Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized trial of the efficacy of bright-light exposure and aerobic exercise on depressive symptoms and serum lipids.

BACKGROUND Season-related subsyndromal depressive symptoms during winter are common among populations at high latitudes. Both physical exercise and exposure to bright light can relieve the fatigue and downturn of mood associated with the shortening length of day. Serum cholesterol level may be related to changes in mood, but the evidence is contradictory. Our objective was to compare the effect of aerobic exercise with or without bright-light exposure on health-related quality of life, mood, and serum lipids in a sample of relatively healthy adult subjects. METHOD A randomized controlled trial was conducted with subjects allocated to group aerobics training in a gym with bright light (2500-4000 lux) (N = 40) or normal illumination (N = 42) or to relaxation/stretching sessions in bright light as a control group (N = 42) twice a week for a period of 8 weeks. Changes in mood were recorded using questionnaires at the beginning of the study, at weeks 4 and 8. and at follow-up 4 months after the study. A blood sample was drawn before and after the 8-week intervention to measure the concentrations of serum lipids. RESULTS Ninety-eight subjects completed the 8-week study. Both exercise and bright light effectively relieved depressive symptoms. Bright light reduced atypical depressive symptoms more than exercise (p = .03), based on the atypical symptoms subscore of the Structured Interview Guide for the Hamilton Depression Rating Scale-Seasonal Affective Disorders Version Self-Rating Format. There were no significant differences between the study groups in the changes in serum lipid levels. CONCLUSION Bright light administered twice a week, alone or combined with physical exercise, seems to be a useful intervention for relieving seasonal mood slumps.
Architectural exploration of Last-Level Caches targeting homogeneous multicore systems

The Last-Level Cache (LLC) influences the overall system performance and power dissipation in multicore systems significantly. This paper evaluates five LLC architectures targeting execution time, dynamic and static power dissipation, and area consumption. They are measured using the widely adopted PARSEC benchmark suite for parallel shared-memory systems. Employing Gem5 full-system simulator and 32 nm technology characterization of the McPAT framework, this work had two interesting findings: (i) the shared LLC has the overall best performance under the PARSEC parallel workload, even for applications with less than 20% of shared data. (ii) A privately accessed cache can reduce up to 20 times the dynamic power dissipation on 32 nm technology and 25 times the area consumption when compared to shared-accessed caches.
Supplementary Information for " Repair Rather than Segregation of Damage Is the Optimal Unicellular Aging Strategy " Supplementary Model Description

Repair rather than segregation of damage is the optimal unicellular aging strategy Model equations. In all cases, growth of active protein depends on substrate concentration S following Monod kinetics: (). (1) If repair of damaged protein takes place, the rate of repair is: (). (2) Basic model: damaged protein inert, and not repaired:
LESBIAN MOTHERHOOD

The identity of lesbian-mother combines a marginalized identity (lesbian) with one of the most revered mainstream identities (mother). With data collected through exploratory in-depth interviews from nine lesbian-mothers, the authors use symbolic interaction framework to explore the strategies that lesbian birth mothers and comothers employ to gain acceptance for their marginal-mainstream identities in their family networks. Respondents experienced varying levels of resistance from their social networks, with comothers being especially vulnerable due to their lack of both biological and legal substantiation. The authors explore the process of identity negotiation in three realms of everyday experience: in relationships with extended families, in relations within the nuclear family, and in lesbian relationships that have ended.
Microvesicles of the neurohypophysis are biochemically related to small synaptic vesicles of presynaptic nerve terminals

Nerve endings of the posterior pituitary are densely populated by dense- core neurosecretory granules which are the storage sites for peptide neurohormones. In addition, they contain numerous clear microvesicles which are the same size as small synaptic vesicles of typical presynaptic nerve terminals. Several of the major proteins of small synaptic vesicles of presynaptic nerve terminals are present at high concentration in the posterior pituitary. We have now investigated the subcellular localization of such proteins. By immunogold electron microscopy carried out on bovine neurohypophysis we have found that three of these proteins, synapsin I, Protein III, and synaptophysin (protein p38) were concentrated on microvesicles but were not detectable in the membranes of neurosecretory granules. In addition, we have studied the distribution of the same proteins and of the synaptic vesicle protein p65 in subcellular fractions of bovine posterior pituitaries obtained by sucrose density centrifugation. We have found that the intrinsic membrane proteins synaptophysin and p65 had an identical distribution and were restricted to low density fractions of the gradient which contained numerous clear microvesicles with a size range the same as that of small synaptic vesicles. The peripheral membrane proteins synapsin I and Protein III exhibited a broader distribution extending into the denser part of the gradient. However, the amount of these proteins clearly declined in the fractions preceding the peak of neurosecretory granules. Our results suggest that microvesicles of the neurohypophysis are biochemically related to small synaptic vesicles of all other nerve terminals and argue against the hypothesis that such vesicles represent an endocytic byproduct of exocytosis of neurosecretory granules.
Prediction of driver fatigue: Approaches and open challenges

