Distributed Vibration Monitoring System for 10 kV-400 kVA 3D Wound Core Transformer under Progressive Short-Circuit Impulses.

As large-scale, high-proportion, and efficient distribution transformers surge into the grids, anti-short circuit capability testing of transformer windings in efficient distribution seems necessary and prominent. To deeply explore the influence of progressively short-circuit shock impulses on the core winding deformation of efficient power transformers, a finite element theoretical model was built by referring to a three-phase three-winding 3D wound core transformer with a model of S20-MRL-400/10-NX2. The distributions of internal equivalent force and total deformation of the 3D wound core transformer along different paths under progressively short-circuit shock impulses varying from 60% to 120% were investigated. Results show that the equivalent stress and total deformation change rate reach their maximum as the short-circuit current increases from 60% to 80%, and the maximum and average variation rate for the equivalent stress reach 177.75% and 177.43%, while the maximum and average variation rate for the total deformation corresponds to 178.30% and 177.45%, respectively. Meanwhile, the maximum equivalent stress and maximum total deformation reach 29.81 MPa and 38.70 µm, respectively, as the applied short-circuit current increased to 120%. In light of the above observations, the optimization and deployment of wireless sensor nodes was suggested. Therefore, a distributed monitoring system was developed for acquiring the vibration status of the windings in a 3D wound core transformer, which is a beneficial supplement to the traditional short-circuit reactance detection methods for an efficient grid access spot-check of distribution transformers.

Wearable ECG Device and Machine Learning for Heart Monitoring.

With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.

Prevalence, Antimicrobial Resistance, and Diversity of Campylobacter Isolated from U.S. Goat Feces: 2019 NAHMS Survey.

Goats are often asymptomatic carriers of Campylobacter, including the foodborne pathogen Campylobacter jejuni. Infections can have significant and economically detrimental health outcomes in both humans and animals. The primary objective of this study was to estimate the prevalence of Campylobacter in U.S. goat herds. Campylobacter species were isolated from 106 of 3,959 individual animals and from 42 of 277 goat operations that participated in fecal sample collection as part of the National Animal Health Monitoring System Goat 2019 study. Weighted animal-level prevalence was 2.3% (SE = 0.5%) and operation prevalence was 13.0% (SE = 3.2%). Animal-level prevalence ranged widely from 0 to 70.0%, however, 52.4% of positive operations (22/42) had only a single isolate. C. jejuni was the most frequently isolated species (68.9%; 73/106), followed by C. coli (29.3%, 31/106). A total of 46.2% (36/78) of viable isolates were pan-susceptible to 8 antimicrobials. Resistance to tetracycline (TET) was observed in 44.9% (35/78) of isolates, while 12.8% (10/78) were resistant to ciprofloxacin (CIP) and nalidixic acid (NAL). Among all isolates, a single resistance profile CIP-NAL-TET was observed in 3.8% (3/78) of isolates. A total of 35 unique sequence types (STs) were identified, 11 of which are potentially new. Multiple C. jejuni STs were observed in 48.1% (13/27) of positive operations. Goats with access to surface water, operations reporting antibiotics in the feed or water (excluding ionophores and coccidiostats), and operations reporting abortions and without postabortion management tasks had significantly greater odds of being Campylobacter positive. This snapshot of the U.S. goat population enriches the limited pool of knowledge on Campylobacter species presence in U.S. goats.

Unveiling the role of taxonomic sufficiency for enhanced ecosystem monitoring.

The use of Artificial substrates (AS) as sampling devices addresses challenges in macrofaunal quantitative sampling. While effectively capturing biodiversity patterns, the time-intensitive identification process at the species level remains a substantial challenge. The Taxonomic Sufficiency approach (TS), where only taxa above species level are identified, arises as a potential solution to be tested across different environmental monitoring scenarios. In this paper, we analyzed three AS macrobenthic datasets to evaluate the odds of TS in improving the cost-effective ratio in AS monitoring studies and establish the highest resolution level to detect assemblage changes under different environmental factors. Results indicated that the family level emerged as a pragmatic compromise, balancing precision and taxonomic effort. Cost/benefit analysis supported TS efficiency, maintaining correlation stability until the family level. Results also showed that reducing resolution to family does not entail a significant Loss of Information. This study contributes to the discourse on TS applicability, highlighting its practicality in monitoring scenarios, including spatial-temporal studies, and rapid biodiversity assessments. Additionally, it highlights the "second best approach" of family-level practicality depending on the specific monitoring scenario and recognizes the importance of the species-level "best approach" before applying TS in monitoring studies.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

Useful of proximal gastrectomy with double-tract reconstruction in preventing glucose spikes.

