Miracle-dz/UniFAR-FTUs huggingface dataset:
Faktor Eksternal dan Internal yang Mempengaruhi Stigma terhadap Orang dengan Epilepsi di Kalangan Akademisi

Latar Belakang: Kualitas hidup orang dengan epilepsi perlu di optimalkan. Stigma memiliki peran penting terhadap kurangnya kualitas hidup orang dengan epilepsi. Faktor-faktor yang berperan terhadap stigma epilepsi di kalangan civitas akademika belum diketahui. Penelitian ini ingin membuktikan faktor-faktor yang berpengaruh terhadap pemberian stigma terhadap orang dengan epilepsi di salah satu universitas swasta di Semarang.Metode: Penelitian ini merupakan studi observasional dengan pendekatan cross-sectional. Sebanyak 236 orang sivitas akademika berusia ≥18 tahun yang diambil secara purposive untuk pemilihan perguruan tinggi dan respondennya dengan proportional random sampling. Data sosiodemografi, faktor internal dan faktor eksternal individu dikumpulkan dengan cara wawancara, sedangkan pengukuran stigma menggunakan scala stigma pada orang dengan epilepsi. Hasil: Hasil penelitian menunjukan sebanyak 54,5 % responden memberikan stigma dengan skor ≥ 20,73. Uji regresi logistik berganda menunjukan faktor yang berpengaruh terhadap stigma pengalaman melihat serangan pada orang dengan epilepsi dengan OR=1,86 artinya pengalaman melihat serangan mempunyai kemungkinan memberikan stigma sebesar 1,8 kali dibandingkan dengan yang tidak pengalama. Sedangkan memiliki sanak/rekan dengan epilepsi mempengaruhi pemberian stigma dengan OR sebesar 0,45 yang berarti memiliki sanak keluarga/rekan dengan epilepsi sebagai protektif pemebrian stigma. Edukasi dan sosialisasi tentang epilepsi diperlukan bagi masyarakat agar stigma yang diberikan kepada ODE menjadi berkurang.Kata kunci: Stigma, epilepsi, kualitas hidup, orang dengan epilepsi (ODE)
State of the Practice

Although nurse-midwives care for women of all ages, including adolescents and mature, postmenopausal women, the best available statistics can be found regarding births. Since birth statistics lag because of the nature of compiling data, the latest available statistics for attendants at birth were published in 2003, covering up to 2002. According to the National Center for Health Statistics, there were 307,527 CNM-attended births in the United States in 2002, accounting for 7.6% of all births, and more than 10% of all spontaneous vaginal births. For the 10-year period from 1992 to 2002, CNM-attended births (97% of which took place in hospitals) rose an astonishing 64%. 1 More than one third of all babies born in New Mexico are born into the hands of a midwife. However, some states lag far behind in the availability of nurse-midwifery care. Nursemidwives have continued to expand the scope of services provided in their practices with the addition of
Elimination of bottlenecks in dynamic dataflow processors

A key component of a dynamic dataflow processor, the matching unit, has been identified as a major bottleneck. An alternative implementation for the matching unit is presented. This implementation increases the operating bandwidth of the unit by allowing token-matching operations to be done concurrently. The adoption of the proposed implementation also reveals throughput limitations of other units within the processor. As a result, a configuration for a dynamic dataflow processor is proposed that is believed to be cost-effective, to give high performance, and to be capable of incremental performance improvement. <<ETX>>
Performance Analysis of Multilevel Punctured Turbo Codes

