Miracle-dz/UniFAR-FTUs huggingface dataset:
Bio-Inspired Rhythmic Locomotion for Quadruped Robots

The mechanisms of locomotion in mammals have been extensively studied and inspire the related researches on designing the control architectures for the legged robots. Reinforcement learning (RL) is a promising approach allowing robots to automatically learn locomotion policies. However, careful reward-function adjustments are often required via trial-and-error until achieving a desired behavior, as RL policy behaviors are sensitive to the rewards. In this paper, we draw inspiration from the rhythmic locomotion behaviors of animals and propose a new control architecture by incorporating a rhythm generator to naturally stimulate periodic motor patterns, which actively participates in the timing of phase transitions in the robot step cycle. To speed up training, we use the joint position increments rather than the conventional joint positions as the outputs of the RL policy. During deployment, the rhythm generator can be reused for the state estimation of quadruped robots. We validate our method by realizing the full spectrum of quadruped locomotion in both simulated and real-world scenarios.
Minimal Effects of Medium-Chain Triglyceride Supplementation on the Intestinal Microbiome Composition of Premature Infants: A Single-Center Pilot Study

Compared to term infants, the microbiota of preterm infants is less diverse and often enriched for potential pathogens (e.g., members of the family Enterobacteriaceae). Additionally, antibiotics are frequently given to preterm infants, further destabilizing the microbiota and increasing the risk of fungal infections. In a previous communication, our group showed that supplementation of the premature infant diet with medium-chain triglyceride (MCT) oil reduced the fungal burden of Candida spp. in the gastrointestinal tract. The objective of this study was to determine whether MCT supplementation impacts the bacterial component of the microbiome. Pre-term infants (n = 17) receiving enteral feedings of either infant formula (n = 12) or human milk (n = 5) were randomized to MCT supplementation (n = 9) or no supplementation (n = 8). Fecal samples were taken at randomization and prior to MCT supplementation (Week 0), on days 5–7 (Week 1) and day 21 (Week 3). After DNA extraction from samples, the QIIME2 pipeline was utilized to measure community diversity and composition (genera and phyla). Our findings show that MCT supplementation did not significantly alter microbiota diversity or composition in the gastrointestinal tract. Importantly, there were no significant changes in the family Enterobacteriaceae, suggesting that MCT supplementation did not enrich for potential pathogens. MCT holds promise as a therapeutic intervention for reducing fungal colonization without significant impact on the bacterial composition of the host gastrointestinal tract.
Computer Graphic Image Design and Visual Communication Design in the Internet of Things Scenario

For the problems of low-illumination reconstructed images with low resolution and long reconstruction time, an image reconstruction system based on the wavelet domain chunking compressed perception algorithm is proposed. A low-illumination image sampling model is established, and the wavelet domain chunking compression perception and information fusion processing are performed using the depth-of-field adaptive adjustment method of the image. The multiscale Retinex algorithm is used for wavelet domain chunking compressed perception and information extraction, and the information entropy feature quantity of the image is extracted. The IoT-based artificial intelligence image detection system has been formed, which effectively enhances the accuracy and timeliness of the image detection system and makes up for the deficiencies of the traditional image detection system. Simulation results show that the method is used for low-illumination image reconstruction with higher resolution, better edge perception, and shorter reconstruction time, which is more efficient for practical application.
Research on the application of computer vision-based structural deformation monitoring

The continuous development of computer vision has brought vitality to the traditional discipline of civil engineering. Structural deformation monitoring based on digital image processing is a new non-contact measurement method that has come a long way since its introduction in the last century. Based on the research results of computer vision technology, the authors firstly perform a grey-scale transformation, filtering and noise reduction, and image enhancement on photographs collected in the laboratory and use edge detection algorithms for contour extraction. Structural displacements are obtained by combining higher-order polynomial fitting, and indirect structural strains using quadratic differential derivatives. In addition, deformation monitoring of a laboratory model was also completed using sensors. The results obtained were compared with the image processing method, which proved to be satisfactory for simple engineering applications.
How and When Humble Leadership Impact Employees Well-Being: A Moderated Mediation Model

