Minute Intervals Research Articles

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

Automated Drying of Moringa Leaves Using an Arduino Uno Microcontroller

The aim of this research is to explore how to design and implement the use of a DHT11 sensor with Arduino in a moringa leaf dryer for temperature and humidity control. The device design process involves the preparation of all necessary equipment and the creation of Arduino source code. This code makes connections between the Arduino uno, DHT11 sensor, LCD display, relay and fan using jumper cables, allowing the results obtained to be displayed on the LCD screen. Temperature and humidity are monitored at 15 minute intervals over a 7 hour period and the fan is automatically activated in the event of temperature fluctuations during each drying phase. The research results show that the device design system includes hardware components such as the Arduino Uno and the DHT11 sensor, LCD display and fan. In addition, the software program for the monitoring system, initialised using the Arduino IDE, has been successfully designed and works effectively. Based on the equipment test results, the average moisture content of the Moringa leaves is 7.2% compared to the initial moisture content of 73%. These results underline the efficiency of the developed system in achieving the desired drying results.

Intelligent device recognition of internet of things based on machine learning

With the rapid popularization and application of Internet of Things technology, smart devices have become an indispensable part of people's daily lives. Therefore, it is crucial to accurately identify these devices as their numbers continue to grow. The research aimed to introduce a lightweight method for identifying Internet of Things devices based on network flow characteristics and scheduling algorithms. This can improve device identification accuracy while maintaining high efficiency. The research constructed a comprehensive optimization algorithm selection framework to achieve performance optimization in different scenarios. This framework took into account many factors, including network traffic characteristics, device identification requirements, and system efficiency, to ensure flexible adaptation in different scenarios and optimize overall performance. Research results showed that the proposed system had an accuracy of 96.8 % at 1-minute intervals, which increased to 99.7 % at 10-minute intervals, and reached 99.9 % at both 30-minute and 60-minute intervals. In 100 experiments, the research method improved the accuracy by an average of 1.5 % compared with the baseline. In fingerprint recognition, the overall accuracy of the long short-term memory network exceeded 90 %, with an area under the curve of 0.99. Most devices had an accuracy of over 95 % in recognition, and the recall rate remained around 90 %, the effectiveness of the method proposed in the study was further verified. The method proposed in the study not only improved the accuracy and efficiency of device recognition, but also provided powerful solutions for the field of network security. This provides useful guidance for research and practical applications in related fields.

3D Kinematics Quantifies Gait Response to Levodopa earlier and to a more Comprehensive Extent than the MDS-Unified Parkinson's Disease Rating Scale in Patients with Motor Complications.

Quantitative 3D movement analysis using inertial measurement units (IMUs) allows for a more detailed characterization of motor patterns than clinical assessment alone. It is essential to discriminate between gait features that are responsive or unresponsive to current therapies to better understand the underlying pathophysiological basis and identify potential therapeutic strategies. This study aims to characterize the responsiveness and temporal evolution of different gait subcomponents in Parkinson's disease (PD) patients in their OFF and various ON states following levodopa administration, utilizing both wearable sensors and the gold-standard MDS-UPDRS motor part III. Seventeen PD patients were assessed while wearing a full-body set of 15 IMUs in their OFF state and at 20-minute intervals following the administration of a supra-threshold levodopa dose. Gait was reconstructed using a biomechanical model of the human body to quantify how each feature was modulated. Comparisons with non-PD control subjects were conducted in parallel. Significant motor changes were observed in both the upper and lower limbs according to the MDS-UPDRS III, 40 minutes after levodopa intake. IMU-assisted 3D kinematics detected significant motor alterations as early as 20 minutes after levodopa administration, particularly in upper limbs metrics. Although all "pace-domain" gait features showed significant improvement in the Best-ON state, most rhythmicity, asymmetry, and variability features did not. IMUs are capable of detecting motor alterations earlier and in a more comprehensive manner than the MDS-UPDRS III. The upper limbs respond more rapidly to levodopa, possibly reflecting distinct thresholds to levodopa across striatal regions.

The predictive role of carotid artery flow time for anesthesia-induced hypotension in high-risk elderly patients.

