Miracle-dz/UniFAR-FTUs huggingface dataset:
Networked Microgrids for Reliable Load Sharing in Remote locations of Armed Force

Abstract— Forward operating bases of the Armed Forces are located in remote areas where grid connection is normally not available and if available are vulnerable to outages due to vagaries of weather or action by adversary. Microgrids are generally more efficient and may provide electric power storage for emergency supply of electricity to mission critical equipment like surveillance systems, sensors, communication, command and control systems of such forward bases. Several small independent microgrids can also be interconnected together to satisfy the energy requirements of the remote military areas. Networking of several self-supervised microgrids is being developed with the aim to enhance efficiency, reliability and resiliency as well as the steadiness of power systems. In this work, an interconnection of microgrids at three remote bases of the armed forces is presented and simulated. The proposed power co-ordination strategy is designed in such a way that during peace time operation it tries to minimize the overall requirement of fossil fuel for generators supplying the critical loads and in case of outages, it can tackle the emergency situation by automatically routing the power to critical loads from other operational bases. The entire system has been simulated and verified using MATLAB 2018. Keywords—Power co-ordination, networked microgrid, PV system, diesel generator, battery system, critical loads, military remote areas.
Compact and Flexible FPGA Implementation of Ed25519 and X25519

This article describes a field-programmable gate array (FPGA) cryptographic architecture, which combines the elliptic curve--based Ed25519 digital signature algorithm and the X25519 key establishment scheme in a single module. Cryptographically, these are high-security elliptic curve cryptography algorithms with short key sizes and impressive execution times in software. Our goal is to provide a lightweight FPGA module that enables them on resource-constrained devices, specifically for Internet of Things (IoT) applications. In addition, we aim at extensibility with customisable countermeasures against timing and differential power analysis side-channel attacks and fault-injection attacks. For the former, we offer a choice between time-optimised versus constant-time execution, with or without Z-coordinate randomisation and base-point blinding; and for the latter, we offer enabling or disabling default-case statements in the Finite State Machine (FSM) descriptions. To obtain compactness and at the same time fast execution times, we make maximum use of the Digital Signal Processing (DSP) slices on the FPGA. We designed a single arithmetic unit that is flexible to support operations with two moduli and non-modulus arithmetic. In addition, our design benefits in-place memory management and the local storage of inputs into DSP slices’ pipeline registers and takes advantage of distributed memory. These eliminate a memory access bottleneck. The flexibility is offered by a micro-code supported instruction-set architecture. Our design targets 7-Series Xilinx FPGAs and is prototyped on a Zynq System-on-Chip (SoC). The base design combining Ed25519 and X25519 in a single module, and its implementation requires only around 11.1K Lookup Tables (LUTs), 2.6K registers, and 16 DSP slices. Also, it achieves performance of 1.6ms for a signature generation and 3.6ms for a signature verification for a 1024-bit message with an 82MHz clock. Moreover, the design can be optimised only for X25519, which gives the most compact FPGA implementation compared to previously published X25519 implementations.
Optimisation of Feature Space for People Detection from TopView on Light Embedded Platform

Detecting people from a top view video feed is comparatively a more difficult problem than pedestrian detection especially on a real time system, as the perceived 2D shape of person from the overhead view is of widely varying nature due to perspective distortions with almost no unique discernible outline shape. Therefore the amount of information available from overhead view is very less. Also, there is a lot of rotational variation of the data from top view. However the advantage of having such a detector is that for a tracking application there would be little occlusion. Hence, this problem is worthy of specialised attention. In recent times there are many deep learning based approaches, but all of them are computationally expensive. We present a method to effectively train a computationally light AdaBoost classifier based detector, which uses the limited amount of information and can give a high accuracy running on a light embedded platform such as Raspberry Pi 3B.
Brain Tumor Segmentation on MR Images Using Anisotropic Deeply Supervised Convolutional Neural Network

