Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bioinspired Control Strategy for the CYBERLEGs Knee-Ankle-Foot Orthosis: Feasibility Study with Lower-Limb Amputees

Lower-limb loss is a dramatic event affecting quality of life and often reducing independence. An active Knee-Ankle-Foot Orthosis (KAFO) could represent an assistive tool for lower-limb amputees to reduce the additional metabolic effort resulting from compensatory strategies due to walking with a passive prosthesis. Within the CYBERLEGs project, a novel active KAFO was designed to assist the knee and the ankle joints during ground level walking. In this paper the mechatronic design of the device is presented for the first time. Moreover, the paper presents the control strategy developed to provide knee and ankle assistance and the experimental results with two volunteers with lower-limb amputation. The KAFO was shown to: (i) fulfill all the design functional requirements to comply with range of motion, torque, speed and power; and (ii) provide assistive torque in the most demanding phases of the gait cycle. Tests with end-users showed that the assistive action resulted in physiological profiles of the knee and ankle angles and torques, showing a first proof of feasibility for the presented system. Both subjects reported comfortable interaction with the exoskeleton, but results on the metabolic consumption were not conclusive. This feasibility study will be extended in the future with an optimized controller to further explore the effectiveness of the system.
Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes

In its landmark paper about Standards and Guidelines for the Interpretation of Sequence Variants, the American College of Medical Genetics and Genomics (ACMG), and Association for Molecular Pathology (AMP) did not address how to use tumor data when assessing the pathogenicity of germline variants. The Clinical Genome Resource (ClinGen) established a multidisciplinary working group, the Germline/Somatic Variant Subcommittee (GSVS) with this focus. The GSVS implemented a survey to determine current practices of integrating somatic data when classifying germline variants in cancer predisposition genes. The GSVS then reviewed and analyzed available resources of relevant somatic data, and performed integrative germline variant curation exercises. The committee determined that somatic hotspots could be systematically integrated into moderate evidence of pathogenicity (PM1). Tumor RNA sequencing data showing altered splicing may be considered as strong evidence in support of germline pathogenicity (PVS1) and tumor phenotypic features such as mutational signatures be considered supporting evidence of pathogenicity (PP4). However, at present, somatic data such as focal loss of heterozygosity and mutations occurring on the alternative allele are not recommended to be systematically integrated, instead, incorporation of this type of data should take place under the advisement of multidisciplinary cancer center tumor‐normal sequencing boards.
Improving existing 6LoWPAN RPL for content based routing

Internet of things has become a subject of interest across a different industry domain. It includes 6LoWPAN (Low-Power Wireless Personal Area Network) which is used for a variety of application including home automation, sensor networks, manufacturing and industry application etc. However, gathering such a huge amount of data from such a different domain causes a problem of traffic congestion, high reliability, high energy efficiency etc. In order to address such problems, content based routing (CBR) technique is proposed, where routing paths are decided according to the type of content. By routing the correlated data to hop nodes for processing, a higher data aggregation ratio can be obtained, which in turns reducing the traffic congestion and minimizes the energy consumption. CBR is implemented on top of existing RPL (Routing Protocol for Low Power and Lossy network) and implemented in contiki operating system using cooja simulator. The analysis are carried out on the basis average power consumption, packet delivery ratio etc.
miR-136-5p Regulates the Inflammatory Response by Targeting the IKKβ/NF-κB/A20 Pathway After Spinal Cord Injury

Background/Aims: miR-136-5p participates in recovery after spinal cord injury (SCI) via an unknown mechanism. We investigated the mechanism underlying the involvement of miR-136-5p in the inflammatory response in a rat model of SCI. Methods: Sprague-Dawley rat astrocytes were cultured in vitro to construct a reporter plasmid. Luciferase assays were used to detect the ability of miR-136-5p to target the IKKβ and A20 genes. Next, recombinant lentiviral vectors were constructed, which either overexpressed miR-136-5p or inhibited its expression. The influence of miR-136-5p overexpression and miR-136-5p silencing on inflammation was observed in vivo in an SCI rat model. The expression of IL-1β, IL-6, TNF-α, IFN-α, and related proteins (A20, IKKβ, and NF-κB) was detected. Results: In vitro studies showed that luciferase activity was significantly activated in the presence of the 3’ untranslated region (UTR) region of the IKKβ gene after stimulation of cells with miR-136-5p. However, luciferase activity was significantly inhibited in the presence of the 3’UTR region of the A20 gene. Thus, miR-136-5p may act directly on the 3’UTR regions of the IKKβ and A20 genes to regulate their expression. miR-136-5p overexpression promoted the production of related cytokines and NF-κB in SCI rats and inhibited the expression of A20 protein. Conclusion: Overexpression of miR-136-5p promotes the generation of IL-1β, IL-6, TNF-α, IFN-α, IKKβ, and NF-κB in SCI rats but inhibits the expression of A20. Under these conditions, inflammatory cell infiltration into the rat spinal cord increases and injury is significantly aggravated. Silencing of miR-136-5p significantly reduces the protein expression results described after miR-136-5p overexpression and ameliorates the inflammatory cell infiltration and damage to the spinal cord. Therefore, miR-136-5p might be a new target for the treatment of SCI.
Color Image Noise Covariance Estimation with Cross-Channel Image Noise Modeling

