Miracle-dz/UniFAR-FTUs huggingface dataset:
The Strength of Parent–Adult Child Ties in Biological Families and Stepfamilies: Evidence From Time Diaries From Older Adults

We examine older partnered parents’ time spent with adult children in biological and step families, treating time together as an indication of relationship strength. Using a unique national sample of U.S. time diaries from the Panel Study of Income Dynamics, we investigate time with all adult children combined and with each adult child. We find that time together depends on family structure and parent–adult child dyadic relationship type embedded in family structure. In analyses of all adult children combined, an older parent is more likely to spend time with adult children in biological families than in stepfamilies only when there is no shared biological child in the stepfamily. In dyadic analyses, a parent’s tie with an adult child who is a biological child of both partners is stronger in stepfamilies than in biological families. Moreover, among stepfamilies, ties are not uniformly stronger with biological children relative to stepchildren; differences emerge only in more complex families when each partner has biological children from previous relationships. Our findings challenge the view that ties with older parents are always weaker with stepchildren in stepfamilies and point to the importance of considering parent–child relationships in the broader family context.
Stable, long-term single-neuronal recording from the rat spinal cord with flexible carbon nanotube fiber electrodes

Objective. Flexible implantable electrodes enable months-long stable recording of single-unit signals from rat brains. Despite extensive efforts in the development of flexible probes for brain recording, thus far there are no conclusions on their application in long-term single neuronal recording from the spinal cord which is more mechanically active. To this end, we realized the chronic recording of single-unit signals from the spinal cord of freely-moving rats using flexible carbon nanotube fiber (CNTF) electrodes. Approach. We developed flexible CNTF electrodes for intraspinal recording. Continuous in vivo impedance monitoring and histology studies were conducted to explore the critical factors determining the longevity of the recording, as well as to illustrate the evolution of the electrode–tissue interface. Gait analysis were performed to evaluate the biosafety of the chronic intraspinal implantation of the CNTF electrodes. Main results. By increasing the insulation thickness of the CNTF electrodes, single-unit signals were continuously recorded from the spinal cord of freely-moving rats without electrode repositioning for 3–4 months. Single neuronal and local field potential activities in response to somatic mechanical stimulation were successfully recorded from the spinal dorsal horns. Histological data demonstrated the ability of the CNTF microelectrodes to form an improved intraspinal interfaces with greatly reduced gliosis compared to their stiff metal counterparts. Continuous impedance monitoring suggested that the longevity of the intraspinal recording with CNTF electrodes was determined by the insulation durability. Gait analysis showed that the chronic presence of the CNTF electrodes caused no noticeable locomotor deficits in rats. Significance. It was found that the chronic recording from the spinal cord faces more stringent requirements on the electrode structural durability than recording from the brain. The stable, long-term intraspinal recording provides unique capabilities for studying the physiological functions of the spinal cord relating to motor, sensation, and autonomic control in both health and disease.
Impact of lowering frequency of dorsal root ganglion stimulation on implantable pulse generator consumption

© American Society of Regional Anesthesia & Pain Medicine 2022. No commercial reuse. See rights and permissions. Published by BMJ. INTRODUCTION Dorsal root ganglion stimulation (DRGS) is a recent advancement in neuromodulation for chronic pain that is superior to spinal cord stimulation in the treatment of complex regional pain syndrome. 2 Recent studies have shown that dosetapering strategies for DRGS using very low frequencies can provide equivalent therapeutic efficacy to standard program settings. 4 Lower charge output requirements for DRGS should also result in increased battery life. This is important because DRG implantable pulse generators (IPGs) are not rechargeable and require surgical replacement when depleted. We assessed whether the remaining voltage readings of DRGS IPGs in patients treated with dosetapering strategies are higher than readings of patients treated with standard program settings.
Oncological big data platforms for promoting digital competencies and professionalism in Chinese medical students: a cross-sectional study

Objectives Advancements in big data technology are reshaping the healthcare system in China. This study aims to explore the role of medical big data in promoting digital competencies and professionalism among Chinese medical students. Design, setting and participants This study was conducted among 274 medical students who attended a workshop on medical big data conducted on 8 July 2021 in Tongji Hospital. The workshop was based on the first nationwide multifunction gynecologic oncology medical big data platform in China, at the National Union of Real-World Gynecologic Oncology Research & Patient Management Platform (NUWA platform). Outcome measures Data on knowledge, attitudes towards big data technology and professionalism were collected before and after the workshop. We have measured the four skill categories: doctor‒patient relationship skills, reflective skills, time management and interprofessional relationship skills using the Professionalism Mini-Evaluation Exercise (P-MEX) as a reflection for professionalism. Results A total of 274 students participated in this workshop and completed all the surveys. Before the workshop, only 27% of them knew the detailed content of medical big data platforms, and 64% knew the potential application of medical big data. The majority of the students believed that big data technology is practical in their clinical practice (77%), medical education (85%) and scientific research (82%). Over 80% of the participants showed positive attitudes toward big data platforms. They also exhibited sufficient professionalism before the workshop. Meanwhile, the workshop significantly promoted students’ knowledge of medical big data (p<0.05), and led to more positive attitudes towards big data platforms and higher levels of professionalism. Conclusions Chinese medical students have primitive acquaintance and positive attitudes toward big data technology. The NUWA platform-based workshop may potentially promote their understanding of big data and enhance professionalism, according to the self-measured P-MEX scale.
Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning

Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (i.e., item-item similarity) matrix to reveal the low dimensional structure in the high dimensional data. The most popular manifold learning algorithms include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap. However, these algorithms only provide the embedding results of training samples. There are many extensions of these approaches which try to solve the out-of-sample extension problem by seeking an embedding function in reproducing kernel Hilbert space. However, a disadvantage of all these approaches is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this thesis, we introduce a novel dimensionality reduction framework, called Spectral Regression (SR). SR casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression as a building block, different kinds of regularizers can be naturally incorporated into our framework which makes it more flexible. SR can be performed in supervised, unsupervised and semi-supervised situation. It can make efficient use of both labeled and unlabeled points to discover the intrinsic discriminant structure in the data. We have applied our algorithms to several real world applications, e.g. face analysis, document representation and content-based image retrieval.
Drug-Protein interaction prediction by correcting the effect of incomplete information in heterogeneous information

MOTIVATION Large-scale heterogeneous data provides diverse perspectives for predicting drug-protein interactions (DPIs). However, the available information on molecular interactions and clinical associations related to drugs or proteins is incomplete because there may be unproven interactions and associations. This incomplete information in the available data is presented in the form of non-interaction and non-correlation, which may mislead the prediction model. Existing methods fuse incomplete and complete information without considering their integrity, so the negative effects of incomplete information still exist. RESULTS We develop a network-based DPI prediction method named BRWCP, which uses the complete information network to correct the prediction results acquired by the incomplete information network. By integrating relevant heterogeneous information that may be incomplete, the feature similarities of drugs and proteins are obtained. Combining the feature similarities and known DPIs, an incomplete information-based drug-protein heterogeneous network is constructed. Then a bidirectional random walk with pruning algorithm is adopted in this heterogeneous network to predict potential DPIs. Next, the predicted DPIs are combined with the chemical fingerprint similarity of drugs and amino acid sequence similarity of proteins to construct the complete information network. The bidirectional random walk with pruning algorithm is applied in the new network to obtain the final prediction results until it converges. Experimental results show that BRWCP is superior to several state-of-the-art DPI prediction methods, and case studies further confirm its ability to tap potential drug-protein interactions. AVAILABILITY The code of BRWCP is available at https://github.com/lyfdomain/BRWCP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Effect of Cichorium intybus on production performance, carcass quality and blood lipid profile of hybrid duck

Objective One hundred hybrid male ducks (Mojosari×Alabio) were used to examine the efficacy of chicory supplementation as nutritional feed manipulation on production performance, and blood lipid profile of hybrid ducks. Methods The ducks were tagged, weighed, and then allotted randomly to one of the four treatment diets using a completely randomized design. The experimental diets were: i) P0 (100% basal diets+0% chicory as control), ii) P1 (95% basal diets+5% chicory), iii) P2 (90% basal diets+10% chicory) and iv) P3 (85% basal diets+15% chicory). For each treatment group, there were 5 replicates of 5 birds each. All experimental diets were isonitrogenous and isocaloric using locally available ingredients. Results Hybrid ducks with fed diets supplemented fresh chicory (5%, 10%, and 15%) showed increased feed intake and body weight gain, as well as feed conversion ratio to be smaller than those ducks fed diets without chicory supplementation (control). The ducks fed 10% chicory supplementation contained significantly (p<0.05) lower ash and higher organic matter contents of meat than those ducks fed other diets. The ducks fed 15% chicory supplementation showed the lowest crude protein and cholesterol content of meat among the treatment diets. Ducks fed chicory supplementation showed lower (p<0.05) blood cholesterol and triglyceride levels than those ducks fed without chicory supplementation, while dietary interventions had no major (p>0.05) influence on low-density lipoprotein and high-density lipoprotein levels in duck blood. Conclusion In this study, 10% chicory supplementation showed the best results characterized by an increase in growth performance, carcass quality, small intestinal histomorphology, and lower cholesterol levels of meat.
Derivation of the Hadronic Structure Functions

