Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrophobic Metals Nanoparticles Encapsulated In A Lipid Bilayer Of Thermosensitive-Liposome

Thermosensitive liposomes (TSLs) have gained significant attention in recent years due to their potential applications in drug delivery and biomedical therapeutics. This study investigates developing and characterising TSLs encapsulated with hydrophobic gold (Au) and silver (Ag) nanoparticles for enhanced therapeutic efficacy. The TSLs were prepared using a thin-film hydration method, and hydrophobic Au and Ag nanoparticles were incorporated into the lipid bilayers [1]. The physicochemical properties of the TSLs, including size, surface charge, and thermal stability, were evaluated using dynamic light scattering, zeta potential measurements, and differential scanning calorimetry. Hydrophobic Au and Ag nanoparticles were encapsulated within the TSLs, resulting in stable and uniform nanocarriers. The average size of the TSLs was determined to be within the desirable range for efficient cellular uptake and circulation in the bloodstream. The presence of hydrophobic nanoparticles did not significantly affect the overall size and surface charge of the TSLs. Furthermore, the thermal stability of the TSLs was evaluated, and it was found that the incorporation of hydrophobic nanoparticles improved the heat sensitivity of the liposomes [2]. This enhanced thermos sensitivity can be exploited for triggered drug release at elevated temperatures, such as hyperthermia-induced tumour targeting
GradQuant: Low-Loss Quantization for Remote-Sensing Object Detection

Convolutional neural network-based methods have shown remarkable performance in remote-sensing object detection. However, their deployment on resource-limited embedded devices is hindered by their high computational complexity. Neural network quantization methods have been proven effective in compressing and accelerating CNN models by clipping outlier activations and utilizing low-precision values to represent weights and clipped activations. Nonetheless, the clipping of outlier activations leads to distortion of object local features. Furthermore, the lack of enhanced overall feature mining exacerbates the degradation of detection accuracy. To address the limitations above, we propose an innovative clipping-free quantization method called GradQuant, which mitigates the model’s quantization accuracy loss caused by clipping outlier activations and the lack of overall feature mining. Specifically, a bounded activation function (sigmoid-weighted tanh, SiTanh) is carefully designed to ensure that object features are represented within a limited range without clipping. On the basis of this, an activation substitution training (AST) method is codesigned to prompt models to focus more on nonoutlier object features instead of outlier-like local ones. Extensive experiments on public remote-sensing datasets demonstrate the effectiveness of the GradQuant method compared with other state-of-the-art quantization methods.
Log Anomaly Detection by Adversarial Autoencoders With Graph Feature Fusion

The exponential growth of scale and complexity in distributed systems necessitates significant maintenance efforts. Logs play an indispensable role in system operation and maintenance since they record crucial runtime information. However, recent studies on log anomaly detection have primarily focused on deep learning methods, which entail high computational complexity for learning temporal and semantic features from logs. Moreover, most deep learning-based approaches for log anomaly detection require supervised training, which is labor intensive. To address these challenges, this article proposes a framework called GAE-Log. GAE-Log leverages event graphs and knowledge graphs to model logs comprehensively. By integrating temporal dynamics through event graphs and incorporating contextual information from knowledge graphs, GAE-Log enhances the understanding of the system's status. Moreover, GAE-Log employs adversarial training of autoencoders for anomaly detection on logs. The effectiveness of GAE-Log is evaluated through an ablation study and comprehensive comparisons using both public and synthetic log datasets. The results demonstrate that GAE-Log outperforms state-of-the-art methods in log anomaly detection, achieving significant performance improvements.
Image-based Localization in Urban Environments

