ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of distribution grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor distribution grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution system monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different power grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photo-voltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors.
Tracking and Pairing Vehicle Headlight in Night Scenes

Traffic surveillance is an important topic in computer vision and intelligent transportation systems and has intensively been studied in the past decades. However, most of the state-of-the-art methods concentrate on daytime traffic monitoring. In this paper, we propose a nighttime traffic surveillance system, which consists of headlight detection, headlight tracking and pairing, and camera calibration and vehicle speed estimation. First, a vehicle headlight is detected using a reflection intensity map and a reflection suppressed map based on the analysis of the light attenuation model. Second, the headlight is tracked and paired by utilizing a simple yet effective bidirectional reasoning algorithm. Finally, the trajectories of the vehicle's headlight are employed to calibrate the surveillance camera and estimate the vehicle's speed. Experimental results on typical sequences show that the proposed method can robustly detect, track, and pair the vehicle headlight in night scenes. Extensive quantitative evaluations and related comparisons demonstrate that the proposed method outperforms state-of-the-art methods.
CoN4-supported Co2N metal clusters for developing sensitive chemiluminescent immunochromatographic assays

In view of the optimal catalytic efficiency (∼100%), single-atom site catalysts are being widely exploited in a range of areas including organic synthesis, energy conversion, environmental remediation, biotherapy, etc. However, low loading ratio of the unitary active sites on single-atom site catalysts dramatically hinders the remarkable improvement of their catalytic activity. Hereby, a facile low-temperature reduction protocol was adopted for synthesizing CoN4-supported Co2N metal clusters on graphitic carbon nitride, which show the remarkably superior chemiluminescent (CL) catalytic capacity than some reported pure single-atom site catalysts. Nitrogen-encapsulated Co2N clusters coupled with isolated Co-N4 moieties (Co2N@Co-N4) endowed the synergetic catalysts with high Co content of 53.2 wt%. Through X-ray absorption spectroscopy, the synergetic active sites (Co2N@Co-N4) afforded the CoN4-supported Co2N clusters with the remarkable catalytic activity for accelerating the decomposition of H2O2 to produce extensive superoxide radical anion rather than singlet oxygen or hydroxyl radical. Therefore, the CoN4-supported Co2N clusters possessed the superb enhancement effect on luminol-H2O2 CL reaction by ∼22829 times. The CoN4-supported Co2N clusters were utilized as signal probes to establish a CL immunochromatographic assay (ICA) platform for quantitating mycotoxins. Herein, aflatoxin B1 was employed as a mode analyte and the limit of detection was as low as 0.33 pg mL-1 (3σ). As a proof-of-principle work, the developed ICA protocol was successfully employed on the detection of aflatoxin B1 spiked in Angelica dahurica and Ganoderma lucidum with acceptable recoveries of 84.0-107.0%. The ideal practicability of the work elucidates that CoN4-supported Co2N clusters showed a new perspective for developing the sensitive CL biosensing.
Off-chip bus power minimization using serialization with cache-based encoding

The data bus is a major component of high power consumption in small process high-performance systems and in systems-on-chip (SoC) design. This paper presents an analysis of different state-of-the-art techniques for reducing the power of off-chip memory bus interface, with proposing an approach overcoming some limitations existing in the state-of-art methods. More precisely, the paper introduces a serialization (S) method combined with cache-based encoding scheme, aiming at saving the optimal possible power for off-chip memory bus. Bus serialization reduces the number of transmission wires, while a Serialization-Widening (SW) approach reduces the bus capacitance and the number of transmission wires. Experimental results show that, for off-chip data bus, the serialization approach with cache-based encoding achieves 31% and 52% power reduction for single-core and multi-core applications, respectively, when using fixed voltage and frequency with 128 bits data bus. (C) 2016 Elsevier Ltd. All rights reserved.
D2C-Based Hybrid Network for Predicting Group Cohesion Scores

