Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting.
DConv-LSTM-Net: A Novel Architecture for Single- and 12-Lead ECG Anomaly Detection

Electrocardiograms (ECGs) can be considered a viable method for cardiovascular disease (CVD) diagnosis. Recently, machine learning algorithms such as deep neural networks trained on ECG signals have demonstrated the capability to identify CVDs. However, existing models for ECG anomaly detection learn from relatively long (60 s) ECG signals and tend to be heavily parameterized. Thus, they require large time and computational resources during training. To address this, we propose a novel deep learning architecture that exploits dilated convolution layers. Our architecture benefits from a classical ResNet-like formulation, and we introduce a recurrent component to better leverage temporal information in the data, while also benefiting from the dilated convolution operation. Our proposed architecture is capable of learning from single- and 12-lead ECG signals and thus offers a flexible solution for CVD diagnosis. In our experiments, we perform subject-independent ten-fold cross-validations (CVs) and compare our results with two existing benchmark models using the PhysioNet atrial fibrillation (AF) challenge dataset, the China Physiological challenge, the PTB-XL repository from PhysioNet, and the Georgia dataset. For all the four datasets, our model archives state-of-the-art performance, with an upto 8% F1 score gain achieved. Our neural conduction plots demonstrate the effectiveness of having convolution layers with varying dilation factors and the use of recurrent networks to capture rhythmic patterns. Our architecture is explainable and has the ability to learn from short ECG segments. Using neural conductance, we reveal interesting hidden patterns learned by our model, which reflect the medical phenomena/characteristics associated with CVD. Code is publically available here.
Continuous sliding‐mode control for singular systems

Singular systems with matched Lipschitz perturbations and uncertainties are considered in this paper. Since continuous solutions of an impulse‐free singular system require continuous input signals, a two‐step continuous sliding‐mode control strategy to compensate matched Lipschitz perturbations and uncertainties in singular systems is proposed. Our suggested methodology is tested in a singular representation of a DC motor pendulum of relative degree two. The performance of the proposed strategy is assessed by comparing the accuracy, in both cases, with and without considering small noise in the output, obtained through other continuous sliding‐mode control, and reconstruction/compensation of perturbations and uncertainties techniques.
Estimation of Conditional Prevalence From Group Testing Data With Missing Covariates

Abstract We consider estimating the conditional prevalence of a disease from data pooled according to the group testing mechanism. Consistent estimators have been proposed in the literature, but they rely on the data being available for all individuals. In infectious disease studies where group testing is frequently applied, the covariate is often missing for some individuals. There, unless the missing mechanism occurs completely at random, applying the existing techniques to the complete cases without adjusting for missingness does not generally provide consistent estimators, and finding appropriate modifications is challenging. We develop a consistent spline estimator, derive its theoretical properties, and show how to adapt local polynomial and likelihood estimators to the missing data problem. We illustrate the numerical performance of our methods on simulated and real examples. Supplementary materials for this article are available online.
Detecting Toe-Off Events Utilizing a Vision-Based Method

Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.
Response of the Sea Breeze to Urbanization in the Pearl River Delta Region

The Pearl River delta (PRD) region has undergone rapid urbanization since the 1980s, which has had significant effects on the sea-breeze circulation in this region. Because the sea breeze plays an important role in pollutant transportation and convective initiation in the PRD region, it is meaningful to study the effects of urbanization on the sea breeze. In this study, three numerical experiments were conducted from 2 June to 31 August 2010 with land-use data from 1988, 1999, and 2010. For each simulation, characteristics of the sea breeze such as the start time, end time, intensity, height, pumping ability, and inland penetration distance were quantified. By comparing the characteristics of the sea breeze in these simulations, its response to urbanization was quantified. The results show that urbanization enhances the duration, height, and intensity of the sea breeze but blocks its inland penetration. One physical mechanism is proposed to dynamically elucidate the response of the sea breeze to urbanization. Because the urban area in the PRD region is concentrated near the coast, urbanization imposes a positive heating gradient on the coastal region and a negative heating gradient on the region farther inland. The positive heating gradient may intensify the sea breeze, and the negative heating gradient may prevent the sea breeze from propagating farther inland.
Regionalization of runoff formation by aggregation of hydrological response units : a regional comparison

