Waste management through open burning is still widely practiced by communities; however, the process is often inefficient due to unstable temperature fluctuations and the production of dense black smoke caused by incomplete combustion. This study aims to design an automatic temperature control and remote monitoring system for a waste incineration furnace based on the Internet of Things (IoT) to improve combustion efficiency. The research employed an experimental approach involving hardware design using a NodeMCU ESP8266 microcontroller, a DS18B20 temperature sensor, and an AC Light Dimmer module based on Pulse Width Modulation (PWM) to dynamically regulate fan speed. The system was also integrated with a Telegram-based interface for real-time temperature monitoring. Experimental results over a 75-minute testing period showed that the automatic mode achieved a maximum temperature of 132.45°C, which is higher than the manual mode that only reached 126.94°C. The implementation of PWM control enabled adaptive oxygen supply regulation, increasing the combustion temperature by 5.51°C and accelerating the reduction of black smoke. Therefore, the proposed IoT-based temperature control system successfully improves combustion efficiency while enabling convenient remote monitoring for operational supervision.

The evolution of healthcare legislation across federal administrations has been instrumental in advancing the digitalization of healthcare. Key legislative milestones- the Health Security Act of 1993, the Health Insurance Portability and Accountability Act (HIPAA), the Health Information Technology for Economic and Clinical Health (HITECH) Act, and the Patient Protection and Affordable Care Act-have collectively transformed the healthcare system, driving it toward greater connectivity and data accessibility. The Health Security Act, a 1993 proposal for universal coverage and a centralized health information system, although never enacted, laid the foundation for subsequent reforms. The HITECH Act, part of the 2009 American Recovery and Reinvestment Act, accelerated electronic health record (EHR) adoption through financial incentives and "meaningful use" criteria, improving patient engagement, data sharing, and care coordination. The 21st Century Cures Act further strengthened data interoperability, supporting secure information exchange between patients and providers. Other Acts throughout the 20th and 21st century encouraged patient-centered care and large-scale system standardization that has culminated in the widespread use of current digital health systems. But what does this journey tell us about the potential and limitations of digital health reform? Significant challenges remain, from high EHR implementation costs and clinician burnout to barriers facing smaller practices. The digital shift has introduced new risks, such as increased medical errors during EHR transitions and clinician alert fatigue, while clerical burdens impact providers' work-life balance and patient interaction quality. As digital health tools like telehealth and remote monitoring become integral to patient care in 2025, the path forward demands improved EHR usability, robust clinician training, and addressing socioeconomic barriers. This article concludes by considering how these efforts can ensure digital health reforms foster equitable, effective care across diverse populations.

Lameness during the breeding season can impair bulls' reproductive performance, but early detection may enable timely treatments or replacements to mitigate pregnancy losses. This study used remote monitoring technologies to investigate whether changes in beef bulls' behavior could be used for early identification of lameness during the breeding season. Twenty-five Angus bulls (12 in year 1; 13 in year 2) considered satisfactory at the breeding soundness exam were enrolled. Collars with GPS and accelerometers were placed on bulls at the start of the breeding season. Bulls were monitored on pastures by video recording 3 times a week, on alternate days, during 2 breeding seasons to identify signs of lameness. Blinded raters analyzed videos, assigning a locomotion score (LS; 0-3 scale). Bulls with LS 2 or 3 were considered lame. The day lameness was identified was D0, and the xth days prior were D-x (e.g., D-1). Machine learning algorithms were used to estimate behaviors from the accelerometer data, including Activity (AC), Fighting (FI), Grazing (GR), Resting (RE), Ruminating (RU), and Walking (WA). State-space models were used to estimate the GPS trajectory to calculate the distance traveled (DIST), the average velocity (VEL), and the home range (HR). Statistical analyses were performed using generalized mixed-effect models. Behaviors of lame and non-lame bulls within the same period and breeding pasture were compared. The baseline of each bull was calculated for all behaviors using the average of D-14 to D-4. All behaviors from D-3 to D-1 of lame bulls were compared to their baseline for each day. Behaviors of non-lame bulls were compared to their baseline during the same period that lame bulls were observed. Lame bulls presented lower AC (D-1, -4.2 percentage points, p.p., P = 0.02), WA (D-2, -2.22 p.p., P = 0.03), DIST (D-1, -1,253 m, P = 0.02), VEL (D-2, -0.015 m/s, P = 0.01; D-1, -0.022 m/s, P < 0.001), and HR (D-1, -158,145 m2 P < 0.001), and higher RU (D-1,+4.4 p.p., P = 0.03) than non-lame bulls. Lame bulls presented a reduction in FI (D-1, -0.66 p.p., P = 0.03), WA (D-3, -2.09 p.p., P = 0.02; D-1, -3.02 p.p., P < 0.001), DIST (D-2, -1,051 m, P = 0.02; D-1, -1,562 m P < 0.001), VEL (D-2, -0.01 m/s, P = 0.04; D-1, -0.02 m/s, P = 0.002), and HR (D-1, -150,189 m2 P = 0.01) prior to lameness. Non-lame bulls presented a reduction in FI (D-3, -1.01 p.p., P < 0.001; D-1, -1.32 p.p., P < 0.001) and an increase in GR (D-3, +3.6 p.p., P < 0.001; D-1, 3 p.p., P = 0.005) within the period lame bulls were identified. Reductions in movement-related metrics (WA, DIST, and VEL) up to 3 days before lameness suggest these metrics may enable earlier lameness detection than visual observation. These results provide insights into key behaviors for early lameness detection in breeding beef bulls and inform future development of monitoring technologies.

