Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromogranin B (CHGB) is dimorphic and responsible for dominant anion channels delivered to cell surface via regulated secretion

Regulated secretion is conserved in all eukaryotes. In vertebrates granin family proteins function in all key steps of regulated secretion. Phase separation and amyloid-based storage of proteins and small molecules in secretory granules require ion homeostasis to maintain their steady states, and thus need ion conductances in granule membranes. But granular ion channels are still elusive. Here we show that granule exocytosis in neuroendocrine cells delivers to cell surface dominant anion channels, to which chromogranin B (CHGB) is critical. Biochemical fractionation shows that native CHGB distributes nearly equally in soluble and membrane-bound forms, and both reconstitute highly selective anion channels in membrane. Confocal imaging resolves granular membrane components including proton pumps and CHGB in puncta on the cell surface after stimulated exocytosis. High pressure freezing immuno-EM reveals a major fraction of CHGB at granule membranes in rat pancreatic β-cells. A cryo-EM structure of bCHGB dimer of a nominal 3.5 Å resolution delineates a central pore with end openings, physically sufficient for membrane-spanning and large single channel conductance. Together our data support that CHGB-containing (CHGB+) channels are characteristic of regulated secretion, and function in granule ion homeostasis near the plasma membrane or possibly in other intracellular processes.
Towards Designing and Learning Piecewise Space-Filling Curves

To index multi-dimensional data, space-filling curves (SFCs) have been used to map the data to one dimension, and then a one-dimensional indexing method such as the B-tree is used to index the mapped data. The existing SFCs all adopt a single mapping scheme for the whole data space. However, a single mapping scheme often does not perform well on all the data space. In this paper, we propose a new type of SFC called piecewise SFCs, which adopts different mapping schemes for different data subspaces. Specifically, we propose a data structure called Bit Merging tree (BMTree), which can generate data subspaces and their SFCs simultaneously and achieve desirable properties of the SFC for the whole data space. Furthermore, we develop a reinforcement learning based solution to build the BMTree, aiming to achieve excellent query performance. Extensive experiments show that our proposed method outperforms existing SFCs in terms of query performance.
Hessian-Laplace Feature Detector and Haar Descriptor for Image Matching

In recent years feature matching using invariant features has gained significant importance due to its application in various recognition problems. Such techniques have enabled us to match images irrespective of various geometric and photometric transformations between images. The thesis being presented here focusses on developing such a feature matching technique which can be used to identify corresponding regions in images. A feature detection approach is proposed, which finds features that are invariant to image rotation and scaling, and are also robust to illumination changes. A description is computed for each feature using the local neighborhood around it and then acts as a unique identifier for the feature. These feature identifiers (or feature descriptors) are then used to identify point to point correspondences between images. A systematic comparison is made between this feature detector, and others that are described in the literature. Later in this work, we apply the feature matching technique developed here to perform image retrieval for panoramic images. Our objective here is to retrieve a panoramic image similar to a query image from a database. We show how such a retrieval task can be performed by giving results for both indoor and outdoor sequences.
Chaotic pattern dynamics on sun-melted snow

This thesis describes the comparison of time-lapse field observations of suncups on alpine snow with numerical simulations. The simulations consist of solutions to a nonlinear partial differential equation which exhibits spontaneous pattern formation from a low amplitude, random initial surface. Both the field observations and the numerical solutions are found to saturate at a characteristic height and fluctuate chaotically with time. The timescale of these fluctuations is found to be instrumental in determining the full set of parameters for the numerical model such that it mimics the nonlinear dynamics of suncups. These parameters in turn are related to the change in albedo of the snow surface caused by the presence of suncups. This suggests the more general importance of dynamical behaviour in gaining an understanding of pattern formation phenomena.
An exploration of laser scanning modalities: from raster and spiral to Lissajous and Risley prisms-based

Laser scanning is employed in a variety of modalities, including the most common raster scanning, as well as spiral, Lissajous, or Risley prism-based (the later a type of rhodonea) scanning. The first two can have the advantage of linearity, while the last two are faster, although they are highly non-linear. The present study performs an exploration of such aspects. Thus, we compare the characteristics of the above-mentioned modalities, with an emphasis on parameters such as field of view (FOV), linearity, resolution, and fill factor (FF). Simulations are carried out regarding different types of scanning patterns, as well as some experimental validations. Also, results from some of our recent studies on galvanometer-, polygon mirror-, and Risley prisms-based scanning are utilized. Some of the specific performances of the considered scanning modalities are discussed, highlighting advantages and drawbacks.
DHSDJArch: An Efficient Design of Distributed Heterogeneous Stream-Disk Join Architecture