Group cohesiveness represents the bonding between members in a group. Indeed, a group with high cohesiveness may easily reach success in their task. Therefore, the most critical element that affects the success of a group is group cohesiveness, which is estimated by Group Cohesion Score (GCS). This study proposed an automatic GCS estimation system for the 7(th) Emotion Recognition in the Wild (EmotiW 2019) challenge in the task of the Group Cohesion Prediction. We proposed a multi-stream hybrid network based on scene-level, skeleton-level, UV coordinates-level, mid-fusion, and face-level, followed by late-fusion to combine these approaches. We also developed a joint training method called Discrete labels to Continuous scores (D2C), where discrete labels (categorical labels) directly participate in generating continuous scores. Our proposed method achieved 0.416 mean squared error on the testing set of the EmotiW 2019 dataset and became a state-of-the-art in this challenge. Furthermore, to confirm the ability of the proposed D2C method, we performed experiments on the AffectNet database and obtained relatively better results than state-of-the-art approaches.
Omalizumab as a corticosteroid-sparing agent in the treatment of bullous pemphigoid

Bullous pemphigoid (BP) is the most common autoimmune blistering skin disease, characterized by the development of autoantibodies against hemidesmosomal components BP180 and BP230. The mainstay of therapy is topical and systemic corticosteroids (CS) and immunosuppressors. As this pathology mainly involves the elderly, subjects often have numerous comorbidities that influence the clinical management. Omalizumab is a recombinant humanized monoclonal anti-IgE antibody which has recently emerged as a promising treatment for BP in patients for whom CS are contraindicated or conventional treatments have failed to control the disease. For this study, we selected five patients who presented with corticosteroid-dependent BP with a contraindication to the use of other immunosuppressive treatments. The objectives of our study were to evaluate the effectiveness of omalizumab in controlling BP and allowing to decrease the dosage of systemic CS, assessing the effects of omalizumab on the clinical manifestations and the titers of circulating anti-BP180 and BP230 antibodies, IgE and eosinophils. A reduction in the dose of systemic CS was possible in 100% of the patients and complete resolution of the clinical picture was seen in 100% for skin lesions and in 40% for pruritus. A reduction of circulating IgE was found in 40%, anti-BP180 and BP230 IgGs were decreased in 60% and eosinophils in 80%.
Emerging Technologies for Next Generation Remote Health Care and Assisted Living

Remote health care is currently one of the most promising solutions to ensure a high level of treatment outcome, cost-efficiency and sustainability of the healthcare systems worldwide. Even though research on remote health care can be traced back to the early days of the Internet, the recent COVID-19 has necessitated further improvement in existing health care systems with invigorated research on remote health care technologies. In this article we delve into the state-of-the-art research in latest technologies and technological paradigms that play a vital role in enabling the next generation remote health care and assisted living. First the need of using the latest technological developments in the domain of remote health care is briefly discussed. Then the most important technologies and technological paradigms that are crucial in enabling remote health care and assisted living are emphasised. Henceforth, a detailed survey of existing technologies, potential challenges in those technologies, and possible solutions is conducted. Finally, missing research gaps and important future research directions in each enabling technology are brought forth to motivate further research in remote health care.
CPiX: Real-Time Analytics Over Out-of-Order Data Streams by Incremental Sliding-Window Aggregation

Stream processing is used in various fields. In the field of big data, stream aggregation is a popular processing technique, but it suffers serious setbacks when the order of events (e.g., stream elements) occurring is different from the order of events arriving to the systems. Such data streams are called "non-FIFO steams". This phenomenon usually occurs in a distributed environment due to many factors, such as network disruptions, delays, etc. Many analyzing scenarios require efficient processing of such non-FIFO streams to meet various data processing requirements. This paper proposes an efficient scalable checkpoint-based bidirectional indexing approach, called CPiX, for faster real-time analysis over non-FIFO streams. CPiX maintains the partial aggregation results in an on-demand manner per checkpoint. CPiX needs less time and space than the state-of-the-art approach. Extensive experiments confirm that CPiX can deal with out-of-order streams very efficiently and is, on average, about 3.8 times faster than the state-of-the-art approach while consuming less memory.
Dynamic Image Quantization Using Leaky Integrate-and-Fire Neurons

