Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformer with Selective Shuffled Position Embedding for Early Detection of Knee Osteoarthritis

Knee OsteoArthritis (KOA) is a common musculoskeletal disorder, which causes reduced mobility for seniors. Early detection of such a disorder is important to limit its impact on people. Computer-Aided Diagnosis (CAD) systems based on deep learning methods have shown success in KOA diagnosis. Due to the high cost of labelling, the lack of sufficient data in the medical field is a significant challenge for training machine learning models. To improve the generalization capability of deep neural network models and avoid overfitting, data augmentation is essential. However, existing data augmentation techniques such as rotation and gamma correction are not effective at increasing the diversity of the original data. In this paper, we propose a novel approach based on the Vision Transformer (ViT) model with a Selective Shuffled Position Embedding (SSPE) strategy that generates different input sequences fixing and shuffling the position embedding of key and non-key patches, respectively, as a novel method of data augmentation for early detection of KOA (KL-0 vs KL-2). Experimental results demonstrate that our approach is valid as it can significantly improve the model’s classification performance.
Deep learning multimodal methods for geophysical inversion : application to glacier ice thickness estimation

Glaciers play a critical role in the Earth’s climate system, and accurate estimates of their behaviours are essential for understanding the impacts of climate change and informing policy decisions. One of the most important parameters for such a task is ice distribution, which is difficult to measure and predict using traditional physics-based models. In this study, we propose a deep learning approach to predict glacier thickness by learning directly from ice velocity and topography. Our approach overcomes the limitations of traditional physics-based models, such as computational cost and the need for expert knowledge to calibrate the models. In addition, deep learning models are flexible enough to explore the relevance of multimodality and multitasking to address the physical problem. Our results demonstrate the feasibility of quickly training a neural network model with sufficient training data and producing stable, high-quality ice thickness estimates. We highlight the importance of some specific input features suggested by geophysicists that have a positive impact on model stability.
PL/PX Platform: Online tool for the evaluation of fun and game experiences

Playability evaluation relies on heuristics as one of the most prominent approaches by virtue of their comprehensiveness and relative ease of implementation. However, due to the vastness of available game-based systems, their varied purposes, and the diversity of the player audience, defining heuristics that focus on thorough playability assessment carries a considerable challenge. This paper presents an online tool designed to evaluate the playability of game experiences. For its development, it has been based on a general-purpose playability assessment tool, which has been improved and adjusted according to previous research results. Efforts have been directed towards a specific population, in this case, the elderly, with the purpose of adapting the tool to a specific practical case. It is expected that the results obtained through this tool will provide a solid means to evaluate the playability of game experiences, not only in the general public, but also in specific populations, such as the elderly. This is intended to provide high quality game-based systems that are attractive and generate a high level of player engagement.
Warunki geologiczne i perspektywy wykorzystania złoża węgla brunatnego w rejonie Nakła nad Notecią

GEOLOGICAL SETTING AND PERSPECTIVES OF USE OF BROWN COAL DEPOSITS FROM THE NAKŁO REGION UPON NOTEC RIVER Summary The studied deposit is situated in a narrow longitudinal depression of the tectonic trough type in the vicinities of Naklo upon Notec river, northern Poland. The Tertiary, directly overlying Lower Jurassic sandstones, is represented by Oligocene, Miocene and Pliocene. Oligocene comprises siltstones and glauconitic sands with intercalations of brown coals of the seam group V and Miocene quartz sands with brown coals of the seam group II in its lower part, and clays and silts with brown coals of the seam group I in the upper. They are overlain by Pliocene clays and silts and Quaternary tills and sands. Coal-bearing Tertiary series comprises Middle and Upper Miocene with the seam groups II and I, respectively. The seams of the group II markedly increase in thickness within the Naklo trough which is reflected by increase of total coal-bearing index from 0.17 on margins of the trough to 0.30 inside of it. The deposit area is about 13 km2, summative thickness of brown coal seams equals 19.5 m on the average, and prognostic deposit resources are estimated at about 275 mln Mg. The deposit is formed of earth coals with mean caloric value equal 7976 kJ/kg and mean ash content 24.22%. They are suitable as fuel for power plant.
Aeroacoustic Noise Prediction and Acoustic Optimization of Mufflers