This study aimed to explore how humble leadership affects employees autonomy need satisfaction and workplace well-being, and whether employees prevention regulation focus can moderate the association between humble leadership and autonomy need satisfaction. A questionnaire survey finally produced 222 valid cases. According to the results, humble leadership significantly impacts employees workplace well-being through autonomy need satisfaction, and employees prevention regulatory focus amplify the positive relationship between humble leadership and autonomy need satisfaction. The current research is extended in terms of humble leadership and workplace well-being.
Analysis Method for Nanometer-Scale Line Edge Roughness Based on Stationary Wavelet Transform

To resolve the problem of measuring semiconductor line edge roughness (LER) in the microelectronic manu-facturing technology, the analysis method for LER based on stationary wavelet transform was presented. First, the topogra-phy of Si line structure was measured using atomic force microscope (AFM), and the feature of LER was obtained by image processing and threshold method. Then the energy distribution of LER in each scale was determined by the multi-scale analysis based on stationary wavelet transform and the parameters of multi-scale characterization were given including the characteristic length l and the roughness exponent R. The simulation data of the line profile with different degree irregulari-ties were generated, and the calculated roughness exponent R is 0.72 and 6.05, respectively. The results show that the roughness exponent R can reflect the irregularities of LER effectively and provide the straightforward spatial information of LER. Applying this multi-scale analysis to the measurement data of Si line, the characteristic length l and the roughness exponent R is 44.56 nm and 12.17, respectively. Finally, the experimental data analyses of three kinds of probes and three scanning intervals show that this analysis method can offer an effective quantitative characterization and analysis of LER.
Evaluation of the Effect of Topical Melatonin Application on Immediately Placed Dental Implants Using Cone Beam Computed Tomography (CBCT)

Aims and objective Immediate implants provide ideal three-dimensional positioning compared to conventional implants. There may be a gap between the surface of the implant and the bone walls of the socket when it is inserted into a recently extracted alveolus. To compensate for this, various grafting materials are used. Recently, melatonin has gained significant attention because of its inhibitory action on bone resorption. So, we conducted the study to evaluate the effects of topical melatonin application on immediately placed implants. Methods The present study was conducted to compare the radiographic outcomes of immediately placed dental implants with and without topical application of melatonin using cone beam computed tomography (CBCT) with nine months of follow-up. A total of 14 patients were selected, of which seven were placed without the application of melatonin and seven were placed along with the application of melatonin. Within four months, all the implants were loaded functionally with permanent prostheses. Mean crestal bone levels, bone volume, and bone density are evaluated in both groups. Results There was no statistically significant difference in the amount of crestal bone loss near immediately inserted implants in the melatonin group as compared to the control group. In the bone volumetric analysis and bone density, the overall mean bone volume loss is less in the melatonin group. The statistical significance of the difference was determined. Conclusions The present study seems to support the hypothesis that the topical application of melatonin in immediately placed dental implants provides with better osseointegration and good survival rates.
Red pigment production by Amycolatopsis sp. UFPEDA 3422

Red biopigments produced by actinobacteria represent an important substitute in the food industry to synthetic pigments, which can cause harmful effects on human health and the environment. In this study, pigment production by actinobacteria strain UFPEDA 3422, identified by polyphasic taxonomy, was investigated. The cultivation parameters related to growth and pigment production were assayed in solid culture media and submerged fermentation. The red cell pigment was extracted with ethyl acetate and analyzed in a UV-Vis spectrophotometer with maximum production of the pigment (34.9 AU540 nm) in Hickey-Tresner (HT) medium for 48 hours at 37 ºC. The maximum biomass concentration (33.4 g/L) was obtained after 96 hours. Polyphasic taxonomy confirms 97.4% similarity of strain UFPEDA 3422 with the Amycolatopsis, a rare genus able to produce pigments on different substrates. In addition, the microorganism has tolerance to temperature variations, pH fluctuations and concentrations of 5% NaCl. The determination of cultivation and pigment production parameters show the potential of Amycolatopsis sp. as an alternative source of natural pigments.
Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions

The training process for deep learning and pattern recognition normally involves the use of convex and strongly convex optimization algorithms such as AdaBelief and SAdam to handle lots of “uninformative” samples that should be ignored, thus incurring extra calculations. To solve this open problem, we propose to design bandit sampling method to make these algorithms focus on “informative” samples during training process. Our contribution is twofold: first, we propose a convex optimization algorithm with bandit sampling, termed AdaBeliefBS, and prove that it converges faster than its original version; second, we prove that bandit sampling works well for strongly convex algorithms, and propose a generalized SAdam, called SAdamBS, that converges faster than SAdam. Finally, we conduct a series of experiments on various benchmark datasets to verify the fast convergence rate of our proposed algorithms.
Longitudinal Relation between Comprehensive Job Resources and Three Basic Psychological Needs at Work

This study aims to understand the long-term relation between comprehensive job resources and the three basic psychological needs at work (autonomy, relatedness and competence). The study was conducted in a progressive design on a sample of 1025 Polish human service professionals. Based on a typology of job resources, the three aggregated job resources index related to the task, leadership and interpersonal relations were created and the effects of each of them on the satisfaction and frustration of the three basic psychological needs, measured after 8 months, were tested. The analysis conducted by using of structural equation modelling showed that task resources are associated with the three basic psychological needs more strongly than two other kinds of resources and that that both leadership and interpersonal resources were related to the satisfaction and frustration of all the needs to the same extent. The results are discussed in the paradigm of the Conservative of Resources and the Self Determination theories.
GFFS: Gravitational-Force Feature Real Time Segmentation of 3D Point Cloud

Segmentation of point cloud is a momentous technology for autonomous vehicles. Due to the diversity and complexity of road environment, instance segmentation based on point cloud is a challenging task. Addressing this challenge, a gravitational-force feature segmentation of 3D point cloud (GFFS) is proposed. Firstly it divides the point into areas, secondly it sets the radius of the spherical neighborhood corresponding to each area based on the size and density of point cloud, thirdly it calculates the gravitational forces between the space of each spherical neighborhood and the barycenter of the area. GFFS performs instance segmentation of all kinds of objects in point cloud scene by analyzing the magnitude and continuity of gravitational forces. Finally, the superiority and effectiveness of the algorithm are verified by experiments.
Screening for dark personalities in Portugal: Intra- and interpersonal correlates, reliability and invariance of the Short Dark Tetrad Portuguese version

ABSTRACT The Dark Tetrad refers to a cluster of four socially aversive and undesirable personality traits: narcissism, Machiavellianism, psychopathy, and sadism. The main aim of the present study was to validate the recently developed Short Dark Tetrad (SD4) scale among a sample of young adults (N = 429, M = 22.11 years, SD = 3.35, range = 18–42) from Portugal, composed of men (n = 192, M = 22.14 years, SD = 3.34, range = 18–35) and women (n = 237, M = 22.08 years, SD = 3.35, range = 18–42). Results revealed that the expected four-factor latent structure of the SD4 obtained an adequate fit. The four factors mostly presented positive moderate significant intercorrelations, and adequate to good reliability. The four subscales showed distinctive correlates with other measures (e.g., low self-control, suppression of aggression, anxiety) and variables (e.g., delinquency acts, substance abuse). Strong cross-gender measurement invariance was demonstrated, with men scoring significantly higher than women on the Narcissism, Machiavellianism, Psychopathy, and Sadism subscales. The current findings support the use of the SD4 in Portuguese.
COMPARING STAKEHOLDER PARTICIPATION IN COMMUNITY-BASED TOURISM (CBT), EXAMPLES FROM THAILAND

