Miracle-dz/UniFAR-FTUs huggingface dataset:
Alterations in Surface Gloss and Hardness of Direct Dental Resin Composites and Indirect CAD/CAM Composite Block after Single Application of Bifluorid 10 Varnish: An In Vitro Study

The surface characteristics of the restorative material are essential to its longevity. Since resin composites are polymeric-based materials, they could be degraded when exposed to oral conditions and chemical treatment. Certain chemical solutions, such as fluoride varnish, have the potential to deteriorate the resin composite’s surface properties such as gloss and hardness. The current study aimed to assess and compare the surface gloss and hardness of different types of dental resin composites (nanohybrid, ormocer, bulk-fill flowable direct composites, and indirect CAD/CAM resin composite blocks (BreCAM.HIPC)) after a single application of Bifluorid 10 varnish. A total of 80 disc-shaped resin composite specimens were evenly distributed in four groups of 20 specimens. These were divided into two equal subgroups of specimens with topical fluoride (TF) application (n = 10) and without TF application (n = 10). The specimens were examined for surface gloss and hardness. Independent sample t-test was used to investigate statistically the effect of TF on the gloss as well as the hardness of each material. One-way ANOVA and post hoc tests were used to assess the difference in gloss and hardness among the materials without and with TF application. The significance level was adjusted to p ≤ 0.05. The results of gloss showed that the TF application led to a significant reduction in gloss values of all tested composites. The gloss among the various materials was significantly different. The TF had no significant effect on the hardness of nanohybrid, bulk-fill flowable, and BreCAM.HIPC composites (p = 0.8, 0.6, and 0.3, respectively). On the other hand, the hardness of ormocer was significantly reduced after TF application. Comparing the different resin composite materials, the hardness significantly differed. This study concluded that surface gloss and hardness seem to be impacted by the type and composition of the resin composites and vary depending on fluoride application.
COYOTE ACTIVITY PATTERNS IN THE SIERRA NEVADA

The winter months in high mountain regions of the Sierra Nevada make survival difficult for predators that exist there. High productivity during the spring and summer enables coyotes (Canis latrans) to survive and reproduce, but little is known about how these animals overwinter in mountainous areas. Hawthorne (1970) suggested that coyotes make altitudinal migrations, but recent data (Gantz 1990, Gese et al. 1996, Shivik et al. 1996) indicate that coyotes can remain in the high mountains throughout the year. Temporal rhythms (e.g., innate behavioral rhythms such as diel cycles and seasonal reproductive cycles) may influence, or help elucidate, coyote activity patterns. We hypothesized that coyotes vary activity levels throughout the year as a result of these seasonal biological requirements. We related coyote activity patterns to the seasons that are likely to influence these patterns. We analyzed coyote activity in the Sagehen Basin of the Sierra Nevada because seasonal activity patterns in this seasonally cold and snowy climate provide insight into how coyotes allocate energy in order to survive and reproduce. Previous analyses of coyote activity used movement data to determine activity levels (Gipson and Sealander 1972, Andelt and Gipson 1979, Smith et al. 1981, Gese et al. 1989). However, some authors argued that using distance traveled is not a good measure of coyote activity (Knowlton et al. 1968, Laundre and Keller 1981). The literature presents a paucity of analyses examining seasonal trends in coyote activity using activity-sensitive collars (especially in mountainous areas). Therefore, we conducted a study using activity-sensitive radiocollars to monitor seasonal coyote activity in a mountainous region of the Sierra Nevada. Materials and Methods
Clinical and Laboratory Predictors of Long-Term Outcomes after Catheter Ablation for a Ventricular Electrical Storm

