Miracle-dz/UniFAR-FTUs huggingface dataset:
Static and Dynamic TCAD Analysis of IMOS Performance: From the Single Device to the Circuit

Impact ionization MOSFET (IMOS) is a device that enables to reach subthreshold slopes as small as 5 mV/dec. This device has an asymmetric doping profile, and only a fraction of the channel is covered by the gate. In the first part of this paper, the purpose is to investigate the impact of some geometrical parameters on the IMOS performance: the gate length, the intrinsic length, and the Si film thickness. This study simulates a p-IMOS device on silicon-on-insulator using ATLAS. It is pointed out that the increase of the ratio LG/LIN allows a drop of the bias voltage, but involves a degradation of the subthreshold slope. A thin Si film improves the overall device performance. In the second part, the performance of an IMOS-based inverter is investigated, and for the first time an IMOS ring oscillator is simulated
Signal Detection and Abrupt-Channel Tracking for MC-CDMA

In this paper, a channel estimation and multiuser detection method is presented for uplink MC-CDMA systems in time-varying environment. Based on the fact that the channel coefficients of two successive symbols are highly correlated, joint channel tracking and signal detection are carried out iteratively. A major contribution of the proposed approach is that it enables robust error propagation control, and is capable of detecting and tracking abrupt channel changes effectively. Simulation examples demonstrate the robustness of the proposed algorithms
Career Woman: The Dual Roles Conflict Management in the Gender Perspective

Human rights are something that is essential and should not be limited only for reasons that refer to gender and social status, especially human rights for women in various aspects, especially the right to a career and self-actualization. Facts on the ground show that women also have the same competence as men in the aspects of public and professional roles. This study aims to analyze the aspects of women's rights and freedoms in carrying out their roles as career women, especially for women who are married and have dual roles. This study examines aspects of social roles and management of role conflicts that are experienced by women with the status of career women. Aspects of social roles are studied based on various theories of social development, studies of women's rights based on the concept of human rights and gender equality. The data in this study were sourced from the results of observations, interviews, and literature studies. The analysis technique used was an interactive model. The results of this study indicate that career women with dual roles have appropriate conflict management techniques, so that their roles have the same priority. This was also supported by the concept of gender equality which is starting to be accepted in society so that women can live a life with the dual roles they have, as well as the guarantee of human rights as human beings who have complete freedom.
MIMO systems with low-resolution ADCs: Linear coding approach

This paper considers a multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we present a new MIMO detection approach using coding theory. The principal idea of the proposed approach is to transform a non-linear MIMO channel to a linear MIMO channel by leveraging both a p-level quantizer and a lattice code where p ≥ 2. After transforming to the linear MIMO channel with the sets of finite input and output elements, efficient MIMO detection methods are proposed to attain both diversity and multiplexing gains by using algebraic coding theory. In particular, using the proposed methods, the analytical characterizations of achievable rates are derived for different MIMO configurations. One major observation is that the proposed approach is particularly useful for a large MIMO system with the ADCs that use a few bits.
Phenotype of early-onset fetal growth restriction in sheep

Introduction Fetal growth restriction (FGR) is a common pregnancy complication, caused by placental insufficiency, with serious adverse consequences for development in utero and postnatal wellbeing. There are no antenatal treatments to improve growth or organ development in FGR, and animal models are essential to mimic the physiological adaptations in FGR and to assess potential interventions. This study aimed to identify the temporal nature of reduced developmental trajectory in fetuses with FGR, and to examine the effects of common factors that may mediate differential growth such as glucocorticoid treatment. We hypothesised that the trajectory of growth would be adversely impacted by FGR. Methods FGR was induced via surgical placental insufficiency in fetal sheep (89 days gestation/0.6 gestation; n=135) and compared to age-matched controls over the last third of gestation and into neonatal life (n=153). Results Body weight of FGR fetuses/lambs was significantly reduced compared to controls (p<0.0001) from 127 days of gestation (term is 148 days), with increased brain:body weight ratio (p<0.0001) indicative of brain sparing. All biometric measures of body size were reduced in the FGR group with the exception of biparietal (head) diameter. The trajectory of body growth in the last trimester of sheep pregnancy was significantly reduced in the FGR group compared to controls, and stillbirth rate increased with longer gestation. Discussion This work provides a well characterised FGR animal model that mimics the known physiological adaptations in human pregnancy and can be used to determine the efficacy of potential interventions.
FlowCover: Low-cost flow monitoring scheme in software defined networks

