Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble-based unsupervised machine learning method for membership determination of open clusters using mahalanobis distance

We present an improved method for the determination of membership of an open cluster using ensemble-based unsupervised machine learning techniques. The working principle of this method relies on two stages: (i) choosing a suitable range of three astrometric parameters (π, μαcos δ, μδ) using k-nearest neighbour (kNN) algorithm on the data downloaded for the cluster within a smaller search radius; (ii) application of two component Gaussian Mixture Modelling (GMM) on the resulting one dimensional Gaussian distribution of Mahalanobis distance (MD) of stars using the range of parameters obtained from the earlier step, but with the data downloaded within a bigger search radius. MD is calculated from the mean of each of the parameters in three dimensions. Thus the use of MD reduces the input of the GMM from the 3D parameter space into a 1D parameter space for the cluster membership determination. The method has been tested on a few clusters including those which have overlaps in some/all the parameters using the data obtained from the Gaia DR3 database. It is found that the approach can easily separate the cluster members from the field stars. The clean colour-magnitude diagrams and similar direction of proper motions of the member stars obtained for the clusters shows that this method is very efficient and robust in segregating the cluster members from the field stars.
Triple-Fact Retriever: An explainable reasoning retrieval model for multi-hop QA problem

Nowadays, multi-hop question answer (QA) problem is challenging and not well solved in the QA community. The dominant bottleneck of the multi-hop QA problem is the need for a reasoning retriever to fetch a document path from an open-domain corpus (e.g., Wikipedia). A reasoning retriever aims to collect an evidence document from large corpora at one hop retrieval and aggregate the evidence for subsequent hop retrieval, which yields a document path after multi-hop retrieval. There exist two challenges, (1) to fetch the evidence document in an efficient and explainable way at one hop retrieval and (2) to update the question information by aggregating the evidence from the retrieved document after each hop retrieval. To address these two challenges, we propose a triple-fact-based retrieval model to effectively retrieve a related document path in an explainable way for each question. We extract a structured representation from the unstructured document and utilize the knowledge of pre-trained language model (PLM) to do the semantic-level matching between the question and document. We evaluate the proposed Triple-fact Retriever model on the recently proposed open-domain multi-hop QA dataset, HotpotQA, and a cross-document multi-step Reading Comprehension dataset, Wikihop. The results11The source code is available on our website: https://github.com/Rebaccamin/triple_retriever. demonstrate that the Triple-fact retriever outperforms the existing baseline retrieval works.
Automated Orientation Control of Motile Deformable Cells

Automated manipulation of deformable objects is challenging due to the object’s deformation behavior. Different from still deformable objects such as wires and cloth, biological organisms such as sperm and worms are both deformable and motile, requiring the control of both deformation and motion. This paper reports automated orientation control of live sperm, as an example of motile deformable cells. Robotic manipulation of human sperm was performed by using a glass micropipette, which is a standard clinical tool, to rotate individual motile sperm. Sperm rotation must be performed before immobilization, as required in clinical cell surgery for infertility treatment. To control tail deformation during sperm rotation, a path planner was designed based on kinematic analysis and manipulation point update. To deal with the intrinsic motion of a motile sperm, a motorized stage was controlled to compensate for sperm swimming motion, and an observer was designed to decouple sperm orientation from its wiggling motion. A sliding mode controller was designed to cope with stiffness variances along the sperm tail and among different sperm. Deep neural networks were developed for robust sperm tail detection, and Kalman filter was used to predict tail motion. Experimental results demonstrated that automated sperm manipulation achieved an orientation error of 0.8° and operation time of 6.8 s, both significantly less than those of manual operation. The designed observer was effective to reduce sperm orientation error by reducing the disturbance from sperm wiggling motion. The developed sliding mode controller outperformed the PID controller in operation time, reducing the time of oocyte exposure to the ambient environment. Note to Practitioners—This work tackled the challenge of rotating a fast-swimming and deformable sperm in clinical cell surgeries. Automated manipulation of deformable objects has wide applications in industrial and service settings such as manipulating wires and folding cloth. However, the intrinsic motion of a motile sperm and the lack of a rotational degree of freedom in standard micromanipulators pose difficulties to automated sperm manipulation. In this paper, we propose automation techniques for sperm orientation control. For sperm tail detection, deep learning was used to handle the variances of shape and length among different sperm. A path planning strategy and a controller were designed to achieve automated rotation of motile sperm, with its deformation and motion both controlled. The developed methods can be generalized to the manipulation of other deformable objects such as wires, cables and cloth. These objects exhibit significant variance of mechanical properties, and calibration is often time-consuming. The designed controller can be used to manipulate deformable objects with robustness to varied mechanical parameters. Path planning was designed by updating the manipulation point based on the object’s deformation behavior, and is suitable in manipulation where constraints are imposed such as the object’s strain.
Meta-Learning Based Classification for Moving Object Trajectories in Mobile IoT

