Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrastive Learning Dimensionality Reduction Method Based on Manifold Learning

With the development of the times, more and more high-dimensional datasets come into people's view. In order to reduce the time complexity and space complexity of downstream tasks, data dimensionality reduction becomes the primary choice. Classical dimensionality reduction algorithms are mainly divided into linear dimensionality reduction algorithms and nonlinear dimensionality reduction algorithms. Some of the traditional dimensionality reduction methods have the problems of not considering the nonlinear structure of the original dataset and the existence of weak generalisation, which makes the dimensionality reduction effect not good or model need to be recalculated because of the addition of new samples. In order to solve these problems, the research in this paper is a comparative learning dimensionality reduction method based on manifold learning. The idea of manifold learning using geodesic distance can fully consider the nonlinear structure of the original dataset. In this paper, comparative learning is the main framework. When the neural network completes the training, it only need to take the new data as input to calculate, the result can be obtained, no need to reconstruct the model which means the generality is high. Starting from the related work, this paper briefly introduces manifold learning, comparative learning and neural network algorithms. Subsequently, an innovative model is proposed, including three modules, Isomap to extract nonlinear structure, expanding neighbourhood to make pseudo-labels, and comparative learning training. Detailed analyses are carried out through experiments, comparing with PCA and LLE algorithms with the geodetic distance retention rate as an indicator, which proves that the data dimensionality reduction method of this model is more effective and ubiquitous.
Micro-Electro-Mechanical Systems Microphones: A Brief Review Emphasizing Recent Advances in Audible Spectrum Applications

The MEMS microphone is a representative device among the MEMS family, which has attracted substantial research interest, and those tailored for human voice have earned distinct success in commercialization. Although sustained development persists, challenges such as residual stress, environmental noise, and structural innovation are posed. To collect and summarize the recent advances in this subject, this paper presents a concise review concerning the transduction mechanism, diverse mechanical structure topologies, and effective methods of noise reduction for high-performance MEMS microphones with a dynamic range akin to the audible spectrum, aiming to provide a comprehensive and adequate analysis of this scope.
Characterization of the spatial and temporal variability in pan-Arctic, terrestrial hydrology

Arctic hydrology represents an important component of the larger global climate system, and there are signs that significant water-cycle changes, involving complex feedbacks, have occurred. This dissertation explores the methods to estimate components of the arctic hydrological cycle, the numerous biases and uncertainties associated with the techniques, and suggestions for future research needs. The studies described here focus on quantitative models and methods for predicting the spatial and temporal variability in pan-Arctic hydrology. This dissertation discusses pan-Arctic water budgets drawn from a hydrological model which is appropriate for applications across the terrestrial Arctic. Including effects from soil-water phase changes results in increases in simulated annual runoff of 7% to 27%. A sensitivity analysis reveals that simulated runoff is far more sensitive to the time-varying climate drivers than to parameterization of the landscape. When appropriate climate data are used, the Pan-Arctic Water Balance Model (PWBM) is able to capture well the variability in seasonal river discharge at the scale of arctic sea basins. This dissertation also demonstrated a method to estimate snowpack thaw timing from radar data. Discrepancies between thaw timing inferred from the microwave backscatter data and the hydrological model are less than one week. The backscatter signal-to-noise values are highest in areas of higher seasonal snow accumulation, low to moderate tree cover and low topographic complexity. An evaluation of snow water equivalent (SWE) estimates drawn from land surface models and microwave remote sensing data suggests that simulated SWE from a hydrological model like PWBM, when forced with appropriate climate data, is far superior to current snow mass estimate derived from passive microwave data. Biases arising from interpolations from sparse, uneven networks can be significant. A bias of well over +10 mm yr-1 was found in the early network representations of spatial precipitation across Eurasia. When examining linkages between precipitation and river discharge, these biases limit our confidence in the accuracy of historical precipitation reconstructions. This dissertation assess our current capabilities in estimating components of arctic water cycle and reducing the uncertainties in predictions of arctic climate change.
La perception de la forêt mixte par les oiseaux forestiers : une analyse multi-échelle

