Miracle-dz/UniFAR-FTUs huggingface dataset:
A new improved algorithm for optimal sizing of battery-supercapacitor based hybrid energy storage systems

One of the major challenges in harvesting energy from renewable energy sources is the intermittent nature of available energy sources like solar, wind etc. Thus, it is has become essential to deploy suitable energy storage devices to compensate for the intermittent and random output power generation from various resources. A hybrid energy storage systems (HESS) consisting of battery and supercapacitor is suitable to overcome the difficulties in battery storage system which is normally used. Differential evolution (DE) is one of the powerful evolutionary optimization and has been successfully applied to solve various optimization problems. But one of major difficulties in DE is the selection of control parameters. A wrong parameter selection may lead to premature convergence and even stagnation. In the this work, a new improved algorithm using differential evolution and chaos theory is proposed for optimal sizing of hybrid energy storage system consisting of battery and super capacitor with an aim to avoid premature convergence. A hybrid system consisting of solar and wind is considered for the present work. A suitable objective function is developed which is optimized under several equality and inequality constraints. Simulation results are presented. A comparison of the results with other heuristic techniques is also presented and it shows that proposed techniques can produce good quality solutions.
Gender di¡erences in asthma prevalence may depend on how asthma is de¢ned

Background : Asthma may be de¢ned either as wheeze within the previous 12 months (current wheeze), doctor-diagnosed asthma (DDA), or current wheeze plus con¢rmed airway hyperresponsiveness (AHR). Aims : We wantedto estimate asthma prevalenceinrandomlyselected adolescentsbased on di¡erentcriteriafor asthmadiagnosis, studygenderdi¡erencesin reported asthma-like symptoms vs DDA, andrelate our ¢ndings to measurements of AHR, levels of exhaled nitric oxide (ENO) and total IgE. Methods : As part of the health survey of North-Tr (cid:1) ndelag (HUNT), 8571 adolescents aged 13^19 years were investigated with an interview on allergic and respiratory symptoms (phase I study). Ofthese,401subjects who reported wheeze withinthe previous12 months (current wheeze) and 213 non-symp-tomaticcontrolswererandomlyselectedandinvestigatedwithallergyscreening, methacholinebronchoprovocationtest and measurements of ENO (phase II study). Results :Inthe phase I study, prevalence ofcurrent wheeze was 26% (30% in girlsand 23% inboys, P o 0.01). Prevalenceof DDAwas10.8% (10.5% ingirlsand11% inboys). Amongsubjectswithcurrent wheeze, thelikelihood of having DDAwasreducedin girls comparedto boys, oddsratio (95% CI) 0.82 (0.68-0.98) which waspartlyexplained byalongerhistoryof wheeze among boys.Inthe phase II study, although more girls than boyswith current wheeze had AHR (62% versus 50%, P o 0.02), more boys than girls reported DDA (44% vs . 32%, P o 0.02). Of the objective parameters, increased levels of ENO most strongly increased the riskof having DDA. Conclusions :When asthma is defined as DDA, there is a risk of underestimating the prevalence of asthma, especially among girls. r 2002
Inertial sensor data based motion estimation aided by image processing and differential barometry

In this paper, a navigation filter is presented that can be used for pedestrian navigation or autonomous micro aerial vehicles (MAV) in the context of search and rescue missions. The proposed system does not rely on external infrastructure and does not make any geometric assumptions on the environment. Point to point navigation tasks are addressed as these are typical for search and rescue scenarios. Therefore, a high accuracy odometry approach is applied. The proposed system fuses sensor data of a mono camera, an IMU and a barometer. In particular, a filter-based approach to visual inertial odometry (VIO) is extended by the fusion with a barometer. Additionally, a tightly coupling between image processing and filter state is applied such that a robust feature tracking and a standstill detection is achieved. The proposed system is evaluated with both simulated and real world datasets. The navigation solution is estimated consistently at IMU rate. The final position error is below one percent of the distance traveled on real world datasets.
Molecular challenges of neuroendocrine tumors ( Review )