Thermal comfort in lecture rooms is an important factor in supporting the effectiveness of learning in higher education. Manual operation of Air Conditioners that is still applied in various educational institutions shows limitations in responding to changes in environmental conditions dynamically. This study aims to develop a prototype of an automatic Air Conditioner temperature control system based on Wemos D1 Mini microcontroller and DHT-22 sensor integrated with the Home Assistant platform to support the Smartclass concept at Universitas Dirgantara Marsekal Suryadarma. An experimental method with an Internet of Things prototype development approach was applied through stages of literature study, hardware and software design, implementation, and system testing. The test results show that the DHT-22 sensor is able to read temperature with a very good level of accuracy, producing an average error of 0.93% for indoor conditions and 0.98% for outdoor. The Infrared transmitter module functions optimally at an effective distance of up to 7 meters with a 100% success rate in sending control commands. The system is proven to be able to maintain room temperature stability according to the setpoint determined automatically without requiring manual intervention. Integration with Home Assistant enables real-time remote monitoring and control, creating a comfortable learning environment while increasing energy efficiency in modern classrooms.

Mycotoxin contamination of grains during storage and transportation represents a significant threat to global food security. Conventional detection methods exhibit limitations in terms of real-time monitoring. This study presents a compact smart gas sensing system for mycotoxins, facilitating non-destructive testing of corn infected with fungi by analyzing the volatile organic compounds (VOCs) emitted during fungal growth. It also facilitates the precise quantitative detection of Aflatoxin B1 (AFB1). Additionally, a dual-branch convolutional neural network (DB-CNN) model has been developed to conduct an in-depth analysis of the temporal and spatial characteristics of VOCs signals. The system achieves 100% accuracy in identifying grains (corn, peanuts, wheat, and rice) infected with Fusarium graminearum and Aspergillus flavus by extracting the characteristic fingerprint spectra of fungal VOCs. In the quantitative analysis, the DB-CNN exhibits good performance (RMSE = 1.0292 μg/kg, R2 = 0.9994). In addition, the designed detection system supports wireless transmission and can be connected to a smartphone for data transfer, thereby facilitating data storage and remote monitoring. The entire detection process is completed within 4 min. This study provides an innovative technical foundation for dynamic real-time monitoring of fungal contamination in the food supply chain, contributing to early warning systems and quality control measures.