Abstract : This report describes an efficient algorithm to accurately determine the position and orientation of a camera in an outdoor urban environment using camera imagery acquired from a single location on the ground. The requirement to operate using imagery from a single location allows a system using our algorithms to generate instant position estimates and ensures that the approach may be applied to both mobile and immobile ground sensors. Localization is accomplished by registering visible ground images to urban terrain models that are easily generated offline from aerial imagery. Provided there are a sufficient number of buildings in view of the sensor, our approach provides accurate position and orientation estimates, with position estimates that are more accurate than those typically produced by a global positioning system (GPS).
Platelet-Rich Fibrin-Enhanced Bone Healing Co-grafted With Either Hydroxyapatite and Beta Tricalcium Phosphate or Demineralized Freeze-Dried Bone in Small Maxillofacial Osseous Defects: A Clinical Comparison

Introduction: This study was conducted to clinically compare a commercially available combination of 70:30 nanocrystalline hydroxyapatite (HA) and beta tricalcium phosphate (βTCP) along with platelet-rich fibrin (PRF) with demineralized freeze-dried bone (DFDB) grafts along with PRF in small maxillofacial osseous defects. Materials and methods: Thirty patients with one osseous defect were randomly distributed into two groups of 15 each: Group A and Group B. Group A patients received HA+βTCP+PRF while Group B received DFDB + PRF. Postoperative pain, swelling, wound dehiscence, and the presence or absence of infection were evaluated at various intervals up to seven postop days and compared between the two groups and within either group. A technetium 99m methylene diphosphonate (MDP 99mTc) scan was also done for a representative patient of either group at the end of three months to evaluate the fate of the graft. Results: We found no significant difference between the two groups for any of our parameters. Significant improvements were noted for pain and swelling within either group at various intervals. The MDP 99mTc scan showed increased tracer uptake for the representing patient of either group. Conclusions: HA+βTCP is more inexpensive than DFDB and more readily available and has no host incompatibility or infection potential, resulting in similar clinical postoperative states as DFDB when either is used with PRF.
Disordered Eating and Eating Disorders in Pediatric Obesity: Assessment and Next Steps

While the exact prevalence of disordered eating in youth who are overweight and have obesity has not been determined, studies show that the odds of a young adult (18–24 years) with obesity engaging in disordered eating behaviors is 2.45 times more likely to occur than in young adults with Body Mass Indexes (BMI) in the normative range. The purpose of this review is to highlight the role that disordered eating and eating disorders may play in pediatric obesity and the importance of screening for these conditions. The ability to identify and assess disordered eating alters the course of treatment. Without an understanding of the intersection of obesity and disordered eating, medical providers may continue treatment-as-usual. Doing so may inadvertently contribute to internalized weight bias in patients with obesity and exacerbate their disordered eating symptoms and behaviors. In addition, understanding the spectrum of disordered eating in pediatric patients with obesity allows providers to tailor treatments, discuss food and physical activity differently, and know when to refer patients to eating-disorder-specific providers for continued treatment.
Building data marts to analyze university faculty activities using power BI

Evaluating the performance of university faculty is a hard task because of the diversity of the work performed. The authors assume that the founder of the university evaluates the effectiveness of the university according to performing the teaching staff. The aim of the study is to improve the monitoring of key performance indicators of the university teaching staff based on data management. The authors present an information system to support decision-making related to the teaching staff at Kemerovo State University. The authors of the paper describe creating such a system using Business Intelligence technologies step by step. The authors identified data sources, designed the structure of the data mart and built ETL-processes for its filling, implemented various analytical dashboards. Implementing the information system in the daily activities of the university allows responding promptly to changes in the key indicators, forecasting their further change, deciding on activation of efforts in the chosen direction or types of work.
Automatic refactoring of conditions and substitutions for B state transition models

The automation of programming, which lies at the intersection of software engineering and artificial intelligence, enables machines to automatically generate programs that satisfy given requirements. In the context of B formal design modeling, one of the challenges is the refactoring of substitutions in design specifications, which often uses state transitions to describe how program or system statuses change during execution. This paper proposes a condition and substitution refactoring algorithm for the B formal specification language. The aim of the work is to automatically derive B operational predicates based on given transitions. The work has been extremely useful to machine‐driven formal design model repair as well as automated design specification generation. Given a set of state transitions, common relations of their state variables can be discovered and clustered into a number of classes. These relations can be further used to synthesize substitutions that derive new states from existing states. To restrict application domains of the synthesized substitutions, conditions that guard these substitutions are generated using first‐order logic. We have implemented the proposed algorithm as an extension to the ProB model checker. Experiments were conducted based on the B model public dataset. The evaluation results demonstrated that our solution is able to synthesize conditions and substitutions for various sets of state transitions in a wide range of B models.
Lightweight Algorithm for Apple Detection Based on an Improved YOLOv5 Model