Noise estimation is crucial in many image processing tasks such as denoising. Most of the existing noise estimation methods are specially developed for grayscale images. For color images, these methods simply handle each color channel independently, without considering the correlation across channels. In this work, we propose a multivariate Gaussian approach to model the noise in color images, in which we explicitly consider the inter-dependence among color channels. We design a practical method for estimating the noise covariance matrix within the proposed model. Specifically, a patch selection scheme is first introduced to select weakly textured patches through thresholding the texture strength indicators. Noticing that the patch selection actually depends on the unknown noise covariance, we present an iterative noise covariance estimation algorithm, where the patch selection and the covariance estimation are conducted alternately. Experimental results show that our method can effectively estimate the noise covariance. The practical usage is demonstrated with color image denoising.
Thermographic Measurement and Simulation of Power Losses Due to Interlaminar Contacts in Electrical Sheets

Ferromagnetic cores of electrical machines and transformers are constructed from laminated electrical sheets to reduce eddy current losses. Burrs are formed at the edges of electrical sheets during punching and cutting. These burrs deteriorate the insulation of adjacent sheets, make interlaminar contacts and cause interlaminar currents to flow across the stack. It is essential to know the interlaminar conductivity and the size of the contacts to estimate additional losses in the electrical machine. In this paper, the size of the interlaminar contacts formed by the burrs is estimated using a novel thermographic measurement method and an analytical method. The initial temperature rise of the contacts is used in the analytical model to estimate the contact size. The electromagnetic and thermal finite element formulations validate the results obtained from the analytical method. Moreover, the results obtained from the measurement, the analytical method, and the finite element method are compared. The study shows that the initial temperature rise method can be used to estimate the interlaminar contact size and electromagnetic losses if the temperature is measured immediately after energizing the sheets.
Design and Development of Movable Furniture

Existing stretchers, wheelchairs or trolleys are designed to move only on plane surface or ramp, and lifts are used to transfer a person or heavy items between the floors. However, many places do not have the facilities and a furniture is required to transfer easily using only stairs. In this project, an attempt is made to modify the existing furniture to meet the needs. Here, proposed concept is to add a machine to the conventional stretcher, such that it enables stretcher to climb stairs when force is applied manually. Aim is to develop smooth and stable mechanism to climb stairs as well as move on a plane surface. The developed model is an add-on to conventional stretcher which consists stair climbing wheels and a mechanism which engages these wheels before climbing and disengages them for motion on a plane surface. One of the main experiment carried out in this project is the calculation of jerks using a sensor and graph is plotted. In the future, the project can be extended to make a life-size model using stronger and light weight materials. Automatic wheelchairs can also be developed by adding motors to the system and controlling them.
Evaluation of ecotourism sites: a GIS-based multi-criteria decision analysis

Purpose The aim of this study is to evaluate the potential geographic locations for ecotourism activities and to select the best one among alternatives. Design/methodology/approach The proposed model consists of four sequential phases. In the first phase, different geographic criteria are determined based on existing literature, and data are gathered using GIS. On equal criteria weighing, alternative locations are determined using GIS in the second phase. In the third phase, the identified criteria are weighted using analytical hierarchy process (AHP) by various stakeholders of potential ecotourism sites. In the fourth phase, the PROMETHEE method is applied to determine the best alternative based on the weighted criteria. Findings A framework including four sequential steps is proposed. Using real data from the Black Sea region in Turkey, the authors test the applicability of the evaluation approach and compare the best alternative obtained by the proposed method for nine cities in the region. Consequently, west of Sinop, east of Artvin and south of the Black Sea region are determined as very suitable locations for ecotourism. Research limitations/implications The first limitation of the study is considered the number of included criteria. Another limitation is the use of deterministic parameters that do not cope with uncertainty. Further research can be conducted for determining the optimum locations for different types of tourism, e.g. religion tourism, hunting tourism and golf tourism, for effective tourism planning. Practical implications The proposed approach can be applied to all area that cover the considered criteria. The approach has been tested in the Black Sea region (nine cities) in Turkey. Social implications Using the proposed approach, decision-makers can determine locations where environmentally responsible travel to natural areas to enjoy and appreciate nature that promotes conservation have a low visitor impact and provide for beneficially active socioeconomic involvement of local individuals. Originality/value To the best knowledge of the authors, this is the first study which applies a GIS-based multi-criteria decision-making approach for ecotourism site selection.
An Image Matching Correction Method of Integrating Least Squares and Phase Correlation Using Window Series