In applications of wireless packet-oriented video and image communications, a special coding scheme, referred to as multilevel coding (MLC), exhibits both power and bandwidth efficiency by jointly optimizing channel coding and modulation. In particular, MLC is suitable for communications over a degraded broadcast channel due to its superposition structure. Moreover, the hierarchical channel coding strategy of MLC matches the typical source encoding structure employed with successive refineable sources. Thus, an important issue of the MLC design is the code rate selection for each level. In order to meet high performance and rate flexibility required, we study MLC employing punctured turbo codes. Based on the random puncturing analysis and reliable channel region theorem for good binary code ensembles, we derive the punctured rate threshold for individual codes at each level, and show that, under multistage decoding (MSD) algorithm, the error probability performance of individual punctured turbo code at each level exhibits threshold behavior with respect to its punctured rate and the set of punctured rates of lower level codes. Moreover, a closed form achievable reliable transmission rate is described, and shown to predict the effective transmission rate of multilevel punctured turbo coding scheme very well at AWGN channel.
Reducing relapse in psychosis through medication management.

The concept of relapse is a complex, multi-dimensional phenomenon that is not uniformly defined in the research literature (Simonelli 2005). It is, however, commonly understood as the re-emergence or worsening of signs and symptoms in individuals who, in this context, have previously been treated for psychoses (Birchwood and Spencer 2001 . Birchwood M, Spencer E (2001) Early intervention in psychotic relapse. Clinical Psychology Review. 21, 8, 1211-1226.
Collaborative filtering models for recommendations systems

Modern retailers frequently use recommendation systems to suggest products of interest to a collection of consumers. A closely related task is ratings prediction, in which the system predicts a numerical rating that a user u will assign to a product p. In this paper, we build three ratings prediction models for a dataset of products and users from Amazon.com and Yelp.com. We evaluate the strengths and weaknesses of each model, and discuss their effectiveness in a recommendation system.
Performance optimization of multi-stage MEMS W-band dielectric-block phase-shifters

This paper reports on the RF performance optimization of multi-stage distributed MEMS devices. The return loss (RL) is optimized, without substantially compromising in overall insertion loss (IL), for a 7-stage MEMS dielectric-block phase shifter by adjusting geometrical parameters of the distributed network, for the nominal frequency of 75 GHz as well as for performance within a 500 MHz and a 1 GHz bandwidth. Different optimization strategies have been investigated by simulations and by measuring fabricated devices. The best optimization concept was found for exponentially increasing distances between the stages, which takes into account the proper actuation sequence for all possible phase-shift combinations. As compared to a non-optimized device previously published, the design offering the best compromise between RL and IL results in a significant average RL improvement of 11.8 dB (simulated) and 6.98 dB (measured), while compromising the average IL by only 0.75 dB (simulated) and 0.92 dB (measured).
Resilient Tit-For-Tat (RTFT) A game solution for wireless misbehaviour

The vulnerability of wireless networks to selfish and misbehaving nodes is a well known problem. The Tit-For-Tat (TFT) strategy has been proposed as a game theoretic solution to the problem, however the TFT suffers from a deadlock vulnerability. We present a modified TFT algorithm, the Resilient Tit-For-Tat (RTFT) algorithm in which we introduce the concept of alternative strategies to complement the default strategy. This combination enables us to model a non-cooperative game in which nodes are able change their strategies in order to maximize their utilities in selfish and misbehaviour scenarios. We demonstrate the viability of our proposal with simulation results.
TFII-I Regulates Vβ Promoter Activity through an Initiator Element

ABSTRACT In our effort to understand the transcriptional regulation of naturally occurring TATA-less but initiator (Inr)-containing genes, we have employed the murine T-cell receptor Vβ 5.2 promoter as a model. Here we show by transient-transfection assays that the Inr binding transcription factor TFII-I is required for efficient expression of the Vβ promoter in vivo. Mutations in the Inr element that reduced binding of TFII-I also abolished the Vβ promoter activity by ectopic TFII-I. We further biochemically identified a protease-resistant N-terminal DNA binding fragment of TFII-I, p70. When ectopically expressed, recombinant p70 bound to the Vβ Inr element with a specificity similar to that of wild-type TFII-I. More importantly, p70, which lacks independent activation functions, behaved as a dominant negative mutant that inhibited Inr-specific function of wild-type TFII-I. However, the activation functions of p70 were restored when fused to the heterologous activation domain of the yeast activator protein GAL4. Taken together, these data suggest that TFII-I functions in vivo require an intact Inr element and that the Inr-specific transcriptional functions of TFII-I are solely dictated by its N-terminal DNA binding domain and do not require its own C-terminal activation domain.
Stress Testing with a Bottom-Up Corporate Default Prediction Model