Network monitoring and measurement are crucial in network management to facilitate quality of service routing and performance evaluation. Software Defined Networking (SDN) makes network management easier by separating the control plane and data plane. Network monitoring in SDN is lightweight as operators only need to install a monitoring module into the controller. Active monitoring techniques usually introduce too many overheads into the network. The state-of-the-art approaches utilize sampling method, aggregation flow statistics and passive measurement techniques to reduce overheads. However, little work in literature has focus on reducing the communication cost of network monitoring. Moreover, most of the existing approaches select the polling switch nodes by sub-optimal local heuristics. Inspired by the visibility and central control of SDN, we propose FlowCover, a low-cost high-accuracy monitoring scheme to support various network management tasks. We leverage the global view of the network topology and active flows to minimize the communication cost by formulating the problem as a weighted set cover, which is proved to be NP-hard. Heuristics are presented to obtain the polling scheme efficiently and handle flow changes practically. We build a simulator to evaluate the performance of FlowCover. Extensive experiment results show that FlowCover reduces roughly 50% communication cost without loss of accuracy in most cases.
Improving identification performance by integrating evidence from sequences

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.
Ray-Shooting Algorithms for Robotics

Ray shooting is a well-studied problem in computer graphics and also has applications in robotics such as collision detection and contact force optimization. Unfortunately, most ray-shooting algorithms developed for graphics applications only allow 3-dimensional (3-D) objects represented as triangle meshes, and therefore are not suited for objects with parametric surfaces or general convex sets in high-dimensional space which robotics applications often require. In contact force optimization, for example, the problem is in the 6-dimensional (6-D) wrench space and it is desirable to consider the nonlinear friction cone without approximating it by a pyramid. This paper discusses existing and novel geometry-based ray-shooting algorithms applicable to general convex sets, and compares their performances in two robotics applications: computing the distance between two convex objects and optimizing contact forces in grasping.
Joint cooperative scheduling and power control for interference-limited wireless networks

In this paper, we consider a joint cooperative scheduling and power control problem in interference-limited wireless ad hoc networks. In particular, we investigate the scenario where multiple pairs of users wish to communicate with their corresponding partners and each communication requires that its Signal-to-Interference-plus-Noise Ratio (SINR) is greater than a predetermined value for QoS guarantees. We propose to employ a decode-and-forward cooperative protocol jointly with the distributed Foschini-Miljanic power control algorithm to maximize the number of scheduled users in the network. In addition, we develop efficient handshaking and interference-aware relay selection mechanisms to achieve good throughput-overhead tradeoff for the proposed distributed protocol. We show that the proposed cooperative scheduling and power control algorithm is guaranteed to achieve better throughput than a non-cooperative scheduling algorithm based on direct communications and illustrate the performance gain through numerical studies.
Rigorous Design of PartitionAware Total Order Broadcast System using EventB

In distributed system, the sites communicate with each other by exchange of messages. A total order broadcast is a reliable broadcast that ensures delivery of messages to the sites in the same order. For the fault tolerant applications, ordering algorithm must support the network partitions and site failure. In this paper, we present a formal model of total order broadcast system in the presence of network partition and site failures. We have used Event-B as a formal technique for the development of our model. In this technique system is built in several stages by gradually introducing the details in the refinement steps. We outline an abstract model specifying total order broadcast and introducing the details in the refinement level for considering the case of network partitioning and site failure. General Terms Group communication primitives, B method, network partition.
Agonistic aptamer to the insulin receptor leads to biased signaling and functional selectivity through allosteric modulation

Due to their high affinity and specificity, aptamers have been widely used as effective inhibitors in clinical applications. However, the ability to activate protein function through aptamer-protein interaction has not been well-elucidated. To investigate their potential as target-specific agonists, we used SELEX to generate aptamers to the insulin receptor (IR) and identified an agonistic aptamer named IR-A48 that specifically binds to IR, but not to IGF-1 receptor. Despite its capacity to stimulate IR autophosphorylation, similar to insulin, we found that IR-A48 not only binds to an allosteric site distinct from the insulin binding site, but also preferentially induces Y1150 phosphorylation in the IR kinase domain. Moreover, Y1150-biased phosphorylation induced by IR-A48 selectively activates specific signaling pathways downstream of IR. In contrast to insulin-mediated activation of IR, IR-A48 binding has little effect on the MAPK pathway and proliferation of cancer cells. Instead, AKT S473 phosphorylation is highly stimulated by IR-A48, resulting in increased glucose uptake both in vitro and in vivo. Here, we present IR-A48 as a biased agonist able to selectively induce the metabolic activity of IR through allosteric binding. Furthermore, our study also suggests that aptamers can be a promising tool for developing artificial biased agonists to targeted receptors.
Efficient Distance-Aware Influence Maximization in Geo-Social Networks