Noise control of large diesel and natural gas generators is achieved through industrial muﬄers. Design of such muﬄers relies heavily on general guidelines. But these guidelines are not suitable for complex muﬄers; instead, automated optimization provides an eﬀective means of design. Optimization of a plug ﬂow muﬄer (PFM) is conducted in this work with two diﬀerent approaches: 1) a relatively simple gradient-descent algorithm (L-BFGS-B) maximizing the transmission loss of the PFM, and 2) a multiobjective (transmission loss and pressure drop) simulation-based optimization using the Eﬃcient Global Optimization (EGO) algorithm. The EGO algorithm is shown to be well suited for muﬄer optimization, performing vastly better than the commonly used NSGA-II algorithm. In addition, the initial steps towards the prediction of aeroacoustic noise (self-noise) in muﬄers is accomplished through the CFD simulation of the tandem cylinder benchmark experiment.
Optimal Cluster Head Selection in Wireless Sensor Network via Combined Osprey-Chimp Optimization Algorithm: CIOO

—The development of Wireless Sensor Network (WSN) has gained significant attention for smart systems due to their potential use in a wide range of areas. WSN consists of tiny, independently arranged sensor nodes that run on batteries. The resources and energy usage for sensor nodes are the major factors. Particularly, the unbalanced nodes’ raises the energy use and reduces the network life-span. Energy efficiency in WSN cluster head selection remains a challenging task. The best method has been developed for reducing node energy consumption is clustering. However, the current clustering strategy failed to properly allocate the energy needs of the nodes without considering energy features, node quantity, as well as adaptability. Hence, there is need for advanced clustering process with new optimization tactics, and accordingly, a new cluster-head selection model in WSN is proposed in this work. Initially, the clustering process is done by the k-means algorithm. The Cluster Head (CH) selection is the subsequent progress under the consideration of node’s energy, distances, delays, and risks as well. A novel CIOO (Chimp Integrated Osprey Optimization) algorithm combining the Osprey and Chimp optimization algorithm is proposed for Cluster Head Selection (CHS). Finally, the performance of proposed model is evaluated over the conventional methods.
Security and Privacy of Cloud Data Auditing Protocols: A Review, State-of-the-art, Open Issues, and Future Research Directions

—Cloud service providers offer a trustworthy and resistant-based storage environment for on-demand cloud services to outsource clients’ data. Several researchers and business entities currently adopt cloud services to store their data in remote cloud storage servers for cost-saving purposes. Cloud storage offers numerous advantages to users like scalability, low capital expenses, and data available from any place, anytime, regardless of location and device. However, as the users lose physical access and control over data, the storage service raises security and privacy issues, such as confidentiality, integrity, and availability of outsourced data. Data integrity is a primary concern for cloud users to confirm whether data integrity is intact or not. This paper presents a comprehensive review of cloud data auditing schemes and a comparative analysis of the desirable features. Furthermore, it provides advantages and disadvantages of the state-of-the-art techniques and a performance comparison regarding the communicational and computational costs of involved entities. It also highlights desirable features of different techniques, open issues, and future research trends of cloud data auditing protocols.
Ontological Context for Gesture Interpretation

This study explores gesture interpretation by utilizing an ontological context. The aim is to store gestures and scene context data in an ontology and use its knowledge graph to actuate the robot arm to perform sets of manipulation tasks used in various environments. The knowledge graph captures the relationships between gestures, objects in the scene, and the desired actions. By putting the ontological context into use, the system can understand the meaning behind the gestures and execute the appropriate actions. The paper focuses on the development of the ontology, including the creation of class properties and the embedding of gestures within the ontology. Additionally, the paper explores how the integration of specifying context interpretation from the ontology may look to enhance the interpretation of gestures. The proposed approach aims to provide more intuitive and adaptive gesture-based supervisory control of robots in general. We tested the proposed ontological system in several tests so that it may be used in our future applications.
Enhancing Production System Performance: Failure Detection and Availability Improvement with Deep Learning and Genetic Algorithm

— A crucial component of industrial operations is the detection of production system failures, which aims to spot any problems before they get worse. By applying cutting-edge methods like deep learning and genetic algorithms, failure detection accuracy may be improved, allowing for preemptive actions to reduce downtime and maximize system availability. These methods improve reactivity to possible errors and solve dynamic issues, which enhances the overall efficiency and reliability of production systems. This study offers a novel method for improving the availability and failure detection of production systems using deep learning techniques and genetic algorithms in a data-driven strategy. The goal of the project is to provide a complete framework for efficient failure detection that incorporates deep learning models, particularly Convolutional Neural Network (CNN) Autoencoder. Furthermore, system configurations are optimized through the use of genetic algorithms, improving overall availability. The suggested model is able to identify complex patterns and connections in the data by being trained on a variety of datasets that contain information about equipment failure. The incorporation of genetic algorithm guarantees flexibility and resilience in system setups, hence augmenting total availability. The study presents a proactive and flexible approach to the dynamic issues encountered in industrial environments, providing a notable breakthrough in failure detection and availability improvement. The proposed model is implemented in Python software. It achieves an astounding 99.32% accuracy rate, which is 3.58% higher than that of current techniques like CNN-LSTM (Long Short-Term Memory), Bi-LSTM (Bi-directional Long Short-Term Memory), and CNN-RNN (Recurrent Neural Network). The data-driven approach's high accuracy highlights its efficacy in forecasting and avoiding problems, which minimizes downtime and maximizes production efficiency.
First report of Pyrrhoderma noxium from rice in northern Thailand