Stress testing plays an important role in risk management by focusing on extreme events that are rare but bear significance. This paper proposes an operational system for credit stress testing which is comprised of (1) the daily updated corporate default prediction system maintained by the Risk Management Institute at National University of Singapore, and (2) a set of stress-testing regressions translating the presumed adverse macroeconomic scenarios into the input values for the default prediction system. Our credit stress testing system produces distributions of corporate defaults for an economy and/or any target portfolio over typical horizons of interest. The current model powering the RMI system employs a forward intensity-based corporate default prediction model and forms a bottom-up approach to aggregating individual obligors’ behaviours into a portfolio’s default profile. Each stress-testing regression relates an input variable to a common set of macroeconomic variables that define stress scenarios. We focus on the Singapore economy in the empirical analysis but also use the US to illustrate its wider applicability. We find that the eight macroeconomic stress variables used in the analysis can explain a large fraction of variations in the input variables of the corporate default prediction model. We also demonstrate that the proposed stress testing system performs well.
Image Resolution Upscaling via Two-Layered Discrete-Time Cellular Neural Network

This paper proposes a novel image resolution up-scaling method using discrete-time cellular neural network (DT-CNN) with multi-level quantization function for output of a cell. The nonlinear interpolative approximation capability of the DT-CNN is used to generate an resolution enhanced image from its low-resolution version. Our proposed method consists of two-layered DT-CNN. At the first layer stage, the DT-CNN is used to obtain the optimal weight parameter which makes possible to represent the original image with a weighted linear combination of finite impulse response function such as Gaussian function and wavelet function. At the second layer stage, the image obtained after transition of the first layer DT-CNN is upsampled with arbitrary size, then the resolution enhanced image is obtained by the convolution with the B-template which is derived by extending the A-template spatially. The experimental evaluation shows that the proposed method produces better results than the conventional image resolution enhancement methods
Local differential calculus over Fedosov algebra

In this paper the local differential calculus over Fedosov algebra is constructed using the trivialization isomorphism. The explicit formulas for deformed derivations are given. The resulting calculus can be used as a "building block" for a theory of Seiberg-Witten map with Fedosov type of noncommutativity.
Do Mothers Benefit from Flexible Work? Cross-National Evidence for Work Time, Job Quality, and Satisfaction

Flexible work is commonly implemented to provide workers with greater discretion to accommodate competing demands. While investigated at the individual and organizational levels, less is known about whether national cultures of flexible work are associated with workers’ ability to match their desired to actual work hours. Applying data from the 2005 International Social Survey Programme (n = 19,134 from 32 nations), we investigate whether workers in countries where flexible work is more common report better work-time compatibility, job quality, and job satisfaction. Paying careful attention to gender and parental status, we find that mothers are more likely to report working their desired hours in countries with more expansive flexible work. Women and mothers also occupy more satisfying jobs in countries with more expansive flexible work. Finally, all workers report working in higher quality jobs and preferences for less than full-time work in countries with more expansive flexible work offerings. Our results highlight the benefits to national-level flexible work across multiple measures of work quality.
Fault Detection and Isolation for Robotic Manipulators

