Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Multipath TCP Performance over WiFi and Cellular Networks: An Analytical Approach

A complete understanding of the dynamics of Multipath TCP (MPTCP) over Cellular and WiFi networks is still lacking. This is a highly challenging issue, as a consequence of the complex interdependencies between the losses, packet reordering due to heterogeneous wireless channel features, errors, and link layer retransmissions, as well as their (joint) influence on MPTCP's control mechanism. In this paper, we develop a comprehensive approach that is capable of assessing the performance of long-lived MPTCP flows with joint WiFi and Cellular network access, taking into account the diverse characteristics of both types of networks. Relying on a parallel queueing model, we develop a framework that features the controllable network parameters, such as the retransmission limit and the buffer sizes, so as to capture their impact on the TCP-level performance. We include a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive MPTCP design. In particular, we design a novel MPTCP algorithm to exploit the route heterogeneity, and demonstrate that the proposed algorithm is capable of learning the network's characteristics. It also identifies the improved MPTCP window increment parameters for any given set of channel errors, retransmission limits, and buffer sizes.
Linking Corporate Social Responsibility with Reputation and Brand of the Firm

There are no agreements among scientists in terms of relationships between Corporate Social Responsibility (CSR), Corporate Identity and Corporate Image (CI), Corporate Reputation (CR), and Corporate Brand (CB), and their mutual impacts as well the definition of these constructs cause a lot of scientific debates among scholars. The paper analyses the relationship between CSR, Corporate Image, Corporate Reputation and Corporate Brand and their outcomes linked to the financial and other benefits of the firm and society. The main studies that are dealing with this subject were analysed, and the theoretical model that is linking these constructs was developed. The proposed model can be used for carrying empirical studies in specific industries and branches of the economy in order to assess the main measures to increase Corporate Reputation and maximise its outcomes.
Strategic human resource management in the Brazilian Federal Legislative: conditions for the implementation

Resumo Esta pesquisa objetivou identificar aspectos interferentes na implementação da gestão estratégica de pessoas (GEP) nos órgãos do Poder Legislativo Federal brasileiro e as possíveis relações entre eles. Por meio de entrevistas e grupos focais realizados com 16 gestores do Senado Federal (SF) e da Câmara dos Deputados (CD) foram identificados 21 aspectos interferentes, divididos em categorias institucionais, políticas, organizacionais e setoriais, bem como foram elaborados modelos teórico-empíricos das influências exercidas por cada um dos aspectos sobre os demais, a partir do que se concluiu que os condicionantes mais significativos teriam natureza política, pois definiriam os recursos e a autonomia disponíveis para as unidades de gestão de pessoas (GP) conduzirem o processo de implementação.
HUBUNGAN ANTARA NEGATIVE EMOTIONAL STATE DENGAN RESILIENSI PADA WARGA BINAAN NARKOTIKA DI LEMBAGA PEMASYARAKATAN KEDUNGPANE SEMARANG

Kasus narkotika merupakan kasus terbesar di Indonesia, baik pengguna atau pengedar maupun bandar. Efek penggunaan narkotika dan tinggal di lapas menuntut warga binaan narkotika untuk memiliki kemampuan bertahan dan bangkit kembali setelah mengalami keterpurukan yang disebut sebagai resiliensi. Kemampuan ini dapat digunakan sebagai upaya pencegahan memburuknya kondisi psikologis warga binaan narkotika berupa negative emotional state (depresi, kecemasan, stres). Tujuan penelitian ini untuk mengetahui hubungan antara negative emotional state dengan resiliensi pada warga binaan narkotika di Lembaga Pemasyarakatan Kedungpane, Semarang. Populasi dalam penelitian ini berjumlah 880 warga binaan, sebanyak 211 warga binaan diambil untuk sampel penelitian dengan teknik purposive sampling. Instrumen penelitian terdiri dari dua skala yaitu DASS 21 (21 aitem dengan α = 0,916) dan Skala Resiliensi (34 aitem dengan α = 0,889). Hasil penelitian dengan analisis korelasional Spearman’s rho menunjukkan bahwa terdapat hubungan negatif yang signifikan antara negative emotional state dengan resiliensi dengan rxy = -0,308 dan p = 0,000 (p<0,01), artinya semakin rendah negative emotional state pada warga binaan narkotika maka resiliensi semakin tinggi, begitu pula sebaliknya. Temuan unik yang didapat yaitu negative emotional state pada warga binaan narkotika Lapas Kedungpane, Semarang berada pada taraf normal dimana penelitian-penelitian sebelumnya menunjukkan kondisi lapas yang cukup menekan psikologis. Program mapenaling dan detoksifikasi membantu negative emotional state pada warga binaan narkotika berada pada taraf normal.
Predictors of Metastasis and Outcome Following True Negative Sentinel Node Biopsy