Endophytic fungi live in plant tissues without causing disease symptoms. However, they can cause disease on their host by changing their life style. Moreover, endophytic fungi can enhance plant growth and health by antagonistic relationships with the host plant. Rice is one of the most important crops in many tropical countries, particularly in Thailand. Therefore, isolation and identification of fungal endophytes associated with rice can be useful for understanding about fungal diversity on rice, disease management and future studies on endophytes. In this study, Pyrrhoderma noxium , formerly known as Phellinus noxius has been isolated from rice variety RD10, collected from Chiang Rai province, Thailand. Identification was based on morphology and phylogenetic analyses, including the ITS and LSU loci. This is the first report of P . noxium associated with rice.
Digital Commons@Becker

In Escherichia coli , FkpA, PpiA, PpiD, and SurA are the four known periplasmic cis - trans prolyl isomerases. These isomerases facilitate proper protein folding by increasing the rate of transition of proline residues between the cis and trans states. Genetic inactivation of all four periplasmic isomerases resulted in a viable strain that exhibited a decreased growth rate and increased susceptibility to certain antibiotics. Levels of the outer membrane proteins LamB and OmpA in the quadruple mutant were indistinguishable from those in the surA single mutant. In addition, expression of P and type 1 pili (adhesive organelles produced by uropathogenic strains of E. coli and assembled by the chaperone/usher pathway) were severely diminished in the absence of the four periplasmic isomerases. Maturation of the usher was signiﬁcantly impaired in the outer membranes of strains devoid of all four periplasmic isomerases, resulting in a defect in pilus assembly. Moreover, this
CLASSIFICATION OF INSTRUMENT SOUNDS WITH IMAGE CLASSIFICATION ALGORITHMS

Classification of audio files using CNN (Convolutional Neural Network) algorithm is an important application in the field of audio processing and artificial intelligence. This process aims to automatically classify audio files into different classes and can be used in speech recognition, emotional analysis, voice - based control systems and many other applications. The aim of this study is to perform spectrum transformation of instrumental sounds and classify them using image classification algorithms. The dataset contains a total of 1500 data from five different instruments. Audio files were processed, and signal and spectrogram images of each audio file were obtained. DenseNet121, ResNet and CNN algorithms were tested in experimental studies. The most successful results belong to the CNN algorithm with 99.34%.
Treatment of Chronic Lymphocytic Leukemia in the Personalized Medicine Era

Chronic lymphocytic leukemia is a lymphoproliferative disorder marked by the expansion of monoclonal, mature CD5+CD23+ B cells in peripheral blood, secondary lymphoid tissues, and bone marrow. The disease exhibits significant heterogeneity, with numerous somatic genetic alterations identified in the neoplastic clone, notably mutated TP53 and immunoglobulin heavy chain mutational statuses. Recent studies emphasize the pivotal roles of genetics and patient fragility in treatment decisions. This complexity underscores the need for a personalized approach, tailoring interventions to individual genetic profiles for heightened efficacy. The era of personalized treatment in CLL signifies a transformative shift, holding the potential for improved outcomes in the conquest of this intricate hematologic disorder. This review plays a role in elucidating the evolving CLL treatment landscape, encompassing all reported genetic factors. Through a comprehensive historical analysis, it provides insights into the evolution of CLL management. Beyond its retrospective nature, this review could be a valuable resource for clinicians, researchers, and stakeholders, offering a window into the latest advancements. In essence, it serves as a dynamic exploration of our current position and the promising prospects on the horizon.
A multiplexed high-throughput neutralization assay reveals a lack of activity against multiple 1 variants after SARS-CoV-2 infection