This thesis documents at different spatial scales relationships between mixedwood forest and forest birds. More precisely, its objective is to determine if these birds perceive mixedwood forest as a forest with a unique identity or whether as an ecotone, a contact zone between coniferous boreal and deciduous temperate forests. The first chapter presents a study realized at a regional scale which related bird distribution data from the Breeding Bird Atlases of Quebec and Ontario to land cover data from Landsat satellite imagery. This study modeled the relationship between the presence, within 1551 squares of 100 km, of 71 forest bird species and the cover proportion of three forest types: coniferous, deciduous and mixedwood forests. The second chapter asks the same question at finer scales. It modeled the occurrence at 57 observation points of 32 bird species with the amount of forest cover (temperate mixedwood, coniferous dominant, and deciduous dominant) present within 50, 100, and 1000 m radius. The third chapter attempts to explain the occurrence of eight bird species in mixedwood forest by their use of trees. The results obtained in the first two chapters show that at many scales several bird species present more important relations with mixedwood forest than with coniferous and deciduous forests. The blackburnian warbler particularly distinguishes itself at multiple scales. The results obtained in chapter 3 indicate that the intrinsic need of coniferous and deciduous trees in equal proportion for singing and foraging activities is not the selection criteria that motivates the occurrence within mixedwood forest of the eight bird species studied at that scale. We conclude that certain bird species do perceive mixedwood forest as a forest that possesses its own characteristics and we point out the importance of considering the distinct nature of that forest in our conservation and management strategies.
A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0

Industry 4.0 is fundamentally based on networked systems. Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency. Industry 4.0-based wireless sensor networks (WSNs) are an integral part of modern industrial operations however, these networks face escalating cybersecurity threats. These networks are always vulnerable to cyber-attacks as they continuously collect data and optimize processes. Increased connections make people more susceptible to cyberattacks, necessitating the use of strong cybersecurity measures to protect sensitive data. This study proposes a predictive framework intended to intelligently prioritize and prevent cybersecurity intrusions on WSNs in Industry 4.0. The proposed framework enhances the cybersecurity of WSNs in Industry 4.0 using a multi-criteria approach. It implements machine-learning and deep-learning algorithms for cybersecurity intrusion detection in WSNs of Industry 4.0 and provides prevention by assigning priorities to the threats based on the situation and nature of the attacks. We implemented three models, i.e., Decision Tree, MLP, and Autoencoder, as proposed algorithms in the framework. For multidimensional classification and detection of cybersecurity intrusions, we implemented Decision Tree and MLP models. For binary classification and detection of cybersecurity intrusions in WSNs of Industry 4.0, we implemented Autoencoder model. Simulation results show that the Decision Tree model provides an accuracy of 99.48%, precision of 99.49%, recall of 99.48%, and F1 score of 99.49% in the detection and classification of cybersecurity intrusions. The MLP model provides an accuracy of 99.52%, precision of 99.5%, recall of 99.5%, and F1 score of 99.5% in the detection and classification of cybersecurity intrusions. The implementation of Autoencoder with binary classification yields an accuracy of 91%, a precision of 92%, a recall of 91%, and an F1 score of 91%. The benchmark models, i.e., Random Forest (RF) for multidimensional classification and Logistic Regression (LR) for binary classification, have also been implemented. We compared the performance of the benchmark models with the models implemented in the proposed framework, revealing that the models in the proposed framework
Three‐Dimensional Architecture, Structural Evolution, and Role of Inheritance Controlling Detachment Faulting at a Hyperextended Distal Margin: The Example of the Err Detachment System (SE Switzerland)

While extensional detachment systems linked to postorogenic or oceanic settings have been described from many places, examples linked to hyperextension and formation of magma‐poor rifted margins remain rare. Here we describe one of the best preserved examples of a detachment system that is exposed over 200 km2 in the Err nappe in SE Switzerland. Based on existing and new mapping, we realized a new map that enables to describe the 3‐D structure of a detachment system and its role in thinning the crust and controlling the architecture and structural evolution of a hyperextended margin. We show that the Err detachment system is formed by at least three in‐sequence detachment faults. The 3‐D architecture of these faults and of their hanging wall blocks is controlled by inherited structures and a prerift evaporite level. The geometrical relationships and the evolution of the detachment system show deviations from the classical rolling hinge model. From the observations it remains unclear how and where the detachment faults rooted at depth. The overall observations made in the Err nappe allow to describe how the final rift evolution preceding mantle exhumation can be explained by extensional detachment systems and how these structures shape the hyperextended continental wedge at distal margins. While these questions are difficult to answer at present‐day rifted margins, the use of a kilometer‐scale field analogue can help to upscale and to interpret extensional detachment systems at present‐day distal, magma‐poor rifted margins.
O26 Polysaccharides as Key Players in Enteropathogenic E. coli Immune Evasion and Vaccine Development

