Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation

With the widespread adoption of smart meters in buildings, an unprecedented amount of high-resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods for the segmentation of customers based on their load profiles, which help utilities and energy providers to promote customized/personalized targeting for energy programs. Existing energy consumption segmentation techniques use assumptions that could result in reduced quality of clusters in representing their members. Therefore, in this paper, we investigated a two-stage clustering method for capturing more representative load shape temporal patterns and peak demands through a cluster merging approach. In the first stage, load shapes are clustered (using classical clustering algorithms such as self-organizing map) by allowing a large number of clusters to accurately capture variations in energy use patterns, and cluster centroids are extracted by accounting for limited shape misalignment within the range of DR timeframes - i.e., ~2 hours. In the second stage, clusters with similar centroids and power magnitude ranges are merged by using Complexity-invariant Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the efficacy of the framework, compared to baseline methods, and to discuss the impact on energy management. The proposed investigated merging-based clustering resulted in an increase in correlation (between cluster centroids and the corresponding members) by 8.2%, 8.9%, and 2.6% for datasets 1 to 3, respectively.
A Facile Strategy for the Ion Current and Fluorescence Dual-Lock in Detection: Naphthalic Anhydride Azide (NAA)-Modified Biomimetic Nanochannel Sensor towards H2S

Inspired by biological channels, the electric signal-based artificial nanochannel system exhibits high sensitivity in various analyses. However, ion current may be affected by many other factors, leading to false-positive signals. For reliable detection, in this work, we apply a facile strategy to combine both current signal and fluorescence. Fluorescent probes were conjugated to the nanochannel surface by covalent bonds. By utilizing the specific reduction of azide groups in the probe to amino groups by H2S, a synchronizing change in fluorescence and nanochannel surface charge was established. As a result, both transmembrane ion current and fluorescence intensity showed significant changes. The photoelectric double-checked locking from temporal and spatial variation validly confirmed the response process and protected detection accuracy. The work may provide new ideas for the development of more sophisticated current and fluorescence dual-index nanochannel systems.
Development of Portable E-Nose System for Fast Diagnosis of Whitefly Infestation in Tomato Plant in Greenhouse

An electronic nose (E-nose) system equipped with a gas sensor array and real-time control panel was developed for a fast diagnosis of whitefly infestation in tomato plants. Profile changes of volatile organic compounds (VOCs) released from tomato plants under different treatments (i.e., whitefly infestation, mechanical damage, and no treatment) were successfully determined by the developed E-nose system. A rapid sensor response with high sensitivity towards whitefly-infested tomato plants was observed in the E-nose system. Results of principal component analysis (PCA) and hierarchical clustering analysis (HCA) indicated that the E-nose system was able to provide accurate distinguishment between whitefly-infested plants and healthy plants, with the first three principal components (PCs) accounting for 87.4% of the classification. To reveal the mechanism of whitefly infestation in tomato plants, VOC profiles of whitefly-infested plants and mechanically damaged plants were investigated by using the E-nose system and GC-MS. VOCs of 2-nonanol, oxime-, methoxy-phenyl, and n-hexadecanoic acid were only detected in whitefly-infested plants, while compounds of dodecane and 4,6-dimethyl were only found in mechanically damaged plant samples. Those unique VOC profiles of different tomato plant groups could be considered as bio-markers for diagnosing different damages. Moreover, the E-nose system was demonstrated to have the capability to differentiate whitefly-infested plants and mechanically damaged plants. The relationship between sensor performance and VOC profiles confirmed that the developed E-nose system could be used as a fast and smart device to detect whitefly infestation in greenhouse cultivation.
Semantic Excel: An Introduction to a User-Friendly Online Software Application for Statistical Analyses of Text Data

Semantic Excel (www.semanticexcel.com) is an online software application with a simple, yet powerful interface enabling users to perform statistical analyses on texts. The purpose of this software is to facilitate statistical testing based on words, rather than numbers. The software comes with semantic representations, or an ordered set of numbers describing the semantic similarity between words/texts that are generated from Latent Semantic Analysis. These semantic representations are based on large datasets from Google N-grams for a dozen of the most commonly used languages in the world. This small-by-big data approach enables users to conduct analyses of small data that is enhanced by semantic knowledge from big data. First, we describe the theoretical foundation of these representations. Then we show the practical steps involved in carrying out statistical calculation using these semantic representations in Semantic Excel. This includes calculation of semantic similarity scores (i.e., computing a score describing the semantic similarity between two words/texts), semantic t-tests (i.e., statistically test whether two sets of words/texts differ in meaning), semantic-numeric correlations (i.e., statistically examine the relationship between words/texts and a numeric variable) and semantic predictions (i.e., using statistically trained models to predict numerical values from words/texts).
Bayesian solvency analysis with autocorrelated observations