This paper deals with the problem of detecting and isolating sensor faults in industrial robot manipulators. To the purpose, an analytical redundancy approach has been pursued, based on a bank of state observers for residual generation. An extended Hinfin approach is adopted to build the bank of residual generators; the compensation of poorly known dynamics in each observer is improved by the use of a neural network. The synthesis of the observer gains is achieved by solving an LMI feasibility problem, where constraint on the position of the estimation error linearized dynamics poles in the complex plane are taken into account Finally, in order to test the effectiveness of the proposed approach, a case study is presented, based on experimental data collected on a six-degree-of-freedom Comau Smart-3 S industrial manipulator
A Genetic Algorithm for Query Optimization in Database Grid by Dynamic Cost Estimation

increasing data volume in the database of organizations especially those that are multicluster and geographically extensive cause a lot of problems in data storage, retrieve and transmition. In addition, data integration related to database existed in different geographical positions is too risky with the respect of security and velocity of transaction executing and generally posses some disadvantages which can be solved by using the grid database and leads to more efficient function. Therefore, with internet extension among the organizations and institutes and also accordance of grid architecture to it, development of database in grid environments is inevitable. In order to establish a structure for data storing in the width of a grid environment in distributed and heterogonous from tries to storage and distribute data in a wide geographical region. Because of extension and high volume of data, transaction processing in such an environment is complexity and time-consuming. It is obvious that if non-optimal queries are used, transaction efficiency in this database will considerably be decreased. But if appropriate optimal algorithms are used, this efficiency will be so much increased. There are a lot of algorithms suggested for optimization of queries. However, due to difference of grid environments, different optimal algorithms are required. In this study presented algorithm is according to the structure of grid measurement and through a novel solving performs a good function in high data volume grid base systems.
Cycle prediction EWMA run-to-run controller for mixed-product drifting process

In semiconductor manufacturing processes, mixed-products are usually fabricated on the same set of process tool with different recipes. Run-to-Run controllers which based on the exponential weighted moving average (EWMA) statistic are probably the most frequently used in industry for the quality control of certain semiconductor manufacturing process steps. However, for mixed-product drifting process, if the break length of a product is large, then the process output at the beginning runs of cycle 1,2,… will be far deviated from target which will lead to a possible high rework rate and lots of waste wafers. Therefore, this study aims to develop a cycle prediction EWMA (CP-EWMA) approach to deal with the problem of large deviations in the first few runs of cycle 1,2,…. Simulation study showed that the proposed approaches are effective.
In Your Face: Risk of Punishment Enhances Cognitive Control and Error-Related Activity in the Corrugator Supercilii Muscle

Cognitive control is needed when mistakes have consequences, especially when such consequences are potentially harmful. However, little is known about how the aversive consequences of deficient control affect behavior. To address this issue, participants performed a two-choice response time task where error commissions were expected to be punished by electric shocks during certain blocks. By manipulating (1) the perceived punishment risk (no, low, high) associated with error commissions, and (2) response conflict (low, high), we showed that motivation to avoid punishment enhanced performance during high response conflict. As a novel index of the processes enabling successful cognitive control under threat, we explored electromyographic activity in the corrugator supercilii (cEMG) muscle of the upper face. The corrugator supercilii is partially controlled by the anterior midcingulate cortex (aMCC) which is sensitive to negative affect, pain and cognitive control. As hypothesized, the cEMG exhibited several key similarities with the core temporal and functional characteristics of the Error-Related Negativity (ERN) ERP component, the hallmark index of cognitive control elicited by performance errors, and which has been linked to the aMCC. The cEMG was amplified within 100 ms of error commissions (the same time-window as the ERN), particularly during the high punishment risk condition where errors would be most aversive. Furthermore, similar to the ERN, the magnitude of error cEMG predicted post-error response time slowing. Our results suggest that cEMG activity can serve as an index of avoidance motivated control, which is instrumental to adaptive cognitive control when consequences are potentially harmful.
Chapter 4 Computational Models of Vascular Mechanics