Glioma is the most common and cancerous type of early stage brain tumors that arise from glial cells. Deep learning based solutions are flexible in brain tumor image analysis and computer-assisted diagnosis but it does not gain the desired accuracy. Manual brain tumor segmentation is a challenging task for clinicians therefore, researchers are working continuously to improve the accuracy for brain tumor segmentation using automatic segmentation. In this paper we segmented the brain tumor in three regions namely whole tumor, enhancing tumor and non-enhancing tumor using Convolutional Neural Network (CNN) implemented by anisotropic dilated convolution with residual blocks additionally using deeply supervised layers. Experiment shows that, our proposed method achieved the dice scores of 0.91, 0.78, 0.84 for whole tumor, enhancing tumor and non-enhancing tumor respectively, which is better than the BraTS 2017 challenge and other reported approaches.
Community Composition and Ex Situ Cultivation of Fungi Associated with UNESCO Heritage Monuments in the Bay of Naples

The Bay of Naples, Italy, is renowned for its archaeological heritage. However, this heritage is threatened by the combination of weathering and the biological activity of microorganisms. Fungi are among the major agents of microbial deterioration of cultural heritage since they can cause cracks and lesions in monuments due to the penetrating force of their hyphae. Such biodeterioration may weaken the stone structures and threaten the longevity of these culturally important monuments. To address this, we collected, identified, and maintained in culture filamentous fungi that colonize the external surface of monuments at five important archaeological sites near Naples, namely Cuma, Ercolano, Nola, Oplonti, and Pompei. We isolated a total of 27 fungal taxa, all of which can be cultivated in the laboratory, and form a part of our reference collection. Many of the described fungal taxa we found belong to groups that are involved in stone biodeterioration and can thus be considered as model organisms for in vitro studies. These results emphasize the importance of identifying and cultivating fungal stock cultures for non-invasive studies on biodeterioration. Our newly developed reference collection represents a useful resource that is available to other researchers to rapidly identify potentially hazardous fungi on other monuments.
Predicting Patagonian Landslides: Roles of Forest Cover and Wind Speed

Dense tree stands and high wind speeds characterize the temperate rainforests of southern Chilean Patagonia, where landslides frequently strip hillslopes of soils, rock, and biomass. Assuming that wind loads on trees promote slope instability, we explore the role of forest cover and wind speed in predicting landslides with a hierarchical Bayesian logistic regression. We find that higher crown openness and wind speeds credibly predict higher probabilities of detecting landslides regardless of topographic location, though much better in low‐order channels and on midslope locations than on open slopes. Wind speed has less predictive power in areas that were impacted by tephra fall from recent volcanic eruptions, while the influence of forest cover in terms of crown openness remains.
Potentially inappropriate prescriptions of anticholinergic drugs in patients with benign prostatic hyperplasia

Abstract Background Prostatic hyperplasia is frequent in the elderly, and it can be associated with urinary retention in patients who use cholinergic antagonists. The objective was to estimate the anticholinergic burden of drugs prescribed to patients diagnosed with benign prostatic hyperplasia. Methods A cross-sectional study using a population database to identify prescriptions of cholinergic antagonists drugs used in the management of benign prostatic hyperplasia. The anticholinergic burden was evaluated using the Anticholinergic Drug Scale. Results Three thousand seven hundred and sixty patients with benign prostatic hyperplasia were identified, with a mean age of 68.26 ± 10.46 years. Of these patients, 2961 (78.8%) received pharmacological treatment mainly with tamsulosin monotherapy (34.7%, n = 1026). Overall, 34.7% (n = 1303) of all patients were taking cholinergic antagonists. Patients aged 75–84 years (OR: 1.985, 95%CI: 1.063–3.709) and those 85 or older (OR: 2.52, 95%CI: 1.287–4.948) had a greater probability of having an anticholinergic burden score ≥3 points. Of the patients not receiving pharmacological treatment for benign prostatic hyperplasia, 35% (n = 280) were taking medications with anticholinergic properties. Conclusions A high proportion of patients with benign prostatic hyperplasia were receiving medical management for the relief of symptoms, mostly via monotherapy. However, one-third of patients received some type of medication with anticholinergic properties, being much more frequent after 75 years.
The Cost of the Second Seizure: Rethinking the Treatment Decision