This paper introduces a novel coding/decoding mechanism that mimics one of the most important properties of the human visual system: its ability to enhance the visual perception quality in time. In other words, the brain takes advantage of time to process and clarify the details of the visual scene. This characteristic is yet to be considered by the state-of-the-art quantization mechanisms that process the visual information regardless the duration of time it appears in the visual scene. We propose a compression architecture built of neuroscience models; it first uses the leaky integrate-and-fire (LIF) model to transform the visual stimulus into a spike train and then it combines two different kinds of spike interpretation mechanisms (SIM), the time-SIM and the rate-SIM for the encoding of the spike train. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by counting the spikes. For that reason, the proposed mechanisms is called Dual-SIM quantizer (Dual-SIMQ). We show that (i) the time-dependency of Dual-SIMQ automatically controls the reconstruction accuracy of the visual stimulus, (ii) the numerical comparison of Dual-SIMQ to the state-of-the-art shows that the performance of the proposed algorithm is similar to the uniform quantization schema while it approximates the optimal behavior of the non-uniform quantization schema and (iii) from the perceptual point of view the reconstruction quality using the Dual-SIMQ is higher than the state-of-the-art.
Identification of Differentially Expressed Genes in Chilling-Induced Potato (Solanum tuberosum L.); a Data Analysis Study

Cold stress, as chilling (<20 °C) or freezing (<0 °C), is one of the frequently exposed stresses in cultivated plants like potato. Under cold stress, plants differentially modulate their gene expression to develop a cold tolerance/acclimation. In the present study, we aimed to identify the overall gene expression profile of chilling-stressed (+4 °C) potato at four time points (4, 8, 12, and 48 h), with a particular emphasis on the genes related with transcription factors (TFs), phytohormones, lipid metabolism, signaling pathway, and photosynthesis. A total of 3504 differentially expressed genes (DEGs) were identified at four time points of chilling-induced potato, of which 1397 were found to be up-regulated while 2107 were down-regulated. Heatmap showed that genes were mainly up-regulated at 4-, 8-, and 12-h time points; however, at 48-h time point, they inclined to down-regulate. Seventy five up-regulated TF genes were identified from 37 different families/groups, including mainly from bHLH, WRKY, CCAAT-binding, HAP3, and bZIP families. Protein kinases and calcium were major signaling molecules in cold-induced signaling pathway. A collaborated regulation of phytohormones was observed in chilling-stressed potato. Lipid metabolisms were regulated in a way, highly probably, to change membrane composition to avoid cold damage and render in signaling. A down-regulated gene expression profile was observed in photosynthesis pathway, probably resulting from chilling-induced reduced enzyme activity or light-triggered ROSs damage. The findings of this study will be a valuable theoretical knowledge in terms of understanding the chilling-induced tolerance mechanisms in cultivated potato plants as well as in other Solanum species.
Randomized placebo-controlled crossover trial of memantine in children with epileptic encephalopathy

Memantine is an N-methyl-D-aspartate receptor antagonist, approved for dementia treatment. There is limited evidence of memantine showing benefit for paediatric neurodevelopmental phenotypes, but no randomized placebo-controlled trials in children with developmental and epileptic encephalopathy. In this randomized double-blind placebo-controlled crossover trial (Trial registration: https://clinicaltrials.gov/ct2/show/NCT03779672), patients with developmental and epileptic encephalopathy received memantine and placebo, each for a 6-week period separated by a 2-week washout phase. Electroencephalography, seizure diary, patient caregivers' global impression, serum inflammatory markers and neuropsychological evaluation were performed at baseline and after each treatment phase. The primary outcome measure was classification as a 'responder', defined as ≥2 of: >50% seizure frequency reduction, electroencephalography improvement, caregiver clinical impression improvement or clear neuropsychological testing improvement. Thirty-one patients (13 females) enrolled. Two patients withdrew prior to initiating medication and two (twins) had to be removed from analysis. Of the remaining 27 patients, nine (33%) were classified as responders to memantine versus two (7%) in the placebo group (P < 0.02). Electroencephalography improvement was seen in eight patients on memantine compared to two on placebo (P < 0.04). Seizure improvement was observed in eight patients on memantine and two on placebo (P < 0.04). Caregivers reported overall clinical improvement in 10 patients on memantine compared to seven on placebo (not significant). Statistical analysis of neuropsychological evaluation suggested improvements in symptoms of attention-deficit hyperactivity disorder and autism. Memantine is a safe and effective treatment for children with developmental and epileptic encephalopathy, having the potential to improve both seizure control and cognitive function.
Adaptive transitions for automation in cars, trucks, buses and motorcycles