Given a social network <inline-formula><tex-math notation="LaTeX">$\mathcal {G}$</tex-math><alternatives> <inline-graphic xlink:href="wang-ieq1-2633472.gif"/></alternatives></inline-formula> and a positive integer <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="wang-ieq2-2633472.gif"/></alternatives></inline-formula>, the influence maximization problem aims to identify a set of <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="wang-ieq3-2633472.gif"/></alternatives></inline-formula> nodes in <inline-formula> <tex-math notation="LaTeX">$\mathcal {G}$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq4-2633472.gif"/> </alternatives></inline-formula> that can maximize the influence spread under a certain propagation model. As the proliferation of geo-social networks, location-aware promotion is becoming more necessary in real applications. In this paper, we study the distance-aware influence maximization (DAIM) problem, which advocates the importance of the distance between users and the promoted location. Unlike the traditional influence maximization problem, DAIM treats users differently based on their distances from the promoted location. In this situation, the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq5-2633472.gif"/> </alternatives></inline-formula> nodes selected are different when the promoted location varies. In order to handle the large number of queries and meet the online requirement, we develop two novel index-based approaches, MIA-DA and RIS-DA, by utilizing the information over some pre-sampled query locations. MIA-DA is a heuristic method which adopts the maximum influence arborescence (MIA) model to approximate the influence calculation. In addition, different pruning strategies as well as a priority-based algorithm are proposed to significantly reduce the searching space. To improve the effectiveness, in RIS-DA, we extend the reverse influence sampling (RIS) model and come up with an unbiased estimator for the DAIM problem. Through carefully analyzing the sample size needed for indexing, RIS-DA is able to return a <inline-formula><tex-math notation="LaTeX">$1 - 1/e -\epsilon$</tex-math><alternatives> <inline-graphic xlink:href="wang-ieq6-2633472.gif"/></alternatives></inline-formula> approximate solution with at least <inline-formula><tex-math notation="LaTeX">$1 - \delta$</tex-math><alternatives> <inline-graphic xlink:href="wang-ieq7-2633472.gif"/></alternatives></inline-formula> probability for any given query. Finally, we demonstrate the efficiency and effectiveness of proposed methods over real geo-social networks.
University of Malaya from the Selectedworks of Huang Nay Ming Simple Room-temperature Preparation of High- Yield Large-area Graphene Oxide Simple Room-temperature Preparation of High-yield Large-area Graphene Oxide

Graphene has attracted much attention from researchers due to its interesting mechanical, electrochemical, and electronic properties. It has many potential applications such as polymer filler, sensor, energy conversion, and energy storage devices. Graphene-based nanocomposites are under an intense spotlight amongst researchers. A large amount of graphene is required for preparation of such samples. Lately, graphene-based materials have been the target for fundamental life science investigations. Despite graphene being a much sought-after raw material, the drawbacks in the preparation of graphene are that it is a challenge amongst researchers to produce this material in a scalable quantity and that there is a concern about its safety. Thus, a simple and efficient method for the preparation of graphene oxide (GO) is greatly desired to address these problems. In this work, one-pot chemical oxidation of graphite was carried out at room temperature for the preparation of large-area GO with ∼100% conversion. This high-conversion preparation of large-area GO was achieved using a simplified Hummer's method from large graphite flakes (an average flake size of 500 µm). It was found that a high degree of oxidation of graphite could be realized by stirring graphite in a mixture of acids and potassium permanganate, resulting in GO with large lateral dimension and area, which could reach up to 120 µm and ∼8000 µm 2 , respectively. The simplified Hummer's method provides a facile approach for the preparation of large-area GO.
Leaders of Welcoming and Affirming Churches in North Carolina Discuss their Roles in HIV/AIDS Awareness and Prevention

Some church leaders work to increase HIV testing and spread messages to reduce stigma. This study focuses on how leaders of North Carolina churches considered welcoming and affirming by the LGBTQ+ community discuss and disseminate information about HIV/AIDS to church members and communities. Semi-structured interviews were conducted with six leaders of different denominations between April and May 2023. A thematic analysis of the transcribed interviews found three main approaches: 1) HIV is not addressed directly, but we are here; 2) provide access to educational resources; and 3) fight stigma. Results confirm welcoming/affirming churches could play a significant role in, not only educating their communities about HIV, but also reducing the stigma associated with the disease.
An interactome map of the nucleocapsid protein from a highly pathogenic North American porcine reproductive and respiratory syndrome virus strain generated using SILAC‐based quantitative proteomics

