Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell Capacity Dispersion Analysis Based Battery Pack Design

Four different batches of 18650 lithium-ion cells have been measured to obtain initial capacity. The obtained measurements are analyzed to evaluate off the shelf capacity dispersion and other statistical parameters which are used to estimate the likelihood of a good battery (similar capacities for all cells) if cells are selected at random. Additionally, capacity dispersion results are discussed to select appropriate cell balancing methodology.
Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model

Braindrop is the first neuromorphic system designed to be programmed at a high level of abstraction. Previous neuromorphic systems were programmed at the neurosynaptic level and required expert knowledge of the hardware to use. In stark contrast, Braindrop’s computations are specified as coupled nonlinear dynamical systems and synthesized to the hardware by an automated procedure. This procedure not only leverages Braindrop’s fabric of subthreshold analog circuits as dynamic computational primitives but also compensates for their mismatched and temperature-sensitive responses at the network level. Thus, a clean abstraction is presented to the user. Fabricated in a 28-nm FDSOI process, Braindrop integrates 4096 neurons in $0.65~\text {mm}^{2}$ . Two innovations—sparse encoding through analog spatial convolution and weighted spike-rate summation though digital accumulative thinning—cut digital traffic drastically, reducing the energy Braindrop consumes per equivalent synaptic operation to 381 fJ for typical network configurations.
Urinary Tract Infections in Older Adults: Current Issues and New Therapeutic Options

Urinary tract infections (UTIs) are the most common infectious problem among older adults both in the community and institutional settings. With the expected increase in this population, UTI-related costs—both human and financial—will rise in a parallel fashion. The diagnosis of symptomatic UTI among older adults is complicated by the high prevalence of asymptomatic bacteriuria, which does not require any treatment, and the difficulty in interpreting the signs and symptoms of UTI in a population in which significant comorbidities can undermine the communication between the patient and the medical team. Another important issue is the constant increase in antimicrobial resistance, especially in long-term care facilities, where antimicrobial use is greater than in the community. Newer agents are now available for the treatment of UTI among older adults, targeting both the usual and the multiresistant uropathogens. Rational use of antimicrobials in the treatment of UTI in the older adult is important to both provide appropriate care and control the spread of resistant organisms in this population.
A Network Intrusion Detection Based on Improved Nonlinear Fuzzy Robust PCA

It is acknowledged in security field that intrusion detection systems are a powerful way to detect intrusions in a computer network. Nevertheless, the network traffic used to construct an IDS is huge with useless and unnecessary information. To address this issue, we should retain just the relevant information using a feature extraction method. The most popular technique used in detection intrusions area is Principal Component Analysis (PCA). However, PCA is sensitive to noise and outliers and it is limited to linear principal components. In this paper, we have proposed a new variant of the Nonlinear Fuzzy Robust PCA (NFRPCA) using the two well-known datasets KDDcup99 and NSL-KDD. Experimental results demonstrated that the new NFRPCA gives a promising performance in comparison to NFRPCA and PCA.
Dynamic Fuzzy Asset Management for Worst Scenarios with Average Value-at-Risks

A dynamic portfolio allocation is discussed in asset management with fuzziness. By perception-based extension for fuzzy random variables, a dynamic portfolio model for average value-at-risks of fuzzy random variables is introduced. By dynamic programming and mathematical programming, this paper derives analytical solutions of the optimization problem for dynamic worst scenarios. A few numerical examples are given to discuss the results. We find the average value-at-risk is a more reasonable criterion than value-at-risk.
A Review of Activity Time Distributions in Risk Analysis

Project Evaluation and Review Technique (PERT) alongside recent modifications is a popular and useful tool in project risk analysis. Over the past seven decades, there have been some modifications in PERT owing to the shift from beta distributed activity times to other activity time distributions. This paper presents a review of activity time distributions in risk analysis as found in literature up to date.
A Mixed Integer Programming Model for Evaluating the Hidden Probabilities of  $N$ - ${k}$  Line Contingencies in Smart Grids

In contingency analysis of large-scale power systems, the high-order contingencies involving a relatively large number of component failures are traditionally assumed to have a very low occurrence probability. However, this may underestimate the probability of some <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> contingencies and thus pose risk to the reliable system operations considering that those contingencies would cause severe consequences. In this paper, we investigate the problem of hidden <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> contingencies and cast the problem of evaluating the hidden probability of an <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> contingency as a mixed integer linear programming problem. The simulation results on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed model.
Series Arc Fault Detection in DC Microgrid Using Hybrid Detection Method