The regionalization approaches developed by the Gottingen Group were based on two contrasting regions: Eschenberg, a low-gradient agricultural area of 2 km2, and Ziegenhagen, a steep (15°) forested area of 15 km2. The variety and spatial distribution of runoff processes in the two regions were effectively derived from the generally available regional characteristics. Findings from these test regions confirmed the validity of the methods used for regionalization, and their effective scale independence.
Quantifying and Validating Soybean Seed Emergence Model as a Function of Temperature

Developing a model for soybean seed emergence offers a tool producers could use for planting date options and in predicting seedling emergence. In this study, temperature effects on soybean seed emergence were quantified, modeled, and validated. The data for seed emergence model development was generated at varying temperatures, 20°C/12°C, 25°C/17°C, 30°C/22°C, 35°C/27°C, and 40°C/32°C, on two soybean cultivars, Asgrow AG5332 and Progeny P 5333 RY. Time for 50% emergence (t50%) was recorded, and seed emergence rate (SER) was estimated as reciprocal to time at each temperature in both the cultivars. No differences were observed between the cultivars in their response to temperature. A quadratic model (QM) best described the relationship between t50% and SGR and temperature (R2 = 0.93). Two sets of experiments were conducted to validate the model. In Experiment 1, 17 time-series planting date studies with the same cultivars were used by utilizing diurnal and seasonal changes in temperature conditions. In the second experiment, sunlit growth chambers with 3 different day/night temperatures, low—20°C/12°C, optimum—30°C/22°C, and high—40°C/32°C, and 64 soybean cultivars belonging MG III, IV, and V, were used. Air temperature and t50 were recorded, and SGR was estimated in all experiments. No differences were recorded among the cultivars for t50% and SGR, but differences were observed among seeding date and temperature experiments. We tested QM and traditionally used Growing Degree Days models against the data collected in validation experiments. Both the model simulations predictions agreed closely with the observed data. Based on model statistics, R2, root mean square errors (RMSE), and comparison of observations and predictions to assess model performance, the QM model performed better than the GDD model for soybean seed emergence under a wide range of cultivars and environmental conditions.
New general decay results for a Moore–Gibson–Thompson equation with memory

In this paper, we consider the Moore–Gibson–Thompson equation with a memory term in the subcritical case, which arises in high-frequency ultrasound applications accounting for thermal flux and molecular relaxation times. For a class of relaxation functions satisfying for H to be increasing and convex function near the origin and to be a nonincreasing function, we establish the optimal explicit and general energy decay result, from which we can recover the earlier exponential rate in the special case.
Multiband bandpass filter with asymmetric dual‐band response based on metacell

The impressive growth in present‐day wireless communication systems demands compact, low‐power, cost‐effective, planar multiband microwave devices to increase the speed of communication and reduce hardware requirements, which subsequently reduce the power requirement of the system. Accordingly, this article aims to design a multiband microstrip bandpass filter with asymmetric dual‐band response based on metacell. The proposed filter is designed for wide and narrow dual‐band responses with fractional bandwidths of 72% and 2.6%, respectively. For the first time in this article, a new application of metacell in the design of dual‐band filters is discussed. A folded asymmetric step‐impedance resonator and a novel metacell structure—Dual loop split ring resonators are used to obtain wideband and dual‐band responses, respectively. The proposed work discusses the design methodology, and analysis of metacell structure based on a quasistatic model and compares simulation results with experimental results. The proposed dual‐band filter presents an insertion loss (S21) of less than 1 dB and input reflection coefficient (S11) greater than 10 dB in both passbands centered at 3.3 and 5.7 GHz. The designed filter is suitable for wireless communication applications—Wireless Access in the Vehicular Environment.
Image Restoration Based on Adaptive Dual-Domain Filtering

Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learning the optimal confidence factors which are adjusted adaptively with Gaussian noise standard deviation. In addition, by generalizing this learning approach to Laplace noise, the learning algorithm of the optimum confidence factors in Laplace denoising is presented. Finally, the proposed ADDF is tactfully plugged into the method frameworks of off-the-shelf image deblurring and single image super-resolution (SISR). The approach, coining the name Plug-ADDF, achieves promising performance. Extensive experiments validate that the proposed ADDF for Gaussian and Laplace noise removals indeed results in visual and quantitative improvements over some existing state-of-the-art methods. Moreover, our Plug-ADDF for image deblurring and SISR also demonstrates superior performance objectively and subjectively.
A Practical Model for Predicting Esophageal Variceal Rebleeding in Patients with Hepatitis B-Associated Cirrhosis