Enteropathogenic Escherichia coli (EPEC) produce a capsule of polysaccharides identical to those composing the O-antigen polysaccharide of its LPS (lipopolysaccharide) molecules. In light of this, the impact of O26 polysaccharides on the immune evasion mechanisms of capsulated O26 EPEC compared to non-capsulated enterohemorrhagic Escherichia coli (EHEC) was investigated. Our findings reveal that there was no significant difference between the levels in EPEC and EHEC of rhamnose (2.8:2.5), a molecule considered to be a PAMP (Pathogen Associated Molecular Patterns). However, the levels of glucose (10:1.69), heptose (3.6:0.89) and N-acetylglucosamine (4.5:2.10), were significantly higher in EPEC than EHEC, respectively. It was also observed that the presence of a capsule in EPEC inhibited the deposition of C3b on the bacterial surface and protected the pathogen against lysis by the complement system. In addition, the presence of a capsule also protected EPEC against phagocytosis by macrophages. However, the immune evasion provided by the capsule was overcome in the presence of anti-O26 polysaccharide antibodies, and additionally, these antibodies were able to inhibit O26 EPEC adhesion to human epithelial cells. Finally, the results indicate that O26 polysaccharides can generate an effective humoral immune response, making them promising antigens for the development of a vaccine against capsulated O26 E. coli.
Integrated Metabolomic and Transcriptomic Profiles Provide Insights into the Mechanisms of Anthocyanin and Carotenoid Biosynthesis in Petals of Medicago sativa ssp. sativa and Medicago sativa ssp. falcata

The petals of Medicago sativa ssp. sativa and M. sativa ssp. falcata are purple and yellow, respectively. Free hybridization between M. sativa ssp. sativa and M. sativa ssp. falcata has created hybrids with various flower colors in nature. Moreover, the flower colors of alfalfa are closely correlated with yield, nutritional quality, stress tolerance and other agronomic characteristics. To elucidate the underlying mechanisms of flower color formation in M. sativa ssp. sativa and M. sativa ssp. falcata, we conducted an integrative analysis of the transcriptome and metabolome of alfalfa with three different petal colors (purple, yellow and cream). The metabolic profiles suggested that anthocyanins and carotenoids are the crucial pigments in purple and yellow flowers, respectively. A quantitative exploration of the anthocyanin and carotenoid components indicated that the accumulations of cyanidin, delphinidin, peonidin, malvidin, pelargonidin and petunidin derivatives are significantly higher in purple flowers than in cream flowers. In addition, the content of carotenes (phytoene, α-carotene and β-carotene) and xanthophylls (α-cryptoxanthin, lutein, β-cryptoxanthin, zeaxanthin, antheraxanthin and violaxanthin derivatives) was markedly higher in yellow flowers than in cream flowers. Furthermore, we found that delphinidin-3,5-O-diglucoside and lutein were the predominant pigments accumulated in purple and yellow flowers, respectively. The transcriptomic results revealed that twenty-five upregulated structural genes (one C4H, three 4CL, twelve CHS, two CHI, one F3H, one F3′H, one F3′5′H and four DFR) are involved in the accumulation of anthocyanins in purple flowers, and nine structural genes (two PSY, one ZDS, two CRTISO, two BCH, one ZEP and one ECH) exert an effect on the carotenoid biosynthesis pathway in yellow flowers. The findings of this study reveal the underlying mechanisms of anthocyanin and carotenoid biosynthesis in alfalfa with three classic flower colors.
Bone and bone derived factors in kidney disease

Purpose of review: Mineral and bone disorder (MBD) is a prevalent complication in chronic kidney disease (CKD), significantly impacting overall health with multifaceted implications including fractures, cardiovascular events, and mortality. Despite its pervasive nature, effective treatments for CKD-MBD are lacking, emphasizing the urgency to advance understanding and therapeutic interventions. Bone metabolism intricacies, influenced by factors like 1,25 dihydroxy vitamin D, parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), along with intrinsic osseous mechanisms, play pivotal roles in CKD. Skeletal abnormalities precede hormonal changes, persisting even with normalized systemic mineral parameters, necessitating a comprehensive approach to address both aspects. Recent findings: In this review, we explore novel pathways involved in the regulation of systemic mineral bone disease factors, specifically examining anemia, inflammation, and metabolic pathways. Special emphasis is placed on internal bone mechanisms, such as hepatocyte nuclear factor 4α, transforming growth factor-β1, and sclerostin, which play crucial roles in the progression of renal osteodystrophy. Summary: Despite advancements, effective treatments addressing CKD-MBD morbidity and mortality are lacking, necessitating ongoing research for novel therapeutic targets.
Mineral Weathering and the Permafrost Carbon‐Climate Feedback