Antiepileptic Drug Treatment After an Unprovoked First Seizure: A Decision Analysis Bao EL, Chao LY, Ni P, et al. Neurology. 2018;91(15):e1429-e1439. doi:10.1212/WNL.0000000000006319 Objective: To compare the expected quality-adjusted life-years (QALYs) in adult patients undergoing immediate versus deferred antiepileptic drug (AED) treatment after a first unprovoked seizure. Methods: We constructed a simulated clinical trial (Markov decision model) to compare immediate versus deferred AED treatment after a first unprovoked seizure in adults. Three base cases were considered, representing patients with varying degrees of seizure recurrence risk and effect of seizures on quality of life (QOL). Cohort simulation was performed to determine which treatment strategy would maximize the patient’s expected QALYs. Sensitivity analyses were guided by clinical data to define decision thresholds across plausible measurement ranges, including seizure recurrence rate, effect of seizure recurrence on QOL, and efficacy of AEDs. Results: For patients with a moderate risk of recurrent seizures (52.0% over 10 years after first seizure), immediate AED treatment maximized QALYs compared to deferred treatment. Sensitivity analyses showed that for the preferred choice to change to deferred AED treatment, key clinical measures needed to reach implausible values were 10-year seizure recurrence rate ≤38.0%, QOL reduction with recurrent seizures ≤0.06, and efficacy of AEDs on lowering seizure recurrence rate ≤16.3%. Conclusion: Our model determined that immediate AED treatment is preferable to deferred treatment in adult first-seizure patients over a wide and clinically relevant range of variables. Furthermore, our analysis suggests that the 10-year seizure recurrence rate that justifies AED treatment (38.0%) is substantially lower than the 60% threshold used in the current definition of epilepsy.
ERS statement on respiratory muscle testing at rest and during exercise

Assessing respiratory mechanics and muscle function is critical for both clinical practice and research purposes. Several methodological developments over the past two decades have enhanced our understanding of respiratory muscle function and responses to interventions across the spectrum of health and disease. They are especially useful in diagnosing, phenotyping and assessing treatment efficacy in patients with respiratory symptoms and neuromuscular diseases. Considerable research has been undertaken over the past 17 years, since the publication of the previous American Thoracic Society (ATS)/European Respiratory Society (ERS) statement on respiratory muscle testing in 2002. Key advances have been made in the field of mechanics of breathing, respiratory muscle neurophysiology (electromyography, electroencephalography and transcranial magnetic stimulation) and on respiratory muscle imaging (ultrasound, optoelectronic plethysmography and structured light plethysmography). Accordingly, this ERS task force reviewed the field of respiratory muscle testing in health and disease, with particular reference to data obtained since the previous ATS/ERS statement. It summarises the most recent scientific and methodological developments regarding respiratory mechanics and respiratory muscle assessment by addressing the validity, precision, reproducibility, prognostic value and responsiveness to interventions of various methods. A particular emphasis is placed on assessment during exercise, which is a useful condition to stress the respiratory system. Diverse methods are available for assessment of the respiratory muscles; the technique used should be tailored to the question posed http://ow.ly/poE830o8y58
Identification of training opportunities in medical education for academic faculty

