Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain’s Distributed Cognitive System - with Sports Culture Cognition as an Example

Cognition is a process of information processing in the brain. Usually intelligence refers to the ability to reconstruct information. In the cognitive system research field, the traditional artificial intelligence which uses symbolic representation for cognitive simulation has encountered challenges. With the development of brain neuroscience, psychology, cognitive science and related computer technologies, the research on cognitive system is progressing toward contextualization and personification. This paper systematically introduces the cognitive system and several cognitive pathways and analyses their differences and relations. It also describes the elements and characteristics of the distributed cognitive system in detail, including representations and calculations, culture cognition and interactions, etc., and takes the sports culture as an example to further elaborate them. The greatest advantage of the distributed cognition system over the traditional cognitive approach is that it emphasizes the importance of the environment to cognitive activities or processes. As a way to look at cognitive activities, it believes that cognitive processes are distributed to some extent and all cognitive activities should be included in them. This is of great significance to the researches on the cognitive system as well as artificial intelligence.
Consistent Wiener filtering: designing generalized time-frequency masks respecting spectrogram consistency

Wiener filtering has been one of the most widely used methods for source separation for several decades, in particular in audio signal processing. To exploit the short-term stationarity of audio signals, it is very often applied on time-frequency representations [1], especially the short-time Fourier transform (STFT). However, classical Wiener filtering does not take into account the intrinsically redundant structure of STFT spectrograms, and its output is actually in general not the optimal solution. We show here that by ensuring that the output spectrograms are “consistent”, i.e., that they correspond to actual time-domain signals, we can obtain a more efficient filtering. As Wiener filtering is widely used as a post-processing for many methods involving the estimation of the power spectrograms of the component signals (non-negative matrix factorization, AR modeling, etc.) or in time-frequency masking in computational auditory scene analysis, it is of tremendous importance to ensure that the information gathered by those algorithms is best exploited. We generalize here the concept of Wiener filtering to time-frequency masks which can involve a manipulation of the phase as well in order to find the true Maximum-Likelihood solution, by focusing on the concept of consistency, which we already exploited in [2] for fast phase restoration and [3] to improve Kameoka et al.’s complex NMF decomposition [4].
On the Nonlinear Dynamics of a Doubly Clamped Microbeam Near Primary Resonance

This work aims to investigate theoretically and experimentally various nonlinear dynamic behaviors of a doubly clamped microbeam near its primary resonance. Mainly, we investigate the transition behavior from hardening, mixed, and then softening behavior. We show in a single frequency-response curve, under a constant voltage load, the transition from hardening to softening behavior demonstrating the dominance of the quadratic electrostatic nonlinearity over the cubic geometric nonlinearity of the beam as the motion amplitudes becomes large, which may lead eventually to dynamic pull-in. The microbeam is fabricated using polyimide as a structural layer coated with nickel from top and chromium and gold layers from the bottom. Frequency sweep tests are conducted for different values of DC bias revealing hardening, mixed, and softening behavior of the microbeam. A multi-mode Galerkin model combined with a shooting technique are implemented to generate the frequency response curves and to analyze the stability of the periodic motions using the Floquet theory. The simulated curves show good agreement with the experimental data.
Investigating the Genetic Background of Spastic Syndrome in North American Holstein Cattle Based on Heritability, Genome-Wide Association, and Functional Genomic Analyses

Spastic syndrome is a chronic, progressive disorder of adult cattle characterized by episodes of sudden involuntary muscle contractions or spasms of the extensor and abductor muscles of one or both hind limbs. In this study, a case-control genome-wide association study (GWAS) was performed on an adult Holstein cattle cohort. Based on the 50 K and high-density (HD) SNP panel GWAS, we identified 98 and 522 SNPs, respectively. The most significant genomic regions identified are located on BTA9 at approximately 87 megabase pairs (Mb) and BTA7 between 1 and 20 Mb. Functional analyses of significant SNPs identified genes associated with muscle contraction, neuron growth or regulation, and calcium or sodium ion movement. Two candidate genes (FIG4 and FYN) were identified. FIG4 is ubiquitously expressed in skeletal muscle and FYN is involved with processes such as forebrain development, neurogenesis, locomotion, neurogenesis, synapse development, neuron migration, and the positive regulation of neuron projection development. The CACNA1A gene, which codes for a calcium channel subunit protein in the calcium signaling pathway, seems the most compelling candidate gene, as many calcium ion channel disorders are non-degenerative, and produce spastic phenotypes. These results suggest that spastic syndrome is of polygenic inheritance, with important genomic areas of interest on BTA7 and BTA9.
Hydrodynamic boundary layer at a rising air bubble and entrapment of fine solids: Gravity effects on particle–bubble interactions