Background Variceal rebleeding is a significant and potentially life-threatening complication of cirrhosis. Unfortunately, currently, there is no reliable method for stratifying high-risk patients. Liver stiffness measurements (LSM) have been shown to have a predictive value in identifying complications associated with portal hypertension, including first-time bleeding. However, there is a lack of evidence to confirm that LSM is reliable in predicting variceal rebleeding. The objective of our study was to evaluate the ability of generating a extreme gradient boosting (XGBoost) algorithm model to improve the prediction of variceal rebleeding. Methods This retrospective analysis examined a cohort of 284 patients with hepatitis B-related cirrhosis. XGBoost models were developed using laboratory data, LSM, and imaging data to predict the risk of rebleeding in the patients. In addition, we compared the XGBoost models with traditional logistic regression (LR) models. We evaluated and compared the two models using the area under the receiver operating characteristic curve (AUROC) and other model performance parameters. Lastly, we validated the models using nomograms and decision curve analysis (DCA). Results During a median follow-up of 66.6 weeks, 72 patients experienced rebleeding, including 21 (7.39%) and 61 (21.48%) patients who rebleed within 6 weeks and 1 year, respectively. In brief, the AUC of the LR models in predicting rebleeding at 6 weeks and 1 year was 0.828 (0.759–0.897) and 0.799 (0.738–0.860), respectively. In contrast, the accuracy of the XGBoost model in predicting rebleeding at 6 weeks and 1 year was 0.985 (0.907–0.731) and 0.931 (0.806–0.935), respectively. LSM and high-density lipoprotein (HDL) levels differed significantly between the rebleeding and nonrebleeding groups, with LSM being a reliable predictor in those models. The XGBoost models outperformed the LR models in predicting rebleeding within 6 weeks and 1 year, as demonstrated by the ROC and DCA curves. Conclusion The XGBoost algorithm model can achieve higher accuracy than the LR model in predicting rebleeding, making it a clinically beneficial tool. This implies that the XGBoost model is better suited for predicting the risk of esophageal variceal rebleeding in patients.
Development of the Power Simulation Tool for Energy Balance Analysis of Nanosatellites

The energy balance in a satellite needs to be designed properly for the satellite to safely operate and carry out successive missions on an orbit. In this study, an analysis program was developed using the MATLAB Ⓡ graphic user interface (GUI) for nanosatellites. This program was used in a simulation to confirm the generated power, consumed power, and battery power in the satellites on the orbit, and its performance was verified with applying different satellite operational modes and units. For data transmission, STK Ⓡ -MATLAB Ⓡ connectivity was used to send the generated power from STK Ⓡ to MATLAB Ⓡ automatically. Moreover, this program is general-purpose; therefore, it can be applied to nanosatellites that have missions or shapes that are different from those of the satellites in this study. This power simulation tool could be used not only to calculate the suitable power budget when developing the power systems, but also to analyze the remaining energy balance in the satellites.
Preliminary clinical testing to inform development of the Critical Care Pain Observation Tool for Families (CPOT-Fam)

ABSTRACT Introduction Many patients in the intensive care unit (ICU) cannot communicate. For these patients, family caregivers (family members/close friends) could assist in pain assessment. We previously adapted the Critical Care Pain Observation Tool (CPOT) for family caregiver use (CPOT-Fam). In this study, we conducted preliminary clinical evaluation of the CPOT-Fam to inform further tool development. Methods For preliminary testing, we collected (1) pain assessments of patients in the ICU from family caregivers (CPOT-Fam) and nurses (CPOT) and determined the degree of agreement (kappa coefficient, κ) and (2) collected openended feedback on the CPOT-Fam from family caregivers. For refinement, we used preliminary testing data to refine the CPOT-Fam with a multidisciplinary working group. Results We assessed agreement between family caregiver and nurse pain scores for 29 patients. Binary agreement (κ) between CPOT-Fam and CPOT item scores (scores ≥2 considered indicative of significant pain) was fair, κ = 0.43 (95% confidence interval [CI] 0.18–0.69). Agreement was highest for the CPOT-Fam items ventilator compliance/vocalization (weighted κ = 0.48, 95% CI 0.15–0.80) and lowest for muscle tension (weighted κ = 0.10, 95% [CI] −0.17 to 0.20). Most participants (n = 19; 69.0%) reported a very positive experience using the CPOT-Fam, describing it as “good” and “easy-to-use/clear/straightforward. ” We iteratively refined the CPOT-Fam over five cycles using the data collected until no further revisions were suggested. Conclusion Our preliminary clinical testing suggests that family involvement in pain assessment in the ICU is well perceived. The CPOT-Fam has been further refined and is now ready for clinical pilot testing to determine its feasibility and acceptability.
A Review on the Roles of Photoacoustic Imaging for Conventional and Novel Clinical Diagnostic Applications