BACKGROUND: Volatile glucose levels after gastrectomy induce dumping syndrome, which adversely affects patient quality of life. This study aimed to evaluate the glycemic variability of proximal gastrectomy with double-tract reconstruction (PGDTR) as a function-preserving procedure. METHODS: This study used a continuous glucose monitoring (CGM) system to record glycemic profiles of patients who underwent PGDTR or total gastrectomy (TG) and compared them. Moreover, this study evaluated postgastrectomy syndrome, including dumping symptoms, between the PGDTR and TG groups using the 37-item Postgastrectomy Syndrome Assessment Scale (PGSAS-37) questionnaire. RESULTS: Of note, 44 patients underwent PGDTR, and 42 patients underwent TG, which included more advanced cases. CGM results showed that the SD, relative SD, and maximum drop in glucose level between 30 min and 2 h after a meal were smaller in the PGDTR group than in the TG group (14.81 vs 22.40 mg/dL [P < .001], 0.14 vs 0.20 mg/dL [P < .001], and 42.06 vs 117.67 mg/dL [P < .001], respectively). For nocturnal glucose levels, SD and percentage time below the range were smaller in the PGDTR group than in the TG group (11.76 vs 15.16 mg/dL [P = .005] and 11.25% vs 35.27% [P < .001]). The PGDTR group generally performed better than the TG group on all the PGSAS-37 questionnaire items. Patients in the PGDTR group without food inflow into the remnant stomach showed similar CGM results as those in the TG group but with stronger dumping symptoms. CONCLUSION: Food inflow into the remnant stomach is essential for PGDTR to be a function-preserving procedure as it leads to the control of dumping symptoms and lower glucose level spikes.

Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study.

BACKGROUND: Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments. OBJECTIVE: This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults. METHODS: A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability. RESULTS: Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18). CONCLUSIONS: The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability. TRIAL REGISTRATION: ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.

Embedded monitoring system and teaching of artificial intelligence online drug component recognition.

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

A phosphorylated guanidine chitosan and UiO-66-NH2 modified magnetic nanoparticle platform for enrichment and detection of multiple bacteria.

Wastewater-based epidemiology (WBE) is a powerful tool for early warning of infectious disease outbreaks. Hence, a rapid and portable pathogen monitoring system is urgent needed for on-site detection. In this work, we first reported synthesis of an artificial modulated wide-spectrum bacteria capture nanoparticle (Arg-CSP@UiO@Fe3O4). Arginine-modified phosphorylated chitosan (Arg-CSP) coating could provide strongly positive charged guanidinium group for pathogen interaction by electrostatic attraction, and UiO-66-NH2 layer could help Arg-CSP graft onto Fe3O4 magnetic particles. The capture efficiency of Arg-CSP@UiO@Fe3O4 reached 92.2 % and 97.3 % for Escherichia coli (E.coli) and Staphylococcus epidermidis (S.epidermidis)within 40 min, in 10 mL sample. To prevent pathogen degradation in sewage, a portable nucleic acid extraction-free method was also developed. UiO-66-NH2 could disintegrate in buffer with high concentration of PO43- for bacterium desorption, and then nucleic acid of the bacteria was released by heating. The DNA template concentration obtained by this method was 779.28 times higher than that of the direct thermal lysis product and 8.63 times higher than that of the commercial kit. Afterwards, multiple detection of bacteria was realized by loop-mediated isothermal amplification (LAMP). Artificial regulated pathogen desorption could prevent non-specific adsorption of nucleic acid by nanoparticles. The detection limit of Arg-CSP@UiO@Fe3O4-LAMP method was 80 cfu/mL for E.coli and 300 cfu/mL for S.epidermidis. The accuracy and reliability of the method was validated by spiked sewage samples. In conclusion, this bio-monitoring system was able to detect multiple bacteria in environment conveniently and have good potential to become an alternative solution for rapid on-site pathogen detection.