The proliferation and ubiquity of GPS-enabled mobile Internet of Things (IoT) devices (e.g., drones and unmanned robotic devices) across many disciplines has generated substantial interest in the analysis and mining of trajectory data for enhancing public services such as air pollution monitoring and road safety. Among all trajectory analysis techniques in mobile IoT, classifying moving device trajectories is a fundamental research problem due to its importance for numerous important mobile IoT applications such as travel demand analysis and animal migration patterns. However, existing classification methods for moving object trajectories either are dynamic programming problems (such as similarity-based methods with dynamic time wrapping or Fréchet distance) with a quadratic time complexity in all cases, or need abundant labeled samples to training classification model, thus limit the scalability of these methods for given resource-constrained mobile IoT devices. In this study, we propose a deep learning-based approach for trajectory classification in mobile IoT with linear time complexity by firstly encoding a trajectory as a vector via deep representation learning, then utilizing a meta-learning based approach to learn the ability of classifying trajectory with few annotated samples. Experiments on a massive trajectory dataset show that the proposed approach outperforms state-of-the-art baselines consistently and significantly.
Al2O3@SiO2-chitosan synthesis via sol-gel process and its application for ciprofloxacin adsorption

This study aims to investigate the solid-phase extraction procedure of the most prescribed antibiotics in the world, ciprofloxacin, by using aluminosilicates grafted chitosan (Al 2 O 3 @SiO 2 -chitosan). The green adsorbent was synthesis via sol–gel process, and cross-linking with chitosan, in the presence of glutaraldehyde as chelating agent. The results show that the extraction yield of ciprofloxacin removal decreases in acidic medium, more than 52% of adsorption yield was obtained with an optimal pH solution of 5.77. The kinetic study shows also that ciprofloxacin adsorption was very fast at the initial stage of contact time up to 30 min when the equilib- rium adsorption was established. The maximal adsorption capacity was found to be 31 mg g –1 , under optimum conditions. Ciprofloxacin uptake process followed the pseudo-second-order rate expression. Equilibrium isotherm for ciprofloxacin has been modeled successfully using Langmuir isotherm model. Thermodynamics study leads to a spontaneous and exothermic process for ciprofloxacin adsorption by Al 2 O 3 @SiO 2 -chitosan (Δ H = –08.49 kJ mol –1 ).
Is species richness mediated by functional and genetic divergence? A global analysis in birds

Abstract Unravelling why species richness shows such dramatic spatial variation is an ongoing challenge. Common to many theories is that increasing species richness (e.g. with latitude) requires a compensatory trade‐off on an axis of species' ecology. Spatial variation in species richness may also affect genetic diversity if large numbers of coexisting, related species result in smaller population sizes. Here, we test whether increasing species richness results in differential occupation of morphospace by the constituent species, or decreases species' genetic diversity. We test for two potential mechanisms of morphological accommodation: denser packing in ecomorphological space, and expansion of the space. We then test whether species differ in their nucleotide diversity depending on allopatry or sympatry with relatives, indicative of potential genetic consequences of coexistence that would reduce genetic diversity in sympatry. We ask these questions in a spatially explicit framework, using a global database of avian functional trait measurements in combination with >120,000 sequences downloaded from GenBank. We find that higher species richness within families is not systematically correlated with either packing in morphological space or overdispersion but, at the Class level, we find a general positive relationship between packing and species richness, but that points sampled in the tropics have comparatively greater packing than temperate ones relative to their species richness. We find limited evidence that geographical co‐occurrence with closely related species or tropical distributions decreases nucleotide diversity of nuclear genes; however, this requires further analysis. Our results suggest that avian families can accumulate species regionally with minimal tradeoffs or cost, implying that external biotic factors do not limit species richness. Read the free Plain Language Summary for this article on the Journal blog.
Intelligence IS Cognitive Flexibility: Why Multilevel Models of Within-Individual Processes Are Needed to Realise This

Despite substantial evidence for the link between an individual’s intelligence and successful life outcomes, questions about what defines intelligence have remained the focus of heated dispute. The most common approach to understanding intelligence has been to investigate what performance on tests of intellect is and is not associated with. This psychometric approach, based on correlations and factor analysis is deficient. In this review, we aim to substantiate why classic psychometrics which focus on between-person accounts will necessarily provide a limited account of intelligence until theoretical considerations of within-person accounts are incorporated. First, we consider the impact of entrenched psychometric presumptions that support the status quo and impede alternative views. Second, we review the importance of process-theories, which are critical for any serious attempt to build a within-person account of intelligence. Third, features of dynamic tasks are reviewed, and we outline how static tasks can be modified to target within-person processes. Finally, we explain how multilevel models are conceptually and psychometrically well-suited to building and testing within-individual notions of intelligence, which at its core, we argue is cognitive flexibility. We conclude by describing an application of these ideas in the context of microworlds as a case study.
Overcoming impediments to virtual team effectiveness