DC microgrid which has more natural interface with renewable energy such solar energy harnessing, and is relatively easier to operate compared with AC microgrid is gaining more and more interest. In this paper, the control methods for PV dominated DC microgrid has been reviewed and testified. In addition, among all the faults, DC arc fault can cause severe damage to PV panel, so the method to detect DC series fault is necessary. This paper proposes a detection method according to DC series fault frequency feature and current feature. In this paper, All-Phase FFT(APFFT)has been used to analyze the frequency response after the fault occurs. A window by window detection method is used to detect the current change when the arc fault occurs. This proposed method has been proved numerically to be effective.
Semi-supervised Classification of Land Cover in Multi-spectral Images Using Spectral Slopes

We propose a spectral-slope based technique to classify land-cover using spectral angle mapper (SAM) classifier, which is a semi-supervised process of labeling the land surface. Initial reference samples for SAM are obtained from spectral-slopes based rules, which are tailored to classify multispectral images obtained by Indian Space Research Organization‘s (ISRO) Linear Imaging Self-Scanning Sensor 3 (LISS-III) and Advanced Wide Field Sensor (AWiFS). All the pixels in the are classified by using these reference samples, which are subsequently used in a SAM algorithm. The rules for selecting the reference samples from the multi-spectral imageries are defined by using the properties of spectral-profiles. The land-cover is classified into five broad classes, namely, water, grass land, built-up, vegetation, and bare land. The classification results are validated using very high resolution (VHR) satellite images.
Immediate Velocity Prediction of a Mixed-Traffic Flow in Urban Road Networks with Spatial-Temporal Correlation Analysis

Enhancing the precision of short-term transportation flow forecasting is vital for traffic engineering applications. By selecting appropriate characteristics from collected data to strengthen the prediction model, the performance and the exactness of predicted velocity would be improved considerably. From the observation of speed tracking figures in a complex urban road network, the velocity at a specific road segment is intimately affected by immediate past traffic conditions of its adjacent links. In this paper, we evaluate various hypotheses on multiple datasets to determine the impact of the spatial besides temporal aspect on the concluding prediction. A recurrent neural networks variation - Long Short-Term Memory - is experimented with thoroughly tuning on the hyper-parameters. The result shows that the more spatial knowledge we add, the more precise the predicting velocity is, especially on crowded intersections. Moreover, the traffic speed does not correlate with extensive historical data and the prediction accuracy is better with the aggregated velocity of each road segment.
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.
Force Transmission Interfaces for Pressure Fluctuation Energy Harvesters

Wireless sensor nodes in state of the art fluid power systems used in monitoring and maintenance prediction demand long lasting power sources that do not rely on batteries. Energy harvesting is a promising technology that can provide the required energy to power wireless sensors. Pressure fluctuation energy harvesters can be employed in conventional hydraulic systems to convert the acoustic pressure fluctuation to electrical power. Present studies have explored the overall efficiency of these devices while experimentally describing losses in piezoelectric and circuit interfaces, nevertheless there is no study on the fluid to mechanical force transmission efficiency. In this paper we investigate the pressure to force transmission rate of two types of fluid to mechanical interfaces: a flat metal plate and a conventional hydraulic piston. The interfaces are investigated in conditions similar to those found in conventional hydraulic systems. The study shows that flat plate exhibit good force transmission for low pressure applications with a constant rate across frequencies, while exhibiting a decrease in force transmission at higher pressures. On the other hand the piston exhibit a more robust pressure design, with a constant force transmission rate at all pressures but with a dampening of force at higher frequencies. It is shown that small differences in force transmission ratios can have a considerable impact on the power generation.
Return Freight Insurance: Implications for Online Platforms, Third-party Retailers and Consumers