Automated vehicles are entering the roads and automation is applied to cars, trucks, buses, and even motorcycles today. High automation foresees transitions during driving in both directions. The driver and rider state become a critical parameter since reliable automation allows safe intervention and transit control to the automation when manual driving is not performed safely anymore. When the control transits from automation to manual an appropriate driver state needs to be identified before releasing the automated control. The detection of driver states during manual and automated driving and an appropriate design of the human-machine interaction (HMI) are crucial steps to support these transitions. State-of-the-art systems do not take the driver state, personal preferences, and predictions of road conditions into account. The ADAS&ME project, funded by the H2020 Programme of the European Commission, proposes an innovative and fully adaptive HMI framework, able to support driver/rider state monitoring-based transitions in automated driving. The HMI framework is applied in the target vehicles: passenger car, truck, bus, and motorcycle, and in seven different use cases.
Estrogenic, androgenic, and glucocorticoid activities and major causative compounds in river waters from three Asian countries

Estrogen, androgen, and glucocorticoid receptors (ER, AR, and GR) agonist activities in river water samples from Chennai and Bangalore (India), Jakarta (Indonesia), and Hanoi (Vietnam) were evaluated using a panel of chemical-activated luciferase gene expression (CALUX) assays and were detected mainly in the dissolved phase. The ER agonist activity levels were 0.011-55 ng estradiol (E2)-equivalent/l, higher than the proposed effect-based trigger (EBT) value of 0.5 ng/l in most of the samples. The AR agonist activity levels were < 2.1-110 ng dihydrotestosterone (DHT)-equivalent/l, and all levels above the limit of quantification exceeded the EBT value of 3.4 ng/l. GR agonist activities were detected in only Bangalore and Hanoi samples at dexamethasone (Dex)-equivalent levels of < 16-150 ng/l and exceeded the EBT value of 100 ng/l in only two Bangalore samples. Major compounds contributing to the ER, AR, and GR agonist activities were identified for water samples from Bangalore and Hanoi, which had substantially higher activities in all assays, by using a combination of fractionation, CALUX measurement, and non-target and target chemical analysis. The results for pooled samples showed that the major ER agonists were the endogenous estrogens E2 and estriol, and the major GR agonists were the synthetic glucocorticoids Dex and clobetasol propionate. The only AR agonist identified in major androgenic water extract fractions was DHT, but several unidentified compounds with the same molecular formulae as endogenous androgens were also found.
Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.
D-galactose protects the intestine from ionizing radiation-induced injury by altering the gut microbiome

This article aims to investigate the protection of the intestine from ionizing radiation-induced injury by using D-galactose (D-gal) to alter the gut microbiome. In addition, this observation opens up further lines of research to further increase therapeutic potentials. Male C57BL/6 mice were exposed to 7.5 Gy of total body irradiation (TBI) or 13 Gy of total abdominal irradiation (TAI) in this study. After adjustment, D-gal was intraperitoneally injected into mice at a dose of 750 mg/kg/day. Survival rates, body weights, histological experiments and the level of the inflammatory factor IL-1β were observed after TBI to investigate radiation injury in mice. Feces were collected from mice for 16S high-throughput sequencing after TAI. Furthermore, fecal microorganism transplantation (FMT) was performed to confirm the effect of D-gal on radiation injury recovery. Intraperitoneally administered D-gal significantly increased the survival of irradiated mice by altering the gut microbiota structure. Furthermore, the fecal microbiota transplanted from D-gal-treated mice protected against radiation injury and improved the survival rate of recipient mice. Taken together, D-gal accelerates gut recovery following radiation injury by promoting the growth of specific microorganisms, especially those in the class Erysipelotrichia. The study discovered that D-gal-induced changes in the microbiota protect against radiation-induced intestinal injury. Erysipelotrichia and its metabolites are a promising therapeutic option for post-radiation intestinal regeneration.
Prehistoric pictographs of Finland: Symbolism and territoriality

Transformation of landscape with symbolic art does not only mark a space as sacred. Such demarcations also serve as symbols of other social functions, such as the delineation of territories and exchange of information. As enduring monuments to the past, they symbolise a territory of time; as part of a cultural landscape, they symbolise a territory of space. A distributional study of evidence of symbolic territoriality in the landscapes of three rock art sites from the Neolithic - Early Metal periods in Finland, this work utilises GIS spatial analysis to locate prehistoric dwelling sites and sacred sites (red ochre burials, cairn burials, cremation burials, and cup-stones) within 5 and 10 km catchment zones of the pictographs sites. The results are interpreted with explanatory models from Information Exchange Theory and territorial analysis. (c) 2017 Elsevier Ltd and INQUA. All rights reserved.