Mechanical Improvement of Gas Monitoring System in Monoplace Hyperbaric Chamber to Advance the Safety and Efficacy.

Introduction: A Monoplace hyperbaric chamber delivers oxygen to the patient's tissues through breathing. Gas monitoring inside the chamber is important because oxygen (O2) is consumed, and carbon dioxide (CO2) is increased because treatment is performed in a closed volume. This study aimed to advance the safety and efficacy of the monoplace hyperbaric chamber (MHC) through mechanical improvement in a gas monitoring system (GMS). Methods: First, as the oxygen supply method was changed to the direction of the patient's face, it was compared the values of O2, CO2, humidity, and temperature were measured in the MHC and the GMS when operating at 2.0 atmosphere absolute (ATA) and 3.0 ATA. Second, to evaluate the effects of variables across measuring time, it was analyzed in a 3-way repeated measure ANOVA (10 min.×20 min.×30 min.). Lastly, the values before and after the optimization of the MHC were compared by applying a cooler to prevent temperature rise inside the MHC. Results: In 2.0 ATA, the average humidity was higher in the MHC than in the GMS (p<0.001). Also, the average temperature was lower in the MHC than in the GMS (p<0.001). In 3.0 ATA, the average CO2 and humidity were higher in the MHC than in the GMS, respectively (p<0.001, p=0.004). The 3-way repeated measures ANOVA revealed a significant difference in most main and interacted factors (p<0.05). O2 and temperature, comparing before and after MHC optimization, revealed a significant difference (p<0.05). Conclusion: Few studies have verified safety and effectiveness by evaluating the pressure, oxygen concentration, etc. of a monoplace hyperbaric chamber. Further research is expected to verify the effectiveness of providing comfort to patients receiving hyperbaric oxygen treatment and increase the treatment effect.

A Comparison of Two Transport Monitor Systems With Regard to Efficiency and Staff Satisfaction in the Perioperative Setting.

BACKGROUND: Medical research aims to improve patient safety and efficiency in the perioperative setting. One critical aspect of patient safety is the intrahospital transfer of patients. Also, reliable monitoring of vital signs is crucial to support the medical staff. This study was conducted to assess two monitoring systems in terms of the handover time and staff satisfaction. METHODS: To assess several aspects, two monitoring systems were compared: an organizational unit-related monitoring system that needs to be changed and brought back to the initial organizational unit after the patient transfer and a patient-specific monitoring system that accompanies the patient during the whole perioperative process. RESULTS: In total, 243 patients were included, and 375 transfers were examined to analyze economic factors, including differences in handover times and user-friendliness. To this end, 30 employees of the Heidelberg University Hospital were asked about their satisfaction with the two monitoring systems based on a systematic questionnaire. It could be shown that, especially during transfers from the operating theater to the intensive care unit or the recovery room, the time from arrival to fully centralized monitoring and the total handover time were significantly shorter with the patient-specific monitoring system (p < 0.001). Furthermore, the staff was more satisfied with the patient-specific monitor system in terms of flexibility, cleanability and usability. CONCLUSION: The increased employee satisfaction and significant time benefits during intrahospital transports may increase patient safety and efficiency of patient care, reduce employee workload, and reduce costs in the overall context of patient care.

Dietary acrylamide disrupts the functioning of the biological clock.