Fatigue is a mental process that grows gradually and affects human reaction time and the consciousness. It is one of the causes of road fatal accidents around the globe. Although it is now generally accepted that fatigue plays an important role in road safety, it is still largely left to individual drivers to manage. The recent research in this area focuses on fatigue detection and the existing systems alert the drivers in severe fatigued stage. These systems use either physiological signs of the fatigue or the behavioural reaction to generate alerts. This research investigates the feasibility of using a group of fatigue symptoms (such as pupil response, gaze patterns, steering reaction and EEG) to build a robust fatigue detection algorithm that can be used in a real-life system for the early prediction and avoidance of fatigue development. Intensive testing and validation stages are required to ensure the reliability and the suitability of the system that should be able to detect fatigue levels at different degrees of tiredness. Moreover, the proposed system predicts subsequent stages of fatigue and generates an approximate behavioural model for each individual driver to enable more personalised and effective intervention.
Extending the vocabulary of touch events with ThumbRock

Compared with mouse-based interaction on a desktop interface, touch-based interaction on a mobile device is quite limited: most applications only support tapping and dragging to perform simple gestures. Finger rolling provides an alternative to tapping but uses a recognition process that relies on either per-user calibration, explicit delimiters or extra hardware, making it difficult to integrate into current touch-based mobile devices. This paper introduces ThumbRock, a ready-to-use micro gesture that consists in rolling the thumb back and forth on the touchscreen. Our algorithm recognizes ThumbRocks with more than 96% accuracy without calibration nor explicit delimiter by analyzing the data provided by the touch screen with a low computational cost. The full trace of the gesture is analyzed incrementally to ensure compatibility with other events and to support real-time feedback. This also makes it possible to create a continuous control space as we illustrate with our MicroSlider, a 1D slider manipulated with thumb rolling gestures.
Emotional Intelligence and Conflict Resolution: Implications for Human Resource Development

The problem and the solution. There continues to be strong interest regarding the emotional intelligence construct,primarily because of the construct’s potential as a predictor of workplace behavior in organizations. Little research has been conducted, however, that considers the implications of emotional intelligence for organizational change and human resource development in organizations.The study outlined in this article explores the connection between emotional intelligence and preferred styles of conflict resolution and examines the implications for human resource development and micro level organizational change specifically. One hundred and thirty-nine respondents were administered the Workgroup Emotional Intelligence Profile, a measure of group members’ emotional intelligence when working in teams, as well as the Thomas-Kilmann Conflict Model Instrument. The results consistently showed that individuals with high emotional intelligence preferred to seek collaborative solutions when confronted with conflict. Implications for human resource development and organizational change are also discussed.
On the use of automatic speech recognition for TV captioning

This study analyses the possible use of automatic speech recognition (ASR) for the automatic captioning of TV programs. Captioning requires: (1) transcribing the spoken words and (2) determining the times at which the caption has to appear and disappear on the screen. These times have to match as closely as possible the corresponding times on the audio signal. Automatic speech recognition can be used to determine both aspects: the spoken words and their times. This paper focuses on the question: would perfect automatic speech recognition systems be able to automate the captioning process? We present quantitative data on the discrepancy between the audio signal and the manually generated captions. We show how ASR alone can even lower the efficiency of captioning. The techniques needed to automate the captioning process are presented.
Optimization of extraction parameters on the antioxidant properties of banana waste.