Most financial institutions are required to comply with a minimum capital rule in order to face their obligations during a given period of time. Due to the random nature of the financial flows involved, the problem of assessing the amount of capital required must be analysed within a stochastic framework and the solution can be reduced to the estimation of a selected quantile. Given the financial impact of a specific capital requirement, a proper and careful choice of the underlying model is of great relevance. Here, we address the problem for insurance companies by proposing an autocorrelated model to describe the relative severity after a suitable transformation and compare the results with those of a model, which assumes independence among observations. We undertake a full Bayesian analysis and derive the reference priors for the models. Results are illustrated with a real data set from the Mexican insurance industry. Copyright © 2006 John Wiley & Sons, Ltd.
DFT / TDDFT insights into excited state intra-molecular hydrogen atom transfer mechanism in Liqcoumarin: an EFP1 study

The theoretical calculations were carried out on Liqcoumarin (LC) and its water complex LC+(H20)4-[LCH] at the ground (S0) / first excited states (S1) by employing density functional theory (DFT) / state specific time-dependent density functional theory (SS-TDDFT). In LC and LCH, there is an intra-molecular hydrogen bond between hydroxyl group and acetyl group along with four inter-molecular hydrogen bonds in the hydrated molecule. The computational studies of molecular structural parameters, electrostatic potential, NBO analysis, molecular orbital’s, and UV-Vis spectra of both molecules under polar solvents were explored by B3LYP / 6–31G (d,p) / PCM / EFP1 method. The intra-molecular hydrogen atom transpires between hydroxyl to acetyl group in both pure andhydrated molecules confirm the ICT process, and it affirms through potential energy surface (PES) scans.
Incidence and Risk Factors of COVID-19-Associated Pulmonary Aspergillosis in Intensive Care Unit—A Monocentric Retrospective Observational Study

Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is an increasingly recognized complication of COVID-19 and is associated with significant over-mortality. We performed a retrospective monocentric study in patients admitted to the intensive care unit (ICU) for respiratory insufficiency due to COVID-19 from March to December 2020, in order to evaluate the incidence of CAPA and the associated risk factors. We also analysed the diagnostic approach used in our medical centre for CAPA diagnosis. We defined CAPA using recently proposed consensus definitions based on clinical, radiological and microbiological criteria. Probable cases of CAPA occurred in 9 out of 141 patients included in the analysis (6.4%). All cases were diagnosed during the second wave of the pandemic. We observed a significantly higher realization rate of bronchoalveolar lavage (BAL) (51.1% vs. 28.6%, p = 0.01) and Aspergillus testing (through galactomannan, culture, PCR) on BAL samples during the second wave (p < 0.0001). The testing for Aspergillus in patients meeting the clinical and radiological criteria of CAPA increased between the two waves (p < 0.0001). In conclusion, we reported a low but likely underestimated incidence of CAPA in our population. A greater awareness and more systematic testing for Aspergillus are necessary to assess the real incidence and characteristics of CAPA.
A Chisel Generator of Parameterizable and Runtime Reconfigurable Linear Insertion Streaming Sorters

Fully streaming linear sorters with their rather simple and low-cost hardware architecture are widely used as the fundamental building blocks in many digital signal processing applications that require sorting operations and continuous data streaming interfaces. Therefore, a parameterizable and runtime reconfigurable generator of fully streaming sorters based on an insertion sort algorithm are captured inside Chisel hardware design language in order to enable fast and efficient agile design space exploration. A broad range of parameter settings are supported with the proposed generator, such as sorting data type and data width in number of bits, discarding element position inside the sorting array, compile and runtime reconfigurable sorter depth, sorting direction, control and status register access bus, etc. Various generator instances have been mapped onto a commercially available FPGA platform, tested and mutually compared. It is proven that such a generator can yield resource and performance competitive sorter designs.
External Counterpulsation Improves Angiogenesis by Preserving Vascular Endothelial Growth Factor-A and Vascular Endothelial Growth Factor Receptor-2 but Not Regulating MicroRNA-92a Expression in Patients With Refractory Angina

