VARIABILIDAD DE LA TEMPERATURA DE SUPERFICIES EN EL NW DE LA PROVINCIA DE NEUQUÉN A PARTIR DE INFORMCIÓN SATELITAL. by Marisa G. Cogliati, Mariana Suárez

“…La temperatura de superficie obtenida utilizando la información de los canales 4 y 5 del sensor AVHRR presenta la distribución espacial de la temperatura del suelo que pone en evidencia la complejidad del comportamiento, asociado al relieve. …”
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Red nacional de monitoreo de pastizales naturales de Argentina: productividad forrajera de la vegetación extrapampeana by L. J. BLANCO, M. Durante, D. Ferrante, R. E. Quiroga, M. Demaría, C. M. Di Bella

“…Los resultados de este trabajo aportan información relevante para la planificación forrajera de la ganadería a escala regional, y también pueden ser utilizados como insumo para la calibración de modelos de estimación de la productividad forrajera basados en el uso de sensores remotos.…”
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Vertical flow immunoassay for multiplex mycotoxins based on photonic nitrocellulose and SERS nanotags by Ruipeng Chen, Jingfang Hu, Hui Wang, Cheng Li, Haiqi Kang, Yuxuan Chen, Liang Yang, Xiangfang Tang, Benhai Xiong, Xiangwei Zhao

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Mechanical and Pharmacological Revascularization Strategies for Prevention of Microvascular Dysfunction in ST-Segment Elevation Myocardial Infarction: Analysis from Index of Microc... by Ji-Hun Jang, Man-Jong Lee, Kyu-Yong Ko, Jin-Hee Park, Yong-Soo Baek, Kwon Sung-Woo, Sung-Hee Shin, Seong-Ill Woo, Dae-Hyeok Kim, Young Ju Suh, Jun Kwan, Sang-Don Park

“…The IMR was measured with a pressure sensor/thermistor-tipped guidewire immediately after primary PCI. …”
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Identifying Digital Markers of Attention-Deficit/Hyperactivity Disorder (ADHD) in a Remote Monitoring Setting: Prospective Observational Study by Heet Sankesara, Hayley Denyer, Shaoxiong Sun, Qigang Deng, Yatharth Ranjan, Pauline Conde, Zulqarnain Rashid, Philip Asherson, Andrea Bilbow, Madeleine J Groom, Chris Hollis, Richard J B Dobson, Amos Folarin, Jonna Kuntsi

“…The RADAR-base Passive App and wearable device collect sensor data in the background, while the Active App involves participants completing clinical symptom questionnaires. …”
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Synaptotagmin-1 attenuates myocardial programmed necrosis and ischemia/reperfusion injury through the mitochondrial pathway by Teng Sun, Jialei Li, Shuang Wang, Yu Han, Xiangyu Tao, Min Yuan, Zhijie Jing, Ting Liu, Yuehong Qi, Siqi Liu, Yanlin Feng, Jiasong Chang, Lan Zhou, Lijuan Gao, Jianyun Shi, Ruihong Ning, Jimin Cao

“…Synaptotagmin-1 (Syt1), a Ca2+ sensor, is originally identified in nervous system and mediates synchronous neurotransmitter release. …”
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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

“…The livestock environment management system sensor and two load cells were used to measure the IT, IRH, ICO2, BW and DE intake of pigs, respectively. …”
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Desempenho da iluminação natural em poços de luz de edifícios residenciais by Aline Gouvêa Leite, João Pedro de Melo Souza, Sabrina Andrade Barbosa, Klaus Chaves Alberto

“…A métrica de avaliação da iluminação utilizada foi o Daylight Autonomy, que considera a média da porcentagem das horas de ocupação, ao longo do ano, em que os sensores registraram iluminância igual ou maior a 150 lux. …”
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Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud by Hang Mi, Bo Ai, Ruisi He, Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Yang Miao

“…First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. …”
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Can electronic monitoring with a digital smart spacer support personalised medication adherence and inhaler technique education in patients with asthma?: Protocol of the randomised... by Job F. M. van Boven, Boudewijn H.J. Dierick, Maria Achterbosch, Sandra Been-Buck, Titia Klemmeier, Susanne J. van de Hei, Paul Hagedoorn, Huib A.M. Kerstjens, Janwillem W.H. Kocks

“…The rechargeable CE-marked smart spacer (Aerochamber Plus with Flow Vu) includes a sensor that monitors adherence and inhalation technique to prescribed dosing regimen of both maintenance and reliever inhalers. …”
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Cloud-edge hybrid deep learning framework for scalable IoT resource optimization by Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, Anjani Kumar Rai, S. M. Padmaja, Khan Vajid Nabilal, Vimal Kumar, Roobaea Alroobaea, Hamed Alsufyani