Objective. To compare the efficacy of various clinical monitoring programs, including a remote monitoring system using automated interactive questionnaire. Materials and methods. The study included 254 patients with COPD, divided into 3 dynamic monitoring groups: the main group of patients (n=77) used a remote monitoring system activities, including an interactive questionnaire; the comparison group (n=75) who used a remote monitoring system activities limited to the assessment of a number of objective parameters: a modified 6-minute walk test (6-MWT test, in steps), daily physical activity (number of steps per day) and peak expiratory flow rate, but the interactive questionnaire was not applied in this group; the control group (n=102), received standard follow-up according to routine clinical practice without remote monitoring in compliance with the Order No. 168n of the Russian Ministry of Health (dated March 15, 2022) “On approval of the Procedure for Dispensary Observation of Adults”. Results. In the main group, where remote monitoring of interactive questionnaire data was combined with the assessment of objective disease criteria, a significant proportion of patient transition from group “E” to group “B” (a course with rare exacerbations) was revealed. In the comparison group, no significant disease rephenotyping was observed. Conversely, in the control group, a significant proportion of patients transitioned from groups “A” and “B” to group “E” (a course with frequent exacerbations). Over the 12 month follow-up period, treatment in the main group was aligned with clinical guidelines; in the comparison group, a less significant redistribution of treatment programs was observed; in the control group, the rate of treatment regimen compliance with clinical guidelines after one year did not exceed 40 %. Conclusions. An approach combining the assessment of subjective (using an interactive questionnaire) and objective criteria for remote monitoring, improves the quality of clinical follow-up in COPD patients, allowing disease rephenotyping, timely detection of disease exacerbations, and achieving symptom control through the prompt adjustment of maintenance therapy, when required.

Quantitative remote wound monitoring has the potential to shorten patient recovery time and alleviate the workload of healthcare professionals. In this study, a nitrogen-doped horizontally grown graphene (NHG) antenna sensor with a working frequency of 2.45 GHz was designed for wireless real-time monitoring of wounds. The sensor comprises 32 NHG microtubes (1 mm in diameter), a porous Cu radiation electrode, a polydimethylsiloxane substrate with a cylindrical channel array, and a Cu ground plane. Its novel structure enables body fluid and its temperature and pH value sensing by tracking dual signals, such as resonance frequency and return loss, thereby facilitating the identification of living organisms and real-time quantitative wound assessment. Notably, the NHG microtubes, which penetrate the Cu electrode and PDMS substrate, regulate the radiofrequency radiation field and enhance the monitoring sensitivity. The sensor exhibits a minimum fluid response volume of 25 μL, a temperature detection range of 34-43 °C, a resolution of 0.1 °C, and a response time of 20 s. Furthermore, the NHG antenna sensor reliably evaluated the pH value, volume, and area of the wound using a machine learning algorithm. The system was successfully validated for real-time monitoring of wound healing in mice and has been preliminarily applied to monitor wounds of various sizes and locations in human patients.

AI-based video analysis for the assessment of upper limb function in children with unilateral cerebral palsy: feasibility of remote monitoring.

Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE &lt; 9.3% for wheat, rice, maize, and soybean; (2) compared with SNAP (10 m), RMSE decreased by 4.83%, 3.10%, 7.51%, and 8.63% for wheat, rice, maize, and soybean, respectively; compared with GEOV3 (300 m), reductions reached 7.88%, 9.49%, 13.63%, and 19.75%, respectively. Further observations showed that the model-derived CropFVC captured intra-field variability and abnormal crop conditions well, enabling more accurate monitoring of crop-specific FVC dynamics across phenological stages. The proposed operational framework enhances CropFVC estimation by improving canopy structural representation and reducing retrieval bias. By enabling more accurate 10 m CropFVC mapping at the field scale, the crop-specific approach provides practical support for precision agriculture and crop-related food security monitoring.

