Miracle-dz/UniFAR-FTUs huggingface dataset:
RIMeshGNN: A Rotation-Invariant Graph Neural Network for Mesh Classification

Shape analysis tasks, including mesh classification, segmentation, and retrieval demonstrate symmetries in Euclidean space and should be invariant to geometric transformations such as rotation and translation. However, existing methods in mesh analysis often rely on extensive data augmentation and more complex analysis models to handle 3D rotations. Despite these efforts, rotation invariance is not guaranteed, which can significantly reduce accuracy when test samples undergo arbitrary rotations, because the analysis method struggles to generalize to the unknown orientations of the test samples. To address these challenges, our work presents a novel approach that employs graph neural networks (GNNs) to analyze mesh-structured data. Our proposed GNN layer, aggregation function, and local pooling layer are equivariant to the rotation, reflection and translation of 3D shapes, making them suitable building blocks for our proposed rotation-invariant network for the classification of mesh models. Therefore, our proposed approach does not need rotation augmentation, and we can maintain accuracy even when test samples undergo arbitrary rotations. Extensive experiments on various datasets demonstrate that our methods achieve state-of-the-art performance.
Energy-Efficient Computation Offloading and Data Compression for UAV-Mounted MEC Networks

The advancement of mobile edge computing (MEC) is driving the utilization of mobile devices (MDs) for real-time video detection. Nonetheless, during emergency scenarios, video detection tasks encounter two primary challenges: 1) time-varying channels over time between the MDs and the unmanned aerial vehicle (UAV); 2) the need for precision while managing energy consumption on the MDs. In this paper, we collaboratively address the optimization of computation offloading, data compression, and resource allocation challenges in the context of UAV-mounted MEC networks, where the UAV offers computational support for MDs. To improve accuracy and reduce energy consumption in time-varying environments, we propose an energy-efficient Deep Deterministic Policy Gradient based computation offloading algorithm (DCOA). By pursuing the long-term goal, DCOA learns to adapt to continuously changing conditions and thus improve accuracy and energy efficiency. Additionally, we introduce a convex optimization algorithm using the Lagrange multiplier method to solve resource allocation issues for offloading tasks, further reducing energy usage. Experimental results show that DCOA achieves high accuracy with low energy consumption compared to existing algorithms.
represent-Derivation of the maintenance energy requirements and efficiency of metabolizable energy utilization for dry and lactating Jersey cows

Maintenance energy is the energy required to conserve the state of an animal when no work is completed. Dietary energy must be supplied to meet maintenance requirements before milk can be produced. The objectives of the current experiment were to quantify the maintenance energy requirement of Jersey cows when lactating or dry. Energetic measures were collected on 8 Jersey cows and evaluated across 3 physiological phases and nutritional planes: lactation, dry cows fed at maintenance, and fasted dry cows. Through total collection of feces and urine as well as using headbox-style indirect calorimeters, energy balance and heat production data were measured across all phases. Lactation data were collected across four 28-d periods. Data for cows fed at maintenance were collected after 14 d and fasting heat production was measured during the last 24 h of a 96-h fast. Net energy for maintenance (NE M ) requirements, and the efficiency of converting metabolizable energy (ME) into net energy were compared between lactating and dry (maintenance or fasting phase) cows.
International Journal of Approximate Reasoning

Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (ii) a compatible symmetric positive deﬁnite (SPD) matrix. In order to ensure positive deﬁniteness in (ii), a dominant diagonal is usually imposed. In this work we investigate different methods to generate random symmetric positive deﬁnite matrices with undirected graphical constraints. We show that if the graph is chordal it is possible to sample uniformly from the set of correlation matrices compatible with the graph, while for general undirected graphs we rely on a partial orthogonalization method.
Microbial Biosystems

The biological synthesis of silver nanoparticles offers a promising alternative to traditional physical and chemical methods due to its inherent simplicity, non-toxic nature, and environmental friendliness. Within this study, endophytic fungi hosted six wild plants in Arish City, North Sinai, Egypt have been isolated by surface sterilization technique on different isolation media. Twelve species, based on their frequency of occurrence, out of thirty recovered taxa were tested for their capability to synthesize extracellular AgNPs. Aspergillus terreus (OQ119631) and Aspergillus flavus (OQ119633) recovered from Hyoscyamus muticus and Zygophyllum album respectively were found to be the best candidate for the production of mycogenic AgNPs among all examined species. Mycogenic AgNPs were characterized by UV-visible spectroscopy and Transmission Electron Microscopy for both taxa. The synthesized silver nanoparticles demonstrated antimicrobial efficacy against various pathogenic bacteria and fungi. Additionally, their cytotoxicity was assessed against the human normal fibroblast cell line (BJ) using a cell viability assay. The results revealed substantial growth inhibition with IC50 concentration of 100 µg/mL, indicating effectiveness against diseased cells while maintaining relative safety towards healthy cells. This underscores the potential of eco-friendly AgNPs produced by native endophytic fungi for targeted cytotoxicity.
Variable Selection in Local Regression Models via an Iterative LASSO