Neuroendocrine tumors (NETs) are a very heterogeneous group that are thought to originate from the cells of the endocrine and nervous systems. These tumors develop in a number of organs, predominantly in the gastrointestinal and pulmonary systems. Clinical detection and diagnosis are reliable at the late stages when metastatic spread has occurred. However, traditional conventional therapies such as radiation and chemotherapy are not effective. In the majority of cases even surgical resection at that stage is unlikely to produce promising reusults. NETs present a serious clinical challenge, as the survival rates remain low, and as these rare tumors are very difficult to study, novel approaches and therapies are required. This review will highlight the important points of accumulated knowledge covering the molecular aspects of the role of neuroendocrine cells, hormonal peptides, the reasons for ectopic hormone production in NET, neuropeptides and epigenetic regulation as well as the other challenging questions that require further understanding.
Magnetic Isolation and Characterization of Porcine Ovarian Putative Stem Cells (Pscs): An In Vitro Study

According to the ruling hypothesis the potential source of tumorigenesis might be cancer stem cells (CSCs) resulting from the accumulation of mutations in other types of stem cells (SCs). Therefore we decided to identify and characterize the somatic stem cells commonly called Putative Stem Cells (PSCs) in the porcine postnatal ovary. To isolate ovarian PSCs, an antibody against SSEA-4 conjugated to magnetic beads was used. PSCs were cultured for 7 days. During the culture spontaneously arising embryoid bodies have been observed. Such behaviour under culture condition is characteristic for stem cells. Using immunofluorescence we showed cytoplasmic localization of the Oct 3/4 and Sox-2, pluripotency markers, proteins responsible for maintaining cells in an undifferentiated state. Using immunocytochemistry we showed transmembrane and cytoplasmic localization of the c-Kit receptor and GDF-9 - protein associated with the initiation of cell differentiation in the process of folliculogenesis. We demonstrated also a cytoplasmic and perinuclear location of the Ki-67 protein associated with cell division. PCR method revealed the presence of stem cell markers such as c-Kit, GDF-9 and proliferation marker Ki-67 at the level of the transcript in the putative stem cells (PSCs). Moreover, by the Western blot analysis we also confirmed the presence of markers including c-Kit, GDF-9, Oct-3/4, Sox-2 and Ki-67 in these PSC cells. The results obtained allow to presume that stem cells are actually present in the porcine ovary.
Superpixel based spectral classification of hyperspectral images in different color spaces

In this paper, superpixel based spectral classification of hyperspectral images are compared using different color spaces. The effective use of spatial and spectral information by using superpixel-based approaches increases classification performance of hyperspectral data. SLIC superpizel algorithm is applied to color images obtained using different datasets. Support vector machine (SVM) is used as classification method. Classification performance of hyperspectral data is compared in RGB space, LAB space, HSV space, XYZ space and SVM. It is shown that superpixel based spectral classification in LAB space gives better classification accuracy than other color spaces and pixel-based approach.
Up-regulated Expressions of Immune Parameters, ppa, propo, sod, and hsp70 in White Shrimp Litopenaeus vannamei Reared at Unfavorable Low Salinities

The expressions of prophenoloxidase activating enzyme (ppA), prophenoloxidase (proPO) I, proPO II, and antioxidant enzymes like cytosolic manganese dismutase (cytMnSOD), mitochondrial manganese dismutase (mtMnSOD), and extracellular copper and zinc dismutase (ecCuZnSOD) as well as heat shock protein 70 (HSP70) were examined in white shrimp Litopenaeus vannamei reared for 24 weeks at salinities of 2.5%, 5%, 15%, 25%, and 35%. The expression levels of ppA, proPO I, proPO II, cytMnSOD, mtMnSOD, ecCuZnSOD, and HSP70 of shrimp reared at 2.5% and 5% were significantly higher than in shrimp reared at 15%, 25%, and 35%. We concluded that white shrimp kept under long-term culture at 2.5% and 5% were under chronic stress had up-regulated expressions of ppA, proPO, MnSOD, CuZnSOD, and HSP70 that may provide candidate biomarkers in low salinity shrimp farming.
Semantic image segmentation with deep features

