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The increasing demand for efficient patient monitoring systems in healthcare and the growing need for remote monitoring, particularly post-pandemic, emphasise the importance of tracking critical parameters such as urine output, blood oxygen saturation, breath rate, and blood pressure. Urine output, a key indicator of kidney function and medical treatment response, is traditionally assessed manually, posing a significant burden on hospital staff and caregivers. Addressing this, our system facilitates continuous, accurate monitoring of urine output, enhancing patient care and healthcare efficiency. We developed a smartphone application leveraging capacitive sensors and a Wi-Fi-enabled control unit, enabling remote monitoring of urine bag volumes. The system alerts when bags are empty for extended periods or full, this is validated through experiments with volumes ranging from 100 to 1000 mL.The corresponding variations in sensor output voltage confirmed the accuracy of the system. To secure patient data, we incorporated AES-256 encryption with dynamic key generation using patient-specific IDs and OTP-based access control, ensuring data privacy and compliance with healthcare regulations. Our approach offers several advantages: ease of attachment to standard urine bags, non-invasiveness, reusability of bags, and remote monitoring through the mobile application. This innovation automates urine output monitoring, secures patient data, reduces the workload of intensive care nurses, and enhances patient care through precise and continuous monitoring. Unlike existing devices that rely on customised containers or short-range Bluetooth transmission, our system is compatible with standard urine bags, employs cost-effective capacitive copper-tape sensors, and integrates AES-256 encryption with dynamic key generation and OTP-based access control for robust data security. These unique features make the system functionally novel, technically secure, and highly practical for deployment in both hospital and home settings.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones’ ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

This paper reports simulation of a carbon monoxide gas sensor using COMSOL Multiphysics whose active sensing material used is a carbon nanocomposite (i.e., 0.1 wt % of single-walled carbon nanotubes along with PEDOT:PSS (poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)) in an equal volume ratio of 1:1. Given the high cost associated with the development of these sensors, it becomes imperative to establish a mathematical model for economically predicting their behavior. The simulation using COMSOL Multiphysics is performed to obtain the surface coverage of the sensor by introducing carbon monoxide gas through a Gaussian pulse feed inlet at concentrations ranging from 1 to 7 ppm. The surface coverage over the range of 14% to 32.94% for the given range of concentrations is achieved giving the information of the amount of gas molecules adsorbed onto the surface of the sensing material at a given time. The surface coverage of the sensor is enhanced by using the nanocomposite materials which in turn enhances the sensitivity of the gas sensors.

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei. Dice similarity coefficient of 0.95 was obtained for nucleus detection. We also compared the proposed method with a state-of-the-art method and the proposed method was found to be better. Shape and texture features of the detected nuclei were used for classifying white blood cells. We considered classification of WBCs using two approaches such as 5-class and cell-by-cell approaches using neural network and hybrid-classifier respectively. We compared the results of both the approaches for classification of white blood cells. Cell-by-cell approach offered 1.4% higher sensitivity in comparison with the 5-class approach. We obtained an accuracy of 100% for lymphocyte and basophil detection. Hence, we conclude that lymphocytes and basophils can be accurately detected even when the analysis is limited to the features of nuclei whereas, accurate detection of other types of WBCs will require analysis of the cytoplasm too.

This comprehensive study examines the pivotal role of technology in seed quality inspection and computer-aided seed germination monitoring. Focusing on cutting-edge automated methods, the review explores how image processing and machine learning techniques are employed for seed quality assessment. It provides a comprehensive overview of the methodologies, image modalities, evaluation approaches, and metrics employed in these advancements, drawing from recent literature sources. The study underscores the importance of real-time monitoring and identifies the requirements for developing automated seed testing systems that minimize human intervention while maximizing productivity in the agricultural sector. By synthesizing supporting literature, the review offers valuable guidance and future directions for researchers seeking to enhance seed quality inspection and implement computer-aided monitoring systems, ultimately improving accuracy and productivity in seed germination processes.

Microscopic evaluation of peripheral blood smear analysis is a commonly used laboratory procedure to diagnose various diseases such as anemia, malaria, leukemia, etc. Manual microscopic evaluation is laborious and hence many research groups have attempted to automate smear analysis. Variations in staining procedure and smear preparation introduces color shade variations into peripheral blood smear images. Illumination provided by point source bulb introduces brightness variations across the smear which affects the performance of an automated method. In this paper we present an image processing algorithm for detection of nuclei of white blood cells which is robust to color and brightness variations. In the proposed method we used two different datasets and also five datasets which were derived from original images by introducing brightness variations. We also compared the results of the proposed method with four state-of-the-art methods. The results demonstrate that the proposed method detects nuclei accurately with an average accuracy of 0.99 and Dice coefficient of 0.965.