Locally weighted regression is a technique that predicts the response for new cases from their neighbors in the training dataset. In this paper we propose to join modern regularization approaches to locally weighted regression. Speci(cid:12)cally, the LASSO method is able to select relevant variables leading to sparse models. We present two algorithms that embed LASSO in an iterative procedure that incrementally discard or add variables, respectively, in such a way that a LASSO-wise regularization path is locally obtained. The algorithms are tested in two di(cid:11)erent datasets from the UCI repository, obtaining promising results
Advantages of mixed tree stands in restoration of upper soil layers on postmining sites: A five‐year leaf litter decomposition experiment

No in‐depth studies covering litter decomposition were previously conducted on afforested postmining sites despite the importance of litter decomposition on soil formation, especially in degraded areas. We aimed to compare leaf litter decomposition rates of 14 tree species growing in mixed stands on a postmining spoil heap, and to compare their decay rates with those obtained in pure stands of tree species that are very often planted in postmining areas. The study was conducted on the Bełchatów Lignite Mine spoil heap, central Poland. We studied litter decomposition rates over 5 years using the litterbag method. Leaf litter decomposition rates (k constants; yr−1) after 5 years of the experiment were arranged from highest to lowest as follows: Prunus serotina (1.04), Alnus glutinosa (0.86), Fraxinus excelsior (0.81), Ulmus laevis (0.73), Acer pseudoplatanus (0.68), Populus tremula (0.63), Populus × canadensis (0.59), Betula pendula (0.54), Populus nigra ‘Italica’ (0.38), Quercus rubra (0.36), Pinus sylvestris (0.34), Robinia pseudoacacia (0.34), Fagus sylvatica (0.25), and Quercus robur (0.22). Decomposition rates increased with Mg, Ca, and summed Mg + Ca + P + K contents in freshly fallen leaves (%). All species‐specific decomposition rates were higher in mixed than in Scots pine stands. Half of them were also higher than in pure stands of other tree species. Our results indicate that tree species composition could significantly affect the physiochemical properties of upper soil horizons on reclaimed areas. Based on these findings, we recommend planting high share of not only A. pseudoplatanus, A. glutinosa, and U. laevis but also F. sylvatica and Q. robur in stands on postmining spoil heaps, because mixed stands can accelerate soil development more than pure stands.
Innovation and Entrepreneurial Ecosystems

Nowadays special attention is paid to ecosystem conditions that encourage innovation and entrepreneurship. This chapter provides a critical review and expands the understanding of the concepts of the innovation ecosystem and entrepreneurial ecosystem. The entrepreneurial ecosystem represents a collection of actors that interact within a geographically bound entrepreneurial environment and factors, which contribute to the development of productive entrepreneurship. Innovation ecosystems represent communities of interacting actors that support innovation processes and create technologies and innovations. The focus of the innovation ecosystem is on value creation through the creation of innovations, while the focus of the entrepre-neurship ecosystem is on the development of entrepreneurship. There are differences between the two concepts, but also the relationships and interactions, which are revealed in the chapter. Also, there are highlighted the framework, components and features of both entrepreneurial and innovation ecosystems.
LIPOTEICHOIC ACID EXTRACTION FROM PLASMA CHROMATOGRAPHY TECHNIQUIES UTILIZING TRUNCATED DERIVATES OF ANTIMICROBIAL PEPTIDES

With increasing incidence rates aligned with poor prognosis; sepsis represents one of the biggest challenges in modern health care. It is a multifactorial syndrome defined as organ dysfunction caused by disturbed systemic response to an infection. Most of the inpatient sepsis are caused by Gram positive bacteria and one of its major constituents of the cell envelope: lipoteichoic acid (LTA). An adjuvant treatment that has gained prominence recently is extracorporeal blood removal therapies i.e., hemoperfusions. The concept is to remove the bacterial virulence factors that triggers immune responses and therefor stabilize the hemodynamic parameters of the patient. The dominating research of this method centres around adsorption of the Gram negative bacterias’ endotoxin lipopolysaccharide (LPS) but not LTA, whose biochemical and physiological properties resembles each other. The aim of this study was to determine whether LTA can be adsorbed using immobilized truncated derivates of antimicrobial peptides (AMPs). LTA was quantified using ELISA comparing before and after passage through columns with immobilized peptides. Further, the absorption abilities of LTA from two different solid phases with distinctive surfaces were investigated. This was of interest to elucidate the nature of the mechanisms behind LTA extractions. All results generated inconclusive data, except for one trial which demonstrated that peptide KEF-19 adsorbed most LTA and that the electrostatic force had the greatest influence of the adsorption. Future studies should however be carried out to validate these statements as well as feasibility and safety estimations for KEF-19 as the sorbent in hemoperfusions for Gram positive bacteria and LTA.
Reinforcement Learning for Ising Model