BACKGROUND Banana is grown worldwide and consumed as ripe fruit or used for culinary purposes. Peels form about 18-33% of the whole fruit and are discarded as a waste product. With a view to exploiting banana peel as a source of valuable compounds, this study was undertaken to evaluate the effect of different extraction parameters on the antioxidant activities of the industrial by-product of banana waste (peel). METHODS Influence of different extraction parameters such as types of solvent, percentages of solvent, and extraction times on total phenolic content (TPC) and antioxidant activity of mature and green peels of Pisang Abu (PA), Pisang Berangan (PB), and Pisang Mas (PM) were investigated. The best extraction parameters were initially selected based on different percentages of ethanol (0-100% v/v), extraction time (1-5 hr), and extraction temperature (25-60°C) for extraction of antioxidants in the banana peels. Total phenolic content (TPC) was evaluated using Folin-Ciocalteu reagent assay while antioxidant activities (AA) of banana peel were accessed by DPPH, ABTS, and β-carotene bleaching (BCB) assays at optimum extraction conditions. RESULTS Based on different extraction solvents and percentages of solvents used, 70% and 90% of acetone had yielded the highest TPC for the mature and green PA peels, respectively; 90% of ethanol and methanol has yielded the highest TPC for the mature and green PB peels, respectively; while 90% ethanol for the mature and green PM peels. Similar extraction conditions were found for the antioxidant activities for the banana peel assessed using DPPH assay except for green PB peel, which 70% methanol had contributed to the highest AA. Highest TPC and AA were obtained by applying 4, 1, and 2 hrs extraction for the peels of PA, PB and PM, respectively. The best extraction conditions were also used for determination of AAs using ABTS and β-carotene bleaching assays. Therefore, the best extraction conditions used have given the highest TPC and AAs. CONCLUSIONS By-products of banana (peel) can be considered as a potential source of antioxidants in food and pharmaceutical industry.
States, schemas, and control: General theories for psychotherapy integration

A common language is necessary for an integration of psychotherapy techniques. This means that each ‘brand name’ must surrender some historically used terminology. A modern cognitive language seems to be an acceptable ground. This paper suggests how a theory of states, schemas, and controls can use such a language while revitalizing dynamic conceptualizations in a way that promotes articulation to behavioural and family systems points of view.
Improved robust stability criteria for networked control systems

In this paper, we concern with robust stability of networked control systems (NCSs) by taking the effects of both the time-varying network-induced delay and data packet dropout into consideration. A new delay decomposition approach to time-varying delay is proposed and an appropriate Lyapunov-Krasovskii functional is constructed to derive some less conservative stability criteria. No slack matrix variable is introduced and overly bounding for some term is avoided. Furthermore, two numerical examples are given to show the effectiveness of the proposed results.
Designing a Multi-dimensional Space for Hybrid Information Extraction

Information extraction systems are developed for various specific application domains to manage an increasing amount of unstructured data. The majority build either upon the knowledge-based approach, which promises high accuracy but involves labour-intensive coding of extraction rules, or upon the automatically trainable systems approach, which produces highly portable solutions but requires an appropriate learning set. In this paper, we present results of a project that aims to provide a new methodology which combines the knowledge-based and the machine learning approach into a hybrid one in order to compensate for their respective shortcomings and to achieve high IE performance. Firstly, we propose the idea of a multi-dimensional space that guides users in selecting appropriate methods, i.e., different hybrid concepts, depending on the extraction task and the level of available features. Secondly, we provide the concept of one hybrid approach, namely the sequential processing of a knowledge-based approach and a selection of different machine learning methods. Thirdly, we present the evaluation of an implementation of the sequential extraction on a curriculum vitae corpus. Thus, we provide first results for filling the multi-dimensional space for hybrid information extraction.
Perceptions of Terrorism Threats at the 2004 Olympic Games: Implications for Sport Events

A legacy of September 11, 2001, and subsequent terrorist attacks such as the Bali, (2002), Madrid (2004) and London (2005) bombings, is evidenced in the increased security measures put in place at major sport events. Heightened attention to safety management and public concern about terrorism threats and perception of risk has now become a fundamental component of the planning and risk management strategies for sport events. On the basis of appraisal-tendency theory (Lerner & Keltner, 2001), we investigated effects of anger and fear on risk judgments of 277 attendees at the 2004 Athens Olympics. Attendees who reported being fearful or feeling unsafe at the Games displayed increased risk estimates and associated concerns, whilst respondents expressing defiance and anger produced opposite reactions. Male respondents had less pessimistic risk perceptions than did females, and men were more likely than women to report that the increased security measures detracted from their Olympic Games experience. Nationality had minimal effect on perceptions of risk except in the case of the host country, with Greek respondents reporting fewer concerns for safety but greater awareness of the security measures present at the Games. The discussion focuses on theoretical, methodological and practical implications.
What makes teacher professional development work? The influence of instructional resources on change in physical education