Photoacoustic imaging is a promising medical imaging modality that enables the visualization of molecular functional and morphological information of biological tissues. Its clinical potential has been widely investigated for assessing and diagnosing various diseases. Currently, several research groups are developing photoacoustic imaging systems for translation from the laboratory to the clinic. In particular, the integration of photoacoustic imaging into existing diagnostic ultrasound applications, such as cancer diagnosis, has shown promising results. Additionally, recent research has explored the application of photoacoustic imaging for novel clinical uses. In this review paper, recent trials of photoacoustic imaging in both conventional and novel clinical applications are summarized and evaluated. Additionally, current limitations and future directions of photoacoustic imaging for successful translation into the clinical world are discussed. The aim of this review is to provide a comprehensive overview of the recent advancements in photoacoustic imaging and highlight its potential for clinical diagnosis and treatment. It is hoped that this review will contribute to the development of improved diagnostic and therapeutic approaches for a wide range of diseases using photoacoustic imaging.
Different Responses of Bacteria and Archaea to Environmental Variables in Brines of the Mahai Potash Mine, Qinghai-Tibet Plateau

Salt mines feature both autochthonous and allochthonous microbial communities introduced by industrialization. It is important to generate the information on the diversity of the microbial communities present in the salt mines and how they are shaped by the environment representing ecological diversification. Brine from Mahai potash mine (Qianghai, China), an extreme hypersaline environment, is used to produce potash salts for hundreds of millions of people. However, halophiles preserved in this niche during deposition are still unknown. In this study, using high-throughput 16S rRNA gene amplicon sequencing and estimation of physicochemical variables, we examined brine samples collected from locations with the gradient of industrial activity intensity and discrete hydrochemical compositions in the Mahai potash mine. Our findings revealed a highly diverse bacterial community, mainly composed of Pseudomonadota in the hypersaline brines from the industrial area, whereas in the natural brine collected from the upstream Mahai salt lake, most of the 16S rRNA gene reads were assigned to Bacteroidota. Halobacteria and halophilic methanogens dominated archaeal populations. Furthermore, we discovered that in the Mahai potash mining area, bacterial communities tended to respond to anthropogenic influences. In contrast, archaeal diversity and compositions were primarily shaped by the chemical properties of the hypersaline brines. Conspicuously, distinct methanogenic communities were discovered in sets of samples with varying ionic compositions, indicating their strong sensitivity to the brine hydrochemical alterations. Our findings provide the first taxonomic snapshot of microbial communities from the Mahai potash mine and reveal the different responses of bacteria and archaea to environmental variations in this high-altitude aquatic ecosystem.
An ecostylistic analysis of selected extracts from Michael Punke’s novel The Revenant

Abstract This article presents an ecostylistic analysis of selected extracts from Michael Punke’s novel The Revenant (2002). The aims of the study are achieved in terms of an eclectic methodological framework. The study aims at revealing the power hierarchy created in the selected texts between human and non-human participants. Also, it aims to show how the elements of nature affect the survival of humans in the fictional context by identifying the ecostylistic features in the literary text. A close ecostylistic reading of six selected extracts from the novel using Systemic Functional Grammar (SFG) tools (Halliday & Matthiessen, 2014[1985]) and Zurru’s (2017) approach is followed. Moreover, the study presents a qualitative investigation of stylistic elements such as foregrounded features and deviated language patterns. Thus, conducting an ecostylistic analysis of the chosen passages, and, demonstrating how the author portrays the environment with all its elements as an effective participant helps to appreciate nature. The findings show that nature acts as the main character’s savior and a threat to his life; it also demonstrates how nature works as a powerful active participant in portraying the events of the novel.