Heterogeneity is the key aspect of complex networks and smart devices for using it as nature of live streams. The heterogeneous stream-disk join is a significant research topic in real-time processing applications because it can directly affect the data analytics. Multiple issues, including stream loss, scalability, disk access cost, and data accuracy, should be considered during heterogeneous stream-disk join transformation. In this work we overcome these issues by introducing a distributed heterogeneous stream-disk join architecture (DHSDJArch) which can prevent stream data loss as well as maintaining balance between heterogeneous distributed data sources and accuracy of stream-disk join. A four phased distributed architecture is proposed for the multi-objective optimization to transform heterogeneous incomplete stream. To prevent stream loss, configuration of log retention is proposed based on the characteristics of distributed event streaming platform (DESP). Specifically, two transformations are proposed to pre-process heterogeneous streams and to join pre-processed stream with distributed disk data by performing real-time disk access while compensating the differences between data sources and streaming application, respectively. We conduct comprehensive experimental study on real datasets to verify the performance of proposed architecture in terms of accuracy, log retention policy, scaling, stability and cloud data storage.
Comparison study of unsupervised paraphrase detection: Deep learning—The key for semantic similarity detection

Automatic detection of concealed plagiarism in the form of paraphrases is a difficult task, and finding a successful unsupervised approach for paraphrase detection is necessary as a precondition to change that. This comparative study identified the most efficient methods for unsupervised paraphrased document detection using similarity measures alone or combined with Deep Learning (DL) models. It proved the hypothesis that some DL models are more successful than the best statistically‐based methods in that task. Many experiments were carried out, and their results were compared. The text similarities between documents are obtained from 60 different methods using five paraphrase corpora, including the new one made by authors, as an important original contribution. Some DL models achieved significantly better results than those obtained by the best statistical methods, especially pre‐trained transformer‐based language models with average values of Accuracy and F1 of 85.8% and 88.3%, respectively, with top values of 99.9% and 98.4% for Accuracy and F1 on some corpora. These results are even better than those of supervised and combined approaches. Therefore, here presented results prove that detecting concealed plagiarism becomes an attainable goal. This study highlighted those language models with the best overall results for paraphrase detection as best suited for further research. The study also discussed the choice of similarity/distance measure paired with embeddings produced by DL models and some advantages of using cosine similarity as the fastest measure. For 60 different methods, complexity has been defined in O notation. Times needed for their implementation have also been presented. The article's results and conclusions are a firm base for future semantic similarity, paraphrasing, and plagiarism detection studies, clearly marking state‐of‐the‐art tools and methods.
WiAi-ID: Wi-Fi-Based Domain Adaptation for Appearance-Independent Passive Person Identification

Wi-Fi signal-based person identification has become a hot research topic due to the widespread deployment of Wi-Fi devices and the fact that these approaches are noncontact, passive, and privacy-preserving. While the existing related methods and systems have achieved good performance for person identification, they also encounter many significant challenges in practical applications. Due to the propagation properties of Wi-Fi signals, the signal at the receiver will change significantly when the user’s appearance changes. This makes single-appearance trained models unusable for cross-appearance recognition tasks. To address this challenge, we propose a deep learning-based framework for appearance-independent identification using Wi-Fi signals (WiAi-ID), the core of which lies in the fact that the domain discriminator and feature extractor are trained together in an adversarial manner, thus forcing the model to extract identity-inherent features independent of human appearance, and introduces a multiscale CNN adaptation module to capture time-span-based features. We collected Wi-Fi signal data of pedestrians with different appearances. The experimental results show that WiAi-ID can effectively eliminate the impact on identification due to pedestrian appearance variations and accordingly outperforms the current state-of-the-art video and wireless signal-based recognition methods.
Cardiac Dimensions in Normal Pregnancy: A Prospective Study