Many of the world’s leading causes of death involve pathology of the vasculature, both arterial and venous. In addition to the biochemical and genetic factors governing vascular health and disease, mechanics strongly modulates the form and function of the vessel wall. Biomechanical analysis is being increasingly used to not only elucidate key disease processes, but also to predict disease progression and response to therapeutic and surgical intervention on a patient-specific basis. This chapter reviews some of the recent advances in computational vascular mechanics, with references to key works in constitutive modeling, fluid-structure interaction, image-based modeling, and atherosclerotic plaque mechanics.
Major depression – a study on the validity of clinicians’ diagnoses in medicolegal assessment

ABSTRACT Major depression ranges among the most frequently diagnosed psychiatric disorders. Accordingly, diagnoses of depression are often underlying insurance, compensation or disability claims. This report evaluates the validity of clinicians’ diagnoses of major depression in a sample of claimants. In 2015, n = 127 consecutive cases were examined for medicolegal assessment. For all of them, a diagnosis of major depression had been established by clinicians. All testees underwent a psychiatric interview, a physical examination, they answered questionnaires for depressive symptoms according to DSM-5, embitterment disorder, post concussion syndrome (PCS) and unspecific somatic complaints. Performance and symptom validity tests were administered. Only 31% fulfilled the diagnostic criteria for DSM-5 major depression according to self-report, while none did so according to psychiatric assessment. Negative response bias was found in 64% of cases, feigned neurologic symptoms in 22%. Symptom exaggeration was indiscriminate rather than depression-specific. By self-report, 64% of the participants qualified for embitterment disorder and 93% for PCS. In conclusion, clinicians’ diagnoses of depression seem often confounded by improper assessment of the diagnostic criteria, confusion of depression with bereavement or embitterment and also by response bias.
Efficiency or Innovation: How Do Industry Environments Moderate the Effects of Firms’ IT Asset Portfolios

Firms invest in a variety of information technologies and seek to align their IT asset portfolios with two key performance outcomes: efficiency and innovation. Existing research makes the universalistic assumption that both outcomes will always be realized through firms' IT asset portfolios. There has been limited research on the conditions under which firms' IT asset portfolios should be oriented more toward efficiency or innovation. Here, we argue that the nature of the industry where a firm competes will have a significant moderating effect on the link between firms' IT asset portfolios and efficiency or innovation outcomes. Using panel data that covers a wide range of industry environments, we find that at lower levels of dynamism, munificence, and complexity, IT asset portfolios are associated with a greater increase in efficiency. In contrast, in environments with higher levels of complexity, IT asset portfolios are associated with a greater increase in innovation (i.e., development of new products and processes, and exploration of growth opportunities). These results provide insights about how firms could realize strategic alignment by tailoring their IT asset portfolios toward an efficiency or innovation focus.
Image segmentation using Shadowed C-Means and Kernel method

A new shadowed c-means clustering based image segmentation method is proposed in this paper. By including the local spatial information in shadowed c-means algorithm and mapping the original data into a high dimensional space via kernel method, we propose the Kernel Spatial Shadowed C-Means (KSSCM) clustering algorithm for image segmentation problems. The KSSCM based approach shows better performance than traditional clustering based approaches on segmenting noised synthetic and real images.
Apatinib inhibits cellular invasion and migration by fusion kinase KIF5B-RET via suppressing RET/Src signaling pathway

The Rearranged during transfection (RET) fusion gene is a newly identified oncogenic mutation in non-small cell lung cancer (NSCLC). The aim of this study is to explore the biological functions of the gene in tumorigenesis and metastasis in RET gene fusion-driven preclinical models. We also investigate the anti-tumor activity of Apatinib, a potent inhibitor of VEGFR-2, PDGFR-β, c-Src and RET, in RET-rearranged lung adenocarcinoma, together with the mechanisms underlying. Our results suggested that KIF5B-RET fusion gene promoted cell invasion and migration, which were probably mediated through Src signaling pathway. Apatinib exerted its anti-cancer effect not only via cytotoxicity, but also via inhibition of migration and invasion by suppressing RET/Src signaling pathway, supporting a potential role for Apatinib in the treatment of KIF5B-RET driven tumors.