Up to 30% of sentinel node-negative patients develop metastases during follow-up. Negative sentinel node biopsies (SNB) can be classified to false (FN) and true negative (TN) categories. Little attention has been paid to the characteristics and outcomes of patients who experience direct distant metastasis following TN-SNB. In this retrospective study of a melanoma database at Tampere university hospital we analyzed characteristics and outcome following metastases after TN-SNB. A total of 506 patients underwent SNB between 2006 and 2016. After review, SNBs were classified FN, TN and true positive (TP). Follow-up was performed until 30.4. 2019. Of SN-negative patients, 74 of 396 (19%) developed recurrence, including 17 (4%) local, 22 (6%) regional lymph node (FN) and 35 (9%) direct distant metastases (TN-D). False negative rate was 16% and negative predictive value 93.8%. Locoregional recurrences occurred earlier compared to distal metastases (median of 2.14 /2.93 years). Compared to patients without recurrence, thickness ≥ 2 mm (univariable p<0.001), male gender (p=0.021), nodular melanoma (p=0.001), ulceration (p<0.001) and location in upper limb region (p=0.062) were predictors of TN-D. The 5-year melanoma specific survival in TN-D patients did not differ significantly from TP patients (2.36 /2.26 years). TN-D is associated with nodular melanomas in upper limb region, male gender, cervical SNBs and ulcerated tumors with Breslow thickness ≥ 2 mm. These patients should be considered at high-risk relapse and mortality. Surveillance imaging to detect distant metastases is mandatory regardless of SNB status. In future, inclusion criteria for therapy trials for high-risk SNB-negative patients might also be worth considering.
Academic Staff Satisfaction with their Work: A Cross-Sectional Study in a Medical University

BACKGROUND: Employees’ work satisfaction, combined with democratic management, are important predictors of future productivity in any organisation. AIM: The aim of this study is to investigate job satisfaction in academic staff as well as the associated working environment factors, using an original self-administered questionnaire. METHODS: A cross-sectional survey was conducted using an original standardised questionnaire. It involved 370 academic staff members at one of the five medical universities in Bulgaria. The questionnaire consists of 17 items (including occupational hazards, management style, conflict solving and demographic characteristics) rated on a 5-point Likert scale. RESULTS: The results revealed that the majority of academic staff (71.7%) works in a risky environment. Employees indicate that “mental strain”, “work with chemical agents and dust” and “work with biological hazards” are the most common risk factors. Democratic leadership and cooperation are most commonly applied management styles. CONCLUSION: The instrument for the measurement of job satisfaction revealed high values of psychometric characteristics for reliability and validity. The study found a high level of satisfaction of academics with their working conditions. It is necessary to conduct similar studies periodically to detect more precisely the decrease in academic staff work satisfaction and take timely and adequate measures to improve it.
Media Coverage of Climate Change and Sustainable Product Consumption: Evidence from the Hybrid Vehicle Market

As sustainable consumption becomes increasingly important, firms must better understand the drivers behind the consumption of these products. This article examines the effects of mass media in the context of the U.S. hybrid vehicle market. Drawing on monthly sales data, the authors provide evidence that the general coverage of climate change or global warming by major media outlets exerts an overall positive impact on the sales of hybrid vehicles. This impact mainly comes from the media reports that assert that climate change is occurring. In contrast, media coverage that either denies climate change or holds a neutral stance on the issue has little impact. The authors provide preliminary evidence that a social norm advocating for environmentally friendly consumption plays an important role in how media coverage affects consumer purchase. They provide implications for theory and practice and call for future research that examines the causal impact of media in general on consumer decisions, especially in domains that are crucial for the society.
Utilización de los servicios de salud de Atención Primaria en los pacientes crónicos según nivel de riesgo.

