Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern of oro-dental disease in dental clinic of a tertiary care center

Introduction: The oro-dental diseases are major public health problem throughout the population in Nepal and other countries of the world. It affects people throughout their lifetime. Unhealthy lifestyles, environment and oral health related behavior are the primary causes of oro-dental diseases. We aimed to study the descriptive variables related to oro-dental dental diseases amongst patients attending tertiary care center. Method: A three-year data (April 2019 to April 2022) of patients registered for the treatment of oro-dental diseases in the record book of dental department of Patan Hospital were collected retrospectively. The collected data were entered in Microsoft Excel, and statistical analysis was carried out by using Statistical Package for the Social Science (SPSS) version 16. Result: In the study, 15,879 patients were included; among them 8,652(54.49%) were female and 7,227(45.51%) were male. The most prevalent oro-dental disease was gingivitis/periodontitis 5,076(31.96%) followed by dental caries 5,028(31.66%), abrasion 3,525(22.2%), malocclusion 951(5.99%), impaction 585(3.68%), pulpitis 384(2.42%) and edentulous 330(2.08%). In male, majority 1,800(24.9 %) belonged to 60 years and above age group, in female, majority 1,749 (20.21 %) belonged to 20-29 y. Conclusion: The most prevalent oro-dental diseases was gingivitis/periodontitis followed by dental caries, abrasion, malocclusion, impaction, pulpitis and edentulous. The female patients had more oro-dental problems than males.
Black-Box Boundary Attack Based on Gradient Optimization

Deep neural networks have gained extensive applications in computer vision, demonstrating significant success in fundamental research tasks such as image classification. However, the robustness of these networks faces severe challenges in the presence of adversarial attacks. In real-world scenarios, addressing hard-label attacks often requires the execution of tens of thousands of queries. To combat these challenges, the Black-Box Boundary Attack leveraging Gradient Optimization (GOBA) has been introduced. This method employs a binary search strategy to acquire an initial adversarial example with significant perturbation. The Monte Carlo algorithm is utilized to estimate the gradient of the sample, facilitating iterative movement along the estimated gradient and the direction of the malicious label. Moreover, query vectors positively correlated with the gradient are extracted to construct a sampling space with an optimal scale, thereby enhancing the efficiency of the Monte Carlo algorithm. Experimental evaluations were conducted using the HSJA, QEBA, and NLBA attack methodologies on the ImageNet, CelebA, and MNIST datasets, respectively. The results indicate that, under the constraint of 3 k query times, the GOBA, compared to other methods, can, on average, reduce perturbation (L2 distance) by 55.74% and simultaneously increase the attack success rate by an average of 13.78%.
Satellite-based large-scale vegetation dynamics in ecological restoration programmes of Northern China

ABSTRACT In the past, large-scale terrestrial vegetation dynamics have been used to evaluate the efficiency of national ecological restoration programmes in northern China. However, complete documentation and timely updates of vegetation dynamics in the Ecological Restoration Programmes Regions (ERPRs) of northern China are rare. Furthermore, the main challenges in analysing large-scale vegetation changes in ERPRs are so far not entirely clear. We chose four ERPR study areas (the Beijing–Tianjin Sand Source Region (BTSSR), the Loess Plateau (LP), and the north-east (NE), and the north-west (NW) of the Three-North Shelterbelt Forest Project (TNSFP)), avoiding the overlap of administrative regions. We used long-term Normalised Different Vegetation Index (NDVI) data to investigate the trends of vegetation activities in these four ERPRs of northern China between 1982 and 2011. A persistent and widespread increase of the growing season NDVI was found in this time span, with 58.54% increase in NE, 89.84% in LP, 54.59% in BTSSR, and 76.13% in NW. The observed increase of NDVI indicates the success of Chinese restoration programmes, which have helped to improve ecological conditions after 2000. However, a comparison of long-term vegetation changes between before and after implementation of restoration programmes is insufficient for assessing the programmes’ efficiency. Large-scale Dynamic Global Vegetation Models (DGVMs) are promising tools for future evaluation of the benefits of ecological restoration programmes.
Bathymetrical zonation of chemoautosynthesis-based communities on the deepest area of the landward slope of the Japan Trench