ABSTRACT A model study of the dynamic interactions of fine non-neutrally buoyant solids with background boundary layer (BL) flow on rising bubbles is developed. The aim is to clarify the specific role of the gravity effects. The approach is based on the acquisition of asymptotic equations about the disturbed flow field in the particle vicinity and accounts for both viscous deformation and sedimentation effects. It is established that two particle density regions are of major interest. In Region I the coupling of hydrodynamic and gravity effects results in granulometric separation of the solids. In Region II the role of particle sedimentation dominates over the purely hydrodynamic interactions. As a result, the lighter the particles are, and the smaller their sizes are, the more important is the granulometric effect inside the BL. For high-density fines and larger bubbles, the gravity effects couple with the BL flow. The particle capture results in significant amplification of the collision chances. The obtained results are expected to refine the approach to recovery of fine species from ground materials in conventional flotation and should be taken into account in the assessment of the overall capture efficiency for fine particles in microflotation and separation processes. GRAPHICAL ABSTRACT
Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies

ABSTRACT In observational studies for the interaction between exposures on a dichotomous outcome of a certain population, usually one parameter of a regression model is used to describe the interaction, leading to one measure of the interaction. In this article we use the conditional risk of an outcome given exposures and covariates to describe the interaction and obtain five different measures of the interaction, that is, difference between the marginal risk differences, ratio of the marginal risk ratios, ratio of the marginal odds ratios, ratio of the conditional risk ratios, and ratio of the conditional odds ratios. These measures reflect different aspects of the interaction. By using only one regression model for the conditional risk, we obtain the maximum-likelihood (ML)-based point and interval estimates of these measures, which are most efficient due to the nature of ML. We use the ML estimates of the model parameters to obtain the ML estimates of these measures. We use the approximate normal distribution of the ML estimates of the model parameters to obtain approximate non-normal distributions of the ML estimates of these measures and then confidence intervals of these measures. The method can be easily implemented and is presented via a medical example.
Development of Group Counseling: A Guidebook in Lesson Study to Increase Creativity

This study aims to develop and test the acceptability of group counseling handbook development in lesson study to enhance student creativity. This handbook is an instrument for counseling guidance teachers in organizing counseling guidance services in schools. The development procedure in this study follows the 4D development model which is limited only to content validation testing. Five experts and guidance counseling practitioners were involved to assess the feasibility of the guidebook. The instrument used for the assessment consisted of 25 items that referred to the acceptability of the manual. The results of the analysis show that the validation of the expert development group counseling guidebook in the setting of lesson study to improve student creativity has a content validity index (CVI) of 0.94 which means very good or special. Based on these results, the development of group counseling handbooks in the setting of lesson studies to enhance student creativity developed in this study has met the eligibility criteria to be used as an instrument in carrying out counseling guidance services.
Biodegradable Polylactic Acid and Its Composites: Characteristics, Processing, and Sustainable Applications in Sports

Polylactic acid (PLA) is a biodegradable polyester polymer that is produced from renewable resources, such as corn or other carbohydrate sources. However, its poor toughness limits its commercialization. PLA composites can meet the growing performance needs of various fields, but limited research has focused on their sustainable applications in sports. This paper reviews the latest research on PLA and its composites by describing the characteristics, production, degradation process, and the latest modification methods of PLA. Then, it discusses the inherent advantages of PLA composites and expounds on different biodegradable materials and their relationship with the properties of PLA composites. Finally, the importance and application prospects of PLA composites in the field of sports are emphasized. Although PLA composites mixed with natural biomass materials have not been mass produced, they are expected to be sustainable materials used in various industries because of their simple process, nontoxicity, biodegradability, and low cost.
Spatial-Temporal Correlation Neural Network for Long Short-Term Demand Forecasting During COVID-19

Demand forecasting is an important method for dealing with the supply-demand relationship in social resource management. The demands of daily life discussed in this study are mainly about hotels, restaurants, gas stations, drugstores, shopping malls, etc. Accurate demand forecasting can help enterprises meet people’s needs properly, especially since COVID-19 has spread worldwide and significantly changed people’s lives. Previous studies have proven the effectiveness of statistical and deep learning models in demand forecasting, including the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and their variants. These methods focus only on one-step prediction of one demand and do not perform well in long-term or multi-step predictions. To address this issue, we propose a framework called the Deep Spatial-Temporal Neural Network (DSTNN) for multi-demand forecasting, which performs well in long short-term forecasting. The DSTNN adopts a transformer encoder-based structure and establishes a direct correlation between the demand distribution and spatiotemporal features. The transformer encoder is designed to learn high-order feature interactions. An auxiliary task of pandemic trend forecasting was used to help the main task learn about pandemic trends. As regional mobility affects the spread of COVID-19, a population mobility graph was designed to achieve a better spatial feature representation. The DSTNN eliminates the dependency on historical sequential data, which helps the model to perform well in long-term forecasting, and its performance does not obviously decline as the prediction period increases. The experimental results show that the DSTNN performs much better than conventional models, particularly in terms of long-term forecasting.
FxTDO ‐based non‐singular terminal sliding mode control for second‐order uncertain systems