Hypotension induced by general anesthesia is associated with postoperative complications, increased mortality, and morbidity, particularly elderly patients. The aim of this study was to investigate the effectiveness of corrected carotid artery flow time (FTc) for predicting hypotension following anesthesia induction in patients over 65 years old.After faculty ethical committee approval and written informed consent, 138 patients (65 years and older, ASA physical status I-III) who scheduled for elective surgery were included in this study. In the pre-operative anesthesia unit, the carotid artery FTc value was measured by ultrasound and hemodynamic values were recorded. Following anesthesia induction with propofol, hemodynamic data were recorded at 1-minute intervals for 3min. Measurements were terminated prior to endotracheal intubation, as direct laryngoscopy and endotracheal intubation could cause sympathetic stimulation and hemodynamic changes.Hypotension occurred in 52 patients (37.7%). The preoperative FTc value of the patients who developed hypotension was statistically lower (312.5 ms) than the patients who did not (345.0 ms) (p < 0.001). The area under the ROC curve for carotid artery FTc was 0.93 (95% CI for AUC:0.89-0.97; p < 0.001) with an optimal cut-off of value for predicting post-anesthesia hypotension 333 ms, a sensitivity of 90.4% and a specificity of 84.9%. As a result of the multiple logistic regression model, carotid artery FTc emerged as the sole independent risk factor for hypotension following anesthesia induction.Preoperative carotid artery FTc measurement is a simple, bedside, noninvasive, and reliable method for predicting anesthesia-induced hypotension in elderly patients.

Utilization of Canistel (Lacuma Nervosa A.DC) Flour in Making Cakes and Pastries

The main purpose of this study was to determine the Utilization of Canistel (Lacuma Nervosa A.DC) Flour in Making Cakes and Pastries conducted at the Municipality of Valencia, Bohol. Specifically, this study aimed to determine its profile in terms of the ingredients and cost, tools and equipment used, procedures, and shelf life, its acceptability in terms of appearance, aroma, texture, and taste and to determine if there is a significant difference in the acceptability of the three treatments. This study used the experimental design, employing the questionnaire as an instrument in gathering data. The researchers asked the permission of the Dean of the College of Teacher Education. The respondents of the study were the cookery students, TLE teacher and cakes and pastry baker in Valencia, Bohol who were selected through purposive sampling. In tasting the products, the respondents were given five minutes interval after every treatment. After the distribution of the sample products, the questionnaire was given to the participants. The respondents evaluated the finished products using a modified sensory evaluation sheet based on Four-Point Hedonic Scale. The data gathered were tabulated using the percentage, weighted mean, One Way Analysis of Variance, and the Scheffe Test for multiple comparison. The study revealed that Canistel (Lacuma Nervosa A.DC) Flour in making Cakes and Pastries in all treatments were “Highly Acceptable” in terms of appearance, aroma, texture, taste and this study determined that the cost of the materials and ingredients were affordable, and the tools and equipment were available. The .05 alpha level was used as the criterion for acceptance or rejection of the null hypotheses. These results led to the conclusion that there is a significant difference that existed in the level of acceptability of Canistel (Lacuma nervosa A. DC) flour in making cakes and pastries in terms of aroma, taste and overall acceptability, therefore the null hypothesis is rejected. However, no significant difference in the level of acceptability of Canistel (Lacuma nervosa A. DC) flour in making cakes and pastries in terms of appearance and texture, therefore the null hypothesis is accepted.

Variability of External Load Measures During Soccer Match Play: Influence of Player Fitness or Pacing?

The aims of this study were to examine the variability of selected external load metrics within 15-minute intervals during soccer match play and examine their relationship with players' high-intensity intermittent fitness. A total of 18 male soccer players were monitored for their external load metrics during 26 matches, which included total distance, high-metabolic-load distance, and mechanical work (defined as the sum of accelerations and decelerations >3m2). Additionally, players completed the 30-15 Intermittent Fitness Test. Total distance had lower coefficients of variation than high-metabolic-load distance and mechanical work (effect size [ES]: 5.2 to 6.4; very large). Within-player Δ-15min showed moderate to large decreases (ES: -0.7 to -1.6) and increases (ES: 0.9 to 1.8) in absolute and coefficient-of-variation values, respectively. Large relationships (r = .55 to .61) were observed between the Intermittent Fitness Test and 15-minmean and 15-minbest in all selected external load metrics. However, small to moderate (0.27 to 0.41) associations were observed between the Intermittent Fitness Test and Δ-15min in selected external load metrics. These findings suggest that players with relatively lower intermittent running capacity might show lower variability during matches, as evidenced by smaller reductions in high-intensity actions during the final 15minutes. We attribute these observations to players' possessing better pacing strategies.