The purpose of this study was to understand factors that make teacher professional development successful and what success might mean in terms of teachers’ instructional practices and feelings about change. Specifically, this study focused on the impact of instructional resources on the large‐scale curricular reform of 30 urban physical education teachers. Data were collected through interviews and class observations. Data were analyzed using inductive analysis, member checks, a research team and triangulation. Analysis of these teachers’ use of and perspectives toward the instructional resources provided in the professional development process suggested that they experienced significant instructional and emotional outcomes. First, the resources enabled them to improve their instruction by: teaching more content, maximizing student learning opportunities, teaching diverse learners, teaching to development, and increasing classroom safety. Second, they also reported strong positive and negative emotions from the new resources that influenced how they viewed change. On the one hand, the resources were inspirational in helping teachers to: meet students’ needs, overcome unsupportive administrators, impress parents, and preserve salaries. On the other hand, resources also led to anxiety and concerns about utilization, storage, maintenance, and preserving relationships. Implications centre on better understanding professional development for teachers.
The role and efficacy of non-linear models in decision making and prognostication

In few types of cancer, genomic abnormalities have been linked to the phenotype and carcinogenesis with a degree of precision. For most cancers, however, this is not the case and the literature provides no clear indication of any logical process. The main difficulties are the great redundancy within the genome and proteome, the vast number of interconnections and the vast number of feedback loops. Such complicated systems can be modelled, but will require highly sophisticated analysis using computational mathematics techniques. Neural networks have been in common use in medical research for the past 20 years. They have been used for classification and for prediction of hazard or failure but are still not widely used for explanation. The binary output can be modified by, for example, adding a Bayesian function to the output stage so that survival probabilities can be given. We looked at the application of probabilistic neural networks in providing prognosis in two types of cancer; laryngeal carcinoma which has a relatively short hazard time and a medium survival rate and ocular melanoma with longer hazard time and higher survival rate. We compared their performance with the more traditional methods and studied their limitations and boundaries.
Development Trends of White Matter Connectivity in the First Years of Life

The human brain is organized into a collection of interacting networks with specialized functions to support various cognitive functions. Recent research has reached a consensus that the brain manifests small-world topology, which implicates both global and local efficiency at minimal wiring costs, and also modular organization, which indicates functional segregation and specialization. However, the important questions of how and when the small-world topology and modular organization come into existence remain largely unanswered. Taking a graph theoretic approach, we attempt to shed light on this matter by an in vivo study, using diffusion tensor imaging based fiber tractography, on 39 healthy pediatric subjects with longitudinal data collected at average ages of 2 weeks, 1 year, and 2 years. Our results indicate that the small-world architecture exists at birth with efficiency that increases in later stages of development. In addition, we found that the networks are broad scale in nature, signifying the existence of pivotal connection hubs and resilience of the brain network to random and targeted attacks. We also observed, with development, that the brain network seems to evolve progressively from a local, predominantly proximity based, connectivity pattern to a more distributed, predominantly functional based, connectivity pattern. These observations suggest that the brain in the early years of life has relatively efficient systems that may solve similar information processing problems, but in divergent ways.
The Charmed Strange Meson System

Motivated by the recent surprising discovery of two new meson states with quark content but unexpectedly low masses and narrow total decay widths, we work out, in a nonrelativistic potential-model approach developed already some two decades ago, the predictions for the energy levels of the corresponding charm–anti-strange quark bound states. In spite of the fact that this simple quark model reproduces the mass spectrum of the previously observed hadrons remarkably well, we are led to the conclusion that, without considerable modifications, both the new states do not fit into this framework.
Layer Recurrent Neural Network based Power System Load Forecasting

This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to perform. In past, various approaches have been applied for forecasting. In this work application of LRNN is explored. The results of proposed architecture are compared with other conventional topologies of neural networks on the basis of Root Mean Square of Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). It is observed that the results obtained from LRNN are comparatively more significant.