The term virtual team denotes an organizational team whose members rarely meet face to face but who nevertheless perform interdependent tasks in pursuit of collective goals. This article identifies the unique aspects of virtual teams that generate major barriers to their effectiveness, and suggests ways in which these may be either overcome or mitigated. A process-oriented model of virtual team effectiveness is presented, identifying issues associated with the development of transactive memory systems, work engagement, and collective efficacy as major challenges to virtual team effectiveness. These issues are illustrated with reference to the experience of virtual teams within a minerals processing firm. Finally, the authors discuss aspects of virtual team leadership and team climate that may help overcome some of the potential process losses associated with virtual teamwork. © 2008 Wiley Periodicals, Inc.
Machine-Learning-Empowered Passive Beamforming and Routing Design for Multi-RIS-Assisted Multihop Networks

This article proposes a novel machine-learning-based routing optimization for the multiple reconfigurable intelligent surfaces (M-RIS)-assisted multihop cooperative networks, in which a practical phase model for reconfigurable intelligent surface (RIS) with the amplitude variation based on the corresponding discrete phase shift is considered. We aim to maximize the end-to-end data rate in the proposed network by jointly optimizing the data transmission path, the passive beamforming design of RIS, and transmit power allocation. To tackle this complicated nonconvex problem, we divide it into two subtasks: 1) the passive beamforming design of the RIS and 2) joint routing and power allocation optimization. First, for the passive beamforming design of RIS, we develop a distributed learning algorithm that employs a cascade forward backpropagation network in each relay node to solve the RIS coefficients optimization problem by directly using the optimization target to train the cascade networks. This solution can avoid the curse of dimensionality of traditional reinforcement learning algorithms in the RIS optimization problem. Then, based on the result of RIS optimization, we introduce the proximal policy optimization (PPO) algorithm with the clipping method to find solutions for joint optimization of routing and power allocation via achieving the long-term benefit in the Markov decision process (MDP). Simulation results show that the proposed learning-based scheme can learn from the environment to improve its policy stability and efficiency in the iterative training process for optimizing routing and RIS and significantly outperform the benchmark schemes.
Diamond-like Carbon Coatings in the Biomedical Field: Properties, Applications and Future Development

Repairment and replacement of organs and tissues are part of the history of struggle against human diseases, in addition to the research and development (R&D) of drugs. Acquisition and processing of specific substances and physiological signals are very important to understand the effects of pathology and treatment. These depend on the available biomedical materials. The family of diamond-like carbon coatings (DLCs) has been extensively applied in many industrial fields. DLCs have also been demonstrated to be biocompatible, both in vivo and in vitro. In many cases, the performance of biomedical devices can be effectively enhanced by coating them with DLCs, such as vascular stents, prosthetic heart valves and surgical instruments. However, the feasibility of the application of DLC in biomedicine remains under discussion. This review introduces the current state of research and application of DLCs in biomedical devices, their potential application in biosensors and urgent problems to be solved. It will be useful to build a bridge between DLC R&D workers and biomedical workers in order to develop high-performance DLC films/coatings, promote their practical use and develop their potential applications in the biomedical field.
Allosteric Binding Sites of the SARS-CoV-2 Main Protease: Potential Targets for Broad-Spectrum Anti-Coronavirus Agents

Abstract The current pandemic caused by the COVID-19 disease has reached everywhere in the world and has affected every aspect of our lives. As of the current data, the World Health Organization (WHO) has reported more than 300 million confirmed COVID-19 cases worldwide and more than 5 million deaths. Mpro is an enzyme that plays a key role in the life cycle of the SARS-CoV-2 virus, and it is vital for the disease progression. The Mpro enzyme seems to have several allosteric sites that can hinder the enzyme catalytic activity. Furthermore, some of these allosteric sites are located at or nearby the dimerization interface which is essential for the overall Mpro activity. In this review paper, we investigate the potential of the Mpro allosteric site to act as a drug target, especially since they interestingly appear to be resistant to mutation. The work is illustrated through three subsequent sections: First, the two main categories of Mpro allosteric sites have been explained and discussed. Second, a total of six pockets have been studied and evaluated for their druggability and cavity characteristics. Third, the experimental and computational attempts for the discovery of new allosteric inhibitors have been illustrated and discussed. To sum up, this review paper gives a detailed insight into the feasibility of developing new Mpro inhibitors to act as a potential treatment for the COVID-19 disease.
On Time-optimal (k, p)-core Community Search in Dynamic Graphs