The Ising spin glasses model [2], a fundamental model in statistical mechanics and condensed matter physics, providing insights into the behavior of interacting spins within a physical system, has been studied for many decades [11, 5, 13], while also being able to formulate many NP-hard problems [10]. However, solving the Ising model for large and complex systems is computationally demanding and often infeasible using traditional methods. In this paper, we present the deterministic REINFORCE algorithm tailored for the Ising model, enabling state-of-the-art performance through learned state transition policies. In our work, we first formulate the Ising model with MaxCut problem as a case study. Secondly, we propose a novel deterministic REINFORCE algorithm incorporating the Local Search approach. Finally, we evaluate our algorithm on well-known datasets and demonstrate state-of-the-art performance.
Bayesian Classiﬁers in Optimization: An EDA-like Approach

Summary. This chapter introduces a new Evolutionary Computation method which applies Bayesian classiﬁers in the construction of a probabilistic graphical model that will be used to evolve a population of individuals. On the other hand, the satisﬁability problem (SAT) is a central problem in the theory of computation as a representative example of NP-complete problems. We have veriﬁed the performance of this new method for the SAT problem. We compare three different solution representations suggested in the literature. Finally, we apply local search methods for this problem.
Estimation, Inference, and Interpretation in the Regression Discontinuity Design

The Regression Discontinuity Design (RDD) has proven to be a compelling and transparent research design to estimate treatment eﬀects. We provide a review of the main assumptions and key challenges faced when adopting an RDD. We cover the most recent developments and advanced methods, and provide the key intuitions that underlie the statistical arguments. Among others, we summarize new insights that we consider to be highly relevant about the choice of bandwidth, optimal inference, discrete running variables, distributional eﬀects, estimation in the presence of covariates, and the regression kink design. We also show how structural parameters can be estimated by combining an RDD identiﬁcation strategy with theoretical models. We illustrate the procedures by applying them to data and we provide codes to replicate the results
Analysis of the Effect of Competence on Customer Complaint Handling Performance with Organizational Commitment as a Moderation Variable: A Study on PT Pos Indonesia (Persero)

s . A company's business continuity is supported by the achievement of performance targets that have been set. This study aims to determine the influence of organizational commitment, employee competence, and the role of organizational commitment moderation in influencing competence on customer complaint handling (CCH) performance at Regional Office 7 East Java Surabaya 60004. The population is all customer service employees at PT Pos Indonesia (Persero), which amounts to 130 people. Sampling uses the total sample method, which is the entire population. This research uses a quantitative approach using deductive methods. The data analysis technique used is the Moderated Regression Analysis (MRA) method using the SPSS For Windows program. The results of this study found that partially the competence and commitment of the organization had a significant positive effect on CCH's performance at PT Pos Indonesia (Persero) Regional Office 7 East Java 60004. In addition, it was found that the organization's commitment could moderate the positive influence of competence on CCH performance at PT Pos Indonesia (Persero) Regional Office 7 East Java 60004.
Population aging in the context of education. Comparison of selected EU countries

The arrival of the fourth industrial revolution hasfundamentally affected the economy of the developed countries. New and developing fields will need workers with quality education corresponding to the needs and demands of technological development. Thus, the share of tertiary educated population significantly affects the economic level of every country. As a result of the projected population ageing in the developed countries, a major problem with the share of the economically active people can be expected across Europe. The increased share of tertiary educated people in economy can positively influence this problem. Therefore, the article deals with the projection of the share of tertiary educated people. On the basis of information on the education structure of individual EU countries, a hierarchical cluster analysis was created, which was used for selecting countries. For the two most significant clusters, the country with values of all selected indicators most similar to the cluster average values has been selected. A population projection was created for these countries, on the basis of which the total dependency ratio, child-dependency ratio and old-age dependency ratio were calculated. In conclusion, it can be stated that even if the population ageing continues, the number of tertiary-educated persons may remain unchanged.
Sleep Quality as the Mediator between Sleep Hygiene and Insomnia, Anxiety, and Depression