Matching is the knotty point in photogrammetry and computer vision. Aiming at inaccurate corresponding points after preliminary matching, this paper proposed an image matching correction method of integrating least squares and phase correlation using window series. The method firstly uses least squares and phase correlation matching to correct corresponding points in utilizing of window series, and simultaneously calculates correlation coefficients using windows of different size. And then the correlation coefficients are used as the index of evaluating whether the corresponding image points are accurate or not. So the matching results with the largest correlation coefficients are chosen as the final results. Based on experimental data-set 1 and data-set 2, the experimental results revealed that the use of window series can significantly improve the correction accuracy of preliminary matching results. And the proposed method can correct the corresponding points of preliminary matching effectively and greatly improve the overall matching accuracy, which is better than least squares matching or phase correlation matching using window series and fixed windows.
Dependence of hidden attractors on non-linearity and Hamilton energy in a class of chaotic system

Non-linearity is essential for occurrence of chaos in dynamical system. The size of phase space and formation of attractors are much dependent on the setting of nonlinear function and parameters. In this paper, a three-variable dynamical system is controlled by different nonlinear function thus a class of chaotic system is presented, the Hamilton function is calculated to find the statistical dynamical property of the improved dynamical systems composed of hidden attractors. The standard dynamical analysis is confirmed in numerical studies, and the dependence of attractors and Hamilton energy on non-linearity selection is discussed. It is found that lower average Hamilton energy can be detected when intensity of nonlinear function is enhanced. It indicates that non-linearity can decrease the energy cost triggering for dynamical behaviors.
Linking Media: adopting Semantic Technologies for Multimodal Media Connection

Today's media and news organizations are constantly generating large amounts of multimedia content, majorly delivered online. As the online media market grows, the management and delivery of contents is becoming a challenge. Computational approaches can help to overcome this challenge by governing different applications such as content creation, production, search, and its promotion and distribution to different audiences. In this abstract we present a success story of the adoption of semantic technologies on the aforementioned applications, which are built on top of a semantic tagging framework, based on a Knowledge Graph (KG). The presented pipeline combines multimodal inputs into a contextual entity linking module, which indexes documents and links them to trends and stories developing on the news. We will describe how documents are linked and provided to media producers through Vilynx's platform, which is currently indexing over 20k media documents a day.
Emergence of chronic myeloid leukemia following autologous stem cell transplantation in a young woman with multiple myeloma

Conclusion The diagnosis of SM requires a cognisance of this rare entity along with a high index of suspicion. It should be considered as one of the differential diagnoses in cases with massive splenomegaly especially when the usual causes have been excluded. The assessment of the immunophenotype of mast cells is helpful in establishing the diagnosis of SM. This case also highlights the role of FCM in identifying abnormal mast cells based on routinely available markers in laboratories performing leukemia FCM.
Formation Control and Trajectory Tracking of Nonholonomic Mobile Robots

In this brief, we design Lyapunov-based control laws to achieve two multi-objective tasks for a network of open-loop unstable, nonholonomic mobile inverted pendulum (MIP) robots, using a connected undirected graph for inter-agent communication. Using the first protocol, translationally invariant formations are achieved along with the synchronization of attitudes and heading velocities to desired values. Using the second protocol, the robots move into a formation and asymptotically track a trajectory. The control laws are based on the kinematic model of the mobile robot, and control torques for the MIPs are extracted using a two-loop control architecture. Both the protocols guarantee boundedness of the linear heading velocity, which is necessary for the stability of the two-loop control architecture. The proposed control laws are experimentally validated on indigenously built MIP robots.
Prognostic value of the combination of microsatellite instability and BRAF mutation in colorectal cancer