The detection algorithm of the apple-picking robot contains a complex network structure and huge parameter volume, which seriously limits the inference speed. To enable automatic apple picking in complex unstructured environments based on embedded platforms, we propose a lightweight YOLOv5-CS model for apple detection based on YOLOv5n. Firstly, we introduced the lightweight C3-light module to replace C3 to enhance the extraction of spatial features and boots the running speed. Then, we incorporated SimAM, a parameter-free attention module, into the neck layer to improve the model’s accuracy. The results showed that the size and inference speed of YOLOv5-CS were 6.25 MB and 0.014 s, which were 45 and 1.2 times that of the YOLOv5n model, respectively. The number of floating-point operations (FLOPs) were reduced by 15.56%, and the average precision (AP) reached 99.1%. Finally, we conducted extensive experiments, and the results showed that the YOLOv5-CS outperformed mainstream networks in terms of AP, speed, and model size. Thus, our real-time YOLOv5-CS model detects apples in complex orchard environments efficiently and provides technical support for visual recognition systems for intelligent apple-picking devices.
Uncovering the Role of Epstein–Barr Virus Infection Markers for Remission in Rheumatoid Arthritis

Epstein–Barr virus (EBV) infection has been shown as a potential risk factor for the development of rheumatoid arthritis (RA). This prospective research aimed to investigate whether EBV infection markers changed during the six-month follow-up period in 133 RA patients (80 newly diagnosed on methotrexate (MTX)—RA-A, and 53 on biologic therapy—RA-B) and whether it was related to a disease outcome. Reduction of disease activity and inflammation was obtained. A significant decline in seroprevalence and titer for anti-VCA-IgM (p = 0.022 and p = 0.026) and anti-EA(D)-IgM (p = 0.022 and p = 0.006) in RA-A, and in seroprevalence and titer of anti-EA(D)-IgG in the RA-B subgroup (p = 0.021 and p = 0.006) were detected after the follow-up. A lower titer of anti-EBNA1-IgG could be considered a significant marker of RA remission in all RA patients regardless of age and gender (OR = 0.99, 95% CI OR = 0.98–0.99, p = 0.038), and also in RA-B patients separately (OR = 0.988, 95% CI OR = 0.98–0.99, p = 0.041). This study supported the basic hypothesis that the immune response to EBV infection is involved in the RA pathogenesis, at the beginning of the disease or during the RA evolution. Moreover, the potential influence of MTX or TNF-alpha inhibitors on the impairment of the host to control EBV infection was indirectly refuted.
Estimate of carbon stock in the soil via diffuse reflectance spectroscopy (vis/nir) air and orbital remote sensing

ABSTRACT Current procedures for determining soil organic carbon (SOC) content are costly, time-consuming, and generate polluting chemical waste. Therefore, developing new protocols using aerial and orbital remote sensing and diffuse reflectance spectroscopy (DRS) for digitally mapping the stock of soil organic carbon (CS) is essential for promoting actions of research and monitoring SOC in Brazilian soils. Given this, three areas of commercial plots in the region of the Middle North of Mato Grosso were studied, where sampling was carried out for the determination of SOC in the layer from 0 to 30 cm, evaluated by the dry combustion method and estimated through DRS in the visible to near -infrared region - Vis-NIR-SWIR/350-2500 nm). To obtain the images by aerial remote sensing, the Carcará II® Unmanned Aerial Vehicle was used, with a MicaSense® multispectral camera (RGB + NIR + RedEdge) attached. The orbital sensors used were the Sentinel 2® and Planet® satellites. This study showed that soil carbon stock values could be predicted using different modeling approaches based on field and laboratory spectral measurements. Predictive models to estimate SOC can be established using remote and near sensing, thus allowing a better understanding of spatial patterns of SOC in crop fields.
Survival implications vs. complications: unraveling the impact of vitamin D adjunctive use in critically ill patients with COVID-19—A multicenter cohort study

