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Context: Sweat glands (SGs) play a vital role in thermal regulation. The function and structure are altered during the different pathological conditions. Objective: These alterations are studied through three techniques: biopsy, sweat analytes and electrical activity of SG. Methods: The morphological study of SG through biopsy and various techniques involved in quantifying sweat analytes is focussed on here. Electrical activities of SG in diabetes, neuropathy and nephropathy cases are also discussed, highlighting their limitations and future scope. Results and Conclusion: The result of this review identified three areas of the knowledge gap. The first is wearable sensors to correlate pathological conditions. Secondly, there is no device to look for its structure and quantify its associated function. Finally, therapeutic applications of SG are explored, especially for renal failure. With these aspects, this paper provides information collection and correlates SG with pathologies related to diabetes. Hence this could help researchers develop suitable technologies for the gaps identified. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove that noise in order to support any decision making for automatic heart disorder diagnosis systems. In this paper, the use of Ant Lion Optimizer (ALO) for optimizing and identifying the cutoff frequency of the ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed by a moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the ALO. The performance of the ALO is investigated. The ALO-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent ALO-based system successfully denoised the ECG signals more effectively than the conventional method. The accuracy percentage increased by 2%. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) based farmer digital expert assistant systems (FDEAS) are capable of analyzing the agricultural field and environmental factors associated with the field. The agricultural yield to be harvested, depends on the liquidity content of the soil, the mineral content of the soil, the geographical position of the field, the microbial content of the soil and the temperature of the environment. Other parameters include irrigation, spraying of chemicals, animals or intruders getting into the field, etc. Such a system monitors all these parameters and provides the appropriate values and suggestions to the farmers. Several factors are monitored using different set of sensors. The location, specified by the GPS, can be compared to existing databases of soil maps and thus provide the nature and type of crop to be cultivated with all specifications to obtain maximum yield at that location. Cameras may be employed to continuously monitor the field and initiate alerts. All sensor outputs and systems can be integrated with a GSM modem which sends alert messages as SMS or recorded audio alerts when required. Such a system would ensure real time monitoring and should provide expert assistance for attaining maximum efficiency. It would act as a holistic system for analyzing, monitoring, alerting and assisting farmers at different stages of farming. [ABSTRACT FROM AUTHOR]