This study evaluated the role of sleep quality as mediator between sleep hygiene and insomnia, anxiety, or depressive symptoms. There were 225 participants with an average age of 30.22 (SD 8.92), the majority were female (75.11%) and residing in metropolitan areas. Measures included the Sleep Hygiene Index, the Pittsburgh Sleep Quality Index, the Insomnia Severity Index, and the Hopkins Symptoms Checklist-25. Causal mediation analysis using PROCESS Macro Model 4 with 5,
NeuralHMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood

Bayesian Inference with Markov Chain Monte Carlo requires the ability to efficiently compute the likelihood function. In some scientific applications, the likelihood can only be computed by a numerical PDE solver, which can be pro-hibitively expensive. We demonstrate that some such problems can be made tractable by amortizing the computation with a surrogate likelihood function implemented by a neural network. This can have the added benefits of reducing noise in the likelihood evaluations and providing fast gradient calculations. We demonstrate these advantages in a model of heliospheric transport of galactic cosmic rays, where our approach enables us to estimate the posterior of five latent parameters of the Parker equation.
Physically Accurate Fast Nanophotonic Simulations with Physics Informed Model and Training

Photonic inverse design has emerged as a powerful technique for creating non-intuitive optical devices, revolutionizing traditional design methodologies. However, the bottleneck in photonic inverse design lies in the computational cost of physically accurate 3D simulations as the high number of optimization iterations or large design foot-print becomes a limiting factor on photonic device design. Here we introduce a novel approach, utilizing a two-staged model combined with a 2D FDFD simulator, to achieve accurate field predictions in a much smaller amount of time than 3D FDTD. The model is trained to emphasize field properties crucial for photonic device optimization such as mode overlap and transmission, enabling rapid and physically accurate simulations. Results demonstrate a remarkable speedup of up to 264.53 times compared to 3D FDTD simulations, opening new avenues for the design of complex and accurate photonic devices.
Classification under Prior Probability Shift in Simulator-Based Inference: Application to Atmospheric Cosmic-Ray Showers

High-energy cosmic rays are informative probes of astrophysical sources in our galaxy. A main challenge is to separate gamma showers (extremely rare events of interest) from the vast majority of hadron showers, when we have access to realistic simulations of the shower production (forward process) but the prior distribution on the shower parameters is unknown. Direct classification of the showers using output data leads to biased predictions and invalid uncertainty estimates, since the prior is chosen by design and is different from the true distribution. We overcome these biases by proposing a new method that casts classification as a hypothesis testing problem under nuisance parameters. The main idea is to estimate ROC curves as a function of all nuisances, devising selection criteria that are valid under a generalized prior probability shift over both shower label and nuisance parameters. Our method yields a set-valued classifier that returns valid confidence sets for all levels α simultaneously without having to retrain the classifier for each level
The Relationship Between Positive Education, Learning with Happiness, Motivation in Learning and Academic Performance in Hong Kong

Research has shown a relationship between learning with happiness and motivation in learning, but the relationship between these aspects of learning and academic performance is not clear. This study's purpose was to understand these associations in sample of first grade students in Hong Kong. The students' parents completed a questionnaire designed for this study. The overall score for constituted two subscales (learning with happiness, intrinsic motivation in learning) that were moderately correlated. Two groups of first graders (N = 155) were assessed for the first time in either the 2018/19 and in 2020/21 school year and followed to second grade in the 2019/2020 or 2021/2022 school year. However, academic performance between first grade and second grade in the academic year 2018/19 was not significantly different, in the academic year 2020/21 second grade academic performance was better than first grade. The results have implications for understanding longitudinal effects of happiness in in learning, motivation in learning, and internal/external motivation in predicting academic performance. This study is significant because it is the first study in this line of research to examine first graders.
OF CORPORATE LEADERSHIP COMPETENCIES IN THE CONTEXT OF MODERN LEARNING 1

The relevance of the topic is determined by the rapid development of global events, the continuing upcoming challenges that affect the working environment and the development of the labour market and the need for managers to cope with new and unprecedented challenges. The learning context is also developing, introducing new opportunities, and learning methods that complement each other. The level of competence was assessed by the respondents for eight of their competences, selected according to the Great Eight Competence Model, which is one of the current perspectives on how to capture the competences required of a manager. Aim of the study: To explore the learning methods chosen by managers in developing competencies, and to make suggestions for managers and organizations to design a competency development process that is relevant to the current learning context. A total of 114 managers participated in the study. Main conclusions: managers highly evaluate their level of development of their competences, but they see a place for growth and further development for all competencies. Managers more often use passive learning methods, which they evaluate lower in terms of effectiveness than active methods that are based on their own experience. There are no significant differences between different level managers and the duration of their experience. There is a difference between the capacity to develop competencies in organizations. The lowest capacity to develop competencies is in the public sector organizations.