We solve a long-standing problem in particle physics: that of deriving the Deep Inelastic structure functions of the proton from the fundamental theory of strong interactions, Quantum ChromoDynamics (QCD). In the Bjorken limit, the momenta of the constituents of the proton (the partons) can be assumed to be in a two-dimensional plane in Minkowski space: a dimensional reduction of QCD to two space-time dimensions. Two dimensional QCD is then shown to be equivalent for all energies and values of number of colors N to a new theory of hadrons, Quantum HadronDynamics (QHD). The phase space of QHD is the Grassmannian (set of subspcaes) of the complex Hilbert space L(R) . The natural symplectic form along with a hamiltonian define a classical dynamical system, which is equivalent to the large N limit of QCD. ’t Hooft’s planar limit is the linear approximation to our theory: we recover his integral equation for the meson spectrum but also all the interactions of the mesons. The Grassmannian is a union of connected components labelled by an integer (the renormalized dimension of the subspace) which has the physical meaning of baryon number. The proton is the topological soliton: the minimum of the energy in the sector with baryon number one gives the structure functions of the proton. We solve the resulting integral equations numerically; the agreement with experimental data is quite good for values of the Bjorken variable xB > 0.2 . rajeev@pas.rochester. edu
Hypercomputation based on quantum computing

We present a quantum algorithm for a (classically) incomputable decision problem: the Hilbert's tenth problem; namely, we present a hypercomputation model based on quantum computation. The model is inspired by the one proposed by Tien D. Kieu. Our model exploits the quantum adiabatic process and the characteristics of the representation of the dynamical algebra su(1,1) associated to the infinite square well. Furthermore, it is demonstrated that the model proposed is a universal quantum computation model.
A Survey of Machine Learning Approaches for Detecting Depression Using Smartphone Data

Depression is one of the most common mental health issues worldwide and has only become more widespread after the emergence of the Covid-19 pandemic. Although depression can be treated through various methods, it often goes undiagnosed and therefore untreated, forcing individuals to go through life with a condition that is nothing short of debilitating. With mobile phones being an integral part of people’s lives, they can provide valuable information about a person’s habits and behaviors, which can then be used to detect depressive tendencies. This paper provides a review of several studies conducted in recent years on the possibility of using machine learning and smartphone data to detect depression.
Structural and Electrical Properties of a-Si Schottky Diode based on ZnO Top Layer and Ag Intermidiate Ultrathin-Films

In this paper, the combination of ZnO top layer and intermediate Ag ultrathin films is proposed to enhance the performance of Amorphous-Silicon (a-Si) Schottky Barrier Diodes (SBDs). The latter was fabricated by means of RF magnetron sputtering technique. Accordingly, ZnO/Ag/ZnO/a-Si/Ag/a-Si embedded thin-films were sputtered at room temperature conditions on glass substrate. The morphological and structural properties of the prepared structure were analyzed by performing XRD and SEM characterizations. Characterizations showed an amorphous-like structure of the prepared samples. The electrical parameters of a-Si SBDs with and without Ag middle layer were extracted from current-voltage measurements. It was revealed that the use of ZnO top layer combined with Ag ultrathin-films leads to enhance the electrical behavior of a-Si SBD, where the device offers low series resistance of $2\times 10^{3}\ \Omega$ and high derived current capability, while maintaining low off current. This improvement is attributed to the enhanced resistive behavior due to the presence of Ag thin-films. Therefore, the present investigation can provide new strategies for the elaboration of highly-performance SBDs based on a-Si platform, which are suitable for the emerging optoelectronic applications.
Matching Techniques for Microstrip Antennas in Classes Teaching Electromagnetics

This paper describes a procedure for determining the optimal position for a probe-fed microstrip patch antenna to achieve 50 Ohm matching. The procedure requires an initial simulation of the driving point impedance of an arbitrary probe position. Next, the impedance locus as seen on the Smith chart is rotated so that the magnitude of the reflection coefficient lies on the horizontal axis of the Smith chart. After that, one algebraic calculation is used to determine the new probe position needed for a 50 Ohm match. Such techniques give important insight for students learning matching in a classroom environment.
Design and Analysis of Soft Prosthetic Hand based on Asymmetric Flexible Hydraulic Actuator