During the Dive # 114 of the ROV KAIKO in April 1999, some colonies of a vesicomyid bivalve were discovered at a depth of 6,805 m in the Japan Trench. Molecular phylogenetic analyses showed that these bivalves and bivalves that were collected within colonies of Calyptogena phaseoliformis on the foot of the Sanriku Escarpment, namely, C. fossajaponica were conspecific. The present results show that three kinds of chemoautosynthesis-based community, dominated by different bivalves that harbor intercellular symbionts in their gills, are distributed on each of the three escarpments in the deepest area of the landward slope of the Japan Trench.
The interaction of central and peripheral processing in L2 handwritten production: Evidence from cross-linguistic variations

The current study explores the interplay between central and peripheral processes in second language (L2) handwriting among bilinguals with diverse orthographic backgrounds. Our investigation delves into the cross-linguistic transfer effect in Spanish–English and Chinese–English bilinguals, emphasizing lexical frequency and phoneme-grapheme (P-O) consistency in spelling-to-dictation and immediate copying tasks. Results reveal that the interaction between central and peripheral processes in L2 handwritten production is shaped by the bilinguals' native language (L1) orthographic characteristics. Spanish–English bilinguals exhibited sensitivity to P-O consistency and the spread of this effect from central to peripheral processes throughout both tasks. Conversely, Chinese–English bilinguals showed heightened sensitivity to lexical frequency during orthographic planning and motor execution, particularly in the immediate copying task. In a broader context, these findings suggest that the parallel and cascading coordination of the L2 writing system is modulated by cross-linguistic variations. The implications of our findings hold relevance for handwriting production and bilingualism research.
Bright/dark switchable mode-locked fiber laser based on alcohol

A passively switchable erbium-doped fiber laser based on alcohol as the saturable absorber (SA) has been demonstrated. The SA is prepared by filling the gap between two optical patch cords with alcohol to form a sandwich structure. The modulation depth of the alcohol – SA is measured to be 6.4%. By appropriately adjusting the pump power and the polarization state in the cavity, three kinds of mode-locked pulse patterns can be achieved and switched, including bright pulse, bright/ dark soliton pair, and dark pulse. These different soliton emissions all operate at the fundamental frequency state, with a repetition rate of 20.05 MHz and a central wavelength of ∼ 1563 nm. To the best of our knowledge, this is the first demonstration of a switchable soliton fiber laser using alcohol as the SA. The experimental results further indicate that organic liquid-like alcohol has great potential for constructing ultrafast lasers.
FlowLever: Leverage Flow Director for Packet Dispatch Acceleration in NFV

The efficiency of dispatching incoming network flow packets to the destination virtual network function (VNF) has been recognized as a key problem in network function virtualization (NFV) platforms. Recent hardware assisted packet dispatch systems tend to rely on expensive software-defined network (SDN) switches or SmartNICs to mitigate the dispatch performance bottleneck, commonly observed in traditional software dispatch systems. In this paper, we revisit a hardware Flow Director (FDir) feature commonly found on commercial off-the-shelf NICs and propose a FDir-based NFV system that greatly accelerates packet dispatch with a much lower total cost of ownership (TCO). FlowLever is carefully designed to resolve nontrivial challenges such as the lack of flow state monitoring in FDir, the limited flow classification schema, heavy flow cache reconfiguration overhead, and high VNF scaling costs. With our software-hardware codesigned dispatch system and newly proposed flow activeness detection and cache replacement mechanisms, our evaluation using real-world network traffic traces shows that FlowLever improves the throughput by 1.6x - 2.8x and costs only 0.2 ms - 0.6 ms for load balance during VNF scaling.
A Unified Label Noise-Tolerant Framework of Deep Learning-Based Fault Diagnosis via a Bounded Neural Network

Supervised fault diagnosis (FD) in mechanical systems, particularly in high-speed train (HST) components, faces significant challenges due to the presence of label noise in annotating large-scale monitoring data. This label noise introduces strict requirements for label noise tolerance and the learning capabilities of FD algorithms. This article presents a unified framework for label noise FD in HST components using a bounded neural network (BNN) to address this issue. The proposed framework consists of multiple basic models with shared weights, enabling the learning of global knowledge across sensor nodes and facilitating the estimation of local states to adapt to dynamic measurement networks. The BNN-based basic model incorporates implicit weighted learning and bounded loss mechanisms, which extract valuable insights from misannotated data. In addition, a tighter bound of loss is introduced, providing theoretical proof and enhancing the label noise tolerance of the BNN. A surrogate training strategy based on an alternative convex search (ACS) is established to ensure the stability of the BNN model during the early training stage. This strategy mitigates the risk of failure in the initial training phase of the BNN model. The feasibility and effectiveness of the proposed method are demonstrated through a real-field test conducted on a mechanical system of an HST component. The code is released on https://github.com/sudao-he/Bounded_Neural_Network.
Deep-Learning-Based Approach in Imaging Radiometry by Aperture Synthesis: An Alias-Free Method