Background Despite insufficient evidence, vitamin D has been used as adjunctive therapy in critically ill patients with COVID-19. This study evaluates the effectiveness and safety of vitamin D as an adjunctive therapy in critically ill COVID-19 patients. Methods A multicenter retrospective cohort study that included all adult COVID-19 patients admitted to the intensive care units (ICUs) between March 2020 and July 2021. Patients were categorized into two groups based on their vitamin D use throughout their ICU stay (control vs. vitamin D). The primary endpoint was in-hospital mortality. Secondary outcomes were the length of stay (LOS), mechanical ventilation (MV) duration, and ICU-acquired complications. Propensity score (PS) matching (1:1) was used based on the predefined criteria. Multivariable logistic, Cox proportional hazards, and negative binomial regression analyses were employed as appropriate. Results A total of 1,435 patients were included in the study. Vitamin D was initiated in 177 patients (12.3%), whereas 1,258 patients did not receive it. A total of 288 patients were matched (1:1) using PS. The in-hospital mortality showed no difference between patients who received vitamin D and the control group (HR 1.22, 95% CI 0.87–1.71; p = 0.26). However, MV duration and ICU LOS were longer in the vitamin D group (beta coefficient 0.24 (95% CI 0.00–0.47), p = 0.05 and beta coefficient 0.16 (95% CI −0.01 to 0.33), p = 0.07, respectively). As an exploratory outcome, patients who received vitamin D were more likely to develop major bleeding than those who did not [OR 3.48 (95% CI 1.10, 10.94), p = 0.03]. Conclusion The use of vitamin D as adjunctive therapy in COVID-19 critically ill patients was not associated with survival benefits but was linked with longer MV duration, ICU LOS, and higher odds of major bleeding.
Influence of contemporary warming on landscape-zonal systems of the East-European sub-continent: Predictive empirical-statistical modeling

Presented in the given article regional geo-ecological prognoses are based on the construction of discrete empirical-statistical models of zonal and regional ecosystems. The analysis was carried out on the examples of the flat territories of the Volga River basin, as well as the northern macro-slope of the Main Caucasian ridge. Regional landscape-ecological calculations and mapping were carried out according to the global climatic models GISS-1988 and E GISS-2007 belong­ing to the family of models of general atmospheric circulation. The strategy of geo-ecological prognosis was as follows: first to identify the selected ecosystem objects (either zonal type of plant formations and regional kinds of landscape) to certain values of contemporary climatic conditions and then to estimate the most probable transformation of the revealed ecological niches of the given objects according to the expected climatic changes for the given prognostic date. The geo-ecological analysis has been performed using mainly two types of empirical models: (a) informational, describing the geo-component interrelations, serving as a basis for the regional bank of their ecological niches that characterizes their parametric space; (b) “fuzzy” set-theoretical models, describing the polysystem units of landscape-zonal organization by operations with the ecological niches as descriptive vectors. Predictions of ecosystem transformations include two stages of analysis: (1) evaluation of the probabilities of changes in the functional states of ecosystems and (2) calculations of the rates of ecosystem transformations. Quantitative predictive analysis is carried out by means of operations with the hydro-thermal niches of zonal-regional ecosystems. The ecological estimates of forthcoming global warming refer first of all to the functional but not structural-morphological prediction. The most probable directions and degree of conversion of the ecosystem are estimated by the maximum values of transformation. The algorithms of predictive calculations are described in detail for both stages of analysis. The results of the zonal-regional prognostic analysis are presented in both graphic-analytical models and small-scale maps.
Monotone Interval Fuzzy Inference Systems

