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The use of community health workers (CHWs) has been explored as a viable option to provide home health education, counselling and basic health care, notwithstanding their challenges in training and retention. In this manuscript, we review the evidence and discuss how the digitalization affects the CHWs programmes for tackling non-communicable diseases (NCDs) in low- and middle-income countries (LMICs). We conducted a review of literature covering two databases: PubMED and Embase. A total of 97 articles were abstracted for full text review of which 26 are included in the analysis. Existing theories were used to construct a conceptual framework for understanding how digitalization affects the prospects of CHW programmes for NCDs. The results are divided into two themes: (1) the benefits of digitalization and (2) the challenges to the prospects of digitalization. We also conducted supplemental search in non-peer reviewed literature to identify and map the digital platforms currently in use in CHW programmes. We identified three benefits and three challenges of digitalization. Firstly, it will help improve the access and quality of services, notwithstanding its higher establishment and maintenance costs. Secondly, it will add efficiency in training and personnel management. Thirdly, it will leverage the use of data generated across grass-roots platforms to further research and evaluation. The challenges posed are related to funding, health literacy of CHWs and systemic challenges related to motivating CHWs. Several dozens of digital platforms were mapped, including mobile-based networking devices (used for behavioural change communication), Web-applications (used for contact tracking, reminder system, adherence tracing, data collection and decision support), videoconference (used for decision support) and mobile applications (used for reminder system, supervision, patients' management, hearing screening and tele-consultation). The digitalization efforts of CHW programmes are afflicted by many challenges, yet the rapid technological penetration and acceptability coupled with the gradual fall in costs constitute encouraging signals for the LMICs. Both CHWs interventions and digital technologies are not inexpensive, but they may provide better value for the money when applied at the right place and time.

Accurate fish segmentation in underwater videos is challenging due to low visibility, variable lighting, and dynamic backgrounds, making fully-supervised methods that require manual annotation impractical for many applications. This paper introduces a novel self-supervised learning approach for fish segmentation using Deep Learning. Our model, trained without manual annotation, learns robust and generalizable representations by aligning features across augmented views and enforcing spatial-temporal consistency. We demonstrate its effectiveness on three challenging underwater video datasets: DeepFish, Seagrass, and YouTube-VOS, surpassing existing self-supervised methods and achieving segmentation accuracy comparable to fully-supervised methods without the need for costly annotations. Trained on DeepFish, our model exhibits strong generalization, achieving high segmentation accuracy on the unseen Seagrass and YouTube-VOS datasets. Furthermore, our model is computationally efficient due to its parallel processing and efficient anchor sampling technique, making it suitable for real-time applications and potential deployment on edge devices. We present quantitative results using Jaccard Index and Dice coefficient, as well as qualitative comparisons, showcasing the accuracy, robustness, and efficiency of our approach for advancing underwater video analysis.

Background Increasing evidence positively links greenspace and physical activity (PA). However, most studies use measures of greenspace, such as satellite-based vegetation indices around the residence, which fail to capture ground-level views and day-to-day dynamic exposures, potentially misclassifying greenspace and limiting policy relevance. Methods We analyzed data from the US-based Nurses’ Health Study 3 Mobile Health Substudy (2018–2020). Participants wore Fitbits™ and provided smartphone global positioning system (GPS) for four 7-day periods throughout the year. Street-view greenspace (%trees, %grass, %other greenspace [flowers/plants/fields]) were derived from 2019 street-view imagery using deep-learning algorithms at a 100-meter resolution and linked to 10-minute GPS observations. Average steps-per-minute for were calculated for each 10-minute period following each GPS observation. Generalized Additive Mixed Models examined associations of street-view greenspace exposure with PA, adjusting for individual and area-level covariates. We considered effect modification by region, season, neighborhood walkability and socioeconomic status (SES), temperature, and precipitation. Results Our sample included 335 participants (mean age = 39.4 years, n  = 304,394 observations). Mean steps-per-minute per 10-minutes were 6.9 (SD = 14.6). An IQR increase (18.7%) in street-view trees was associated with a 0.36 steps-per-minute decrease (95%CI: -0.71, -0.01). In addition, an IQR increase (10.6%) in grass exposure was associated with a 0.59 steps-per-minute decrease (95% CI: -0.79, -0.40); however, the association was non-linear and flattened out after the 75th percentile of street-view grass. Conversely, an IQR increase (1.2%) in other greenspace was associated with a 1.99 steps-per-minute increase (95%CI: 0.01, 3.97). Associations were stronger in the spring and in higher SES neighborhoods, and among residents of the Northeast. Conclusions In this prospective cohort, momentary street-view exposure to trees and grass was inversely associated with PA, while exposure to other greenspace was positively associated. Future research should confirm these results in other populations and explore the mechanisms through which specific greenspace components influence PA.