Acrylamide (ACR) is a known carcinogen and neurotoxin. It is chronically consumed in carbohydrate-rich snacks processed at high temperatures. This calls for systematic research into the effects of ACR intake, best performed in an experimental model capable of detecting symptoms of its neurotoxicity at both high and low doses. Here, we study the influence of 10 µg/g (corresponding to the concentrations found in food products) and, for comparison, 60, 80 and 110 µg/g dietary ACR, on the fruit fly Drosophila melanogaster. We show that chronic administration of ACR affects lifespan, activity level and, most importantly, the daily and circadian pattern of locomotor activity of Drosophila. ACR-treated flies show well-defined and concentration-dependent symptoms of ACR neurotoxicity; a reduced anticipation of upcoming changes in light conditions and increased arrhythmicity in constant darkness. The results suggest that the rhythm-generating neural circuits of their circadian oscillator (biological clock) are sensitive to ACR even at low concentrations if the exposure time is sufficiently long. This makes the behavioural readout of the clock, the rhythm of locomotor activity, a useful tool for studying the adverse effects of ACR and probably other compounds.

How can physicians improve medication adherence and outcomes in dermatological conditions?

INTRODUCTION: Medication non-adherence is a major contributor to suboptimal disease treatment across medical specialties and is a particular hurdle with topicals. While adherence is a patient behavior affected by many socioeconomic and health system factors, physicians can play an important role in encouraging good adherence. AREAS COVERED: We discuss methods for measuring adherence, including ethics of such research, provide select examples of dermatology-specific adherence studies, and conclude with physician-focused practices to improve patients' adherence. Articles were selected from a PubMed search spanning 2003 to 10 December 2023, using the following terms: 'dermatology,' 'medication,' 'treatment,' 'adherence,' 'compliance,' and 'intervention.' EXPERT OPINION: Poor adherence to treatment is a major cause of poor treatment outcomes. As the goal of medical care is to achieve successful treatment outcomes, encouraging good adherence may be as much a foundation of care as making the right diagnosis and prescribing the right treatment. Taking a doctor-centric perspective on reasons for non-adherence may be more productive than simply finding fault with the patient. Establishing trust and accountability is a foundation for good adherence; after establishing the provider-patient relationship, physicians can improve adherence by incorporating behavioral and counseling strategies, communicating through technology, and advocating for distribution of validated educational information.

Early Dinner Improves the Glycemic Profile in Habitual Late Eaters With Uncontrolled Type 2 Diabetes Mellitus in the Short Term.

Background Late dinner (LD) can worsen the glucose profile in type 2 diabetes (T2D). We assessed the short-term effect of early dinner (ED) on glycemic control in habitual late eaters with uncontrolled T2D. Methodology This interventional, single-arm, within-group trial recruited 10 habitual late eaters with uncontrolled T2D (glycosylated hemoglobin: 7-9% and either fasting plasma glucose (FPG): ≥140 mg/dl or post-prandial plasma glucose: ≥200 mg/dl). They had their usual LD (beyond 22:00 hours) on Days 0-3 and ED (before 20:00 hours) on Days 4-10. Continuous glucose monitoring system (CGMS) parameters, two-hour post-dinner, and fasting (10-hour post-dinner) investigations were analyzed. Bedtime hunger was assessed using a Labeled Magnitude Satiety Scale. Results The mean dinner time was reduced from 22:28 hours to 19:29 hours. CGMS revealed that ED lowered the 10-hour post-dinner incremental area under the curve (22,587.9 ± 5,168.3 mg/dl×mins vs. 15,886.3 ± 4,288.7 mg/dl×mins, P < 0.002), 10-hour post-dinner average blood glucose (ABG) (137.5 ± 9.3 mg/dl vs. 125 ± 7.9 mg/dl, P < 0.002), 24-hour ABG (132.2 ± 7.5 mg/dl vs. 124.8 ± 5.4 mg/dl, P = 0.037), and night mean amplitude of glucose excursion (83.7 ± 5.8 mg/dl vs. 69.3 ± 7.5 mg/dl, P = 0.027). ED also reduced FPG (119.8 ± 7.3 mg/dl vs. 105.2 ± 5.7 mg/dl, P = 0.015), fasting insulin (15.0 ± 4.3 µIU/ml vs. 9.7 ± 2.7 µIU/ml, P < 0.002), and HOMA-IR (4.36 ± 1.2 vs. 2.56 ± 0.79, P < 0.002). Post-dinner glucose, insulin, and inflammatory markers were unchanged. Bedtime hunger increased significantly on Days 4 and 5 but returned to baseline by Day 10. Conclusions A simple modification of dinner time in habitual late eaters with uncontrolled T2D improves FPG, glycemic control, and insulin resistance in the short term.