Deep convolutional neural networks (CNN) have shown significant success in many classification problems including semantic image segmentation. However training of deep networks is time consuming and requires large training datasets. A network trained in one dataset could be applied to another task or dataset through transfer learning and retraining. As an alternative to transfer learning, feature vectors that are extracted from network layers could be directly used for classification purposes. In this paper we investigate the improvement in classification performance when features extracted from generic CNN architectures are used in an image labeling algorithm that does not require training. We show that the use of ‘learned’ features from deep networks together with ‘hand-crafted’ features such as SIFT increases the labeling accuracy. Since existing pre-trained networks are used, the proposed approach could be easily applied to any dataset without any retraining. The proposed method is tested in two datasets and labeling accuracies are compared with similar existing methods.
Android malware detection based on runtime behaviour

As the number of applications on Android markets grows, there is also a significant increase in the number of malicious applications that aim to harm users and devices. Therefore, mobile malware detection systems are developed and deployed for both Android markets and mobile devices. However, most malwares employ techniques such as code obfuscation, dynamic code loading in order to evade from static analysis based detection systems. For this reason, a dynamic analysis based detection method is proposed in this study. By examining the behaviour of malicious applications at runtime, features are extracted to distinguish them from benign applications, and a detection system is developed by using machine learning techniques.
Ambient Systems , Networks and Technologies ( ANT 2018 ) Routing for E ffi cient Alarm Aggregation in Smart Grids : A Genetic Algorithm Approach

We propose a Genetic Algorithm based routing scheme to maximize the number of alarm messages received at the sink node in a Wireless Sensor Network embedded in an electric power grid upon a fault/outage detection. In this scenario, sensor nodes rely on their scarce energy storage to operate and report alarm messages to the sink. Sensed data in neighboring nodes tend to be correlated. Therefore, nodes will send redundant data to the sink while wasting energy and congesting the network. In order to mitigate the energy waste and increase the message delivery rate, a data aggregation scheme is proposed. c © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs.
A hybrid mode of operation for RPL newtorks

RPL became the widely accepted routing protocol for Internet of Things. RPL suggests different modes of operation (MOP) to satisfy different use cases effectively. However, interconnectivity between different modes has not been possible so far. In this research, a new method to provide interconnection between different modes of operation is proposed and compared to current studies. Simulation results show that suggested approach reduces average hop count by 33% while providing interconnection between different modes.
Personalidade e bem-estar subjetivo na excelência profissional: validação de escalas junto de gestores portugueses

The present study aimed to validate the Satisfation With Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985) and the Core Self-Evaluations Scale (CSES; Judge, Erez, Bono, & Thoresen, 2003) with a sample of Portuguese managers (N = 108). The SWLS assesses the cognitive component of subjective well-being, also described as the perceived satisfaction with the quality of one’s life. The CSES focuses on the person’s core self-evaluations, and includes four personality dimensions: self-esteem, generalized self-efficacy, neuroticism, and locus of control. The findings suggest that both instruments present adequate reliability indices and that the original theoretical constructs presented by the authors are partially observed in the dimensional structure of the scales, in the current Portuguese sample, with greater similarity in the SWL. In addition, a positive and significant correlation was observed between personality traits measured with the CSES and life satisfaction assessed with the SWLS, particularly for the items reported to perceived self-efficacy.
Multiphase Biodegradable Scaffolds for Tissue Engineering a Tendon-Bone Junction