Permafrost thaw in the Arctic enables the biogeochemical transformation of vast stores of organic carbon into carbon dioxide (CO2). This CO2 release has significant implications for climate feedbacks, yet the potential counterbalance from CO2 fixation via chemical weathering of minerals exposed by thawing permafrost is entirely unstudied. We show that thermokarst in the western Canadian Arctic can enable rapid weathering of carbonate tills, driven by sulfuric acid from sulfide oxidation. Unlike carbonic acid‐driven weathering, this caused significant and previously undocumented CO2 production and outgassing in headwater streams. Increasing riverine solute fluxes correspond with long‐term intensification of thermokarst and reflect the regional predominance of sulfuric acid‐driven carbonate weathering. We conclude that thermokarst‐enhanced mineral weathering has potential to profoundly disrupt Arctic freshwater carbon cycling. While thermokarst and sulfuric acid‐driven carbonate weathering in the western Canadian Arctic amplify CO2 release, regional variation in sulfide oxidation will moderate the effects on the permafrost carbon‐climate feedback.
Mitigation of Salt Stress in Rice by the Halotolerant Plant Growth-Promoting Bacterium Enterobacter asburiae D2

Salinity is a major abiotic stress that seriously affects crop growth worldwide. In this work, we aimed to isolate potential halotolerant plant growth-promoting rhizobacteria (PGPR) to mitigate the adverse impacts of salt stress in rice. An isolate, D2, with multiple plant growth-promoting (PGP) characteristics was identified as Enterobacter asburiae D2. Strain D2 could produce indole-3-acetic acid and siderophore. It also exhibited phosphate solubilization and 1-aminocyclopropane-1-carboxylic deaminase activity. Genome analysis further provided insights into the molecular mechanism of its PGP abilities. Strain D2 inoculation efficiently stimulated rice growth under both normal and saline conditions. Compared with the non-inoculated plants, a significant increase in plant height (18.1–34.7%), root length (25.9–57.1%), root dry weight (57.1–150%), and shoot dry weight (17.3–50.4%) was recorded in inoculated rice seedlings. Meanwhile, rice seedlings inoculated with strain D2 showed improvement in chlorophyll and proline content, while the oxidant damage was reduced in these plants in comparison with the control group. Moreover, the K+/Na+ ratio of the inoculated rice seedlings exposed to NaCl and Na2CO3 was higher than that of the uninoculated groups. These results imply that Enterobacter asburiae D2 is a potential PGPR that can be used for alleviation of salt stress in rice.
Never too late: Lynch syndrome diagnosed at the age of 71

Lynch syndrome is an autosomal dominantly inherited disease characterized by a mutation in one of the deoxyribonucleic acid mismatch repair genes. In consequence of this defect, patients have higher risk of developing early colorectal and endometrial cancer (among others types of cancer). This syndrome accounts for 3% of all colorectal cancers and 10% to 19% of colorectal cancers diagnosed before the age of 50. Total abdominal colectomy is the best treatment for patients with Lynch syndrome who develop colorectal cancer.
Characterization of STEM teacher education programs for disciplinary integration: A systematic review

STEM education is established as an alternative for developing 21st century skills, with the premise of integrating its component disciplines. Although numerous studies exist on the subject, STEM teacher training programs are not widely discussed. Therefore, a systematic literature review was conducted in Scopus and Web of Science to identify the intentions of the training and the design and implementation of such teacher training programs. Among the 15 articles identified, there are three groups of intentions: Improving knowledge, developing competencies and skills, and changing attitudes and perceptions. Five methodological strategies were identified: project-based learning, problem-based learning, collaborative learning, ODR (observation/discussion/reflection) approach, and design-based learning. Disciplinary integration can be achieved through content or competencies. It is concluded that design-based learning is the most appropriate strategy for disciplinary integration. It is recommended that research be conducted to measure the impact of modality and time of training on the development of STEM competencies.
Keragaman Gender di Dewan Komisaris dan Risiko Perusahaan: Perspektif Teori Agensi Perilaku

The purpose of this study is to verify the influence of gender diversity in the Board of Commissioners on the financial risks of the company from the perspective of behavioral agency theory, where the presence of gender diversity in the board of commissioners can improve the ability of the board of commissioners to manage company risks. However, there is also the possibility that gender diversity on the board of commissioners may increase the company's risk. This can happen if there is a conflict between members of the board of commissioners of different genders, thereby disrupting the smooth decision-making process. The company's financial risk indicators measured in this study are Debt to Equity Ratio (DER) and Debt to Asset Ratio (DAR). This research study was conducted on companies incorporated in LQ45 with a research period from 2015 to 2020 as many as 51 companies. This study was conducted using secondary data and the data was processed using unbalanced panel data regression with the help of the EVIEWS 12 application. The results of this study show that gender diversity in the Board of Commissioners has a significant effect on DAR where an increase in the composition of the number of men will reduce DAR by -0.53 this means that gender diversity will increase the company's risk in DAR indicators. But on the contrary, this gender diversity does not have a significant effect on DER.