A new approach based on deep learning methods is presented for reconstructing $L$-band brightness temperature images from the inversion of interferometric data, namely, complex visibilities here simulated from observations of the Soil Moisture and Ocean Salinity (SMOS) interferometric radiometer. A specific deep neural network (DNN) architecture composed of a fully connected layer followed by a contracting and expansive path is proposed to learn the relationship between the simulated visibilities and the brightness temperature maps. The performances of the DNN are compared with those of algebraic inversions based on Fourier theory, which are all affected by strong aliases in the synthesized field of view (FOV), as a consequence of the spacing between the elementary antennas of SMOS, which does not satisfy the Nyquist criteria. In the alias-free FOV (AFFOV) of the algebraic reconstructions, these latter are outperformed by the DNN reconstructions: average mean absolute error (MAE) of about 0.7 K for the DNN instead of 3.7 K. Outside the AFFOV of the algebraic reconstructions, the DNN reconstructions do not show significant signs of field aliasing although the MAE increases: average MAE of about 1.5 K in the whole FOV. An analysis of the role of different neurons in the hidden layers is presented, and it is shown that some neurons are specialized in reconstructing what corresponds roughly to the AFFOV region of algebraic approaches, while other neurons are specialized in dealing with the external regions of this AFFOV.
Intravitreal injection of methotrexate in persistent diabetic macular edema: a 6-month study

Background: Diabetic macular edema (DME) affects approximately 10% of patients with diabetes mellitus. This condition can cause blurred or distorted vision, which significantly affects the quality of life of these patients. We evaluated the therapeutic effects of intravitreal methotrexate (MTX) injections on persistent DME. Methods: This prospective interventional case series included patients with confirmed persistent DME that was unresponsive to previous standard treatments. The patients underwent comprehensive eye examinations and macular imaging with optical coherence tomography (OCT). A single intravitreal MTX injection (400 µg MTX in 0.16 mL solution) was administered, followed by patient assessments at 1, 3, and 6 months after injection. Best-corrected distance visual acuity (BCDVA), intraocular pressure (IOP), macular thickness (MT), and central subfield thickness (CST) were measured at baseline and post-injection to evaluate treatment efficacy. Results: We included 33 eyes of 30 patients with a mean (standard deviation [SD], range) age of 62.7 (8.3, 44 to 77) years, of whom 17 (56.7%) were men and 13 (43.3%) were women. All participants had type 2 diabetes mellitus, with a mean (SD, range) duration of 17.0 (6.8, 10 to 31) years. Most participants (n = 27 eyes, 81.8%) had non-proliferative diabetic retinopathy, and six eyes (18.2%) had regressed proliferative diabetic retinopathy. Four eyes (12.1%) had undergone prior macular laser photocoagulation. The mean (SD) number of prior intravitreal bevacizumab injections was 3.4 (0.8), and 29 eyes (87.8%) had received one intravitreal triamcinolone injection. During the study period, a statistically significant difference was observed in CST (P < 0.05); however, no statistically significant differences were observed in BCDVA, MT, or IOP (P > 0.05). Pairwise comparison revealed a significant decrease in CST at 6 months post-injection compared to the baseline value (P < 0.05). During the investigation period, no side effects of MTX, such as macular edema, retinal tears, vitreous hemorrhage, endophthalmitis, or vision loss, were observed. Conclusions: A single intravitreal MTX injection significantly reduced CST in patients with persistent DME, without relevant safety concerns. However, no significant improvement in functional outcomes was observed. Therefore, there is no strong evidence to recommend its use as a treatment for pDME. Further studies, preferably randomized clinical trials with long-term follow-ups, are warranted to assess the long-term efficacy, safety, and potential benefits of intravitreal MTX for the treatment of persistent DME.
Biogeochemistry of greenhouse gases in coastal upwelling systems: Processes and sensitivity to global change