Recommendations on the use of the flash continuous glucose monitoring system in hospitalized patients with diabetes in Latin America.

BACKGROUND: Continuous glucose monitoring can improve glycemic control for hospitalized patients with diabetes, according to current evidence. However, there is a lack of consensus-established recommendations for the management of hospitalized patients with diabetes using flash continuous glucose monitoring system (fCGM) in Latin America. Therefore, this expert consensus exercise aimed to establish guidelines on the implementation of fCGM in the management of hospitalized patients with diabetes in Latin America. METHODS: The modified Delphi method was applied on a panel of nine specialists, establishing consensus at 80%. A twenty-two-question instrument was developed to establish recommendations on the use of fCGM in hospitalized patients living with diabetes. RESULTS: Based on consensus, experts recommend the use of fCGM in hospitalized patients with diabetes starting at admission or whenever hyperglycemia (> 180 mg/dl) is confirmed and continue monitoring throughout the entire hospital stay. The recommended frequency of fCGM scans varies depending on the patient's age and diabetes type: ten scans per day for pediatric patients with type 1 and 2 diabetes, adult patients with type 1 diabetes and pregnant patients, and seven scans for adult patients with type 2 diabetes. Different hospital services can benefit from fCGM, including the emergency room, internal medicine departments, intensive care units, surgery rooms, and surgery wards. CONCLUSIONS: The use of fCGM is recommended for patients with diabetes starting at the time of admission in hospitals in Latin America, whenever the necessary resources (devices, education, personnel) are available.

Diffusion-driven fed-batch fermentation in perforated ring flasks.

PURPOSE: Simultaneous membrane-based feeding and monitoring of the oxygen transfer rate shall be introduced to the newly established perforated ring flask, which consists of a cylindrical glass flask with an additional perforated inner glass ring, for rapid bioprocess development. METHODS: A 3D-printed adapter was constructed to enable monitoring of the oxygen transfer rate in the perforated ring flasks. Escherichia coli experiments in batch were performed to validate the adapter. Fed-batch experiments with different diffusion rates and feed solutions were performed. RESULTS: The adapter and the performed experiments allowed a direct comparison of the perforated ring flasks with Erlenmeyer flasks. In batch cultivations, maximum oxygen transfer capacities of 80 mmol L-1 h-1 were reached with perforated ring flasks, corresponding to a 3.5 times higher capacity than in Erlenmeyer flasks. Fed-batch experiments with a feed reservoir concentration of 500 g glucose L-1 were successfully conducted. Based on the oxygen transfer rate, an ammonium limitation could be observed. By adding 40 g ammonium sulfate L-1 to the feed reservoir, the limitation could be prevented. CONCLUSION: The membrane-based feeding, an online monitoring technique, and the perforated ring flask were successfully combined and offer a new and promising tool for screening and process development in biotechnology.

Internet of things-based study on online monitoring system of building equipment energy saving optimization control using building information modeling.

Smart building equipment monitoring is a well-established field focused on enhancing contemporary building comfort. The proliferation of Internet connectivity, facilitated by the internet of things (IoT), has transformed buildings from static structures into interactive environments. IoT has witnessed substantial growth across various aspects of daily life, from monitoring environmental conditions to managing building systems and storing data in the cloud. One critical application is the intelligent monitoring and control of building equipment, such as air conditioners, to optimize energy efficiency-a matter of increasing concern for building owners, design experts, and system integrators. Achieving comprehensive energy savings demands a meticulous approach to energy-efficient design and control. This paper's primary objective is to explore and analyze IoT-based energy-saving optimization techniques for intelligent building equipment, integrating building information modeling (BIM) technology. It particularly delves into the energy conservation control algorithm for air-conditioning systems. The research presents a challenge rooted in energy-saving optimization, established upon specific objective functions, followed by a detailed explanation of the energy-saving control algorithm. To validate their approach, the paper outlines a comprehensive experimental design. Over three sessions in August, they conducted control experiments in two distinct areas. Area 1 implemented the energy-saving control methodology discussed in the paper, utilizing virtual parameter enhancement mechanisms, while Area 2 adhered to conventional control methods. The results were enlightening. Area 1 demonstrated superior energy efficiency, consuming 735 kWh compared to Area 2's 819 kWh, signifying an impressive 11.43% reduction in energy consumption thanks to the optimized control strategy. This research underscores the practicality and significance of implementing IoT-based energy-saving strategies, with a focus on smart thermostats, HVAC controllers, and daylight sensors, in intelligent building equipment management to achieve substantial energy conservation gains.