Background Ventricular electrical storm (VES) is characterized by the occurrence of multiple episodes of sustained ventricular arrhythmias (VA) over a short period of time. Radiofrequency ablation (RFA) has been reported as an effective treatment in patients with ventricular tachycardia (VT). Objective The aim of the present study was to indicate the short-term and long-term predictors of recurrent VA after RFA was performed due to VES. Methods A retrospective, single-centre study included patients, who had undergone RFA due to VT between 2012 and 2021. In terms of the short-term (at the end of RFA) effectiveness of RFA, the following scenarios were distinguished: complete success: inability to induce any VT; partial success: absence of clinical VT; failure: inducible clinical VT. In terms of the long-term (12 months) effectiveness of RFA, the following scenarios were distinguished: effective ablation: no recurrence of any VT; partially successful ablation: VT recurrence; ineffective ablation: VES recurrence. Results The study included 62 patients. Complete short-term RFA success was obtained in 77.4% of patients. The estimated cumulative VT-free survival and VES-free survival were, respectively, 28% and 33% at the 12-month follow-up. Ischemic cardiomyopathy and complete short-term RFA success were predictors of long-term RFA efficacy. Neutrophil to lymphocyte ratio (NLR) and GFR <60 mL/min/1.73 m2 were associated with VES recurrence. NLR ≥2.95 predicted VT and/or VES recurrence with a sensitivity of 66.7% and specificity of 72.2%. Conclusion Ischemic cardiomyopathy and short-term complete success of RFA were predictors of no VES recurrence during the 12-month follow-up, while NLR and GFR <60 ml/min/1.73 m2 were associated with VES relapse.
Based on Robot Positioning and Mapping Decomposition Research on Improving PHD-SLAM Method

Aiming at the phenomenon of high clutter in the scene and a large number of missed detections by sensors, a PHD-SLAM method with untracked Kalman filtering under robot pose building decomposition is proposed. This method avoids complex data association based on stochastic finite set theory. Environmental map estimates are based on estimated vehicle trajectories The first statistical moment of the set propagated by the probability hypothesis density filter effectively reduces the computational burden, and the unscented Kalman filter is introduced in the updating process of the Gaussian term function to improve the accuracy of the SLAM method. Through simulation experiments and KITTI data set verification, compared with the EKFSLAM algorithm, This method avoids the complicated data association process and improves the estimation accuracy of map features and robot attitude. It is worth noting that in the data set verification stage, ORB feature extraction algorithm is used to obtain the feature points in the visual image information and realize the coordinate transformation between continuous image frames. In the subsequent SLMA stage, the position of the feature point after the coordinate change is taken as the measured value, which becomes a new idea to solve the problem of robot positioning and mapping.
Transformative Automation: AI in Scientific Literature Reviews

—This paper investigates the integration of Artificial Intelligence (AI) into systematic literature reviews (SLRs), aiming to address the challenges associated with the manual review process. SLRs, a crucial aspect of scholarly research, often prove time-consuming and prone to errors. In response, this work explores the application of AI techniques, including Natural Language Processing (NLP), machine learning, data mining, and text analytics, to automate various stages of the SLR process. Specifically, we focus on paper identification, information extraction, and data synthesis. The study delves into the roles of NLP and machine learning algorithms in automating the identification of relevant papers based on defined criteria. Researchers now have access to a diverse set of AI-based tools and platforms designed to streamline SLRs, offering automated search, retrieval, text mining, and analysis of relevant publications. The dynamic field of AI-driven SLR automation continues to evolve, with ongoing exploration of new techniques and enhancements to existing algorithms. This shift from manual efforts to automation not only enhances the efficiency and effectiveness of SLRs but also marks a significant advancement in the broader research process.
Genome-Wide Identification of the GhANN Gene Family and Functional Validation of GhANN11 and GhANN4 under Abiotic Stress

Annexins (ANNs) are a structurally conserved protein family present in almost all plants. In the present study, 27 GhANNs were identified in cotton and were unevenly distributed across 14 chromosomes. Transcriptome data and RT-qPCR results revealed that multiple GhANNs respond to at least two abiotic stresses. Similarly, the expression levels of GhANN4 and GhANN11 were significantly upregulated under heat, cold, and drought stress. Using virus-induced gene silencing (VIGS), functional characterization of GhANN4 and GhANN11 revealed that, compared with those of the controls, the leaf wilting of GhANN4-silenced plants was more obvious, and the activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) were lower under NaCl and PEG stress. Moreover, the expression of stress marker genes (GhCBL3, GhDREB2A, GhDREB2C, GhPP2C, GhRD20-2, GhCIPK6, GhNHX1, GhRD20-1, GhSOS1, GhSOS2 and GhSnRK2.6) was significantly downregulated in GhANN4-silenced plants after stress. Under cold stress, the growth of the GHANN11-silenced plants was significantly weaker than that of the control plants, and the activities of POD, SOD, and CAT were also lower. However, compared with those of the control, the elasticity and orthostatic activity of the GhANN11-silenced plants were greater; the POD, SOD, and CAT activities were higher; and the GhDREB2C, GhHSP, and GhSOS2 expression levels were greater under heat stress. These results suggest that different GhANN family members respond differently to different types of abiotic stress.
Oxy-foitite, [] (Fe2+ Al2)Al6 (Si6 O18 )(BO3)3 (OH)3 O, a new mineral species of the tourmaline supergroup