Abstract Introduction: Clinician-educators are responsible for providing education to trainees in medical centers. There is no clear overview of what opportunities exist for training clinician-educators in medical education related skills and techniques. Methods: We conducted a systematic review of multiple websites and a medical educator listserve to identify medical education training opportunities for clinician-educators. We included certificate level programs or programs with comparable recognition and excluded masters programs, programs specific to one medical specialty or institution, and brief modules/sessions. We categorized results by training/focus area(s) and program details relevant for faculty. Results: We identified 53 programs. Most focus on general medical education skills (N = 19, 36%), leadership (N = 18, 34%), or learner assessment (N = 16, 30%). Fourteen programs (26%) were exclusively online, 27 (56%) exclusively in-person, and 12 (23%) require in-person and distance components. Time requirements for completion vary greatly, ranging from 1 day to 3 years, as did program costs, ranging from $327 to $15,000. Conclusions: Although training programs in medical education for clinical faculty exist, most focus on general medical education, leadership, and assessment. More programs focused on other topics, such as simulation or educational research, may be needed. Future investigations to understand the needs of this population would be valuable.
Reconstruction in deep learning of highly under-sampled T2-weighted image with T1-weighted image.

T1-weighted image (T1WI) and T2-weighted image (T2WI) are routinely acquired in MRI protocols, which can provide complementary information to each other. However, the acquisition time for each sequence is non-trivial, making clinical MRI a slow and expensive procedure. With the purpose to shorten MRI acquisition time, we present a deep learning approach to reconstruct T2WI from T1WI and highly under-sampled T2WI. Our results demonstrate that the proposed method could achieve 8 or higher acceleration rate while keeping high image quality of the reconstructed T2WI.
A Biopsychosocial Perspective of User-Generated Innovation in Open Innovation Models: A Moderated-Mediation Analysis

User-generated innovation has contributed to the growth of the democratization of open-innovation models. One of the most common forms of user-generated innovation is evident on social media platforms. The purpose of this study is to investigate nonpecuniary motivations that drive innovation among user innovators on social media platforms. Furthermore, the study examines the underlying sociopsychological and biological dispositions that influence nonpecuniary motivation. The experimental and control group consisted of 204 user innovators on different social media platforms who filled out a self-reporting questionnaire in this exploratory research design. The study assessed endocrinal biomarkers through a proxy measure of 2D:4D ratio associated with behavioral, emotional, and social behavior. It developed a moderated-mediation model evaluating the indirect conditional relationships through a regression-based analysis with bootstrapped estimations. The findings support the moderated-mediation model, indicating that nonpecuniary motivation primarily explains user innovator behavior. Hedonic emotions, characterized by aesthetics, experiential enjoyment, and satisfaction-related feelings, mediate this relationship. A critical finding of the study is that endocrinal testosterone moderates this mediated relationship. This study is the first to apply a biopsychosocial lens to examine motivational drives influencing user-generated innovation using a moderated-mediation model. It contributes to understanding user innovators’ tricky motivational purposes, emphasizing the role of human agency in advancing the open-innovation agenda.
Effect of perforation size and substrate bag fruiting position on the morphology of fruiting bodies and clusters in Pleurotus ostreatus (Jacq.) P. Kumm

Perforation or fruiting hole size on substrate bags control cluster sizes and morphology in exotic mushroom cultivation. The effect of three different perforation sizes on substrate bags (factor A: 50, 100, and 150 mm) and their positioning on the shelves (factor B: Horizontal, vertical, and slant) on the crop and various morphological characteristics in Pleurotus ostreatus was studied. Microclimatic conditions for fruiting were 16±1°C, 87±3% RH; 230±42 lux illumination. The formula for calculating area of ellipse was modified and used for the area of mushroom cap. Results indicated that the total fruit body yield and biological efficiency (BE) in the bags set in horizontal position were 10% lower than other treatments. The effect of perforation size on mushroom cluster sizes was more on the substrate blocks in the horizontal position. There was a linear correlation between perforation size and fruiting body cluster sizes. Results suggest that the 50 mm perforation on bags in vertical and slant positions gave fruiting body clusters sizes 186–196 mm width and 122–154 mm height, with 92.36±6.48% BE. The cluster size indicated is the best fit for standard packaging containers used in commercial oyster mushroom production in Ukraine.
Political dimensions of gender inclusive writing in Parisian universities