Learning collaboratives are a widely used implementation strategy for supporting the spread of complex innovations, but little is known about how learning collaboratives develop and sustain over time. The OncoPRO initiative, a PCORI-funded national learning collaborative focused on implementing remote symptom monitoring (RSM) using electronic patient-reported outcomes (ePROs) in oncology, provides a unique opportunity to explore this process. By examining how OncoPRO fosters collaboration, shares strategies, and adapts to diverse sites, this study offers critical insights into both the development of learning collaboratives and their ability to support the long-term success of complex healthcare initiatives. This study employed a multi-methods implementation science approach to examine the development and first year of the OncoPRO initiative. From conception through year 1 (March 2023-December 2024), OncoPRO provided support to 12 independent health systems. We identified cross-organizational barriers encountered during the development of a national learning collaborative, and the implementation strategies employed to address them, using field notes generated during all OncoPRO-related meetings, site-level communications, and site presentations during meetings. We systematically identified and categorized barriers and implementation strategies using the Consolidated Framework for Implementation Research (CFIR) 2.0 and the Expert Recommendations for Implementing Change (ERIC) frameworks. Strategies were then categorized into domains based on their alignment with each other and learning collaborative implementation components or processes. We identified 29 overarching barriers (e.g., lack of best practices; clinician buy-in) that were addressed through 37 foundational implementation strategies relevant to developing and facilitating the learning collaborative. These implementation strategies were organized into six domains: building a multi-level foundation, engaging and onboarding implementation sites, building shared learning structures, supporting technical rollout, embedding feedback loops and quality monitoring, and stimulating demand for RSM and collaborative participation. Most barriers were addressed using multiple strategies, and individual strategies often targeted several barriers simultaneously. Broad strategies addressing multiple barriers (e.g. build a coalition; identify early adopters) were deployed early to develop a base for the collaborative. As the initiative matured, strategies targeting specific barriers (e.g. develop and implement quality monitoring systems) were added to support site-level operationalization and continuous improvement. This study describes our approach to building a national learning collaborative for ePRO-enabled RSM implementation in oncology, focused on the initial phase of implementation. It offers a case study and potential roadmap for others involved in the initial development of large-scale collaboratives for complex interventions. This descriptive process analysis lays the groundwork for future analyses of implementation variation and strategy effectiveness across participating health systems, and highlights how learning collaboratives can support the implementation of complex quality initiatives like RSM in oncology.

Effectiveness of remote foetal health monitoring in improving maternal and foetal outcomes among high-risk pregnancies: A systematic review and meta-analysis.

Healthbots for conducting clinical screening and remote monitoring with patient mood assessment: A scoping review.

Medical Apps for Physicians: Leveraging MHealth to Enhance Healthcare.

Compact fluorescence sensor with silicon photomultiplier and neural network enhancement for real-time total organic carbon monitoring in water.

TPS570 Background: Cabozantinib plus nivolumab is a first-line standard treatment for advanced clear-cell renal cell carcinoma (RCC) based on the pivotal CheckMate 9ER trial (Motzer, Lancet Oncol 2022). However, clinical trial populations usually differ from real-life settings, and physician-reported adverse events (AEs) often underestimate symptom burden. Remote Patient Monitoring (RPM) of self-reported outcomes (PROs) can improve management of treatment-related toxicities and adherence. Methods: CANIQOL is a French prospective, multicenter, real-life, single-arm phase IV study evaluating the impact of RPM of self-reported symptoms on treatment management in patients (pts) receiving cabozantinib (40 mg PO daily) and nivolumab (240 mg IV q2w or 480 mg IV q4w) for advanced clear-cell RCC. Pts will report weekly PRO-CTCAE data using the RPM Cureety platform up to 6 months after start of the combined treatment. The RPM solution automatically generates alerts for the healthcare team, allowing adjustments for unplanned clinical actions and optimizing treatments management. This trial aims to assess the impact of RPM of self-reported symptoms on treatment management adjustments in pts receiving cabozantinib plus nivolumab for advanced RCC in real-life settings. The primary endpoint is the rate of pts with adjustment in treatment management (unplanned consultation, phone follow-up, advice, treatment discontinuation, hospitalization) within 3 months after combination initiation. Secondary endpoints include the evolution of self-reported side effects (PRO-CTCAE), fatigue (FACIT-F), quality of life (FKSI-10), anxiety/depression (HADS), the satisfaction regarding RPM (F-SUS), and the characterization of alerts. Duration of treatment, adherence to the oral treatment at 3 and 6 months (Girerd), and clinical outcomes (RECIST 1.1, objective response rate at 3 and 6 months, IMDC subgroup analysis) will also be assessed. Assuming the rate of adjustment of treatment management within 3 months after the combination start is around 25%, 70 assessable pts are required to estimate this rate with a 95% confidence interval (CI) of width 20%. We plan to enroll 83 pts to anticipate 15% of non-assessable pts, over 36 months of inclusion. The first patient was enrolled in October 2025. Follow-up is 6 months per patient. CANIQOL will provide real-world evidence on feasibility and clinical utility of RPM of PRO (side effects and quality of life) in pts receiving cabozantinib plus nivolumab for advanced RCC, assessing its impact on toxicity management, adherence, and quality of life. Trial registration: NCT07028125. Sponsor: Centre François Baclesse, Caen, France. Funding: IPSEN; logistical support – Cureety. Clinical trial information: NCT07028125 .