Background: Pregnancy, a unique physiologic state, is associated with several changes in the various body systems. The cardiovascular system is one of the systems affected, with chronic volume overload being one of the characteristic changes experienced during pregnancy. Cardiovascular disease in pregnancy is the leading cause of non-obstetric maternal death worldwide. Aim: This study aims to determine and describe the changes in left and right ventricular and atrial sizes in systole and diastole in the course of normal pregnancy. Methods and materials: A cohort study was conducted among healthy pregnant women between the age of 18 and 40 who attended the antenatal clinic of Federal Medical Centre (FMC), Yenagoa, Bayelsa State. Fifty women were recruited during the first trimester (T1) of pregnancy and followed up until six weeks postpartum. Ethical approval was obtained from the Research Ethics Committee of Federal Medical Centre, Yenagoa, with approval number FMCY/REC/ECC/2019/JAN/150. Clinical evaluation, hematologic, biochemical, and anthropometric assessments, and two-dimensional M-mode and Doppler echocardiography were done for the participants in each trimester of pregnancy and at six weeks postpartum. The clinical and echocardiographic parameters were analyzed using Statistical Package for the Social Sciences (SPSS) version 22.0 (IBM Corp., Armonk, NY, USA). Results: The mean trend of left ventricular posterior wall thickness in diastole (LVPWd) and left ventricular posterior wall thickness in systole (LVPWs) increased progressively from the first to third trimester (T3) (not statistically significant) but dropped toward initial values in postpartum to the level that was statistically significant for LVWPd alone when compared to baseline first trimester values. The left atrial diameter in systole (LADs) was largest in the third trimester, and the left atrial volume index (LAVI) and right ventricular basal diameter (RVD1) also showed a similar trend. The left ventricular internal diameter (LVID) in both systole and diastole increased progressively from the first to the third trimesters, but the increase was only statistically significant between the third trimester (T3) and the first trimester (T1). The right atrial diameter (RAD) and right atrial volume (RAV) also increased progressively from the first to the third trimesters, but the increase was only statistically significant between the third trimester (T3) and the first trimester (T1). Conclusion: Changes were noticed in the cardiac chamber sizes during pregnancy. However, this reversed back to levels similar to the first trimester during the postpartum period. To aid in the early detection and treatment of cardiovascular disorders in pregnancy, screening of apparently healthy pregnant women who later developed complaints is advised as cardiovascular changes could be significant during pregnancy.
Cutaneous metastasis of rectal adenocarcinoma: a case report and literature review.

According to the latest data provided by Globocan 2020, the incidence of colorectal cancer ranks third, after lung cancer and breast cancer, becoming a more and more important global health issue. Of the cases diagnosed with colorectal cancer, more than 25% are diagnosed in the metastatic stage, with the presence of secondary tumors more frequently in the liver, lung and bone. Skin metastases from colorectal cancer are still rare today (< 4%). We want to present a rare, unique case in our department of a 74-year-old patient diagnosed 9 years ago with a malignant rectal tumor who, after a disease-free period of approximately 8 years and a half, developed multiple skin metastases of rectal adenocarcinoma.
Model-Predictive Control of Multilevel Inverters: Challenges, Recent Advances, and Trends

Model-predictive control (MPC) has emerged as a promising control method in power electronics, particularly for multiobjective control problems such as multilevel inverter (MLI) applications. Over the past two decades, improving the performance of MPC and tackling its technical challenges, such as computational load, modeling accuracy, cost function design, and weighting factor selection, have attracted great interest in power electronics. This article aims to discuss the current state of MPC strategies for MLI applications, describing the significance of each challenge with the reported effective solutions. Through this review, the MPC methods are categorized into two groups: direct MPC (without modulator) and indirect MPC (with modulator). The recent advances of each category are presented and analyzed, focusing on direct MPC as the most applied method for MLI topologies. In addition, some of the important concepts are experimentally validated through a case study and compared under the same operating conditions to evaluate the performance and highlight their features. Finally, the future trends of MPC for MLI applications are discussed based on the current state and reported developments.
in the Neighborhood of Singularities

In this paper, a new singularity control method for a fully parallel robot manipulator is proposed. The damped least-square method has frequently been used to solve the singularity problem of resolved-acceleration control schemes. It works by damping accelerations of the end-effector, so that accelerations in the degenerated directions are zero at a singular point. However, its velocities may not be zero in some situations, in those cases they will encounter fluctuations around the singular point. In this paper, the control using damped velocity, being called the damped rate resolve acceleration control scheme (DRRAC) is proposed to overcome this drawback. We will show that the DRRAC is asymptotically stable, and discuss its convergent properties. The main advantage of the DRRAC in the 3RPS parallel manipulator control system is not to plan its path to avoid the singular point, and could improve the workspace. Illustrative examples are given to show its effectiveness in practical applications, and experimental results are taken to verify the proposed control scheme.
Distributed Ledger Technology for Fault Tolerant Distribution Grid Operations