Oxy-foitite, □(Fe 2+ Al 2 )Al 6 (Si 6 O 18 )(BO 3 ) 3 (OH) 3 O, is a new mineral of the tourmaline supergroup. It occurs in high-grade migmatitic gneisses of pelitic composition at the Cooma metamorphic Complex (New South Wales, Australia), in association with muscovite, K-feldspar and quartz. Crystals are black with a vitreous luster, sub-conchoidal fracture and gray streak. Oxy-foitite has a Mohs hardness of ∼7, and has a calculated density of 3.143 g/cm 3 . In plane-polarized light, oxy-foitite is pleochroic ( O = dark brown and E = pale brown), uniaxial negative. Oxy-foitite belongs to the trigonal crystal system, space group R 3 m , a = 15.9387(3) A, c = 7.1507(1) A and V = 1573.20(6) A 3 , Z = 3. The crystal structure of oxy-foitite was refined to R 1 = 1.48% using 3247 unique reflections from single-crystal X-ray diffraction using Mo K α radiation. Crystal-chemical analysis resulted in the empirical structural formula: X ( □ 0.53 Na 0.45 Ca 0.01 K 0.01 ) ∑ 1.00 Y ( Al 1.53 Fe 2 + 1.16 Mg 0.22 Mn 2 + 0.05 Zn 0.01 Ti 4 + 0.03 ) ∑ 3.00 Z ( Al 5.47 Fe 3 + 0.14 Mg 0.39 ) ∑ 6.00 [ ( Si 5.89 Al 0.11 ) ∑ 6.00 O 18 ] ( BO 3 ) 3 V ( OH ) 3 W [ O 0.57 F 0.04 ( OH ) 0.39 ] ∑ 1.00 . Oxy-foitite belongs to the X -site vacant group of the tourmaline-supergroup minerals, and shows chemical relationships with foitite through the substitution Y Al 3+ + W O 2− → Y Fe 2+ + W (OH) 1− .
Data Augmentation with Pseudo-Infrared Night-Vision Image Conversion for Improved Nighttime Object Detection

This paper presents a novel data augmentation method with pseudo-infrared night-vision image conversion for RGB images captured under daylight conditions. Our proposed data augmentation method is to apply gamma correction after grayscale conversion to RGB images and composites them with a mask image that simulates infrared irradiation. Our goal is to improve the accuracy of nighttime object detection using infrared night-vision cameras. We exclusively utilized data from the "person" class in the PASCAL VOC dataset and conducted a nighttime detection performance comparison between models trained on this person dataset (original dataset) and models trained on the person dataset to which we applied our proposed data augmentation method (converted dataset). The experimental results showed that the Average Precision (AP) for the model trained on the original dataset is 74.9%. In contrast, the AP for the model trained on our converted dataset is 78.7%.
Climatology of precipitation in West Africa through observational studies

The occurrence of precipitation in the Sahelian belt is highly variable and unpredictable. Incessant droughts and spasmodic flash floods cause residents to experience crop failures due to drought and subsequent famine. These unfortunate occurrences served as the motivation for this study. This research utilized the Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) tool to detect and evaluate storms, summarize the precipitation regime of Mali, West Africa, and compare storm statistics of observed squall lines and Mesoscale Convective Systems (MCS) for the study period of 2006-2008. The results of this research are promising for the future of precipitation occurrence in the Sahelian region. Time series and storm distribution analysis showed that the region experienced atypical rainy seasons for the last three years, with storms in 2008 being the most intense and producing the most precipitation. The comparison of MCS and squall line statistics allowed for the preliminary designation of storm characteristics. Results showed that compared to chosen MCS cases, squall lines tend to be more intense, produce more precipitation, last longer, and have higher storm heights. Future work includes scrutinizing additional storms and storm statistics and performing model verification.
Land Policies and Farm Productivity in Thailand