An artificial limb is required to provide movement to manage an amputee's daily vital life. The mechanical design and simulation of an anthropomorphic multi-fingered soft prosthetic hand with 5 degrees of freedom are discussed in this study. The suggested design features 5 fingers, each of which is driven directly by two flexible hydraulic actuators (FHAs). With FEA analysis, the asymmetric flat FHA made of nitrile butadiene rubber achieved a good bending deflection of 84 degrees at 6 bar operating pressure. The simulation findings from ANSYS reveal that the bending of FHA is affected by the shape profile of the chamber and the proportion ratio of top chamber thickness to the bottom plate thickness. Myoelectric data gathered from three sensor electrodes inserted on to the surface of the forearm muscles governs the open and closure actions of fingers. With the aid of a microcontroller, a MyoWare sensor is used to plot the EMG graph. SOLIDWORKS software is used to simulate the grasping abilities of the hand while holding diverse shapes of things.
A Review of Epidemic Surveillance Systems for Infectious Diseases

The ongoing Covid-19 pandemic revealed that various healthcare systems in the world are fundamentally weak. Compared with many developed countries, the ability of India to tackle a pandemic seems to be much lower and the medical system in the nation is grossly inadequate. Some disease outbreaks, if not detected and controlled at an early stage, have the potential to turn into an epidemic or even a pandemic, which can disrupt the commercial growth of the nation and the health of human resources. By extracting disease outbreak information scattered in online newspapers and hospital records, the onset of deadly diseases can be detected at an early stage and health officials can be alerted. The objective of this paper is to study various approaches used for the development of biosurveillance systems existing in the world. A review of the current surveillance system in India, specifically Kerala is also done. Existing surveillance systems are capable of sensing outbreaks happening in the world or in a particular country. It can provide comprehensive information which can alert health officials. The proposed research is intended to develop an epidemic intelligent system by mining structured and semi-structured data available in online news ports and publishing outbreak information in real-time, considering the health domain of Kerala in particular. It is a novel approach for investigating local or regional level outbreaks happening in various parts of Kerala to enable authorities to act upon the information received to prevent the spread of deadly diseases.
RuleCache: Accelerating Web Application Firewalls by On-line Learning Traffic Patterns

Web Application Firewall (WAF) is widely deployed in cloud to protect web applications, whose performance becomes one of the major bottlenecks for web services. In this paper, we comprehensively analyze several root causes that downgrade WAF’s efficiency. Inspired by that, we build a caching system RuleCache to devise optimization strategies for improving WAF’s performance. Among, Rule Ordering Cache is online learning an optimal order of the ruleset for a better performance of blocking. Rule Result Cache reuses rule results of targets, saving large repetitive computations. Additionally, Rule Prepruning Cache aims to cut extra overhead by processing the static rules in the offline stage. Our evaluation demonstrates that the prototype can improve the performance by up to 3.85x, 1.57x, and 2.4x respectively with the above modules, and up to 5.5x in total.
Multipath Routing for Traffic Engineering with Hypergraph Attention Enhanced Multi-Agent Reinforcement Learning

Traffic Engineering (TE) is an important mechanism of network performance optimization. Some traditional TE approaches mainly focus on static mapping of traffic flow to some available paths without considering the current network utilization or traffic load. Another class of TE methods is heuristic algorithm based on exact mathematical models, which has low adaptability in the face of complex and changing network environments. In this paper, we design a model-free and experience-driven TE method based on Reinforcement Learning (RL). In particular, we present a Multi-Agent RL (MARL) modelling for multipath routing with the objective to minimize the average flow completion time (FCT). Each router in the network is treated as an independent agent and decides how to split the flow originated from it across multiple paths. An efficient learning algorithm for MARL is developed. It employs the hypergraph attention mechanism to extract some relational representations of the network state by considering the graph nature of the routing paths. Moreover, it relies on the Software Defined Network (SDN) for centralized training, while it allows distributed execution without the need for a central controller. To evaluate our approach, comprehensive tests are conducted on two popular network topologies. The results show that 1) our approach significantly reduces FCT and provides better throughput compared to four baseline approaches, and 2) our approach is robust under various traffic load environments and the advantage over other methods is more significant in high traffic load environments.
Hardware Architecture for Adaptive Edge-Directed Interpolation Algorithm

Demosaicing refers to the reconstruction of full color image by the incomplete color samples produced by the single-chip image sensor. So there is a need of interpolation to obtain the missing color pixels. In this work a hardware architecture has been proposed for the adaptive edge-directed interpolation algorithm which uses an edge estimator for the interpolation. The proposed hardware architecture is implemented in Verilog HDL (Hardware Description Language) and synthesized using Cadence Genus compiler with 90nm technology in typical mode. For the proposed architecture, the power dissipation is found to be 26 mW, delay is 7.2 ns and requires 2.3 mm2 area. The demosaiced images obtained using the proposed architecture is observed to have better image quality in terms of peak signal-to-noise ratio and structural similarity while comparing with existing architectures.