Rapid growth in technology has created opportunities to design and develop high-end applications and tools. Conventional mowers in practice are mostly fuel-powered and require personnel assistance for operation. This work develops a smart lawn mower powered by a solar photovoltaic (PV) panel and controlled by an Internet of Things- (IoT-) based technique. The designed lawn mower comprises one brushless direct current (BLDC) motor, four gear motors, sensors, an Arduino-based charge controller, and a Raspberry Pi-powered renewable energy source making it a sustainable device. A lawn mower is operated and controlled through an Android application. Raspberry Pi is used as an edge computing device for transmitting data through the Internet and for communication with Android applications. Arduino UNO is used for energy management and motor control operation. The main novelty of this research is IOT-based motion control feature which provides the user the provision to operate the mower remotely. Results of the designed model depict an average of 89.5% electrical efficiency of the system based on varying weather conditions. Application of the designed model is golf clubs, playgrounds, and lawns eliminating operator costs, saving energy, reducing noise pollution, and achieving environmental sustainability goals.

Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: ‘i.cluster’ was used to generate image classes; ‘i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as ‘i.group’, ‘r.support’, ‘r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa.

Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species--giant panda, dugong, and California condor--to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.

Forage identification is primarily realized by human experts at a low efficiency, which does not meet the requirements of a digital grassland management. In this study, we propose an automatic identification system for gramineous grass seed, an important category of forage in grassland, based on seed images using Gabor filters and local preserving projections (LPP). The system includes four modules: image acquisition, image preprocessing, feature extraction, and feature matching. Seed images are first captured by a common digital camera, and then preprocessed by a morphological operation to obtain the ROI. In the feature extraction module, the integration of Gabor filters and LPP can provide robust features for varying brightness and image contrast while preserving the manifold structure of the images for efficient dimensionality reduction. The nearest neighbor classifier and linear discriminant analysis (LDA) classifier are used for classification. The novelty of the system lies in two aspects; one is that gramineous grass seeds in the study is automatically identified as valuable resources in grassland, instead of the certain species of weed to be distinguished from crops in the previous weed classification. The other is that Gabor filter and LPP are applied to extract the textural manifold features for the identification of gramineous grass, rather than the geometric features of appearance of gray-scale images, for more robust performance. The experimental results demonstrate the effectiveness of the seed identification system.

The purpose of this work was to investigate the air-to-air channel model (A2A-CM) for unmanned aerial vehicle- (UAV-) enabled wireless communications. Specifically, a low-altitude small UAV needs to characterize the propagation mechanisms from ground reflection. In this paper, the empirical path loss channel characterizations of A2A ground reflection CM based on different scenarios were presented by comparing the wireless communication modules for UAVs. Two types of wireless communication modules both WiFi 2.4 GHz and LoRa 868 MHz frequency were deployed to study the path loss channel characterization between Tx-UAV and Rx-UAV. To investigate the path loss, three types of experimental channel models, such as CM1 grass floor, CM2 soil floor, and CM3 rubber floor, were considered under the ground reflection condition. The analytical A2A Two-Ray (A2AT-R) model and the modified Log-Distance model were simulated to compare the correlation with the measurement data. The measurement results in the CM3 rubber floor scenario showed the impact from the ground reflection at 1 m to 3 m Rx-UAV altitudes both 2.4 GHz and 868 MHz which was converged to the A2AT-R model and related to the modified Log-Distance model above 3 m. It clear that there is no ground reflection effect from the CM1 grass floor and CM2 soil floor. This work showed that the analytical A2AT-R model and the modified Log-Distance model can deploy to model the path loss of A2A-CM by using WiFi and LoRa wireless modules.

This article outlines Taiwan’s experience in developing smart cities, including visions, implementation strategies and application cases. To take global trends and local needs into account, Taiwan has applied a dual development model that combines top‐down (theme‐based)/bottom‐up (needs‐based) approaches for a synergy effect in balancing innovations and local needs. Furthermore, a public–private partnership program has been adopted to prompt collaboration between central/local authorities with local businesses.Meanwhile, Taiwan uses a private finance initiative program and a global marketing strategy for strengthening the scalability and sustainability of smart city solutions. Three visions in the project help achieve the transformation to ensure smarter urban governance, more comprehensive industrial business models and better livelihoods of residents. Moreover, this article also presents five application cases in the top‐down approach and four application cases in the bottom‐up approach with proven track records which covered eight industry sectors: agriculture, healthcare, education, mobility, retail, energy, governance and environment.