social media largely influences consumers' attitudes towards counterfeit products and motivates them to visit counterfeit markets intentionally. The study used primary data collected from counterfeit markets of Delhi, Kolkata, and Mumbai. A two-part structured questionnaire consisting of demographic profiles and social media determinants was circulated among the consumers who were actually visited the counterfeit markets between February-March 2025. The study got a total of 406 valid responses, and subsequently, the collected data were analysed through PLS-SEM. The study's findings indicate that social media platform and user-generated content positively influence consumer purchase intention. However, the study found no significant relationship between social media word of mouth and purchase intention. The study also checked for the mediation effect of attitude, showing that attitude mediates the relationship between social media determinants and consumers' purchase intention towards counterfeit products.
Vehicular emissions are a major contributor to deteriorating urban air quality; conventional bottom-up emission inventories apply a uniform citywide average Vehicle Kilometre Travelled (VKT) derived from limited manual counts. This oversimplification overlooks the heterogeneous composition of traffic and diurnal activity, leading to inflated and poorly resolved emission estimates. This study estimates a traffic-type-based road categorisation framework to generate link-specific VKT and quantify vehicular emissions for Nashik city, India. Classified vehicle count, dominant traffic category, road hierarchy and hourly activity duration were integrated across a 24-hour cycle, and emissions of PM, NOx, CO and HC were estimated using category-specific emission factors. Relative to the uniform VKT method, the proposed framework estimated 69% lower PM, 63% lower NOx, 53% lower CO and 55% lower HC and a more realistic emission load. Temporal assessment revealed that two-wheelers dominated daytime (internal road activity), contributing 63.1% of CO and 46.6% of HC, while Light-Duty Vehicles (LDV) and Heavy-Duty Vehicles (HDV) dominated the early hours, contributing 45.4% of NOx and 29% of PM. Unlike the conventional methods that yielded consistent patterns, the proposed emission inventory method highlighted spatial output with distinct emission hotspots along the freight corridors. The novelty of this methodology lies in traffic-type-based estimation of vehicle kilometre travelled, enabling explicit quantification of intra-urban and diurnal heterogeneity. This framework offers practicability for urban local bodies, transport planners and pollution regulatory authorities by providing an adaptable, data-efficient and policy-relevant methodology for air pollution control, emission hotspots identification, and city traffic management in rapidly motorising Indian cities.
Urban noise pollution, driven by high population density and rapid development, poses significant risks to public health and quality of life. However, studies examining noise perception and health impacts based on age and socioeconomic gradient across diverse urban populations in India remain limited. This study aimed to assess the health risks of urban noise exposure among Mumbai residents using a comprehensive questionnaire. Data were collected from 844 participants spanning varied age groups (adolescent children to older adults) and socio-economic statuses. The survey captured sociodemographic details, noise exposure sources, and up to seventeen auditory and non-auditory health effects. Vehicular traffic and construction emerged as the main noise sources. The findings clearly show age-based differences in health impacts. Headaches and sleep issues were common across all ages, with adults and older adults more affected due to longer exposure and stress. Children showed high rates of headaches, annoyance, and distraction but less anxiety, while adults reported more stress and mental fatigue. Behavioural issues, like poor concentration and communication, were highest in children and working-age adults. Adults faced the greatest risk of temporary hearing loss and tinnitus, though children also showed early signs of auditory vulnerability. Socio-economic disparities were pronounced, with children in lower-income groups experiencing more severe symptoms, while adults in higher-income groups reported the highest overall burden. The study is unique in integrating age, socio-economic, and source-specific data to record noise-induced health risks in an Indian metro context. The findings provide valuable, quantifiable insights essential for evidence-based policy interventions to safeguard urban health and wellbeing.
Industry 4.0 (I4.0) based on the proliferation of emerging digital technologies like Internet of Things (IoT), AI, CPS, big data analytics and cloud is transforming manufacturing & production around the globe. Meanwhile, the Circular Economy (CE) paradigm has emerged as a driving force of sustainable industrial sectors which focuses on the improvement of resource optimization and waste generation minimization to ensure economy developed for sustainable benefit. Though significant industry has done a lot of work on the integration of I4.0-the reality is that from a CE perspective, Small and Medium Enterprises (SMEs) are constrained by financial, structural as well as operational restrictions implicated in engaging with the concept. This paper explores literature regarding emerging diagnostics and assessment models for the nexus of Industry 4.0 with circular economy readiness in SMEs. The paper reviews common research methods, discusses critical gaps in the extant literature, and suggests a multi-level readiness framework based on technological, organizational, and environmental levels. The model is underpinned by fuzzy logic and MCDM methods. By integrating sustainability targets with digital transformation, the paper provides actionability for SME managers, policy makers and industry practitioners in search of competitive, agile and environmentally friendly production systems.
Insect infestations in stored grains continue to pose a serious threat to food quality, safety, and supply, often leading to significant post-harvest losses. Studies show that insects alone contribute to more than 10% of the total post-harvest losses in grains and cereals. Traditional detection methods are not only time-consuming and labor-intensive but also prone to inaccuracies. To address these challenges, this study presents a noninvasive and scalable approach that combines acoustic signal analysis with feature fusion techniques. By analyzing insect-generated sounds from three standard datasets, the system captures movement and feeding activity. A fusion of spectral, cepstral, and statistical features enhances detection performance, while a phase-based speech enhancement method helps reduce background noise for clearer signal interpretation. These features are then used with standard audio classification models to determine insect presence and activity levels. Experimental results show the method achieves an average detection accuracy of 94%. Designed to be both practical and efficient, this solution offers a reliable way to protect stored grains and reduce losses across large-scale storage facilities.