Los pacientes cronicos sufren mayor numero de problemas de salud y tienen mayores necesidades de asistencia y cuidados. El objetivo de este estudio fue describir la utilizacion de servicios de salud de Atencion Primaria en los pacientes cronicos segun el nivel de riesgo asignado por los grupos de morbilidad ajustados (GMA), asi como analizar los factores asociados.
Predictive decoding of neural data

PREDICTIVE DECODING OF NEURAL DATA by Yaroslav 0. Halchenko In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder's sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5. PREDICTIVE DECODING OF NEURAL DATA by Yaroslav 0. Halchenko A Dissertation Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science Department of Computer Science
Association between Frailty Syndrome and Anemia in Community-Dwelling Elderly

Frailty is an important and complex phenomenon in the fields of geriatrics and gerontology and a highly relevant topic. Frailty syndrome (FS) is a multidimensional clinical condition characterized by loss of physiological and cognitive reserves across multiple organ systems. The Fried frailty phenotype is the main model used to categorize FS. Recent studies have shown an association between anemia, low hemoglobin concentration and frailty. This study aimed to assess the association between FS and anemia in community-dwelling elderly. An observational cross-sectional design was used, with 308 elderly subjects. Most participants were women (78.2%) between 60 and 69 years old (50.6%). The average schooling level was 9.7 years. Anemia was detected in 9.7% of participants. The frequency of frailty, pre-frailty and non-frailty was 8.2, 74.0 and 17.8%, respectively. The multivariate ordinal logistic regression model identified low levels of brain-derived neurotrophic factor (BDNF) (OR = 0.9, 95% CI = 0.9; 0.9) as risk factor associated with FS. No significant association was found between frailty and anemia. Moreover, FS may be influenced by low schooling levels, despite the non-significant result obtained in the statistical model. These findings contribute to elucidating other factors associated with FS, especially in community-dwelling elderly.
The Importance of Taking Psychology: A Comparison of Three Levels of Exposure

We compared the performance of senior psychology majors, introductory psychology students, and similarly aged individuals with no college psychology on seven different outcome measures including multiple-choice knowledge tests, attitudes, and open-ended scenarios requiring qualitative responses. On all measures, both senior psychology majors and introductory psychology students significantly outperformed individuals with no college psychology. However, the absolute magnitude of introductory psychology student performance would be depicted as a failing grade at most institutions (i.e., below 60% correct). We discuss this pattern of results in the context of judging the effectiveness of the major, the role of the introductory psychology course, and judging the effectiveness of the assessment/outcome measures utilized.
A Meta-analysis of the Effect of Planned Exercise on their Self Efficacy, Depression, Life Satisfaction

The aim of this study was to survey the effects of planned exercise over the age of 65 in male and female with regard to their self-efficacy, depression, and life satisfaction, based on the literatures of 14 master and doctoral dissertations published between 2004 and 2014. The subjects with program exercise habit were chosen considering the generalization of the physical characteristics of elderly subjects. The exercise duration was 50 minutes for 3 times a week for 12 to 24 weeks. Exercise intensity was an optimal aerobic exercise plan that the subjects themselves could perform. The average effect size of self-efficacy was 0.793 (26.1% increased), depression was -0.91(31.9% decreased), and life satisfaction was 1.506 (43.4% increased). These indicate that post-exercise psychological variables had a positive effect on self-efficacy and life satisfaction by lowering depression after regular exercise. Therefore, it is necessary to increase regular exercise participation and to provide sustainable scientific information for the aged and healthy living of the elderly.
The development of international students' motivation, self-regulation, and writing from source texts