Abstract Écriture inclusive (EI) has long been the topic of public debates in France. These debates have become more intense in recent years, often focusing on the higher education system and culminating in the formulation of three separate laws banning it for public administration. In this paper, we investigate the foundations of these conflicts through a large quantitative corpus study of the (non)use of EI in Parisian undergraduate brochures. Our results suggest that Parisian university professors use EI not only to ensure gender neutral reference but also as a tool to construct their political identities. We show that both the use of EI and its particular forms are conditioned by how brochure writers position themselves on non gender‐related‐related issues within the French university's political landscape, which explains how conflicts surrounding a linguistic practice have become understood as conflicts about larger issues in French society. Our paper thus provides new information to be taken into account in the formulation and promotion of nonsexist language policies and sheds light on how feminist linguistic activism and its opposition are deeply intertwined with other kinds of social activism in present‐day France.
A Correlation Index Between Two Different Text and Web Resource Classification Systems

Classifying content on the Web has been a common subject of research, since the amount of available data on the Web, especially in text format, grows every day. In this paper it is proposed a correlation index to measure how close a classification system based on Wikipedia categorization is of a service provided by Watson IBM that has the same purpose: text and resourceclassification on the Web.
Effect of Adaptive Changes of Lysophosphatidylethanolamine Content on Ampicillin Resistance of Yersinia Pseudotuberculosis

Most antibiotics are targeted at intracellular processes. Therefore, their effects are determined by an ability to penetrate bacterial membranes. Mechanism of reducing permeability of porin channels in Gram-negative bacteria is the least known one among the possible reasons of antibiotic resistance. The adaptive accumulation of lysophosphatidylethanolamine (LPE), which is observed under conditions typical for the parasitic phase (in particular, the availability of glucose) of Gram-negative bacteria Yersinia pseudotuberculsis is accompanied by rearrangements in conformation of outer membrane protein OmpF that may impede the porin channel permeability for ß-lactam antibiotics. In present study, we report that adaptive accumulation of LPE in membranes of Y. pseudotuberculosis grown in the presence of glucose reduces antibacterial effect of ampicillin. In turn, polyphenol extract from buckwheat husks (PEBH) induces both the decrease in the level of LPE and resistance of bacteria to ampicillin. Therefore, PEBH acts synergistically with ampicillin in vivo by lowering its MICs and therefore can be used as antibiotic adjuvant to improve an antibiotic's ability to cross the outer membrane. These results showed that strategies for regulation of adaptive changes in lipid matrix of bacterial membranes is a new potentially effective way to increase the sensitivity of pathogens to known antibiotics.
Classification of Gunshots with KNN Classifier

In this article a system of detection and classification of gunshots is proposed, which consists of using the KNN classifier in the presence and absence of Gaussian additive noise. The results guarantee that the classifier reaches up to 94 % of performance in the absence of noise and only using 10 attributes. The attributes proposed in this article are easy to obtain and in the time domain. Finally, brief comparison of popular classifiers in WEKA is also performed to confirm the KNN classifier's advantage in the presence of noise.
Measuring microbial growth in damp buildings: Ongoing efforts at an age-old public health problem

For over 2000 years, building dampness has been considered harmful. Through epidemiological efforts, we have established that damp or moldy building materials are linked to increased risk of respiratory problems. While this is presumably because of unhealthy microbial emissions, the specific causative agent(s) responsible for the health effects remain unknown. In order to identify damp buildings, we want to identify a consistent signature of microbial growth in buildings that can be detected among the rich microbial milieu normally present. This talk will provide a summary of the state of the field (hopefully addressing some long-standing misconceptions) and present findings on promising new strategies, including using sequence-based technologies, for identifying microbial growth in damp buildings. Current research on the moisture conditions in buildings under which microbes are active and contribute to volatile chemistry will also be presented. Peer-review under the responsibility of the organizing committee of the ICMB21.