The Utility of BIS™ Monitoring in Anesthesia for Elderly Patients.

Aging leads to anatomic and physiologic changes in the brain, making it more sensitive to the depressant effects of anesthetic medications and increasing the risk of postoperative neurocognitive complications such as postoperative delirium and postoperative cognitive dysfunction. This article explores the implications of anesthesia on elderly patients' brain health, emphasizing the heightened risk of postoperative neurocognitive disorders, and describes the BIS™ Monitoring System as a neuromonitoring tool for anesthesia professionals to assess the depth of anesthesia. The integration of the BIS Monitoring System into clinical practice can contribute to a more tailored and patient-centered approach to anesthesia management, ultimately improving perioperative outcomes and safety.

Comparison of two different respiratory monitoring systems with 4D-CT images for target volume definition in patients undergoing para-aortic nodal irradiation.

OBJECTIVE: Today, respiratory movement can be monitored and recorded with different methods during a simulation on a four-dimensional (4D) computed tomography (CT) device to be used in radiotherapy planning. A synchronized respiratory monitoring system (RPM) with an externally equipped device is one of these methods. Another method is to create 4D images of the patient's breathing phases without the need for extra equipment, with an anatomy-based software program integrated into the CT device. Our aim is to compare the RPM system and the software system (Deviceless) which are two different respiratory monitoring methods used in tracking moving targets during 4D-CT imaging and to assess their clinical usability. METHODS: Ten patients who underwent paraaortic nodal irradiation were enrolled. The simulation was performed using intravenous contrast material on a 4D-CT device with both respiratory monitoring methods. The right/left kidneys and renal arteries were chosen as references to evaluate abdominal organ movement. It was then manually contoured one by one on both sets of images. The images were compared volumetrically and geometrically after rigid reconstruction. The similarity between the contours was determined by the Dice index. Wilcoxon test was used for statistical comparisons. RESULTS: The motion of the kidneys in all three directions was found to be 0.0 cm in both methods. The shifts in the right/left renal arteries were submillimetric. The Dice index showed a high similarity in both kidney and renal artery contours. CONCLUSION: In our study, no difference was found between RPM and Deviceless systems used for tracking and detection of moving targets during simulation in 4D-CT. Both methods can be used safely for radiotherapy planning according to the available possibilities in the clinic.

Type 2 diabetes in latin America: recommendations on the flash glucose monitoring system.

OBJECTIVE: To establish recommendations through the consensus of a Latin American experts panel on the use of the flash glucose monitoring system (fCGM) in people living with type 2 diabetes mellitus (T2DM) regarding the benefits and challenges of using the fCGM. METHODS: An executive committee of experts was created, comprised by a panel of fifteen physicians, including endocrinologists and internal medicine physicians, with expertise in management of adult patients with T2DM. The experts were from various countries: Colombia, Chile, Peru, Mexico, Argentina, and Brazil. The modified Delphi method was used, considering a consensus level of at least 80% of the participants. A seventeen-item instrument was developed to establish recommendations on the use of fCGM in patients with T2DM in Latin American. RESULTS: The number of glucose scans recommended per day with the fCGM for patients managed with oral antidiabetic drugs or basal insulin was a median of 6 scans per day, and for those managed with multiple insulin doses, a median of 10 scans per day was recommended. Additionally, a holistic and individualized management approach was recommended, taking into account new treatment directions and identifying patients who would benefit from the use of the fCGM. CONCLUSION: Continuous use of the fCGM is recommended for people living with T2DM, regardless of their type of treatment. These metrics must be evaluated individually for each patient profile.