Previous research has demonstrated that regular exercise modulates motor cortical plasticity and cognitive function, but the influence of short-term high-intensity interval training remains unclear. In the present study, the effect of short-term HIIT on neuroplasticity and executive function was assessed in 32 sedentary females. Half of the participants undertook 2 weeks of HIIT. Paired-pulse transcranial magnetic stimulation was used to measure motor cortical plasticity via short intracortical inhibition and intracortical facilitation. We further adapted the Stroop task using (...) functional near-infrared spectroscopy to evaluate executive function in the participants. The results indicated that, compared with the control group, the HIIT group exhibited decreased ICF. In the Stroop task, the HIIT group displayed greater activation in the left dorsolateral prefrontal cortex and left orbitofrontal cortex even though no significant difference in task performance was observed. These findings indicate that short-term HIIT may modulate motor cortical plasticity and executive function at the neural level. (shrink)

In this paper, we first defined soft intervalvalued neutrosophic rough sets(SIVN- rough sets for short) which combines interval valued neutrosophic soft set and rough sets and studied some of its basic properties. This concept is an extension of soft interval valued intuitionistic fuzzy rough sets( SIVIF- rough sets). Finally an illustartive example is given to verfy the developped algorithm and to demonstrate its practicality and effectiveness.

In this paper, we define a new cosine similarity between two interval valued neutrosophic sets based on Bhattacharya’s distance [19]. The notions of interval valued neutrosophic sets (IVNS, for short) will be used as vector representations in 3D-vector space. Based on the comparative analysis of the existing similarity measures for IVNS, we find that our proposed similarity measure is better and more robust. An illustrative example of the pattern recognition shows that the proposed method is simple and effective.

Intervals in boolean algebras enter into the study of conditional assertions (or events) in two ways: directly, either from intuitive arguments or from Goodman, Nguyen and Walker's representation theorem, as suitable mathematical entities to bear conditional probabilities, or indirectly, via a representation theorem for the family of algebras associated with de Finetti's three-valued logic of conditional assertions/events. Further representation theorems forge a connection with rough sets. The representation theorems and an equivalent of the boolean prime ideal theorem yield an (...) algebraic completeness theorem for the three-valued logic. This in turn leads to a Henkin-style completeness theorem. Adequacy with respect to a family of Kripke models for de Finetti's logic, Łukasiewicz's three-valued logic and Priest's Logic of Paradox is demonstrated. The extension to first-order yields a short proof of adequacy for Körner's logic of inexact predicates. (shrink)

According to the local risk-neutrality theorem an agent who has the opportunity to invest in an uncertain asset does not buy it or sell it short iff its expected value is equal to its price, independently of the agent's attitude towards risk. Contrary to that it is shown that, in the context of expected utility theory with differentiable vNM utility function, but without the assumption of stochastic constant returns to scale, nondegenerate intervals of no-trade prices may exist. With (...) a quasiconcave expected utility function they do if, and only if, the agent is risk averse of order one. (shrink)

Sub-Saharan emerging countries experience electrical shortages resulting in power rationing, which ends up hampering economic activities. This paper proposes an approach for very short-term blackout forecast in grid-tied PV systems operating in low reliability weak electric grids of emerging countries. A pilot project was implemented in Arusha-Tanzania; it mainly comprised of a PV-inverter and a lead-acid battery bank connected to the local electricity utility company, Tanzania Electric Supply Company Limited. A very short-term power outage prediction model framework based (...) on a hybrid random forest algorithm was developed using open-source Python machine learning libraries and using a dataset generated from the pilot project’s experimental microgrid. Input data sampled at a 15-minute interval included day of the month, weekday, hour, supply voltage, utility line frequency, and previous days’ blackout profiles. The model was composed of an adaptive similar day module that predicts 15 minutes ahead from a sliding window lookup table spanning 2 weeks prior to the prediction target day, after which ASD prediction was fused with RF prediction, giving a final optimised RF-ASD blackout prediction model. Furthermore, the efficacy analysis of the short-term blackout prediction of the formulated RF, ASD, and RF-ASD regression and classification algorithms was compared. Considering the stochastic nature of blackouts, their performance was found to be fair in short-term blackout predictions of the test site’s weak grid using limited input data from the point of coupling of the user. The models developed were only able to predict blackouts if they occurred frequently and contiguously, but they performed poorly if they were sparse or dispersed. (shrink)