Objective: External counterpulsation (ECP) provides long-term benefits of improved anginal frequency and exercise tolerance in patients with refractory angina (RA). This is postulated as a result of improved angiogenesis and endothelial function through an increase in shear stress. Angiogenesis is mainly represented by vascular endothelial growth factor-A (VEGF-A) and its receptor, vascular endothelial growth factor receptor-2 (VEGFR-2). The microRNA-92a (miR-92a) is a flow-sensitive miRNA that regulates atherosclerosis and angiogenesis in response to shear stress. Thus, ECP beneficial effect might be achieved through interaction between VEGF-A, VEGFR-2, and miR-92a. This study aims to evaluate the ECP effect on VEGF-A, VEGFR-2, and miR-92a in patients with RA in a sham-controlled manner. Methods: This was a randomized sham-controlled trial, enrolling 50 patients with RA who have coronary artery disease (CAD). Participants were randomized (1:1 ratio) to 35 sessions of either ECP (n = 25) or sham (n = 25), each session lasting for 1 h. Plasma levels of VEGF-A and VEGFR-2 were assayed by the ELISA technique. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to measure miR-92a circulating levels in plasma. Result: External counterpulsation significantly preserved VEGF-A and VEGFR-2 level compared to sham [ΔVEGF-A: 1 (−139 to 160) vs.−136 (−237 to 67) pg/ml, p = 0.026; ΔVEGFR-2: −171(-844 to +1,166) vs. −517(−1,549 to +1,407) pg/ml, p = 0.021, respectively]. Circulating miR-92a increased significantly in ECP [5.1 (4.2–6.4) to 5.9 (4.8–6.4), p < 0.001] and sham [5.2 (4.1–9.4) to 5.6 (4.8–6.3), p = 0.008] post-intervention. The fold changes tended to be higher in ECP group, although was not statistically different from sham [fold changes ECP = 4.6 (0.3–36.5) vs. sham 2.8 (0–15), p = 0.33)]. Conclusion: External counterpulsation improved angiogenesis by preserving VEGF-A and VEGFR-2 levels. Both ECP and sham increased miR-92a significantly, yet the changes were not different between the two groups. (Study registered on www.clinicaltrials.gov, no: NCT03991871, August 8, 2019, and received a grant from the National Health Research and Development of Ministry of Health of Indonesia, No: HK.02.02/I/27/2020).
HD-Map Aided LiDAR-INS Extrinsic Calibration

Sensor calibration is a prerequisite for autonomous driving and is vital for accurate perception, ensuring precise planning and control of the autonomous vehicle. The modern self-driving car considers data inputs from multiple sensors to construct an understanding of its surroundings. These sensors are inherently susceptible to errors arising from measurement uncertainty, and the risk is multiplied under sensor fusion, a critical function for vehicle localization and object detection. Existing works in this field of study largely focus on offline sensor calibration methods where the vehicle is assumed to have undergone the calibration process prior to vehicle operation on the road, typically using a known calibration target such as a checkerboard pattern target. This paper proposes a sensor calibration method for the LiDAR and the inertial navigation system (INS) using high definition (HD) maps as a readily available source of ground truth, giving potential to an online approach to calibration. To match the data between the LiDAR and the INS system, the trajectory from LiDAR odometry based on the normal distributions transform (NDT) and the trajectory from the INS are utilized. The HD map acts as an online calibration target and provides real positional coordinates which can correct for errors inherent to the sensor outputs and enhance the trajectory estimations. Upon the detection of miscalibration, the algorithm finds the optimum transformation matrix that aligns the LiDAR with the INS. Real-world data was used to verify the proposed approach and can be applied to find the correct extrinsic transformation matrix.
The Feature Description of Formal Context Based on the Relationships among Concept Lattices

Three-way concept analysis (3WCA) is extended research of formal concept analysis (FCA) by combining three-way decision. The three-way object oriented concept lattice (OEOL) is one of the important data structures which integrates rough set, concept lattice and three-way decision in 3WCA. In the paper, we investigate the characteristics of formal context based on the isomorphic relationship among the kinds of concept lattices with OEOL. Firstly, II-dual intersectable attributes and II-dual intersectable context are proposed and the relationship between the type I-dual intersectable context(dual intersectable context) and the type II-dual intersectable context are studied. In addition, the relationship among the kinds of concept lattices with OEOL are studied when the formal context is both I-dual intersectable context and II-dual intersectable context. Finally, the inverse problems of the above conclusions are discussed and the following two conclusions are obtained: (1) the formal context is the type II-dual intersectable context, when the object oriented concept lattice and OEOL are isomorphic. (2) In addition, the formal context is the type I-dual intersectable context, when the concept lattice and OEOL are anti-isomorphic.
Glove

Cornstarch replaced tissue-irritating Lycopodium powder and talc, but even cornstarch can impede healing if it gets into tissues (as during surgery). As such, unpowdered gloves are used more often during surgery and other sensitive procedures. Special manufacturing processes are used to compensate for the lack of powder. There are two main types of medical gloves: examination and surgical. Surgical gloves have more precise sizing with a better precision and sensitivity and are made to a higher standard. Examination gloves are available as either sterile or non-sterile, while surgical gloves are generally sterile.
Grit utility in explaining job and contextual performance

