Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Exploration in Deep Reinforcement Learning

Posterior sampling of value functions can give efficient exploration for value-based reinforcement learning algorithms. We introduce BayesianExplore (BE), a posterior sampling-based method for reinforcement learning based on Bayesian function-space variational inference over stochastic processes. This Bayesian formalism allows us to formalize domain knowledge as a prior over the value function. Our approach, therefore, provides an alternative to reward shaping with the added benefit that the algorithm keeps seeking an optimal policy in the original environment instead of the one with altered rewards. We show that BE produces state-of-the-art efficiency in exploration with flat priors, and that it is easy to significantly improve performance by incorporating domain knowledge using simple priors.
Risk of Microbial Growth in Ventilation Ductwork Located in the Humid and Cold Conditions

High humidity inside ductworks could be a potential risk for microbial growth and there is also a hypothesis that lower night-time ventilation increases the risk of growth. This study investigates the possibility of microbial growth in ventilation ductwork exposed to humid and cold conditions. Two different typical night-time ventilation strategies for public buildings were investigated: ventilation rate was either continuously the same (0.15 L/s, m2) or no airflow during the night-time. Experimental data were collected over a four-month period. In the experiment, microbial media was released inside the ductwork initially. During the test period, air temperature and relative humidity inside the ductwork were controlled between 11–14 °C and 70–90%. Wipe, swab and air samples were taken at the beginning, monthly and at the end of the test period. The study results showed the extinction of colonies by the end of the experiment regardless of the chosen night-time ventilation strategy. The colony count in the air was low throughout the study period. Therefore, the results indicate that the long-term growth on the walls of air ducts is unlikely and the risk of microbial transfer from the air ductworks to room space is low.
Design Procedure for a Fast and Accurate Parallel Manipulator

This paper presents a design procedure for a two-degree of freedom translational parallel manipulator, named IRSBot-2. This design procedure aims at determining the optimal design parameters of the IRSBot-2 such that the robot can reach a velocity equal to 6~m/s, an acceleration up to 20~G and {a multi-directional repeatability} up to 20~$\mu$m throughout its operational workspace. Besides, contrary to its counterparts, the stiffness {of} the IRSBot-2 should be very high along the normal to the plane of motion of its moving-platform. A semi-industrial prototype of the IRSBot-2 has been realized based on the obtained optimum design parameters. This prototype and its main components are described in the paper. Its accuracy, repeatability, elasto-static performance, dynamic performance and elasto-dynamic performance have been measured and analyzed as well. It turns out the IRSBot-2 has globally reached the prescribed specifications and is a good candidate to perform very fast and accurate pick-and-place operations.
Lifetime calculations in energy levels of Kr vii

Calculations of the lifetimes for all experimentally known energy levels of the spectrum of the six times-ionized krypton (Kr vii) are presented. The relativistic Hartree–Fock method including core-polarization effects were used. The energy matrix was calculated using energy parameters adjusted to the experimental energy levels. We also present a calculation based on a relativistic multiconfigurational Dirac–Fock approach. For some energy levels, a comparison of these results with the bibliography data was made.
Flexible Correlation Structure for Accurate Prediction and Uncertainty Quantification in Bayesian Gaussian Process Emulation of a Computer Model

Gaussian processes are widely used in the analysis of data from a computer model. Ideally, the analysis will yield accurate predictions with correct coverage probabilities of credible intervals. In this paper, we first review several existing Bayesian implementations in the literature. We show that Bayesian approaches with squared-exponential correlation structure do not always quantify well the uncertainty in prediction. Thus, we propose new Bayesian approaches with power-exponential or Matern correlation structure, which have more flexibility. Through application examples and a simulation study, we show that the proposed Bayesian methods not only have superior prediction accuracy but are closer to having the correct coverage probability.
Security Weaknesses in IoT Management Platforms

A diverse set of Internet of Things (IoT) devices are becoming an integrated part of daily lives, and playing an increasingly vital role in various industry, enterprise and agricultural settings. The current IoT ecosystem relies on several IoT management platforms to manage and operate a large number of IoT devices, their data, and their connectivity. Considering their key role, these platforms must be properly secured against cyber attacks. In this work, we first explore the core operations/features of leading platforms to design a framework to perform a systematic security evaluation of these platforms. Subsequently, we use our framework to analyze a representative set of 52 IoT management platforms, including 42 web-hosted and ten locally deployable platforms. We discover a number of high-severity unauthorized access vulnerabilities in 9/52 evaluated IoT management platforms, which could be abused to perform attacks, such as remote IoT SIM deactivation, IoT SIM overcharging, and IoT device data forgery. More seriously, we also uncover instances of broken authentication in 13/52 platforms, including complete account takeover on 8/52 platforms along with remote code execution on 2/52 platforms. In effect, 17/52 platforms were affected by vulnerabilities that could lead to platform-wide attacks. Overall, vulnerabilities were uncovered in 33 platforms, out of which 28 platforms responded to our responsible disclosure. We were also assigned 11 common vulnerabilities and exposures and awarded bounty for our findings.
Analysis of Determinants of Village Administrative Service Quality