Virtual care in residential aged care and primary care settings: a systematic literature review using the SEIPS framework.

Digital health in managing type 2 diabetes among indigenous populations: a scoping review.

Background and objectives Decentralised clinical trials leverage digital technologies to enhance trial accessibility, patient engagement, and improve operational efficiency in clinical research. This study assesses the adoption of these trials among 47 Indian clinical research site professionals from a global survey (n=288). Methods A sub-analysis of a global cross-sectional survey conducted between August–September 2024 assessed respondents’ roles, experience, trial types, therapeutic areas, perceived benefits, challenges, patient satisfaction, and expectations from the Indian researchers involved in decentralised clinical trials. Data were analysed using descriptive statistics, weighted averages (scale: 1=significant challenge to5=significant benefit), and mean rank analysis. Results Hybrid trials (n=33;71%) were more prevalent than full trials (n=14;29%). Frequently adopted components included home health visits and remote monitoring (n=10;16% each), particularly in oncology (n=8;17%) and cardiovascular trials (n=7;15%). Key perceived benefits included improved patient convenience (mean score: 3.8) and enhanced participant diversity (3.5). The most significant challenges were limited digital literacy (2.3) and regulatory uncertainty (mean rank 6.1). Patient satisfaction averaged 4.0, with 83% of respondents anticipating continued growth in adoption of hybrid trials. Interpretation and conclusions Hybrid decentralised clinical trials offer promising avenues to enhance inclusivity and efficiency in India’s clinical research landscape, especially for non-communicable diseases. Gaps in digital literacy and regulatory uncertainties hinder their scale-up. Strategic investments in workforce training, digital infrastructure, and regulatory clarity are critical to unlocking the full potential of decentralised trials.

Drought is the most severe natural hazard threatening agricultural production. Mulberry (Morus alba L.) is an important crop for the sericulture industry, and its drought tolerance has been extensively studied. In this study, the phenotypic and physiological responses of two different mulberry tree genotypes (711 and NS8) to drought stress were investigated, with the aim of screening potential nondestructive traits and understand interrelationships. The significant reductions of digital biomass (DB), leaf area (LA), and projected leaf area (PLA) in morphological traits indicated that drought led to a decrease in mulberry yield. The change of color traits RFarRed and RNIR were associated with pigments and leaf morphology. Vegetation indexes were also significantly affected by drought stress. Due to their had high correlation coefficients and good linear relationships with yield, DB and LA can be used as yield proxy traits for this measure. Drought-sensitive traits were identified using PCA and correlation analysis, and the results showed that greenness (GR) was a proxy predictor of drought stress. For antioxidant defenses, CAT activity and phenolic compound content were significantly decreased. Metabolomics analysis revealed that genotype 711 exhibited 1691 differential metabolites under drought stress; these mainly comprised amino acids, lipids, and phenolic acids, which were mainly enriched in secondary metabolism and flavonoid biosynthesis. Drought also reprogrammed carbohydrate, secondary compounds, and amino acid metabolism. The results revealed that the phenotypic response of two mulberry trees to drought, as well as the integration of phenotypic traits with metabolic traits, could help us to understand drought tolerance mechanisms and benefit efficient selection and breeding of fitter genotypes.

Urban development has caused significant vegetation loss, leading to air pollution, increased temperatures, and ecological imbalance. Vertical gardening addresses space constraints in cities, but manual maintenance is inefficient and costly. This work presents an IoT-based intelligent vertical garden system using a NodeMCU microcontroller and environmental sensors to continuously monitor plant conditions and automatically control irrigation. A 30-day field study showed 44% water savings, 90% cost reduction, and 98.7% system reliability, making it suitable for residential, commercial, and public spaces. The system is supported by an Android application that enables remote monitoring and control. Users can view real-time soil moisture, temperature, humidity, and pH values, switch between automatic and manual modes, adjust thresholds, and initiate irrigation instantly. The app also provides alerts for low moisture, system failure, or abnormal behavior, ensuring timely intervention. This integrated hardware-software approach makes automated vertical gardening practical for homes, hostels, and small institutions