In this paper, we investigate the effect of return freight insurance (RFI) in an online platform setting where two competing third-party retailers provide RFI to consumers. First, we find that RFI generally increases the price level on the platform. Second, we find that when retailers have same base return rates, retailers’ profits are not changed while the platform’s profit is increased. Whereas, when retailers have different base return rates, RFI only benefits the retailer with high base return rate and harms the retailer with low base return rate, and RFI benefits the platform if and only if the product differentiation degree is relatively large. Third, we find RFI increases customer surplus if RFI’s return hassle cost reduction (HCR) and/or willingness-to-pay enhancement (WTPE) effect is relatively strong. Moreover, we find RFI increases social welfare if RFI’s HCR and/or WTPE effect is relatively strong and the product differentiation degree is relatively large. Such findings offer guidelines for online platforms and third-party retailers to design appropriate RFI provision strategies.
Shapes Similarity and Feature Reconstruction Comparison Based Active Contour Model

The medical Internet of things is the foundation of smart medical. Medical image is the main resource for transmission on the medical Internet of things. Ultrasound image, as a primary medical image, is widely used in computer-aided therapy. The segmentation of lesion region in ultrasound image sequences plays a crucial role in computer-aided therapy. Active contour models are widely used in ultrasound image segmentation to extract the lesion boundary through the low level appearance cues of lesion region. However, due to diseases and imaging artifacts, the low level appearance cues might cause weak or misleading features which corrupts the performance of active contour. In this situation, the shape prior becomes a powerful tool to aid active contour to resist the interference with misleading features. However, the various ways to model the prior of shapes are usually learnt from a large set of annotated data, which is not always feasible in practice. It is doubted that the existing shapes in the training set will be sufficient to model the new instance in the testing image. In this paper, a novel active contour based on shape similarity and feature reconstruction comparison is proposed to segmenting ultrasonic image sequence. In our works, the similarity of object shapes in the image sequence is modeled as a shape prior in a active contour model, which can be interpreted as an unsupervised approach of shape prior modeling without a large number of annotated data. Furthermore, a novel sparse representation based object boundary searching strategy, named feature reconstruction comparison, is proposed by exploiting both the low level appearance cues comparison of the object and background to reduce the error of searching, which is also used to resist the defects of ultrasound image. In order to verify the performance of our method, the clinical image sequences were used as the training and test set to validate our method. The proposed method was compared with three well-known methods in the same test set. The results demonstrates that the proposed method can consistently improve the performance of active contour models and increase the robustness against image defects consequently, it improves the efficiency and effect of the computer assisted therapy
Global Initiative for Chronic Obstructive Lung Disease (GOLD) Revisions 2001-2017: Historical and Critical Perspective

In 2001, the first Global Initiative for Chronic Obstructive Lung Disease (GOLD) document was published with the goal to improve the diagnosis, prevention and treatment of chronic obstructive pulmonary disease (COPD). Since then, three major revisions (GOLD 2007, 2011 and 2017) have been released. In this review, we describe the basic principles of the first document and how it has evolved over time. Initially, assessment was based exclusively on the level of airflow limitation. Nowadays, airflow limitation, respiratory symptom load and exacerbation history are taken into account. Bronchodilators have always been first-line. However, the indication for inhaled corticosteroids (ICS) has evolved over time. Non-pharmacological therapies are now considered to be of equal importance. In the latest version, interventional methods, palliative care, and end-of-life strategies have been added. The prevention and adequate treatment of exacerbations are highlighted and an expanded discussion of comorbidities has been added. (BRN Rev. 2017;3:151-65) Corresponding author: Claus F. Vogelmeier, Claus.Vogelmeier@med.uni-marburg.de
Defending Against Adversarial Samples Without Security through Obscurity

It has been recently shown that deep neural networks (DNNs) are susceptible to a particular type of attack that exploits a fundamental flaw in their design. This attack consists of generating particular synthetic examples referred to as adversarial samples. These samples are constructed by slightly manipulating real data-points that change "fool" the original DNN model, forcing it to misclassify previously correctly classified samples with high confidence. Many believe addressing this flaw is essential for DNNs to be used in critical applications such as cyber security. Previous work has shown that learning algorithms that enhance the robustness of DNN models all use the tactic of "security through obscurity". This means that security can be guaranteed only if one can obscure the learning algorithms from adversaries. Once the learning technique is disclosed, DNNs protected by these defense mechanisms are still susceptible to adversarial samples. In this work, we investigate by examining how previous research dealt with this and propose a generic approach to enhance a DNN's resistance to adversarial samples. More specifically, our approach integrates a data transformation module with a DNN, making it robust even if we reveal the underlying learning algorithm. To demonstrate the generality of our proposed approach and its potential for handling cyber security applications, we evaluate our method and several other existing solutions on datasets publicly available, such as a large scale malware dataset and MNIST and IMDB datasets. Our results indicate that our approach typically provides superior classification performance and robustness to attacks compared with state-of-art solutions.
Experimental Verification of a Passively Cooled Large Air-Gap 6/8-Flux-Switching Permanent Magnet Machine Including Manufacturing