Short-term limb immobilization results in skeletal muscle decline, but the underlying mechanisms are incompletely understood. This study aimed to determine the neurophysiologic basis of immobilization-induced skeletal muscle decline, and whether repetitive Transcranial Magnetic Stimulation could prevent any decline. Twenty-four healthy young males underwent unilateral limb immobilization for 72 h. Subjects were randomized between daily rTMS using six 20 Hz pulse trains of 1.5 s duration with a 60 s inter-train-interval delivered at 90% resting Motor Threshold, or Sham rTMS throughout (...) immobilization. Maximal grip strength, EMG activity, arm volume, and composition were determined at 0 and 72 h. Motor Evoked Potentials were determined daily throughout immobilization to index motor excitability. Immobilization induced a significant reduction in motor excitability across time. The rTMS intervention increased motor excitability at 0 h. Despite daily rTMS treatment, there was still a significant reduction in motor excitability, loss in EMG activity, and a loss of maximal grip strength after immobilization. Interestingly, the increase in biceps and posterior forearm skinfold thickness with immobilization in Sham treatment was not observed following rTMS treatment. Reduced MEPs drive the loss of strength with immobilization. Repetitive Transcranial Magnetic Stimulation cannot prevent this loss of strength but further investigation and optimization of neuroplasticity protocols may have therapeutic benefit. (shrink)

In this paper we focus our attention on tableau methods for propositional interval temporal logics. These logics provide a natural framework for representing and reasoning about temporal properties in several areas of computer science. However, while various tableau methods have been developed for linear and branching time point-based temporal logics, not much work has been done on tableau methods for interval-based ones. We develop a general tableau method for Venema's \cdt\ logic interpreted over partial orders (\nsbcdt\ for short). It (...) combines features of the classical tableau method for first-order logic with those of explicit tableau methods for modal logics with constraint label management, and it can be easily tailored to most propositional interval temporal logics proposed in the literature. We prove its soundness and completeness, and we show how it has been implemented. (shrink)

Background: Monitoring the daily change in resting heart rate variability can provide information regarding training adaptation and recovery status of the autonomic nervous system during training camps. However, it remains unclear whether postural stabilization is essential for valid and reliable ultra-short-term recordings in short-term overseas training camps.Design: Observational and longitudinal study.Purpose: This study aimed to investigate ultra-short-term heart rate variability recordings under stabilization or post-stabilization periods in four overseas training camps.Participant: Twenty-seven U-20 male national team futsal players (...) voluntarily participated in this study.Method: Resting HRV was evaluated for 10 min during the early morning of each training camp. The natural logarithm of the root mean square of successive normal-to-normal interval differences was used for comparisons. Time segments of HRV were divided into two periods with three measures within each: the first 30-s, the first 60-s, and the 5-min standard during stabilization; the first 30-s, the first 60-s, and the 5-min standard after stabilization.Result: The results demonstrated trivial to small ES, very large to nearly perfect ICC, and narrow range of SEM when all time segments of HRVUST were compared to the 1st_5 min and 2nd_5 min HRV. Furthermore, the magnitude of the correlation coefficients ranged from very high to nearly perfect for all the time segments. The HRVUST posted excellent agreement in all time segments with/without postural stabilization. Trivial to small levels of effect size in all time segments of LnRMSSDmean and LnRMSSDcv across overseas training camps was identified.Conclusion: The first 30 or 60-s LnRMSSD recordings can be used to evaluate daily cardiac-autonomic function during overseas training camps in futsal players. The process for stabilization seems to be unnecessary for measuring the morning resting LnRMSSD in overseas training camps among young adult futsal players. (shrink)

Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced (...) data preprocessing strategy, a novel optimization model, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming. (shrink)

ABSTRACTNegative emotion influences cognitive control, and more specifically conflict adaptation. However, discrepant results have often been reported in the literature. In this study, we broke down negative emotion into integral and incidental components using a modern motivation-based framework, and assessed whether the former could change conflict adaptation. In the first experiment, we manipulated the duration of the inter-trial-interval to assess the actual time-scale of this effect. Integral negative emotion was induced by using loss-related feedback contingent on task performance, and measured (...) at the subjective and physiological levels. Results showed that conflict-driven adaptive control was enhanced when integral negative emotion was elicited, compared to a control condition without changes in defensive motivation. Importantly, this effect was only found when a short, as opposed to long ITI was used, suggesting that it had a short time scale. In the second experiment, we controlled for effe... (shrink)

Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based on (...) short-sequence enhancement and temporal self-attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo-historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time-aware self-attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art. (shrink)

Ideas of previous constructions are combined into a short proof of topological completeness of modal logic S4 first for rational numbers and after that for real numbers in the interval.