Ticketing and Crowd Management System for Attraction Facilities: An Aquarium Case Study

In tourist facilities, managing ticket sales can reduce congestion imbalances. This study reports on the results of a pilot experiment conducted at Kaiyukan in Osaka, one of the largest aquariums in Japan. The experiment utilized pre-sale ticket data for controlling admission time intervals, smoothing the number of admissions through dynamic pricing, and predicting visitor numbers. The study reviewed the effectiveness of each of these approaches to alleviate congestion inside Kaiyukan. We then report on a method to predict the number of visitors from the pre-sale conditions of Kaiyukan admission tickets. It was found that setting entry times to 15-minute intervals was most operationally advantageous for the Kaiyukan. Moreover, the behavioral effects induced by variable pricing were more effective when prices were changed based on time slots rather than on a daily basis. Compared to the Holt–Winters’ method, we were able to maintain stable prediction accuracy even during consecutive holidays and long school vacation seasons.

Influence of fentanyl, medetomidine-fentanyl or acepromazine-fentanyl premedication on oesophageal and rectal temperature in dogs under anaesthesia

ObjectiveTo compare changes in oesophageal (T-Oeso) and rectal (T-Rec) temperature in dogs during general anaesthesia and premedicated with fentanyl, medetomidine–fentanyl or acepromazine–fentanyl. Study designProspective, randomized, blind clinical study. AnimalsA total of 120 healthy dogs, aged 2–10 years and weighing 5–20 kg. MethodsDogs were randomly allocated to one of three groups. Animals of F group were premedicated with fentanyl (0.01 mg kg–1), MF group with medetomidine (0.005 mg kg–1) and fentanyl (0.01 mg kg–1) and AF group with acepromazine (0.01 mg kg–1) and fentanyl (0.01 mg kg–1). Anaesthesia was induced with propofol and maintained with isoflurane in oxygen–air mixture. Fentanyl was administered continuously (0.01 mg kg–1 hour–1). The T-Oeso, T-Rec and ambient temperatures were recorded after induction (T0) and subsequently at 10 minute intervals for 60 minutes (T10–T60). Data were analysed using anova or their non-parametric equivalents (p < 0.05). ResultsMedian T-Oeso was significantly higher in MF group between T0–T20 compared with other groups. Median T-Oeso significantly decreased in F group from 38.0 °C (T0) to 37.4 °C (T30), 37.1 °C (T40), 36.9 °C (T50) and 36.6 °C (T60), in MF group from 38.3 °C (T0) to 37.7 °C (T30), 37.5 °C (T40), 37.2 °C (T50) and 37.1 °C (T60) and in AF group from 37.7 °C (T0) to 37.3 °C (T40), 37.2 °C (T50) and 37.1 °C (T60). The T-Rec significantly decreased in F group from 38.0 °C (T0) to 37.4 °C (T40), 37.2 °C (T50) and 36.9 °C (T60), in MF group from 38.3 °C (T0) to 37.5 °C (T50) and 37.4 °C (T60) and in AF group from 38.2 °C (T0) to 37.6 °C (T40), 37.5 °C (T50) and 37.4 °C (T60). Conclusions and clinical relevancePremedication with fentanyl, medetomidine–fentanyl or acepromazine–fentanyl in the doses used decreased the T-Oeso and T-Rec. The T-Oeso at the beginning of anaesthesia was higher after premedication with medetomidine–fentanyl. However, this difference was not clinically significant.