This mixed methods study was conducted over the course of a four-week EAP course and examined the development of L2 international students’ motivation, self-regulation, and writing from source texts. Data regarding motivation and self-regulation were collected using a questionnaire at the beginning and end of the EAP course. Furthermore, the participants completed an integrated writing task at the beginning and end of the course and the resulting data were analysed for writing quality and use of sources. Interviews were conducted in the first and final weeks of the course. Descriptive statistics and t-tests showed motivation and self-regulatory strategy use to remain stable over time, except for self-efficacy measure which increased significantly. In addition, scores on the integrated writing task and use of paraphrases increased significantly, while use of direct quotations and percentage of borrowed words remained stable. Correlation analysis confirmed the strong inter-relationship between self-efficacy and self-regulation at both times. At the end of the course, mastery goals, performance goals, and utility value were found to be significantly correlated to essay scores. A number of theoretical implications are highlighted in the study. First, the findings indicate that there was learning transfer due to of the EAP instruction as the participants could apply their recently acquired knowledge to a test taken under timed conditions. It can therefore be assumed that attendance on a pre-sessional course can help students to develop in their cognitive processing of completing tasks that involve writing from sources. Second, the current research found various developmental trajectories for participants in terms of paraphrasing from source texts, Third, a model of EAP students’ motivation from Confucian heritage cultures is hypothesised in which achievement goals and utility value directly impact on writing achievement. Finally, a model of international students’ academic adjustment over the course of an EAP programme is posited that highlights the unique adjustment journey of novice L2 academic writers.
Global aerosol modeling with MADE3 (v3.0) in EMAC (based on v2.53): model description and evaluation

Abstract. Recently, the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, third generation) was introduced as a successor to MADE and MADE-in. It includes nine aerosol species and nine lognormal modes to represent aerosol particles of three different mixing states throughout the aerosol size spectrum. Here, we describe the implementation of the most recent version of MADE3 into the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model, including a detailed evaluation of a 10-year aerosol simulation with MADE3 as part of EMAC. We compare simulation output to station network measurements of near-surface aerosol component mass concentrations, to airborne measurements of aerosol mass mixing ratio and number concentration vertical profiles, to ground-based and airborne measurements of particle size distributions, and to station network and satellite measurements of aerosol optical depth. Furthermore, we describe and apply a new evaluation method, which allows a comparison of model output to size-resolved electron microscopy measurements of particle composition. Although there are indications that fine-mode particle deposition may be underestimated by the model, we obtained satisfactory agreement with the observations. Remaining deviations are of similar size to those identified in other global aerosol model studies. Thus, MADE3 can be considered ready for application within EMAC. Due to its detailed representation of aerosol mixing state, it is especially useful for simulating wet and dry removal of aerosol particles, aerosol-induced formation of cloud droplets and ice crystals as well as aerosol–radiation interactions. Besides studies on these fundamental processes, we also plan to use MADE3 for a reassessment of the climate effects of anthropogenic aerosol perturbations.
A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according to the government’s strategic framework by applying the second order autoregressive-structural equation model (second order autoregressive-SEM). The model is validated by various measures, fits with the best model standards, meets all criteria of the goodness of fit, and is absent from any issues of heteroskedasticity, multicollinearity, autocorrelation, and non-normality. The proposed model is very distinct from other alternatives in that it produces the optimal outcome. Its mean absolute percentage error (MAPE) is 1.02% while the root mean square error (RMSE) is 1.51%. A comparison of the above results is carried out to compare the same values from other models, namely the regression linear model (ML model), back propagation neural network (BP model), artificial neural natural model (ANN model), gray model, and the autoregressive integrated moving average model (ARIMA model). The second order autoregressive-SEM is a model that is appropriate for long-term forecasting (2020–2035), and accounts for the specifics of the Thai government strategy set under the Industry 4.0 policy framework. The results of the long-term analysis indicate that the current political policy will result in continuous economic growth, where the gross national product (GNP) growth rate will climb up to 6.45% per annum by 2035, while the environment is being negatively affected. The study predicts that CO2 emissions will rise up to 97.52 Mt CO2 Eq. (2035). The forecasting model also reflects that the economy factor has an adjustment ability to equilibrium stronger than that of the environment factor; further, it shows that the relationship between the factors is causal. In addition, the political policy , economy , and environment factors are found to have both direct and indirect effects. As to the results, this study illustrates that the Industry 4.0 policy is still inefficient, as the carbon dioxide emissions are projected to be higher than the threshold for environment hazards and disasters which set to the limit of 80 Mt CO2 Eq. by 2035. The effect of such policy will put the environment at risk, and the government must take immediate action to respond to this urgency. Thus, the second order autoregressive-SEM model remains a significant model embedded with the adjustment ability to equilibrium and the applicability for various contexts in different sectors. This introduced model is a vital tool for assisting the national government to create policy that is effective and sustainable, and lead to positive development of the nation. This second order autoregressive-SEM model can be used as a resource for the management of both public policy and private enterprise.