This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers’ privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.01% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.
Vortex dynamics of stratospheric sudden warmings: A reanalysis data study using PV contour integral diagnostics

The dynamics of the polar vortex underlying stratospheric sudden warming (SSW) events are investigated in a data‐based diagnostic study. Potential vorticity (PV) contour integral quantities on isentropic surfaces are discussed in a unified framework; new expressions for their time evolution, particularly suitable for evaluation from data, are presented and related to previous work. Diagnostics of mass and circulation are calculated from ERA‐40 reanalysis data for the stratosphere in case‐studies of seven winters with different SSW behaviour. The edge of the polar vortex is easily identifiable in these diagnostics as an abrupt transition from high to low gradients of PV, and the warming events are clearly visible. The amount of air stripped from the vortex as part of a preconditioning leading up to the warming events is determined using the balance equation of the mass integral. Significant persistent removal of mass from the vortex is found, with several such stripping events identifiable throughout the winter, especially in those during which a major sudden warming event occurred. These stripping episodes are visible in corresponding PV maps, where tongues of high PV are being stripped from the vortex and mixed into the surrounding surf zone. An attempt is made to diagnose from the balance equation of the circulation the effect of frictional forces such as gravity wave dissipation on the polar vortex.
Content categories on Instagram: from promotional to interactivity for business development

Purpose — The study aimed to analyze the content on Instagram and to examine the relationship between the content categories and the reach and impressions of the posts.Method — This study analyzed 58 content posts from the Instagram account @Generasi_kenzu using purposive sampling, divided into five categories and measured based on accounts reached and impressions, using descriptive statistics, a Kolmogorov-Smirnov test for normality, and a Kruskal-Wallis test for comparison of mean reach and impressions across the categories.Result — The content analysis revealed that promotion was the most prevalent category, followed by information and education, with a mean reach of 64.22 and a significant proportion of views from the user's profile, and found hashtags to be an effective method of increasing visibility, though with a considerable degree of variation among individual posts. The Kruskal-Wallis test showed no significant difference in the distribution of accounts reached, reach from follower, reach from non-follower, impressions, from profile, from hashtags, from home and from other across the five content categories.Contribution — This study contributes by adding knowledge about content categories' effectiveness on social media platforms, providing valuable insights into content and reach of Instagram posts, and suggesting the need for further research to explore impacting factors.
Extension of a viscous thread with temperature-dependent viscosity and surface tension

We consider the evolution of a long and thin vertically aligned axisymmetric viscous thread that is composed of an incompressible fluid. The thread is attached to a solid wall at its upper end, experiences gravity and is pulled at its lower end by a fixed force. As the thread evolves, it experiences either heating or cooling by its environment. The heating affects the evolution of the thread because both the viscosity and surface tension of the thread are assumed to be functions of the temperature. We develop a framework that can deal with threads that have arbitrary initial shape, are non-uniformly preheated and experience spatially non-uniform heating or cooling from the environment during the pulling process. When inertia is completely neglected and the temperature of the environment is spatially uniform, we obtain analytic solutions for an arbitrary initial shape and temperature profile. In addition, we determine the criteria for whether the cross-section of a given fluid element will ever become zero and hence determine the minimum stretching force that is required for pinching. We further show that the dynamics can be quite subtle and leads to surprising behaviour, such as non-monotonic behaviour in time and space. We also consider the effects of non-zero Reynolds number. If the temperature of the environment is spatially uniform, we show that the dynamics is subtly influenced by inertia and that the location at which the thread will pinch is selected by a competition between three distinct mechanisms. In particular, for a thread with initially uniform radius and a spatially uniform environment but with a non-uniform initial temperature profile, pinching can occur either at the hottest point, at the points near large thermal gradients or at the pulled end, depending on the Reynolds number. Finally, we show that similar results can be obtained for a thread with initially uniform radius and uniform temperature profile but exposed to a spatially non-uniform environment.