In this paper, we introduce the notion of a monotone fuzzy partition, which is useful for constructing a monotone zero-order Takagi–Sugeno–Kang Fuzzy Inference System (ZOTSK-FIS). It is known that a monotone ZOTSK-FIS model can always be produced when a consistent, complete, and monotone fuzzy rule base is used. However, such an ideal situation is not always available in practice, because a fuzzy rule base is susceptible to uncertainties, e.g., inconsistency, incompleteness, and nonmonotonicity. As a result, we devise an interval method to model these uncertainties by considering the minimum interval of acceptability of a fuzzy rule, resulting in a set of monotone interval-valued fuzzy rules. This further leads to the formulation of a Monotone Interval Fuzzy Inference System (MIFIS) with a minimized uncertainty measure. The proposed MIFIS model is analyzed mathematically and evaluated empirically for the Failure Mode and Effect Analysis (FMEA) application. The results indicate that MIFIS outperforms ZOTSK-FIS, and allows effective decision making using uncertain fuzzy rules solicited from human experts in tackling real-world FMEA problems.
Impact of endosymbionts on tick physiology and fitness

Abstract Abstract Ticks transmit pathogens and harbour non-pathogenic, vertically transmitted intracellular bacteria termed endosymbionts. Almost all ticks studied to date contain 1 or more of Coxiella, Francisella, Rickettsia or Candidatus Midichloria mitochondrii endosymbionts, indicative of their importance to tick physiology. Genomic and experimental data suggest that endosymbionts promote tick development and reproductive success. Here, we review the limited information currently available on the potential roles endosymbionts play in enhancing tick metabolism and fitness. Future studies that expand on these findings are needed to better understand endosymbionts’ contributions to tick biology. This knowledge could potentially be applied to design novel strategies that target endosymbiont function to control the spread of ticks and pathogens they vector.
Interaction rupture-flambage, le cas du «splitting» de tube métallique : approche expérimentale et numérique

Lorsqu’on decoupe un feuillard a l’aide d’un outil, ou lorsqu’on decoupe un tube selon son axe, au fur et a mesure que l’on propage la fissure qui traduit la decoupe il arrive que des ondulations de flambage perturbent les deux bords libres generes par la propagation de la fissure. Cette etude vise a analyser les origines de ces ondulations. Nous avons mene une campagne experimentale, dans laquelle des tubes en acier inox avec differentes geometries (rayon/epaisseur) sont « decoupes » selon une generatrice. Une instrumentation adequate, couplant des mesures ponctuelles, a l’aide de jauges de deformation, et une methode champ par correlation d’image, nous a permis de correctement mettre en exergue la phenomenologie, en particulier les cinematiques induites a l’echelle geometrique de la fissure (front de fissure) ainsi qu’a l’echelle du tube, avec les longueurs d’onde de flambage observees a l’aval de la fissure. La modelisation numerique menee en non lineaire geometrique (flambage), materiau (dechirure ductile), et conditions aux limites (contact) est aussi abordee a l’aide du code de calcul Abaqus/Standard. Pour la gestion de la propagation de la fissure, deux modeles de rupture sont proposes. Le premier modele dit zone cohesive est developpe et implante dans le code Abaqus via la subroutine UEL. Pour la deuxieme modelisation, nous avons utilise le modele dit « d’endommagement ductile » du code Abaqus. La modelisation via des elements massifs ou des elements coques volumiques ainsi que l’utilisation de ces modeles de rupture permettent de corroborer les observations experimentales. Ces travaux montrent que l’augmentation de la charge inherente au deplacement de l’outil de « decoupe », induit une extension dans la direction circonferentielle et donc une striction dans la direction radiale amenant finalement la rupture. Lors de la rupture, un « sillage plastique » apparait, relativement large, pres et parallele aux bords de la fissure. « Confine » par les autres parties du tube qui restent elastiques, des contraintes de compression axiale residuelles apparaissent dans ce sillage plastique, a l’aval de la fissure, leur intensite est suffisante pour produire les ondulations des bords libres qui traduisent un flambage local. Les contraintes residuelles liees a l’operation de decoupe induisent donc le flambage.