Positive strand RNA viruses replicate in the cytoplasm of an infected cell and encode nucleocapsid proteins. These proteins function to promote encapsidation of the RNA genome and virus particle assembly as well as playing potential roles in viral RNA synthesis. Nucleocapsid proteins can also associate with cellular proteins and signaling cascades. The arterivirus nucleocapsid (N) protein is no exception and localizes to both the cytoplasm and the nucleolus in virus‐infected cells. This study generated an interactome map of the N protein from a highly virulent North American strain of porcine reproductive and respiratory syndrome virus (PRRSV). This is a major pathogen of swine resulting in significant morbidity and mortality. Crucial to the study was the use of SILAC coupled to affinity purification using GFP‐traps and LC‐MS/MS. This approach has not been applied before to the investigation of host/viral protein interactomes and this study revealed that the PRRSV N protein interacts with the host cell protein synthesis machinery especially at the level of translation initiation as well as with the RNA post‐transcriptional modification machinery. Applications of the dataset can include studies of virus/host interactions and the design of live attenuated recombinant vaccines.
The efficacy of mindfulness-based cognitive therapy in recurrent depressed patients with and without a current depressive episode: a randomized controlled trial

Background The aim of this study is to examine the efficacy of mindfulness-based cognitive therapy (MBCT) in addition to treatment as usual (TAU) for recurrent depressive patients with and without a current depressive episode. Method A randomized, controlled trial comparing MBCT+TAU (n=102) with TAU alone (n=103). The study population consisted of patients with three or more previous depressive episodes. Primary outcome measure was post-treatment depressive symptoms according to the Hamilton Rating Scale for Depression. Secondary outcome measures included the Beck Depression Inventory, rumination, worry and mindfulness skills. Group comparisons were carried out with linear mixed modelling, controlling for intra-group correlations. Additional mediation analyses were performed. Comparisons were made between patients with and without a current depressive episode. Results Patients in the MBCT+TAU group reported less depressive symptoms, worry and rumination and increased levels of mindfulness skills compared with patients receiving TAU alone. MBCT resulted in a comparable reduction of depressive symptoms for patients with and without a current depressive episode. Additional analyses suggest that the reduction of depressive symptoms was mediated by decreased levels of rumination and worry. Conclusions The study findings suggest that MBCT is as effective for patients with recurrent depression who are currently depressed as for patients who are in remission. Directions towards a better understanding of the mechanisms of action of MBCT are given, although future research is needed to support these hypotheses.
Heat-Shock Protein 72 and Suppression of Oxidative Stress Thermal Treatment Attenuates Neointimal Thickening With Enhanced Expression of

Motoi Okada, Naoyuki Hasebe, Yoshiaki Aizawa, Kazuma Izawa, Jun-ichi Kawabe and Kenjiro Heat-Shock Protein 72 and Suppression of Oxidative Stress Thermal Treatment Attenuates Neointimal Thickening With Enhanced Expression of Print ISSN: 0009-7322. Online ISSN: 1524-4539 Copyright © 2004 American Heart Association, Inc. All rights reserved. is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Circulation doi: 10.1161/01.CIR. 0000124226.88860. 55 2004;109:1763-1768; originally published online March 29, 2004; Circulation. http://circ.ahajournals. org/content/109/14/1763 World Wide Web at: The online version of this article, along with updated information and services, is located on the
Early-Stage Disease Prediction from Various Symptoms Using Machine Learning Models

Development and exploration of several Data analytics techniques in various real-time applications (e.g., Industry, Healthcare Neuroscience) in various domains have led to exploitation of it to extract paramount features from datasets. Following the introduction of new computer technology, the health sector had a significant transformation that compelled it to produce more medical data, which gave rise to a number of new disciplines of study. Quite a few initiatives are made to deal with the medical data and how its usage can be helpful to humans. This inspired academics and other institutions to use techniques like data analytics, its types, machine learning and different algorithms, to extract practical information and aid in decision-making. The healthcare data can be used to develop a health prediction system that can improve a person's health. Based on the dataset provided, making accurate predictions in early disease prediction benefits the human community.