The work discusses and finally verifies the working principle of a passive stator cooling concept employing conventional heat pipes. For this purpose a test-setup based on a small power medium speed 6/8-flux-switching permanent magnet machine with an extraordinarily large air-gap of δ= 4 mm is manufactured, commissioned and measurements are recorded proving the concept of both: the electromagnetic machine design and particularly the passive stator cooling concept. The elegance of the presented passive cooling principle especially lies in easy adaptability on an existing electrical machine which is already in service, its simple structure, minor requirements of additional cooling equipment and its cooling capabilities. However, some limitation in the maximum dissipatable power and manufacturing are experienced as well.
An Ellipse Fitted Training-Less Model for Pedestrian Detection

The problem of pedestrian detection has gained much popularity in the computer vision community in recent times. We have noted that the existing solutions to this problem are mostly supervised in nature. However, it is difficult to guarantee availability of labelled training data in all situations. In this paper, we propose a training-less solution of pedestrian detection. Some of the additional challenges for pedestrian detection are proper handling of viewpoint dependencies, background clutter, illumination variation and occlusion. We design an ellipse fitting model, as a part of our training-less solution, for accurate pedestrian detection. In this model, we fit an ellipse to each competing bounding box (proposal). An area and entropy based quality factor is introduced for every such (fitted) ellipse to discriminate among the proposals. We filter out proposals with low quality factors. Performance comparisons with some well-known supervised pedestrian detection approaches on publicly available PETS2009 dataset demonstrate that our solution is highly promising.
GINA: Group Gender Identification Using Privacy-Sensitive Audio Data

Group gender is essential in understanding social interaction and group dynamics. With the increasing privacy concerns of studying face-to-face communication in natural settings, many participants are not open to raw audio recording. Existing voice-based gender identification methods rely on acoustic characteristics caused by physiological differences and phonetic differences. However, these methods might become ineffective with privacy-sensitive audio for two main reasons. First, compared to raw audio, privacy-sensitive audio contains significantly fewer acoustic features. Moreover, natural settings generate various uncertainties in the audio data. In this paper, we make the first attempt to identify group gender using privacy-sensitive audio. Instead of extracting acoustic features from privacy-sensitive audio, we focus on conversational features including turn-taking behaviors and interruption patterns. However, conversational behaviors are unstable in gender identification as human behaviors are affected by many factors like emotion and environment. We utilize ensemble feature selection and a two-stage classification to improve the effectiveness and robustness of our approach. Ensemble feature selection could reduce the risk of choosing an unstable subset of features by aggregating the outputs of multiple feature selectors. In the first stage, we infer the gender composition (mixed-gender or same-gender) of a group which is used as an additional input feature for identifying group gender in the second stage. The estimated gender composition significantly improves the performance as it could partially account for the dynamics in conversational behaviors. According to the experimental evaluation of 100 people in 273 meetings, the proposed method outperforms baseline approaches and achieves an F1-score of 0.77 using linear SVM.
Perceived Barriers and Drivers of Health Information Systems Adoption by Public Hospitals in Alhasa

A health information system or an electronic medical record system (HIS/EMR) is a centralized electronic record system that stores all patient health information and support the main functions. The adoption rates of such systems among hospitals in Saudi Arabia in general are still low. In Alahsa city, still most hospitals use paper-based patient records. This city is one of the largest cities in Saudi Arabia in terms of population. However, until this time the HIS/EMR have not been adopted in all public hospitals, whereas managing a large amount of patient records is challenging. This research study attempts to identify the barriers to the adoption of HIS/EMR in public hospitals in Alahsa city as perceived by physicians and other workers in these hospitals. The research also identifies factors influencing the acceptance of such systems. This research uses a quantitative research model consisting of constructs related to health information systems barriers and acceptance and impact. The model is validated by cross sectional survey questionnaire sample data collected from two public hospitals in Alahsa city. The study found that the technical barriers are perceived as main barriers to adopt HIS/EMR. The perceived acceptance driving factors of such system are performance expectancy and effort expectancy and user attitude.