Estimation of Solar Power Generation Through the Use of Effective Forecasting Algorithms

The unpredictable nature of solar power generation poses significant challenges to energy management, particularly in power grids with high solar penetration, leading to potential imbalances between supply and demand. This has increased interest in developing accurate forecasting methods for photovoltaic power generation. This study focuses on short-term forecasting of solar power generation using MATLAB, based on historical generation trends through three different hybrid models. It uses a multilayer perceptron neural network as the forecasting tool, which is optimized through various algorithms to fine-tune the network's hyperparameters, such as the weights and biases of the input and hidden layers, for optimal performance. The Root Mean Square Error is used as the objective metric to minimize the discrepancy between actual and predicted values. The models use Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm, and Genetic Algorithm for network optimization. This research analyzes PV generation data from Belgium in July and August 2022, taken at 15-minute intervals, and evaluates model performance using six different error metrics, including Mean Absolute Error. The results show the accuracy and reliability in forecasting solar power generation time series.

How does rest Interval duration affect performance? An experiment on high-intensity sprint exercises

Introduction: The purpose of this study was to examine the effects of interval and high-intensity sprint training (RST) with different rest intervals on the respiratory function, aerobic and anaerobic power. (2) Materials and Methods: The study involved the voluntary participation of 20 male participants. The participants were divided into two groups: 2IRG (2-minute rest between repeated sprints) and 4IRG (4-minute rest between repeated sprints). The changes in the respiratory function, aerobic, and anaerobic power parameters were examined in the 2IRG and 4IRG athletes who performed two training sessions per week for 4 weeks. (3) Results: The findings indicated that there was no improvement in the respiratory function among the athletes, but there was a significant improvement in aerobic and anaerobic power parameters. However, this improvement was similar in both the 2IRG and 4IRG groups. (4) Conclusions: A 4-week high-intensity interval training program with two RST sessions per week was insufficient to elicit improvements in the respiratory function. However, RST protocols with 2-minute and 4-minute rest intervals provided similar aerobic and anaerobic benefits.

Noise Pollution and its Impacts in Selected Secondary Schools in Ikorodu Division Area of Lagos, South-Western Nigeria

Noise pollution has been a major challenge in schools which is adjudged to affect the health of the students. The noise levels of twelve selected secondary schools in Ikorodu division of Lagos were measured with the aid of a smart sensor sound level meter AS844+. Noise level, humidity and temperature of the schools were measured at three points within the school premises between 9 am – 10 am, 11 am – 12 Noon and 1 pm to 2 pm. The measurements were taken at 1-minute intervals for 1 hour at each period. 120 questionnaires were distributed to students to determine the effects of noise on their well-being. The Results revealed that the Duncan PostHoc mean noise levels of the classrooms of the schools selected for this study were in the range of 57.84±2.74 dBA to 86.68±4.12 dBA, mean humidity ranges from 54.33±6.10 % to 74.72±2.42 % and temperature ranges from 30.45±0.67 0C to 37.29±2.43 0C. This noise level was significantly above the World Health Organization (WHO) recommended classroom and outdoor noise level. Statistical analysis of the responses from the questionnaires distributed shows that headache and stress are the major effects of noise on the respondents with 84.17% and 63.33% agreement respectively. It is recommended that effects and ways of reducing noise in schools should be introduced into the curriculum of secondary schools for optimum academic performance and improved well-being.

Abstract 2306: A deep learning framework for automated segmentation and analysis of 3D breast cancer spheroid invasion dynamics