By observing 99 samples of the Bunga Village Community, Bone Raya Subdistrict, Bone Bolango Regency, this study attempts to explain the influence of village administrative staff competence and environmental factors on the Quality of Village Administration Services. Data were analyzed using the PLS-SEM technique through SMARTPLS version 3 software. Samples were selected and collected through purposive sampling by setting criteria for people who had received administrative services and distributing them via online questionnaires via the Google form. The results show that the competence of village administration officers has a significant effect, with a T Statistical value of 2.846 and P Values of 0.005 while environmental factors have a significant effect, with a T Statistical value of 2.047 and P Values of 0.041 on the Quality of Village Administration Services.
Benefits of Cultural Activities on People with Cognitive Impairment: A Meta-Analysis

Background: Museums and cultural institutions are increasingly aware of both the interests and needs of society. Accordingly, these institutions are becoming allies in terms of health and well-being due to the importance of their social functions. Presently, many institutions create cultural activities aimed at cognitively impaired people, a group on the rise owing to the prevalence of dementia and the aging of society. Nevertheless, scientific evidence in this field remains scarce. As a result, the main objective of this research was to empirically evaluate and identify the benefits that cultural interventions can bring to cognitively impaired participants. Method: A meta-analysis (MA) was performed following PRISMA guidelines. When inclusion and eligibility criteria had been established, articles were subsequently selected through a strategic search of Web of Science, SCOPUS, PubMed, and Medline. Results: Twenty-six studies met the eligibility criteria, involving a total of 1201 participants with cognitive impairment. The results showed a statistically non-significant effect size when analyzing these cultural interventions for cognitively impaired people overall. However, when conducting partial meta-analyses (MA’), focusing on studies related to a specific disease, a particular type of treatment, or a specific type of evaluation, the results concurred with the conclusion of the previous systematic review (SR). Conclusion: Despite the high heterogeneity of the studies, benefits were identified in emotional well-being and social aspects but not in clinical ones such as the deterioration of cognitive or motor function, among others.
Triple-mode wideband bandpass filter using simple perturbation in metallic-loaded rectangular waveguide cavity

ABSTRACT This paper presents a simple perturbation technique using an off-centered metal cylinder in rectangular waveguide cavity to realize a triple-mode wideband bandpass filter. A TM mode along with two degenerate hybrid modes namely HM+ and HM-, are excited by an off-centered perturbation in a single rectangular metal cavity. The filter operates at central frequency of 2.4 GHz with fractional bandwidth of 58%. Finally, the filter is fabricated using brass. Measured results are in good agreement with the simulated results.
Counselor self-efficacy, multicultural competency, and perceived wellness among counselors in training during covid-19: a pre- and peri-analysis

ABSTRACT The COVID-19 pandemic disrupted how counselors-in-training (CITs) provided services to clients. The present cross-sectional causal-comparative study (N = 131) examined the differences between CIT (a) self-efficacy, (b) multicultural competence, and (c) perceived wellness pre- and during the pandemic. Independent samples t-tests and one-way between-groups multivariate analysis of variance (MANOVA) were used to explore group differences. Our findings suggest that CITs did not perceive significant negative impacts in these areas as a result of COVID-19.
The Pierre Auger Observatory status and latest results

Ultra-high energy cosmic rays represent the most energetic particles available to scientists. These particles have macroscopic energies, exceeding 1 EeV,but their detectionisa challenge, their flux beingvery weak, around one particle per square kilometer per century for the highest energies. The Pierre Auger Observatory, in Argentina, is the present flagship experiment studying ultra-high energy cosmic rays. The combination of a large array of surface detectors covering 3000 km2 and fluorescence telescopes enhances the performances of the extensive air shower detection and measurements, resulting in both high statistics and unprecedented data quality. Moreover, the operation of a denser sub-array has extended the sensitivity to lower energies. Altogether, these well performing detectors contribute to providing important information on key questions on cosmic rays in the energy range from 0.1 EeV up to 100 EeV, as highlighted in this presentation of the latest results obtained by the Pierre Auger Collaboration. Despite a large number of valuable results, the understanding of the nature and the origin of the highest energy cosmic rays remains an open science case that the Auger Collaboration is willing to address with the AugerPrime project.
Effects of Squeeze Film and Initial Deflection on the Resonance Frequencies and Modal Damping of Circular Microplates