Community search aims to find cohesive subgraphs containing certain vertices, attracting increasing interest recently. However, existing cohesive models such as k-core mainly focus on the dense connections inside the community, and neglect the interactions with the vertices outside. In this paper, we study the (k,p) -core community search (KPCS) problem in dynamic graphs, i.e., find the maximal connected subgraph containing a query vertex where each vertex has at least k neighbors and at least p fraction of its neighbors in the subgraph. Such fraction and connectivity constraints bring non-trivial challenges to the online community search in dynamic graphs. Thus, we design a space-efficient $O(m)$ where $m$ is the edge number) index KPForest which can support time-optimal (k,p) -core community search. We also propose novel construction and maintenance algorithms to record and update the (k,p) value and the connectivity information for dynamic graphs correctly and efficiently. Extensive experimental studies on ten real-world datasets show that our index can support community search with two orders of magnitude speedup at a small cost of construction and maintenance compared with the baseline algorithms.
A Scoping Study of United States Extension Professional Competencies

This scoping study aimed to answer the question: What is known from existing research studies about the major competencies required of Extension professionals? Scoping studies are characterized by searching the literature to summarize major concepts on a research topic, and they are valuable as they show evidence for the major concepts. This study was limited to research studies of United States’ Extension professionals. The major conclusion from the scoping study is that existing research studies have yielded a rich literature base regarding Extension professional competencies. This scoping study identified 15 Extension professional competency domains: communication, diversity and cultural competence, flexibility, interpersonal relations, knowledge of Extension, leadership, professionalism, program planning and evaluation, resource management, subject matter competence, teaching methodology and delivery, technology, thinking and problem-solving, understanding community needs, and volunteer management. It is recommended that the results inform Extension professional job descriptions and professional learning programs.
Functional Characterization of Tomato ShROP7 in Regulating Resistance against Oidium neolycopersici

ROPs (Rho-like GTPases from plants) are a unique family of small GTP-binding proteins in plants and play vital roles in numerous cellular processes, including growth and development, abiotic stress signaling, and plant defense. In the case of the latter, the role of ROPs as response regulators to obligate parasitism remains largely enigmatic. Herein, we isolated and identified ShROP7 and show that it plays a critical role in plant immune response to pathogen infection. Real-time quantitative PCR analysis revealed that the expression of ShROP7 was significantly increased during incompatible interactions. To establish its requirement for resistance, we demonstrate that virus-induced gene silencing (VIGS) of ShROP7 resulted in increased susceptibility of tomato to Oidium neolycopersici (On) Lanzhou strain (On-Lz). Downstream resistance signaling through H2O2 and the induction of the hypersensitive response (HR) in ShROP7-silenced plants were significantly reduced after inoculating with On-Lz. Taken together, with the identification of ShROP7-interacting candidates, including ShSOBIR1, we demonstrate that ShROP7 plays a positive regulatory role in tomato powdery mildew resistance.
Neuropathic Pain Relief after Surgical Neurolysis in Patients with Traumatic Brachial Plexus Injuries: A Preliminary Report

Objective To evaluate the usefulness of surgical neurolysis for neuropathic pain relief in patients with posttraumatic brachial plexus injury (BPI). Methods A prospective, longitudinal, nonrandomized, self-controlled before and after study was performed to evaluate the pain changes according to their intensity using the Visual Analogue Scale (VAS), and the sensory recovery after surgery using the British Medical Research Council (BMRC) scale for sensory recovery. To establish significant changes, a paired T-test was performed, and in order to determine the magnitude of these changes, an effect size was measured. α = 0.05. Results Ten patients were included with an average follow-up of 61.9 ± 53.62 months. The main mechanism of injury was vehicular trauma (70%). A significant decrease in pain after the surgical intervention was observed resulting from an average preoperative state according to VAS of 8.4 ± 1.58, to a postoperative state of 3.4 ± 3.27 (59.52%, p = 0.005, Δ = 1.572), added to a mean sensory improvement (25%) from 2.8 ± 1.62 to 3.5 ± 0.97 after surgery according to BMRC, without statistically significant changes (p=0.062), showing a moderate effect size (Δ = 0.413). Almost all patients showed improvement in the continuous and paroxysmal pattern of pain. No postoperative complications were observed. Discussion. These results suggest that in cases of BPI that originates from a compressive syndrome secondary to the posttraumatic fibrosis that surrounds the nerve structures causing strangulation and inducing hypernociception, the use of surgical neurolysis is an appropriate alternative for patients with medically refractory neuropathic pain.