Abstract Invasion of neighboring tissues is a cardinal feature of malignancy, notably observed in aggressive cancers like breast cancer, where it can lead to significant morbidity. To explore cancer invasion dynamics, the implementation of a three-dimensional (3D) tumor spheroid invasion assay offers a swift approach to mimicking a tumor micro-region or micro-metastasis. Differential Interference Contrast (DIC) time-lapse imaging was chosen for its fluorescence-free and non-destructive advantages in capturing live spheroid movement. However, the subsequent analysis posed challenges, including out-of-focus cells and a voluminous dataset with diverse microscope planes and time points. Our study introduces a segmentation framework featuring in-focus spatial stacking algorithm and novel training architecture that enabling in-depth automated analysis of 3D spheroid invasion behaviors within a microenvironment. Firstly, the MDA-MB-231 breast cancer spheroids were cultured in a microwell dish and then incorporated into a collagen type I matrix for the 3D spheroid invasion assay. Capturing spheroid invasion dynamics involved acquiring 15 z-plane images (spaced at 20 µm intervals) per timepoint for each spheroid using DIC microscopy at 15-minute intervals over a 28-hour period, employing a 20X objective lens. We refined the Focus Stacking framework into Multiple Slices Blurry Stacking (MSB-Stack) by pre-filtering blurred areas before stacking images. This process produced 6390 slices consolidated into 426 labeled stacked images, highlighting Invasive area, Spheroid core, and Invasive cells post-invasion. We then enhanced Mask R-CNN (Region based-Convolutional Neural Networks) with a weak-to-strong augmentation mechanism, leveraging well-segmented samples from MSB-Stack for training on both fully stacked and blurred images. Blurry Consistency Mask R-CNN (BCMask R-CNN) demonstrates overall improvement across feature extractors, outperforming Mask R-CNN. Using Mean average precision (mAP) as the main metric for instance segmentation and detection, our proposed model achieves 73.0% and 65.2% on mAP @[0.5, 0.95], showcasing robustness. Notably, our best results for mAP @0.5 reach 96.6% and 96.7% on both tasks, underscoring the effectiveness of consistency training in mitigating unclear boundary issues during preprocessing. In conclusion, our proposed method, MSB-Stack, effectively addresses the challenge of unclear boundaries in 3D breast cancer cell DIC microscopy datasets. The integration of weak-to-strong consistency training in our model, BCMask R-CNN, not only mitigates biases but also enhances performance across different backbones, setting the stage for future advancements in cell-tracking tasks and broader migratory analyses. Citation Format: Thi Kim Ngan Ngo, Thanh-Huy Nguyen, Fang-Yu Lin, Ting-Yuan Tu. A deep learning framework for automated segmentation and analysis of 3D breast cancer spheroid invasion dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2306.

Abstract 4217: An engineered breast tumor microenvironment model, with single-cell spatial resolution, to assess spatial dynamics of tumor evolution

Abstract Cellular heterogeneity is a prominent feature of the tumor microenvironment, and the organization of these cells has been linked to clinical outcomes such as disease progression and drug resistance. While retrospective analysis of patient tumors has produced a myriad of insights, samples are limited and highly complex making it challenging to validate causality. Here we present an engineered breast tumor microenvironment model, with single cell spatial resolution, to systematically identify which cell phenotypes and their spatial arrangements may be driving ductal carcinoma in situ disease progression. A microfluidic dispenser (Biopixlar, Fluicell) was optimized to enable the spatial patterning of single cells to replicate native histology. To demonstrate the high spatial precision of our method, we first replicated an annotated 2D section of a breast tumor biopsy region of interest (ROI) using MCF10A, MDA-MB-231, primary mammary fibroblasts, THP-1 derived macrophages, and primary mesenchymal stem cells. The XY coordinate of each cell was identified to create a print map matching the original biopsy. Deposited cells adhered to their intended target with an average print fidelity of 2.4 µm. Next, the stromal compartment of the ROI was altered to include phenotypes associated with tumor promoting microenvironments (i.e. cancer associated fibroblasts, M2 macrophages) and the core of the MCF10A ring filled with MDA-MB-231. Each deposited cell contained a fluorescent tag corresponding with its cell type to facilitate real time spatial tracking and annotation. The tumor microenvironments were live cell imaged for 24 hours at 5 minute intervals. MCF10A cells spread to form their junctions and create a cohesive ring, MDA-MB-231 cells proliferated and rapidly moved around within the confined space, and the cells of the stromal compartment spread to create a dense tissue like structure. The microenvironments were cultured for an additional 2-6 days, fixed, and stained for markers of proliferation, phenotype shifts, cell-cell junctions, and morphology. Unlocking the relative contributions of the specific cell types in ductal carcinoma in situ tumor microenvironment to the overall evolution of invasive breast cancer may hold the answer for many questions in early detection, treatment, and cancer avatar models. Citation Format: Haylie R. Helms, Kody A. Oyama, Alexander E. Davies, Ellen M. Langer, Luiz E. Bertassoni. An engineered breast tumor microenvironment model, with single-cell spatial resolution, to assess spatial dynamics of tumor evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4217.