Tendons play an important role in transferring stress between muscles and bones and in maintaining joint stability. Tendon tears are difficult to heal, and are associated with high recurrence rates. So the objective of this study was to develop a biodegradable scaffold for tendon-bone junction regeneration. Two types of polylactic acid (PLA) yarns, having fibers with round and four deep grooved cross-sections, were braided into tubular scaffolds and cultured with murine TGF-β Type II receptor (TGFBR2)-expressing joint progenitor cells. The scaffolds were designed to mimic the mechanical, immuno-chemical and biological properties of natural mouse tendon-bone junctions. Three different tubular scaffolds measuring 2 mm in diameter were braided on a Steeger 16-spindle braiding machine using these PLA yarns. The three different scaffold structures were: 1) PLA hollow tube using round fibers, 2) PLA hollow tube using grooved and round fibers, and 3) PLA multicomponent tube containing round fibers in the sheath and grooved core fibers inserted within the lumen. The dynamic tensile strength and initial Young’s modulus of the three scaffolds were monitored on an Instron mechanical tester, and cell attachment, viability, proliferation and migration were measured at different time points. The three different braided structures provided a wide range of mechanical properties that mimicked the various zones of the tendon bone junction. The biological tests confirmed that cell viability, attachment and proliferation occurred throughout all three scaffolds, indicating that they have the potential to be used as scaffolds for the regeneration of a tendon bone tissue junction.
Combat mobile malware via N-gram based deep learning

Today, mobile devices are beginning to be used in every aspect of life. In addition to being able to perform financial transactions such as banking and shopping, mobile devices can also store personal information such as pictures / videos on these platforms, and important information about the current surroundings of the phone, such as location / sound, can be obtained. Among the mobile platforms, the popularity of the Android operating system and its open source code make it the main target for malware developers. Today's antivirus software is not effective against malicious software that has been tampered with or encountered for the first time while it is effective against pre-existing threats because they are mostly signature-based. New threats need to be detected as quickly as possible, considering new-signatured versions of the same malware can be created quickly and easily with automatic tools. For this reason, researches based on machine learning and deep learning have been conducted in the last years. In this study deep learning methods, which have been tried to be used successfully in all areas of life in recent years, are tested in mobile malware detection. The opcodes of Android applications were grouped in groups of 2 and 3, their features were extracted, weights were optimized using stacked denoising auto encoder and classified by a multi-layered artifical neural network. As a result of the classification, harmful software was detected with an accuracy of 92.04%.
Visual saliency region detection by combination of soft- and hard-segmentation-wise approaches

Abstract. Recent studies in saliency detection have exploited contrast value as a main feature and background prior as a secondary feature. To apply the background prior, most approaches are based on soft- or hard-segmentation mechanisms, and a significant improvement is seen. However, because of contrast feature usage, the soft-segmentation (SS)-wise models have many technical challenges when a high interobject dissimilarity exists. Although hard-segmentation-wise saliency models intuitively use the background prior without usage of the contrast feature, this model suffers from local noises due to undesirable discontinuous artifacts. By analyzing the drawbacks of the existing models, a combination saliency model, reflecting both soft- and hard-segmentation techniques is shown. The proposed model consists of the following three phases: SS-wise saliency, hard-segmentation-wise saliency, and a final saliency combination. In particular, we proposed an iterative reweighting processing for which an influence of outlier segmentation maps is decreased to improve the hard-segmentation-wise saliency. As shown in the experimental results, the proposed model outperforms the state-of-the-art models on various benchmark datasets, which consist of single, multiple, and complex object images.
A visualization platfom for disk failure analysis

It has become a norm rather than an exception to observe multiple disks malfunctioning or whole disk failures in places like big data centers where thousands of drives operate simultaneously. Data that resides on these devices is typically protected by replication or erasure coding for long-term durable storage. However, to be able to optimize data protection methods, real life disk failure trends need to be modeled. Modelling helps us build insights while in the design phase and properly optimize protection methods for a given application. In this study, we developed a visualization platform in light of disk failure data provided by BackBlaze, and extracted useful statistical information such as failure rate and model-based time to failure distributions. Finally, simple modeling is performed for disk failure predictions to alarm and take necessary system-wide precautions.
Characterizing Glioblastoma Heterogeneity via Single-Cell Receptor Quantification