“…The hybrid DQN-PPO-GNN-RL model addresses bottlenecks by dynamically managing computing and network resources, allowing for efficient operations in low-latency, high-demand environments such as autonomous systems, sensor networks, and real-time monitoring. The use of Graph Neural Networks (GNNs) improves the accuracy of resource representation, while reinforcement learning-based scheduling allows for seamless adaptation to changing workloads. …”
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Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale by Minghan Cheng, Xiuliang Jin, Chenwei Nie, Kaihua Liu, Tianao Wu, Yuping Lv, Shuaibing Liu, Xun Yu, Yi Bai, Yadong Liu, Lin Meng, Xiao Jia, Yuan Liu, Lili Zhou, Fei Nan

“…Considering this, we extracted four maize growth process parameters using Leaf Area Index (LAI) obtained from UAV (equipped with multispectral sensor, centimeter-level) and satellite (MODIS, 1 km) observations: PP_a (representing the duration of the crop growth period), PP_b (representing the peak growth stage of the crop), PP_c (representing the initial state of the crop), and LAImax (maximum LAI). …”
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Novel approach for noninvasive pelvic floor muscle strength measurement using extracorporeal surface perineal pressure measurement and machine learning modeling by Ui-jae Hwang, Sun-hee Ahn, Hyeon-ju Lee, Yurin Jeon, Myung Jae Jeon

“…ESPP measurements were obtained using a 10 × 10 pressure array sensor during maximal voluntary PFM contractions in a seated position. …”
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The Effects of Self-Monitoring Using a Smartwatch and Smartphone App on Stress Awareness, Self-Efficacy, and Well-Being–Related Outcomes in Police Officers: Longitudinal Mixed Desi... by Herman Jaap de Vries, Roos Delahaij, Marianne van Zwieten, Helen Verhoef, Wim Kamphuis

“… Abstract BackgroundWearable sensor technologies, often referred to as “wearables,” have seen a rapid rise in consumer interest in recent years. …”
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Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard by Marius Hobart, Michael Pflanz, Nikos Tsoulias, Cornelia Weltzien, Mia Kopetzky, Michael Schirrmann

“…These spatial orchard characteristics can be determined by low-cost drone technology with a consumer grade red-green-blue (RGB) sensor. Flights were conducted in a specified setting to enhance the signal-to-noise ratio of the orchard imagery. …”
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Effects of E-Cigarette Liquid Ratios on the Gravimetric Filter Correction Factors and Real-Time Measurements by Austin Close, Jane Blackerby, Heather Tunnell, Jack Pender, Eric Soule, Sinan Sousan

“…The higher correction factor values for the 90PG/10VG ratio may have resulted from greater PG volatility relative to VG and sensor losses. The correction factors (ratios 1–4) for PM2.5 were SMPS + APS data (4.96–7.62), MiniWRAS (2.02–3.64), pDR (0.50–1.07), and SidePak (0.22–0.40). …”
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The effect of depth data and upper limb impairment on lightweight monocular RGB human pose estimation models by Gloria-Edith Boudreault-Morales, Cesar Marquez-Chin, Xilin Liu, José Zariffa

“…The aims of this work are to (1) Determine how depth data affects lightweight monocular red–green–blue (RGB) HPE performance (accuracy and speed), to inform sensor selection and (2) Validate HPE models using data from individuals with physical impairments. …”
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Ethnic disparities in HbA1c and hypoglycemia among youth with type 1 diabetes: beyond access to technology, social deprivation and mean blood glucose by Suma Uday, John Stuart Pemberton, Zhide Fang, Stuart A Chalew

“…There were no differences between groups for age, MBG, time in range (3.9–10.0 mmol/L), diabetes duration, gender, insulin delivery method (multiple daily injections vs continuous subcutaneous insulin infusion), or percent sensor use (PSU). In multiple variable analysis, MBG (p<0.0001), ethnicity (p<0.0001), age (p<0.001), duration of diabetes (p<0.01) and PSU (p<0.05) accounted for 81% of the variability in HbA1c. …”
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Morning versus Nocturnal Heart Rate and Heart Rate Variability Responses to Intensified Training in Recreational Runners by Olli-Pekka Nuuttila, Heikki Kyröläinen, Veli-Pekka Kokkonen, Arja Uusitalo

“…The participants recorded daily their nocturnal HR and HRV (the natural logarithm of the root mean square of successive differences; LnRMSSD) with a photoplethysmography-based wrist device and performed an orthostatic test (2-min supine, 2-min standing) every morning with a chest-strap HR sensor. The HR and LnRMSSD segments that were analyzed from the nocturnal recordings included start value (SleepStart), end value (SleepEnd), first 4-h segment 30 min after detected sleep onset (Sleep4h), and full sleep time (SleepFull). …”
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Actualización de la cartografía eólica en el centro y sur de Neuquén by Samuel D. Troncoso Schenker, Lidia Ferreyra, Claudia Palese

“…Se utilizaron datos de sensores remotos para caracterizar el relieve y la rugosidad superficial. …”
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