The purpose of this qualitative research program was to study and compare stakeholder participation in community-based tourism (CBT) in communities in Phitsanulok Province and Sukhothai Province in Thailand, using case studies. Data were collected from 35 key informants from 9 villages, selected by purposive sampling, and using a structured interview form as a research tool in face-to-face, in-depth interviews. The program was undertaken during July 2021. Issues identified in an analysis of the data collected were grouped and coded. A number of different issues were identified between CBT communities. A high level of participation in CBT by leaders, homestay owners, and CBT operators was observed. Citizen control level of participation was found in both provinces, but consultation as a participation strategy was found only in Sukhothai Province. As well, differences in participation activities were identified between older participants and young participants and also between the two provinces. Volunteer work attitude and participation by visitors were also differences identified between these two provinces, indicating the different types of tourism, such as develop gastronomic tourism and creative tourism products, that should be offered by CBT communities in the two provinces.
A Compute-in-Memory Hardware Accelerator Design With Back-End-of-Line (BEOL) Transistor Based Reconfigurable Interconnect

Compute-in-memory (CIM) paradigm using ferroelectric field effect transistor (FeFET) as the weight element is projected to exhibit excellent energy efficiency for accelerating deep neural network (DNN) inference. However, two challenges exist. On the technology level, the chip area scaling is stalled due to the lack of logic voltage compatible FeFET at leading-edge technology node, e. g. 7nm. On the system level, CIM-based inference engine designs are usually customized for a specific DNN model, lacking the flexibility to support different DNN models. Besides, communication latency varies across different DNN models and can bound the total inference latency. Therefore, a reconfigurable interconnect is desired to be adaptive to different workloads, which can induce high area cost due to the reconfigurable circuit modules. To solve these issues, in this work, a system-technology co-design (STCO) of a monolithic 3D (M3D) reconfigurable CIM accelerator is performed, where back-end-of-line (BEOL) compatible oxide channel MOSFET and FeFET technologies are utilized. On the technology level, W-doped indium oxide (IWO) NMOS is utilized to design area-efficient M3D write circuit. On the system level, a reconfigurable interconnect design that inserts workload-specific express link is proposed, where the IWO-based NMOS and FeFET are adopted as the building element of the mux and crossbar switch in the router. The algorithm for interconnect configuration is also devised to achieve optimal latency for different workloads. From the system-level evaluation results, M3D IWO FeFET design (utilizing a hybrid 22nm/7nm M3D partition) shows $3.1\times $ times higher energy efficiency than a 7nm 2D SRAM design with comparable chip area. With the proposed reconfigurable interconnect scheme, the interconnect latency is reduced by 9%~32% compared to the baseline with a regular mesh network.
Transient Global Amnesia Recurrence

Background and Objectives Transient global amnesia (TGA) is an acute amnestic disorder with unclear pathophysiology. Although considered a benign phenomenon, the possibility of a recurrence is a major concern for the patient. Our objective is to identify the prevalence and risk factors of relapse to help clinicians counsel patients about it. Methods According to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidance, we screened 1,658 studies from MEDLINE, Lilacs, and Embase databases, published from 1985 to April 2021, in English or Spanish. We included 36 observational case-control and cohort studies that included patients with TGA according to the Caplan or Hodges and Warlow diagnostic criteria. We performed a meta-analysis with a random effect model for proportions and calculation of odds ratio (OR) for identified risk factors. Methodological quality was assessed according to the Newcastle-Ottawa scale. Results We identified 4,514 TGA cases and 544 recurrence events (12.73%). A follow-up had no effect on its variance. We identified a statistically significant association between recurrence and sexual activity as a trigger, a personal history or current state of migraine and depression (OR 1,481 95% CI [1.0341–2.1222] p = 0.04; OR = 2.0795 95% CI [1.3892–3.1128] p = 0.003; and OR = 4.4871 95% CI [1.890–10.651] p = 0.0288, respectively). Discussion The analysis showed that approximately 1 of 8 participants may experience recurrence, with an increased risk in the case of a history or current state of migraine, depression, or sexual intercourse before the event. A personal history of migraine and depression was associated with 2 and 4 times risk, respectively.
JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism

Accurate travel time prediction is one of the most promising intelligent transportation system (ITS) services, which can greatly support route planning, ride-sharing, navigation applications, and effective traffic management. Several factors, like spatial, temporal, and external, have big effects on traffic patterns, and therefore, it is important to develop a mechanism that can jointly capture correlations of these components. However, spatial sparsity issues make travel time prediction very challenging, especially when dealing with the origin-destination (OD) method, since the trajectory data may not be available. In this paper, we introduce a unified deep learning-based framework named joint spatial-temporal correlation (JSTC) mechanism to improve the accuracy of OD travel time prediction. First, we design a spatiotemporal correlation block that combines two modules: self-convolutional attention integrated with a temporal convolutional network (TCN) to capture the spatial correlations along with the temporal dependencies. Then, we enhance our model performance through adopting a multi-head attention module to learn the attentional weights of the spatial, temporal, and external features based on their contributions to the output and speed up the training process. Extensive experiments on three large-scale real-world traffic datasets (NYC, Chengdu, and Xi’an) show the efficiency of our model and its superiority compared to other methods.
Maximum power point tracking based on the fuzzy controller in photovoltaic power systems

A new maximum power point tracking (MPPT) algorithm based on the fuzzy logic controller (FLC) is proposed, consisting of a high-efficiency DC/DC buck-boost converter, for the photovoltaic power systems. The proposed fuzzy MPPT algorithm ensures the photovoltaic module always operates at its maximum power point (MPP) as the temperature and insolation vary. We also compare the frequently used hill-climbing MPPT algorithm with ours to emphasize our method’s efficiency and availability. The validity and performance of the proposed algorithm are verified by MATLAB/Simulink simulations.
Differential Regulation of NK Cell Receptors in Acute Lymphoblastic Leukemia

Cancer immunotherapies are preferred over conventional treatments which are highly cytotoxic to normal cells. Focus has been on T cells but natural killer (NK) cells have equal potential. Concepts in cancer control and influence of sex require further investigation to improve successful mobilization of immune cells in cancer patients. Acute lymphoblastic leukemia (ALL) is a hematological malignancy mainly of B cell (B-ALL) and T cell (T-ALL) subtypes. Influence of ALL on NK cell is still unclear. Targeted next-generation sequencing was conducted on 62 activating/inhibitory receptors, ligands, effector, and exhaustion molecules on T-ALL (6 males) and normal controls (NC) (4 males and 4 females). Quantitative PCR (q-PCR) further investigated copy number variation (CNV), methylation index (MI), and mRNA expression of significant genes in T-ALL (14 males), NC (12 males and 12 females), and B-ALL samples (N = 12 males and 12 females). Bioinformatics revealed unique variants particularly rs2253849 (T>C) in KLRC1 and rs1141715 (A>G) in KLRC2 only among T-ALL (allele frequency 0.8-1.0). Gene amplification was highest in female B-ALL compared to male B-ALL (KLRC2, KLRC4, and NCR3, p < 0.05) and lowest in male T-ALL cumulating in deletion of KLRD1 and CD69. MI was higher in male ALL of both subtypes compared to normal (KIR2DL1-2 and 4 and KIR2DS2 and 4, p < 0.05) as well as to female B-ALL (KIR3DL2 and KIR2DS2, p < 0.05). mRNA expressions were low. Thus, ALL subtypes potentially regulated NK cell suppression by different mechanisms which should be considered in future immunotherapies for ALL.