Purpose The aim of this study was to investigate the prognostic value of the combination of microsatellite instability (MSI) and BRAF V600E mutation in colorectal cancer (CRC). Materials and methods We compare the prognosis difference among CRC patients with four subtypes according to MSI and BRAF mutation, ie, microsatellite stable/BRAF wild type (MSS/BRAFwt), MSS/BRAF mutation (MSS/BRAFmut), MSI/BRAFwt, and MSI/BRAFmut, by pooling the previous related reports and public available data sets till December 2017 for the first time. Results Twenty-seven independent studies comprising 24,067 CRC patients were included. Meta-analysis suggested that, compared with MSS/BRAFwt subtype, MSS/BRAFmut was associated with shorter overall survival (OS) (N=25, HR = 2.018, 95% CI = 1.706–2.388, P=2.220E-16), while there was a trend of association of MSI/BRAFmut with OS (N=13, HR = 1.324, 95% CI = 0.938–1.868, P=1.096E-01) and no association of MSI/BRAFwt with OS (N=17, HR = 0.996, 95% CI = 0.801–1.240, P=9.761E-01). Compared with MSI/ BRAFwt subtype, MSI/BRAFmut was a poor factor for OS (N=22, HR = 1.470, 95% CI = 1.243–1.740, P=7.122E-06). Compared with MSS/BRAFmut subtype, both MSI/BRAFwt (N=11, HR = 0.560, 95% CI = 0.433–0.725, P=1.034E-05) and MSI/BRAFmut (N=16, HR = 0.741, 95% CI = 0.567–0.968, P=2.781E-02) were favorable for OS. Subgroup analysis revealed similar results in all subgroups except the subgroup of stage IV cancer, in which MSI showed poor effects on OS in BRAF wild-type patients (N=6, HR = 1.493, 95% CI = 1.187–1.879, P=6.262E-04) but not in BRAF-mutated patients (N=5, HR = 1.143, 95% CI = 0.789–1.655, P=4.839E-01). Meta-analysis regression and test of interaction revealed no interaction of MSI with BRAF mutation when evaluating the associations of MSI/BRAF mutation subtypes with OS in CRC. Conclusion Among the four subtypes according to MSI and BRAF mutation, MSS/BRAFmut was a poor prognostic factor, while MSS/BRAFwt and MSI/BRAFwt were comparable and favorable and MSI/BRAFmut was moderate in CRC. The combination of MSI/BRAF mutations could facilitate the planning of individualized treatment strategies and prognosis improvement in CRC.
Dynamic Task Assignment in Crowdsensing with Location Awareness and Location Diversity

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issue of matching workers to their assigned tasks. In this paper, we reexamine this issue by considering the spatiotemporal worker mobility and task arrivals, which more fits the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the nonlinear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Performance evaluation on real data set validates the proposed algorithm, where 116% gain of fairness is achieved at the expense of 12% loss of sensing value on average.
Local Binary Pattern with Random Forest for Acoustic Scene Classification

This paper presents an approach for acoustic scene classification using the local binary pattern (LBP) and random forest (RF). The audio signal is converted to a Constant-Q transform (CQT) representation and LBP is used to extract the features from this time-frequency representation. The CQT representations are divided into a number of sub-bands to obtain more localized features relevant to the spectral information. We then use random forest to select the most important features for each band of extracted LBP features. For further performance enhancement, we use feature level fusion of LBP and HOG features. The proposed system has achieved an accuracy of 85% on the DCASE 2016 dataset.
LUMOS: A Fast and Efficient Optical Circuit Switch Scheduling Technique

Contemporary optical circuit switch (OCS) scheduling techniques are still struggling to balance the tradeoff between scheduling efficiency and computational run time. To diminish this gap, we introduce Lumos—a fast and efficient OCS scheduling technique. Lumos negotiates the tradeoff between the cost of prolonging the current configuration and the time penalty of circuit reconfigurations. An extensive evaluation shows that Lumos matches the state of the art in scheduling efficiency while running orders of magnitude faster.
Breast ultrasound lesions recognition: end-to-end deep learning approaches

Abstract. Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. To assess the performance, we conduct fivefold cross validation using the following split: 70% for training data, 10% for validation data, and 20% testing data. The results showed that our proposed method performed better on benign lesions, with a top “mean Dice” score of 0.7626 with FCN-16s, when compared with the malignant lesions with a top mean Dice score of 0.5484 with FCN-8s. When considering the number of images with Dice score >0.5, 89.6% of the benign lesions were successfully segmented and correctly recognised, whereas 60.6% of the malignant lesions were successfully segmented and correctly recognized. We conclude the paper by addressing the future challenges of the work.
Development of High Voltage Pulse Power Supply for Microwave Tube Applications

This paper describes about the design and development of high voltage DC pulse power supply for high power applications. The system has variety of features such as independent control of Pulse Repetition Frequency (PRF), Pulse Duration $({T_{on}})$ and pulse amplitude. PC based wireless pulse generation and control unit with Graphical User Interface (GUI) having variable/controlled PRF (10Hz-1 kHz) and Ton $(10-100{\mu} \mathrm{s})$ is developed for gating pulse generation. Two sets of −25kV, 10A pulse power supplies are connected in series to achieve −50kV, 10A pulse output voltage. High voltage DC pulse power supply is used for the characterization of high power microwave tube and testing applications. To verify the proposed design, simulation and experimental results on resistive load are presented in this paper.