This study assesses the economic implications of land ownership security in rural Thailand. It uses data from this country to rigorously analyze several aspects of land ownership security. It provides both qualitative and quantiative information on the effects of ownership security. The study presents a conceptual model and literature review and is followed by separate discussions on the evolution of land rights in Thailand; the study methodology and the nature of the data; and the credit market. A formal model of land acquisition and ownership security underlies the empirical discussions presented in subsequent chapters on land values; capital formation and land improvements; and, input use and farm productivity. The impact of usufruct certificates is then assessed, and is followed by an analysis of the benefits and costs of land titling. The study demonstrates and concludes that land ownership security in Thailand has a substantial impact on farmers' agricultural performance.
A Route Map to Calibrate Spatial Interaction Models from GPS Movement Data

The development of sensors such as GPS trackers that capture movement data in realtime and at detailed spatial and temporal scales has transformed our ability to collect mobility data (Kwan and Neutens, 2014). Currently, however, these developments in movement data collection technologies are much further ahead than current methods for extracting meaningful patterns from such data (Laube et al., 2007; Long and Nelson, 2012). Furthermore, there have been very few studies that have tried to explore decision-making processes related with movements using GPS data and similar sources. There is a need therefore to discover if new forms of mobility data can be translated into new insights into movement behaviour. Here we do this through an examination of the calibration of spatial interaction with GPS data. As part of this experiment, we discover what needs to be done to transform raw GPS data into origin-destination matrices of aggregated flows.
Elicitation of Anxiety Without Time Pressure and Its Detection Using Physiological Signals and Artificial Intelligence: A Proof of Concept

Stress can be defined as a state of anxiety (or mental tension) caused by a particular situation. Everybody experiences stress to some level, but how we respond to stress significantly affects our well-being. Various events generate anxiety that leads to stress. For example, not having enough time to complete a task or being late are situations where anxiety (and stress) depends on a temporal factor: the scarcity of time. But people also slide into anxiety as they live in a condition that causes them to be tense, independently of time. The studies eliciting anxiety in laboratory settings have less widely considered this variant. This paper presents a proof of concept (PoC) that investigated the possibility of stimulating anxiety without time pressure through a purposely edited horror movie trailer, giving new insights into the emotional experiences evoked by controlled audiovisual stimuli. The PoC comprised an AI-based classifier to detect a person’s emotion among anxiety, relaxation, and none of the two based on the galvanic skin response (GSR), photoplethysmogram (PPG), and heart rate (HR), achieving an accuracy higher than 95%. Key application areas include media and marketing, and psychology. Media producers could improve their content to capture the audience better; psychologists could create tailored exposure experiences to promote gradual desensitization to stress triggers.
Characterisation of pigeon pea (Cajanus cajan) landraces grown in two climatic zones in KwaZulu-Natal province, South Africa

Pigeon pea is an important legume in arid and semi-arid areas of the Sub-Saharan Africa. Knowledge of adaptation characteristics of genotypes would significantly enhance its production in diverse environments of the region. Field experiments were conducted at two sites, namely Makhathini and Newlands Research Stations, both in KwaZulu-Natal province, South Africa, to assess the variation in agronomic characteristics among six local landraces obtained from smallholder farmers. The six landraces were categorised based on seed coat colour according to farmers’ perception. Significant differences were observed among landraces with respect to plant height and time to 50% flowering, indicating phenotypic variability. The Light Brown-seeded landrace planted at Newlands flowered earlier (106 d after planting). The Dark Brown-seeded landrace flowered within 150 d at Makhathini and reached physiological maturity in 185 d, earlier than other landraces. The results of principal component analysis suggested that yield of the Light Brown-seeded landrace was associated with two important agronomic parameters, namely harvest index (HI) and pod mass. The Light Brown-seeded and Cream with Brown-seeded landraces were associated with desirable yield traits including grain yield, HI and pod mass and can, therefore, be recommended as suitable parental lines in crop improvement programmes to improve pigeon pea grain yield.
Cost-effective Seismic Reflection Imaging Using Seismic Interferometry for Imaging of Enhanced Geothermal System - A Case Study in the Neuquén Basin