Major coastal upwelling systems are among the most productive marine ecosystems in the world. They contribute disproportionately to the cycling of carbon and nutrients in the ocean and influence marine biogeochemistry beyond their productive regions. Characterized by intense microbial respiration (both aerobic and anaerobic), major coastal upwelling systems are also hotspots for the production and outgassing of potent greenhouse gases (GHG) such as CO2, N2O, and CH4. Quantifying and understanding these roles in the context of a changing climate is therefore a subject of great interest. Here we provide a short synthesis of the current knowledge of the contributions of major coastal upwelling systems to the cycling of GHG. Despite variations within and among different systems, low-latitude coastal upwelling systems typically act as a net carbon source to the atmosphere, while those at higher latitudes function as weak sinks or remain neutral regarding atmospheric CO2. These systems also significantly contribute to oceanic N2O and CH4 emissions, although the extent of their contribution to the latter remains poorly constrained. We also overview recent and future changes to upwelling systems in the context of a warmer climate and discuss uncertainties and implications for GHG production. Although rapid coastal warming is anticipated in all major coastal upwelling systems, the future changes in upwelling-favorable winds and their implications within the context of increased stratification are uncertain. Finally, we examine the major challenges that impede our ability to accurately predict how major coastal upwelling systems will respond to future climate change, and present recommendations for future research to better capture ongoing changes and disentangle natural and forced variability.
Automated surgical step recognition in transurethral bladder tumor resection using artificial intelligence: transfer learning across surgical modalities

Objective Automated surgical step recognition (SSR) using AI has been a catalyst in the “digitization” of surgery. However, progress has been limited to laparoscopy, with relatively few SSR tools in endoscopic surgery. This study aimed to create a SSR model for transurethral resection of bladder tumors (TURBT), leveraging a novel application of transfer learning to reduce video dataset requirements. Materials and methods Retrospective surgical videos of TURBT were manually annotated with the following steps of surgery: primary endoscopic evaluation, resection of bladder tumor, and surface coagulation. Manually annotated videos were then utilized to train a novel AI computer vision algorithm to perform automated video annotation of TURBT surgical video, utilizing a transfer-learning technique to pre-train on laparoscopic procedures. Accuracy of AI SSR was determined by comparison to human annotations as the reference standard. Results A total of 300 full-length TURBT videos (median 23.96 min; IQR 14.13–41.31 min) were manually annotated with sequential steps of surgery. One hundred and seventy-nine videos served as a training dataset for algorithm development, 44 for internal validation, and 77 as a separate test cohort for evaluating algorithm accuracy. Overall accuracy of AI video analysis was 89.6%. Model accuracy was highest for the primary endoscopic evaluation step (98.2%) and lowest for the surface coagulation step (82.7%). Conclusion We developed a fully automated computer vision algorithm for high-accuracy annotation of TURBT surgical videos. This represents the first application of transfer-learning from laparoscopy-based computer vision models into surgical endoscopy, demonstrating the promise of this approach in adapting to new procedure types.
A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data

The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R² = 0.98, RMSE = 0.07) and lycopene content (R² = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R² of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R² value of −0.35. Shapley additive explanation’s (SHAP) summary plot analysis indicated that both models are effective in predicting °Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models’ efficiency (especially in °Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost’s superiority in handling complex agronomic data for quality assessment.
RISK SCORE PREDICTION MODEL FOR DEMENTIA IN PATIENTS WITH TYPE 2 DIABETES MELLITUS : AN UPDATE SYSTEMATIC REVIEW

Background: Dementia is a substantial global health issue. Reduction in future numbers of dementia cases through effective preventive strategies could significantly affect the personal and socioeconomic burdens of dementia. WHO has recommended that countries urgently develop national public health programmes to reduce the impact of dementia. The aim: The aim of this study to show about risk score prediction model for dementia in patients with type 2 diabetes mellitus. Methods: By the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. This search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed, SagePub, and Google Scholar were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search get 67 articles, whereas the results of our search on SagePub get 210 articles, on Google Scholar 1350 articles. Records remove before screening are 667, so we get 960 articles fos screening. After we screened based on record exclude, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: RxDx-Dementia risk index to predict dementia in patients with type 2 diabetes and hypertension. The RxDx-Dementia risk index which incorporated diagnosis and prescription-based information in a single summary score performed better than diagnosis- or prescription-based comorbidity scores or its combinations. The RxDx-Dementia risk index can be used for prognostic purpose or to control confounding in epidemiological studies.
Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Finally, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.