We investigate the effects of squeeze air film and initial deflection on the resonance frequencies and modal damping of capacitive circular microplates. The equation of motion of a circular microplate, which are derived from the von kármán plate theory, coupled with the Reynolds equation are discretized using the Differential Quadrature Method (DQM). The eigenvalues and eigenvectors of the multiphysical problem are determined by perturbing the system of equations around a static solution. Therefore, the resonance frequencies, modal damping coefficients and mode shapes of the plate and the fluid can be determined. The advantage of using DQM is that the solution of the system can be obtained with only few grid points. The obtained numerical results are compared with the experimental data for the case of a capacitive circular microplates with an initial deflection. The increase of the static pressure leads to a shift in the resonance frequencies due to the increase in the stiffness of the plate. Also the initial deflection change the resonance frequencies due to the change in the effective gap distance. The developed model is an effective tool to predict the dynamic behavior of a microsystem under the effect of air film and with initial deflection.
Assessment of population dynamics of T. putitora (Ham, 1822) in the Nayar river basin

The present study analyzed the population characteristics of Tor putitora in their breeding and nursery grounds in the Nayar river basin. The growth parameters such as asymptotic length, growth rate and growth performance index were estimated as 80.01 cm, 0.810 per year and 3.71 per year while the potential longevity was estimated as 4.6 years. The mortality parameters such as total, natural and fishing mortality were estimated as 2.88 /year, 1.05/ year and 1.83 / year respectively. The length at 25 %, 50% and 75% susceptibility to capture was estimated as 40.87 cm, 38.11 cm and 35.34 cm respectively. The fishing mortality and exploitation beyond optimum level and maximum sustainable exploitation ratio (Emax) indicate the overly exploitation of Mahseer resources in their breeding and nursery grounds. Currently, the breeding grounds are facing intense growth and recruitment overfishing which may result in stock collapse in the forthcoming years.
Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

Learning effective representations is crucial for understanding proteins and their biological functions. Recent advancements in language models and graph neural networks have enabled protein models to leverage primary or tertiary structure information to learn representations. However, the lack of practical methods to deeply co-model the relationships between protein sequences and structures has led to suboptimal embeddings. In this work, we propose CoupleNet, a network that couples protein sequence and structure to obtain informative protein representations. CoupleNet incorporates multiple levels of features in proteins, including the residue identities and positions for sequences, as well as geometric representations for tertiary structures. We construct two types of graphs to model the extracted sequential features and structural geometries, achieving completeness on these graphs, respectively, and perform convolution on nodes and edges simultaneously to obtain superior embeddings. Experimental results on a range of tasks, such as protein fold classification and function prediction, demonstrate that our proposed model outperforms the state-of-the-art methods by large margins.
Analysis of D-amino acid in Japanese post-fermented tea, Ishizuchi-kurocha

The D-amino acid content of Ishizuchi-kurocha, a post-fermented tea produced in Ehime, Japan, was measured. Ishizuchi-kurocha mainly contains D-glutamic acid and D-alanine, but it also contains a small amount of D-aspartic acid. Two types of lactic acid bacteria, Lactiplantibacillus plantarum and Levilactobacillus brevis, are the main species involved in lactic acid fermentation during the tea fermentation process. Therefore, the D-amino acid-producing abilities of strains of these two species isolated from Ishizuchi-kurocha were examined. Specifically, the production of D-aspartic acid, D-alanine, and D-glutamic acid by L. brevis and L. plantarum strains was observed. The amount of D-aspartic acid produced by L. plantarum was low. D-glutamine was detected in culture supernatant but not in bacterial cells. D-arginine was detected in bacterial cells of the L. plantarum strains but not in the culture supernatant. Both the L. brevis and L. plantarum strains possessed at least three kinds of putative racemase genes: alanine racemase, glutamate racemase, and aspartate racemase. However, their expression and enzyme activity remain unknown. L. plantarum and L. brevis could play an important role in the production of D-amino acids in Ishizuchi-kurocha. In fact, Ishizuchi-kurocha is expected to possess the effective physiological activities of D-amino acids.
Dromi: Python package for parallel computation of similarity measures among vector-encoded sequences