In this study, a composite robust control approach with non-singular terminal sliding mode control and fixed-time disturbance observer (FxTDO) is proposed for a class of second-order non-linear systems in the presence of matched uncertain disturbances. A bi-limit-weighted homogeneous FxTDO is designed based on the efficient utilisation of power functions obtained by the differentiation error amplification strategy. The FxTDO enjoys a fast convergence rate and simple parameters tuning process. By introducing the disturbance estimation to directly compensate the total disturbance in the composite control law, only a particular small gain-based discontinuous control term is needed to guarantee the existence of sliding mode, and chattering phenomenon can be attenuated. The simulation results revealed the effectiveness of the proposed method.
Safety climate perceived by pre-hospital emergency care personnel—an international cross-sectional study

Introduction Improving patient safety is one of the most critical components of modern healthcare. Emergency medical services (EMS) are, by nature, a challenging environment for ensuring patient safety. It is fast-paced, physically dangerous, and highly stressful, requiring rapid decision-making and action. This can create risks not only for patients but also for employees. We assessed variations in perceptions of safety culture in prehospital emergency care among an international sample of paramedics and nurses. Methods The Emergency Medical Services Safety Attitudes Questionnaire (EMS-SAQ) was used for the study. The instrument measures six domains of safety culture in the workplace: teamwork climate, job satisfaction, safety climate, working conditions, stress recognition, and perceptions of management. A total of 1,128 EMS from 9 countries participated in this study. Results Safety Climate was 81.32/100 (SD 6.90), Teamwork Climate 84.14/100 (SD 8.74), Perceptions of Management 76.30/100 (SD 10.54), Stress Recognition 89.86/100 (SD 5.70), Working Conditions 81.07/100 (SD 9.75), and Job Satisfaction 70.71/100 (SD 7.21). There was significant variation in safety culture scores across countries for teamwork climate (TWC), working conditions (WC), and job satisfaction (JS). Among the individual variables (age, gender, level of education, and work experience), variations in safety culture scores were unaffected by age, gender, or work experience. Organizational characteristics: employment status and position type were linked to significant variations in safety culture domain scores. Conclusion Participants’ perceptions of the patient safety climate were not particularly satisfactory, confirming that there is still a need to develop a culture of patient safety in prehospital emergency care.
Retinal Vascular Lesions in Patients with Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis

Background: This systematic review and meta-analysis aimed to summarize and compare data on retinal vascular lesions between patients with nonalcoholic fatty liver disease (NAFLD) and individuals without the disease. Methods: Search was performed in PubMed, Scopus and Cochrane Library, complemented by handsearching (PROSPERO ID: CRD42022345558). Thirty-six studies comprising 24,985 individuals (12,387 NAFLD patients and 12,598 controls) were selected for the meta-analysis. Results: Apart from retinopathy, no study with a different type of retinal vascular lesion was retrieved. Overall, there was no significant difference in the presence of retinopathy in NAFLD patients compared to controls (Odds Ratio (OR) = 1.20; 95% Confidence Interval (CI): 0.91–1.59). Heterogeneity among studies was high (I2 = 93%; p < 0.00001), while Egger’s test revealed no publication bias (p = 0.60). However, subgroup analysis showed positive association between retinopathy and NAFLD in type 1 diabetes mellitus (T1DM) (OR = 2.35; 95% CI: 1.53–3.60), but not in type 2 diabetes mellitus patients. Meta-regression analysis exploring potential confounders revealed no significant association. Conclusions: The presence of retinopathy was not overall different between individuals with and without NAFLD; however, T1DM patients with NAFLD had higher rates of retinopathy compared to T1DM patients without NAFLD, a finding warranting further research to show whether NAFLD may predict retinopathy in T1DM patients.
Gold Nanobipyramids for Near-Infrared Fluorescence-Enhanced Imaging and Treatment of Triple-Negative Breast Cancer