Peripheral blood smear analysis is a gold-standard method used in laboratories to diagnose many hematological disorders. Leukocyte analysis helps in monitoring and identifying health status of a person. Segmentation is an important step in the process of automation of analysis which would reduce the burden on hematologists and make the process simpler. The segmentation of leukocytes is a challenging task due to variations in appearance of cells across the slide. In the proposed study, an automated method to detect nuclei and to extract leukocytes from peripheral blood smear images with color and illumination variations is presented. Arithmetic and morphological operations are used for nuclei detection and active contours method is for leukocyte detection. The results demonstrate that the proposed method detects nuclei and leukocytes with Dice score of 0.97 and 0.96 respectively. The overall sensitivity of the method is around 96%.

The cashew industry produces extremely toxic effluent that seriously endangers life. Furthermore, cashew nut shell liquid (CNSL) a by-product, from the cashew industry is underutilized resulting in its presence in effluent. This liquid is extremely toxic and poses a threat to the environment if discharged without removal. Therefore, this comprehensive review delves into the intricacies of cashew nut processing, with a particular focus on the production of CNSL, its chemical profiling, and the imperative need for thorough characterization to ascertain its chemical composition. The manuscript underscores the potential of CNSL as a promising solution in the agricultural sector due to its skyrocketing potential as an insecticidal, fungicidal, antioxidant, anticorrosive, and termite resistant, and its ability to be blended with biodiesel as it improves lubrication properties in comparison with traditional diesel and helps extend the lifespan of engines, further necessitating minimal maintenance. It explores the necessity for chemical modifications in CNSL, presenting recent insights and advancements, particularly in the realm of phyto-nano-emulsions of CNSL with increased bioavailability. Additionally, it highlights the burgeoning role of artificial intelligence and machine learning models in predicting CNSL emissions, yield, crop health, and cashew kernel quality checks, offering a holistic decision support system for supply chain optimization. By succinctly mapping out the roadmap for CNSL production, chemical enhancements, and its application as an antifungal agent, the manuscript advocates for the integration of AI and ML to enhance agricultural outcomes and boost farmers' profits.Article HighlightsChemical profiling and characterization details of Cashew nut shell liquid- an underutilized cashew industry by-product which is rich in chemical constituents.Recent insights on the exceptional multifaceted potential of Cashew Nut Shell Liquid (CNSL) to achieve sustainability in the agricultural sector.An Update on how the integration of AI and machine learning in CNSL production and applications can enhance decision support systems-ultimately increasing farmers’ profits.

Multiwalled carbon nanotubes (MWCNTs) were synthesized by the reduction of ethyl alcohol with sodium borohydride (NaBH4) under a strong basic solvent with the high concentration of sodium hydroxide (NaOH). Nanocomposites of different concentration of MWCNT dispersed in poly(3,4-ethylene dioxythiophene) polymerized with poly(4-styrene sulfonate) (PEDOT:PSS) were prepared and deposited on a flexible polyethylene terephthalate (PET) polymer substrates by the spin coating method. The thin films were characterized for their nanostructure and subsequently evaluated for their piezoresistive response. The films were subjected to an incremental strain from 0 to 6% at speed of 0.2 mm/min. The nanocomposite thin film with 0.1 wt% of MWCNT exhibits the highest gauge factor of 22.8 at 6% strain as well as the highest conductivity of 13.5 S/m. Hence, the fabricated thin film was found to be suitable for piezoresistive flexible strain sensing applications.

The development of flexible electronics is an emerging research field in the area of wearable health monitoring devices and flexible displays. Due to mechanical flexibility with superior electrical properties, polymer nanocomposite thin films including Carbon Nanotubes (CNTs) are one of the potential materials for high-performance flexible electronics. This experimental paper discusses the fabrication of thin films with various weight percentages of chiral Single-Walled Carbon Nanotubes (SWCNTs) and Multi-Walled Carbon Nanotubes (MWCNTs), separately integrated into the conductive polymer, i.e., Poly(3,4-ethylenedioxythiophene) Polystyrene Sulfonate (PEDOT:PSS) on Polyethylene Terephthalate (PET) flexible substrates by the spin-coating deposition method. Characterization of nanocomposite thin films is performed by FESEM, XRD and Raman to study the nanostructures and surface morphology of thin films. Electrical and stress–strain responses of the thin films are comprehensively studied and compared. Lastly, the optimized concentrations of CNTs in conductive polymer and gauge factor of thin films are reported.