Calculating similarities among sequences (i.e biological sequences) can be a challenging task. Here I introduce Dromi, a simple python package that can compute different similarity measurements (i.e percent identity, cosine similarity, kmer similarities) across aligned vector-encoded sequences. This is a crucial step required to perform both upstream and downstream sequence machine learning tasks such as sequence clustering [1, 2, 3], sequence analysis [4] and other pre- or post-processing demands on sequences. Additionally, this package introduces the calculation of the measure referred as positional weights. These represent the cosine similarities or residue-conservation across sequence elements (i.e amino acids in peptide sequences) in the same site (column). The program can also deal with sequences of variable length since end-padded positions are not considered for the calculations. The presented implementations are an incorporation into the arsenal of tools to measure similarity among small peptide sequences such as epitopes.
Cytokine storm mitigation for exogenous immune agonists

Cytokine storm is a life-threatening inflammatory response characterized by hyperactivation of the immune system. It can be caused by various therapies, auto-immune conditions, or pathogens, such as respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes coronavirus disease COVID-19. Here we propose a conceptual mathematical model describing the phenomenology of cytokine-immune interactions when a tumor is treated by an exogenous immune cell agonist which has the potential to cause a cytokine storm, such as CAR T cell therapy. Numerical simulations reveal that as a function of just two model parameters, the same drug dose and regimen could result in one of four outcomes: treatment success without a storm, treatment success with a storm, treatment failure without a storm, and treatment failure with a storm. We then explore a scenario in which tumor control is accompanied by a storm and ask if it is possible to modulate the duration and frequency of drug administration (without changing the cumulative dose) in order to preserve efficacy while preventing the storm. Simulations reveal existence of a “sweet spot” in protocol space (number versus spacing of doses) for which tumor control is achieved without inducing a cytokine storm. This theoretical model, which contains a number of parameters that can be estimated experimentally, contributes to our understanding of what triggers a cytokine storm, and how the likelihood of its occurrence can be mitigated.
DESIGNING AN AUTOMATED SYSTEM FOR PLANT LEAF RECOGNITION

This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using three different shape modelling techniques, the first two based on the Moments-Invariant (M-I) model and the Centroid-Radii (C-R) model and the third based on a proposed technique of Binary-Superposition (B-S). For the M-I model the first four central normalized moments have been considered. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the shape vector. The proposed approach consists of comparing binary versions of the leaf images through superposition and using the sum of non-zero pixel values of the resultant as the feature vector. The data set for experimentations consists of 180 images divided into training and testing sets and comparison between them is done using Manhattan, Euclidean and intersection norms. Accuracies obtained using the proposed technique is seen to be an improvement over the M-I and C-R based techniques, and comparable to the best figures reported in extant literature.
Complex Sphingolipid Profiling and Identification of an Inositol Phosphorylceramide Synthase in Dictyostelium discoideum

Dictyostelium discoideum is a professional phagocyte frequently used as experimental model to study cellular processes underlying the recognition, engulfment and infection course of microbial pathogens. Sphingolipids are abundant components of the plasma membrane that bind cholesterol, control vital membrane properties, participate in signal transmission and serve as adhesion molecules in recognition processes relevant to immunity and infection. While the pathway of sphingolipid biosynthesis has been well characterized in plants, animals and fungi, the identity of sphingolipids produced in D. discoideum, an organism at the crossroads between uni- and multicellular life, is not known. Combining lipidomics with a bioinformatics-based cloning strategy for key sphingolipid biosynthetic enzymes, we show here that D. discoideum produces phosphoinositol-containing sphingolipids with predominantly phytoceramide backbones. Cell-free expression of candidate inositol-phosphorylceramide (IPC) synthases from D. discoideum in defined lipid environments enabled identification of an enzyme that selectively catalyses the transfer of phosphoinositol from phosphatidylinositol onto ceramide. The corresponding IPC synthase, DdIPCS1, is non-homologous to but shares multiple sequence motifs with yeast IPC and human sphingomyelin synthases and localizes to the Golgi apparatus as well as the contractile vacuole of D. discoideum. Collectively, these findings open up important opportunities for exploring a role of sphingolipids in phagocytosis and infection across major evolutionary boundaries.
Comparative Study of Exact Continuous Orthogonal Moments Applications : Local Feature Extraction and Data Compression

This paper present an improved reconstruction algorithm of the multi-gray level images based on overlapping block method using exact continuous moments computation: Legendre , Zernike, Pseudo-Zernike and Gegenbauer moments . We solve the artifact issue caused by unitary block reconstruction which affects the visual image quality. This method aim to ensure high accuracy and low computation time, using only small finite number of moments. Our approaches aims to introduce these moments in the field of data compression and local feature extraction for pattern recognition. Experimental results show the superiority of our proposed approaches over the existing methods.