Clinical assessment of recovery from neuromuscular blockers, is it a safe practise - An observational study

The incidence of residual neuromuscular paralysis (RNMB) with Train of four ratio&amp;#60;0.9 remains as high as 16% in PACU even after administration of reversal when neuromuscular monitoring is not done. Reversal with standard dose of neostigmine and extubation are done based on the clinical signs. We observed the clinical signs of neuromuscular recovery and correlated with neuromuscular monitoring to assess the degree of residual blockade in the post-operative period.100 Patients posted for surgery under general anaesthesia with endotracheal tube intubation and controlled ventilation were enrolled for the study after obtaining human ethical approval. Standard anaesthesia technique using morphine, propofol, vecuronium, isoflurane with low flow anaesthesia were administered for all patients. TOF was noted at the end of surgical procedure when patient resumed spontaneous respiration, during extubation, and at 15 minutes interval for one hour in the postoperative period. Hemodynamics were observed including respiratory rate. Overall 32 percent of patients had residual paralysis. 27% at 15 min, 26% at 30min, 6% at 45min and 3% at one hour in the postoperative period showed RNMB. Subgroup analysis showed that at the time administration of reversal 72 patients had (TOFR&amp;#62;0.4) and 28 had (TOFR&amp;#60;.0.4), showed significant difference in improvement in TOFR between 2 subgroups before reversal, immediate extubation and 15 min post extubation (p=0.00,0.001,0.003,) respectively. Clinical findings of neuromuscular reversal is not foolproof for complete recovery and standard dose neostigmine given during shallow block will accentuate the residual neuromuscular paralysis.

Sedative and cardiorespiratory effects of dexmedetomidine alone or combined with acepromazine in healthy cats

The purpose of this study was to assess sedation, emesis and cardiovascular effects of dexmedetomidine alone or combined with acepromazine in healthy cats. Fourteen male cats aged 0.9 ± 0.5 years and weighing 3.7 ± 0.7 kg were randomly assigned to one of two experimental groups: GD, dexmedetomidine 5 µg/kg; and GDA, dexmedetomidine 5 µg/kg with acepromazine 0.03 mg/kg, all intramuscularly. Measurements were recorded at baseline, at 20 minutes and then at 10–minute intervals following sedation and included heart rate (HR), respiratory rate (FR), systolic arterial pressure (SAP), rectal temperature (RT), number of episodes of emesis and sedation score (0–4). Data were compared using ANOVA for repeated measures followed by Šídák and Dunnet test. Sedation scores were compared between groups at T20 using Mann-Whitney test. Significance was considered when P <0.05. At T20, HR was significantly lower in GDA (99 ± 14 beats/min) compared with GD (133 ± 19 beats/min) and SAP was significantly lower in both groups compared with baseline (126 ± 14 vs. 148 ± 26 and 111 ± 13 vs. 144 ± 17 mmHg in GD and GDA, respectively). Duration of sedation was similar between groups, although sedation scores differed significantly at T20, with 1 (0–4) in GD and 4 (4–4) in GDA. More episodes of emesis were recorded in GD compared with GDA. The combination of dexmedetomidine and acepromazine produced more profound sedation with faster onset and lower incidence of emesis compared with dexmedetomidine alone in healthy cats.

A dataset of recorded electricity outages by United States county 2014–2022

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018–2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

Explainable machine learning methods for predicting water treatment plant features under varying weather conditions

Accurately predicting key features in WWTPs is essential for optimizing plant performance and minimizing operational costs. This study assesses the potential of various machine learning models for predicting the inflow to anoxic sludge reactors. Firstly, it conducts a comprehensive evaluation of diverse machine learning models, including k-Nearest Neighbors (kNN), Random Forest (RF), XGBoost, CatBoost, LightGBM, and Decision Tree Regression (DTR), for predicting the flow into the Anoxic section under various weather conditions (dry, rainy, and stormy). Secondly, the study introduces parsimonious models guided by variable importance from the XGBoost algorithm. Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. Data from the COST Benchmark Simulation Model (BSM1) is used to verify the investigated models' effectiveness. Each dataset consists of 14 days of influent data at 15-minute intervals, with 80% of the data used for model training. Results show that ensemble learning methods, particularly CatBoost and XGBoost, demonstrate satisfactory predictive results for Anoxic section flow in the presence of increased variability under rainy and stormy conditions. Notably, the CatBoost and XGBoost models achieve average Mean Absolute Percentage Error values of 1.33% and 1.59%, outperforming the other methods.