We investigate the applicability of passive seismic interferometry using P-wave coda from local earthquakes for the purpose of retrieving reflections for imaging enhanced geothermal systems. For this, we use ambient-noise data recorded in the Neuquen basin, Argentina, where the Peteroa and Los Molles geothermal fields are present nearby. After retrieving reflections, we proceed to process them following a standard processing sequence to obtain images of the crustal structures. Examining crosscorrelation, crosscoherence, and multidimensional deconvolution approaches, we find that multidimensional deconvolution, based on the truncated singular-value decomposition scheme, gives us slightly better structural imaging than the other two approaches. Our results provide higher-resolution imaging of the crustal structures down to the lower boundary of the Moho in comparison with previous passive seismic imaging by receiver function and global-phase seismic interferometry in this region. We also interpret the deep basement thrust fault that has been indicated by active-seismic reflection profile and nearby exploration well. The method we propose could be used as a low-cost alternative to active-source acquisition for imaging and monitoring purposes of deeper geothermal reservoirs, e.g., in enhanced geothermal systems, where the target structures are down to 10 km depth.
The Gospel of Anti-Americanism in South Korea: Mass Media Exposure and Negative Audience Perception Running Head: Media Exposure, Perceptions &Evaluation

Although there are many reasons behind anti-Americanism in South Korea in the 21 st century, it is believed by some experts that the anti-Americanism originated over a decade ago from the 2002 Olympic short track speed skating controversy, and the issues with the Boeing F-15K. Based on exemplification and social cognitive theories, this study examines the extent to which exposure to the South Korean mass media coverage of these two concrete exemplars of negative American images, Olympic short track speed skating and the Boeing F-15K, affected South Koreans' negative perceptions of America. Results revealed that the South Korean mass media coverage of negative American images serves as the basis for evaluating two well-known American-coded issues: the American soccer team's performance in the 2002 South Korea and Japan World Cup, and the terrorist attack on the World Trade Center in 2001.
A normative modeling approach to quantify white matter changes and predict functional outcomes in stroke patients

Objectives The diverse nature of stroke necessitates individualized assessment, presenting challenges to case-control neuroimaging studies. The normative model, measuring deviations from a normal distribution, provides a solution. We aim to evaluate stroke-induced white matter microstructural abnormalities at group and individual levels and identify potential prognostic biomarkers. Methods Forty-six basal ganglia stroke patients and 46 healthy controls were recruited. Diffusion-weighted imaging and clinical assessment were performed within 7 days after stroke. We used automated fiber quantification to characterize intergroup alterations of segmental diffusion properties along 20 fiber tracts. Then each patient was compared to normative reference (46 healthy participants) by Mahalanobis distance tractometry for 7 significant fiber tracts. Mahalanobis distance-based deviation loads (MaDDLs) and fused MaDDLmulti were extracted to quantify individual deviations. We also conducted correlation and logistic regression analyses to explore relationships between MaDDL metrics and functional outcomes. Results Disrupted microstructural integrity was observed across the left corticospinal tract, bilateral inferior fronto-occipital fasciculus, left inferior longitudinal fasciculus, bilateral thalamic radiation, and right uncinate fasciculus. The correlation coefficients between MaDDL metrics and initial functional impairment ranged from 0.364 to 0.618 (p < 0.05), with the highest being MaDDLmulti. Furthermore, MaDDLmulti demonstrated a significant enhancement in predictive efficacy compared to MaDDL (integrated discrimination improvement [IDI] = 9.62%, p = 0.005) and FA (IDI = 34.04%, p < 0.001) of the left corticospinal tract. Conclusion MaDDLmulti allows for assessing behavioral disorders and predicting prognosis, offering significant implications for personalized clinical decision-making and stroke recovery. Importantly, our method demonstrates prospects for widespread application in heterogeneous neurological diseases.