Simple Summary Gold nanoparticles have gained significant attention in the field of cancer research and treatment due to their unique properties and potential applications to medical imaging, targeted drug delivery, and combined use for cancer diagnosis and therapy. In this study, we developed novel gold nanoparticles with a bipyramidal shape, which have been functionalized to improve fluorescence imaging in the near-infrared biological window and facilitate the targeted delivery of chemotherapy to triple-negative breast tumors. We successfully tested these nanoparticle formulations in both in vitro and preclinical tumor models. Their ability to be combined with a wide range of fluorophores, therapeutics, and targeting agents demonstrates their clinical potential against various cancer types. Abstract There is an imminent need for novel strategies for the diagnosis and treatment of aggressive triple-negative breast cancer (TNBC). Cell-targeted multifunctional nanomaterials hold great potential, as they can combine precise early-stage diagnosis with local therapeutic delivery to specific cell types. In this study, we used mesoporous silica (MS)-coated gold nanobipyramids (MS-AuNBPs) for fluorescence imaging in the near-infrared (NIR) biological window, along with targeted TNBC treatment. Our MS-AuNBPs, acting partly as light amplification components, allow considerable metal-enhanced fluorescence for a NIR dye conjugated to their surfaces compared to the free dye. Fluorescence analysis confirms a significant increase in the dye’s modified quantum yield, indicating that MS-AuNBPs can considerably increase the brightness of low-quantum-yield NIR dyes. Meanwhile, we tested the chemotherapeutic efficacy of MS-AuNBPs in TNBC following the loading of doxorubicin within the MS pores and functionalization to target folate receptor alpha (FRα)-positive cells. We show that functionalized particles target FRα-positive cells with significant specificity and have a higher potency than free doxorubicin. Finally, we demonstrate that FRα-targeted particles induce stronger antitumor effects and prolong overall survival compared to the clinically applied non-targeted nanotherapy, Doxil. Together with their excellent biocompatibility measured in vitro, this study shows that MS-AuNBPs are promising tools to detect and treat TNBCs.
Experimental Study: The Effect of Pore Shape, Geometrical Heterogeneity, and Flow Rate on the Repetitive Two-Phase Fluid Transport in Microfluidic Porous Media

Geologic subsurface energy storage, such as porous-media compressed-air energy storage (PM-CAES) and underground hydrogen storage (UHS), involves the multi-phase fluid transport in structurally disordered or heterogeneous porous media (e.g., soils and rocks). Furthermore, such multi-phase fluid transport is likely to repeatedly occur due to successive fluid injections and extractions, thus, resulting in cyclic drainage–imbibition processes. To complement our preceding study, we conducted a follow-up study with microfluidic pore-network devices with a square solid shape (Type II) to further advance our understanding on the effect of the pore shape (aspect ratio, Type I: 5–6 > Type II: ~1), pore-space heterogeneity (coefficient of variation, COV = 0, 0.25, and 0.5), and flow rates (Q = 0.01 and 0.1 mL/min) on the repetitive two-phase fluid flow in general porous media. The influence of pore shape and pore-space heterogeneity were observed to be more prominent when the flow rate was low (e.g., Q = 0.01 mL/min in this study) on the examined outcomes, including the drainage and imbibition patterns, the similarity of those patterns between repeated steps, the sweep efficiency and residual saturation of the nonwetting fluid, and fluid pressure. On the other hand, a higher flow rate (e.g., Q = 0.1 mL/min in this study) appeared to outweigh those factors for the Type II structure, owing to the low aspect ratio (~1). It was also suggested that the flow morphology, sweep efficiency, residual saturation, and required pressure gradient may not severely fluctuate during the repeated drainage-–imbibition processes; instead, becoming stabilized after 4–5 cycles, regardless of the aspect ratio, COV, and Q. Implications of the study results for PM-CAES and UHS are discussed as a complementary analysis at the end of this manuscript.
Dynamic Factorization and Partition of Complex Networks

Finding the reduced-dimensional structure is critical to understanding complex networks. Existing approaches such as spectral clustering are applicable only when the full network is explicitly observed. In this paper, we focus on the online factorization and partition of implicit large-scale networks based on observations from an associated random walk. We propose an efficient and scalable nonconvex stochastic gradient algorithm. It is able to process dependent data dynamically generated by the underlying network and learn a low-dimensional representation for each vertex. By applying a diffusion approximation analysis, we show that the nonconvex stochastic algorithm achieves nearly optimal sample complexity. Once given the learned low-dimensional representations, we further apply clustering techniques to recover the network partition. We show that, when the associated Markov process is lumpable, one can recover the partition exactly with high probability. The proposed approach is experimented on Manhattan taxi data.
COMPARISON OF THE PERFORMANCES OF DIFFERENTIAL EVOLUTION, PARTICLE SWARM OPTIMIZATION AND HARMONY SEARCH ALGORITHMS ON BENCHMARK FUNCTIONS

In the literature, there are many efficient metaheuristic algorithms for optimizing real-valued objective functions. In this work, Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and Harmony Search (HS) algorithm are compared on the basis of speed of convergence of the algorithms. In order to achieve this goal, a set of benchmark functions that have different characteristics are used.