The study aims to analyze if grit brings incremental validity in the prediction of job performance criteria (task performance, organizational citizenship behaviors and counterproductive work behaviors), in addition to some classical predictors such as emotional intelligence and some personality factors (conscientiousness and agreeableness). The sample consists of 170 employees from various fields of activity. Regression analysis has shown that grit has no incremental validity to personality and emotional intelligence in predicting any of the work performance criteria (task performance, organizational citizenship behaviors and counterproductive work behaviors). The study therefore brings to the academic literature one more proof for the fact that it is difficult to consider grit an independent, self-constructed concept.
Reasoning about Potential Hidden Traffic Participants by Tracking Occluded Areas

Autonomous vehicles face big challenges guaranteeing provable safety during driving. One of the major problems is the uncertainty arising from the perception of the surrounding environment, especially due to occlusions. Recent approaches to tackle these uncertainties either minimize collision risk probabilistically or assume worst-case vehicles coming out of occluded areas. The former does not provide any safety guarantees, while the latter tends to produce overly conservative driving behavior. Human drivers, however, can reason about possible traffic participants (TPs) in occlusions with the knowledge about the street, more importantly, with the continuous observation on the changes of the field of view when moving forward. In this paper, we present an approach that imitates this human-like intelligence and can reason about all the potential TPs in occlusions. By moving forward and propagating the initial knowledge with new observations, the approach can refine the possible states of the TPs in occlusions instead of always adopting a worst-case assumption. By planning w.r. t. the set-based occupancy prediction from the refined state intervals, the vehicle can drive more efficiently under occlusions while guaranteeing safety under reasonable assumptions. The proposed method is evaluated with numerical experiments showing that all the possible hidden TPs can be covered by our refined state intervals, and with that achieving significantly more driving efficiency under occlusions while being safe.
Aid for Trade and the Real Exchange Rate

ABSTRACT The current analysis contributes to the literature on the real exchange rate effect of development aid by separating the real exchange rate effect of the development aid allocated to the trade sector (Aid for Trade – AfT) from the effect of other aid flows. The empirical findings show that higher AfT flows lead to a depreciation of the real exchange rate in recipient economies, while other development aid flows generate an appreciation of the real exchange rate. The real exchange rate effect of total AfT flows works through trade openness, export product diversification, and inward foreign direct investment stock.
The importance of social supports in education: survey findings from students with disability and their families during COVID-19

Abstract Emergency situations such as pandemics typically widen inequities, and Australian children and young people with disability already face significant inequities in the education system. In this paper we draw on survey data from over 700 respondents exploring education experiences during the COVID-19 pandemic. Many families reported being left behind, finding it difficult to access education remotely, and that this was having a significant impact on wellbeing and mental health. We find that of all support offered by schools, social supports have a stronger association with learner engagement than educational interventions. This finding indicates the importance of social and emotional supports in learning. Points of interest The COVID-19 pandemic has significantly disrupted education for most learners; There is good evidence to suggest this impact is felt more keenly by students with disability, because there is disability discrimination in the Australian education system; This paper reports survey responses from over 700 families of students with disability exploring the impact of COVID-19 on education; Many families report being left behind and that students found it difficult to learn away from the school campus, which for many students and families led to poorer wellbeing and mental health; Our results suggest that supporting students to have social contact with peers made the biggest impact on students with disability feeling engaged with learning during this time. This shows it is important for teachers and schools to pay attention to social as well as educational aspects of school life when thinking about how to educate students with disability.
FWB: Funneling Wider Bandwidth Algorithm for High Performance Data Collection in Wireless Sensor Networks

Many applications in Wireless Sensor Networks (WSNs) require collecting massive data in a coordinated approach. To that end, a many-to-one (convergecast) communication pattern is used in tree-based WSNs. However, traffic near the sink node usually becomes the network bottleneck. In this work, we propose an extension to the 802.15. 4 standard for enabling wider bandwidth channels. Then, we measure the speed of data collection in a tree-based WSN, with radios operating in these wider bandwidth channels. Finally, we propose and implement Funneling Wider Bandwidth (FWB), an algorithm that minimizes schedule length in networks. We prove that the algorithm is optimal in regard to the number of time slots. In our simulations and experiments, we show that FWB achieves a higher average throughput and a smaller number of time slots. This new approach could be adapted for other relevant emerging standards, such as WirelessHART, ISA 100.11a and IEEE 802.15. 4e TSCH.