Dysregulation of tyrosine kinase receptor (RTK) signaling pathways play important roles in glioblastoma (GBM). However, therapies targeting these signaling pathways have not been successful, partially because of drug resistance. Increasing evidence suggests that tumor heterogeneity, more specifically, GBM-associated stem and endothelial cell heterogeneity, may contribute to drug resistance. In this perspective article, we introduce a high-throughput, quantitative approach to profile plasma membrane RTKs on single cells. First, we review the roles of RTKs in cancer. Then, we discuss the sources of cell heterogeneity in GBM, providing context to the key cells directing resistance to drugs. Finally, we present our provisionally patented qFlow cytometry approach, and report results of a “proof of concept” patient-derived xenograft GBM study.
The Use of Phage Display and Yeast Based Expression System for the Development of a Von Willebrand Factor Propeptide Assay: Development of a Von Willebrand Factor Propeptide Assay

Background The diagnosis of von Willebrand disease is complex due to the heterogeneity of the disease. About eighty percent of von Willebrand disease patients are diagnosed with a quantitative defect of von Willebrand factor (VWF) where fifty percent is due to an increased clearance of von Willebrand factor. These patients do not respond well to the treatment of choice, Desmopressin (DDAVP) due to decreased efficacy. The ratio between the VWF propeptide and the mature VWF antigen is used to diagnose these patients. Commercial VWF propeptide assays are too expensive for use in developing countries. In this study, we developed a cost-effective ELISA assay. Methods We first displayed VWF propeptide on yeast. Antibody fragments were selected against the displayed VWF propeptide by using phage display technology. The antibodies were used to develop a cost-effective VWF propeptide assay and compared to a commercial VWF propeptide assay. Results Two of these antibody fragments bound specific to the VWF propeptide and not to the yeast used for the expression of the propeptides. These purified antibody fragments were able to detect VWF propeptide in normal plasma. Conclusion Our assay performed well when compared to a commercial kit. It also showed a higher binding affinity for VWF propeptide in plasma at especially lower plasma concentrations.
Polysaccharides From Chrysanthemum morifolium Ramat Ameliorate Colitis Rats via Regulation of the Metabolic Profiling and NF-κ B/TLR4 and IL-6/JAK2/STAT3 Signaling Pathways

Studies have indicated that Chrysanthemum polysaccharides (CP) could prominently ameliorate colitis rats, but its possible mechanism remains unclear. In this study, the underlying mechanism of CP was explored by the metabolic profiling analysis and correlated signaling pathways. TNBS/ethanol induced colitis was used to investigate the intervention efficacy following oral administration of CP. The levels of cytokines such as TNF-α, IL-6, IFN-γ and IL-1β, and the activities of SOD, MPO, and MDA were determined. We also performed western-blot for p65, TLR4, p-JAK2, and STAT3 protein expression in the colon tissue to probe their mechanisms of correlated signaling pathways. What’s more, the metabolic changes in plasma and urine from colitis rats were investigated based on UPLC-Q-TOF/MS combined with MetabolynxTM software. The potential biomarkers and metabolic pathways were also tentatively confirmed. The metabolic profiles of plasma and urine were clearly improved in model rats after oral administration of CP. Thirty-two (17 in serum and 15 in urine) potential biomarkers were identified. The endogenous metabolites were mainly involved in linoleic acid, retinol, arachidonic acid, glycerophospholipid and primary bile acid metabolism in plasma, and nicotinate and nicotinamide, ascorbate and aldarate, histidine and β-alanine metabolism in urine. After polysaccharides intervention, these markers turned back to normal level at some extent. Meanwhile, the elevated expression levels of pp65, TLR4, p-STAT3, and p-JAK2 were significantly decreased after treatment. Results suggested that CP would be a potential prebiotics for alleviation of TNBS-induced colitis. The study paved the way for the further exploration of the pathogenesis, early diagnosis and curative drug development of the colitis.