Analgesia-nociception index accurately predicts inadequate pectoralis muscle fascia block (PECS) in patients undergoing breast surgery: A prospective observational study

BackgroundPostoperative opioid administration has been largely replaced by regional anesthesia techniques. We aimed to determine whether intraoperative Analgesia-Nociception Index (ANI) can aid in early evaluation of the effectiveness of regional blocks such as the pectoralis muscle fascia block (PECS, pectoserratus and interpectoral plane blocks) and predicting the need for analgesics postoperatively. MethodsThis prospective observational study enrolled 30 women (age: 20–80 years) undergoing unilateral, non-intubated, breast tumor excision alone or in conjunction with sentinel lymph node biopsy. PECS block was performed following sedation. ANI readings were obtained at 1-min intervals, and polar coordinates were assigned to the distance from the nipple (0.5-cm intervals) and o'clock position (15-min intervals) for each reading. Pain scores were assessed using a numeric rating scale from 0 to 10, and analgesics were administered depending on pain score post-operatively. Results8 (27%), 19 (63%), and 3 (10%) patients received morphine, tramadol, and no analgesics, respectively. In total, 954 ANI measurements were obtained. At the proposed cut-off of 50, the sensitivity and specificity of the ANI nadir for need of post-operative opioids were 0.875 and 0.932, respectively. Block effectiveness was most satisfactory in the upper lateral quadrant of the breast with nipple–areolar complex (NAC) sparing effect. Most average ANI measurements for the NAC were <50. No patient experienced postoperative nausea/vomiting, although one reported dizziness. ConclusionsThe intraoperative ANI nadir <50 was strongly correlated with need for postoperative opioids. The ANI may aid in objectively evaluating the effectiveness of pectoralis muscle fascial blocks and predicting postoperative need for analgesics.

Examining the Effects of Strength Training with Load Progression on Sleep Parameters and Mental Health in College Students

Abstract Objective To compare the effects of strength training with load progression after 4 weeks on sleep parameters and mental health in college students. Methods A total of 17 university students (11 women, 6 men), ranging from 18 to 21 years old, were randomized into a strength training group (STG) and a control group (CG). The Pittsburgh sleep quality index (PSQI), insomnia severity questionnaire, hospital anxiety and depression (HAD) scale, profile of mood states (POMS), and chronotype were used to evaluate the main outcomes. Training consisted of 60 minute·d−1 (2 times/week, for 4-weeks), with 3 sets of 10 to 12 repetitions, and a 1-minute rest interval between sets and exercises. Baseline and postintervention differences were analyzed using generalized estimating equations (GEE). Results After 4 weeks of ST, a significant time effect on the chronotype (β: 1.33; p &lt; 0.05) was observed in the STG. Additionally, there was a significant time and group effect in the reduction of tension (β: 5.00; p &lt; 0.05), depression (β: 15.41; p &lt; 0.05), anger (β: 8.00; p &lt; 0.05), and confusion (β: 6.50; p &lt; 0.05). For fatigue (β: 2.66; p &lt; 0.05), there was a significant time effect difference in its reduction. Vigor was meaningfully increased in the STG group (β: −1.75; p &lt; 0.05). Furthermore, a significant positive relationship was observed between sleep quality and anxiety (r = 0.54; p = 0.03). Finally, insomnia was positively related with an increase in confusion (r = 0.70; p = 0.04) and anxiety (r = 0.52; p = 0.04), as well as with a decrease in vigor (r = −0.71; p = 0.03). Discussion Short-term strength training for 4 weeks was effective for improving mental health, helping achieve characteristics of a positive mood profile, that is, low values for negative factors and a high value for the positive factor.