The detection of SARS-CoV-2-specific antibodies in the serum of an individual indicates prior infection 35 or vaccination. However, it provides limited insight into the protective nature of this immune response. 36 Neutralizing antibodies recognizing the viral Spike are far more revealing, yet their measurement 37 traditionally requires virus- and cell-based systems that are costly, time-consuming, poorly flexible and 38 potentially biohazardous. Here we present a cell-free quantitative neutralization assay based on the 39 competitive inhibition of trimeric SARS-CoV-2 Spike protein binding to the angiotensin converting 40 enzyme 2 (ACE2) viral receptor. This high-throughput method matches the performance of the gold 41 standard live virus infectious assay, as verified with a panel of 206 seropositive donors with varying 42 degrees of infection severity and virus-specific IgG titers, achieving 96.7% sensitivity and 100% 43 specificity. Furthermore, it allows for the parallel assessment of neutralizing activities against multiple 44 SARS-CoV-2 Spike variants of concern (VOC), which is otherwise unpredictable even in individuals 45 displaying robust neutralizing antibody responses. Profiling serum samples from 59 hospitalized 46 COVID-19 patients, we found that although most had high activity against the 2019-nCoV Spike and to 47 a lesser extent the B.1. 1.7 variant, only 58% could efficiently neutralize a Spike derivative containing 48 mutations present in the B.1. 351 variant. In conclusion, we have developed an assay that has proven 49 its clinical relevance in the large-scale evaluation of effective neutralizing antibody responses to VOC 50 after natural infection and that can be applied to the characterization of vaccine-induced antibody 51 responses and of the potency of human monoclonal antibodies.
MIT Open Access Articles Non-Invasive Mapping of the Gastrointestinal Microbiota Identifies Children with Inflammatory Bowel Disease

Background: Pediatric inflammatory bowel disease (IBD) is challenging to diagnose because of the non-specificity of symptoms; an unequivocal diagnosis can only be made using colonoscopy, which clinicians are reluctant to recommend for children. Diagnosis of pediatric IBD is therefore frequently delayed, leading to inappropriate treatment plans and poor outcomes. We investigated the use of 16S rRNA sequencing of fecal samples and new analytical methods to assess differences in the microbiota of children with IBD and other gastrointestinal disorders. Methodology/PrincipalFindings: We applied synthetic learning in microbial ecology (SLiME) analysis to 16S sequencing data obtained from i) published surveys of microbiota diversity in IBD and ii) fecal samples from 91 children and young adults who were treated in the gastroenterology program of Children’s Hospital (Boston, USA). The developed method accurately distinguished control samples from those of patients with IBD; the area under the receiver-operating-characteristic curve (AUC) value was 0.83 (corresponding to 80.3% sensitivity and 69.7% specificity at a set threshold). The accuracy was maintained among data sets collected by different sampling and sequencing methods. The method identified taxa associated with disease states and distinguished patients with Crohn’s disease from those with ulcerative colitis with reasonable accuracy. The findings were validated using samples from an additional group of 68 patients; the validation test identified patients with IBD with an AUC value of 0.84 (e.g. 92% sensitivity, 58.5% specificity)
Research on Fractal Characteristics of Building Energy Consumption Time Series

The problem of nonlinear building energy consumption characteristics is studied. The fractal energy theory and the rescaled range analysis method are used to construct the building energy consumption analysis model. Through the analysis of the building energy consumption curve, it is concluded that the building energy consumption changes have a certain periodic law and self-similarity in terms of geometry. In terms of geometry, building energy consumption has self-similarity. By calculating the fractal dimension, it is found that the building energy consumption has self-similarity at the time scale. At the same time, the analysis of different types of buildings energy consumption characteristics reveals that there is no difference in the fractal characteristics. It provides a new perspective for the study of building energy consumption characteristics, building energy consumption predictions and troubleshooting.
1 to inhibit papillary thyroid carcinoma progression

Thyroid cancer is the most common tumour of the endocrine system, and its incidence rate has markedly increased over the past several decades. Aberrantly expressed microRNAs (miRNAs) are reportedly involved in the formation and progression of papillary thyroid carcinoma (PTC) by regulating their target genes. Thus, miRNAs may be potential molecular biomarkers for the prediction and prognosis of PTC, and also as novel therapeutic targets for patients with PTC. miR-139 has recently been reported to be aberrantly expressed in several types of cancer. However, the expression levels, biological functions and the associated molecular mechanism of miR-139 in PTC have not been clearly elucidated. The results of the present study revealed that miR-139 expression was downregulated in PTC tissues and cell lines when compared with adjacent normal tissues and normal human thyroid cells, respectively. The restoration of miR-139 expression suppressed cellular proliferation and invasion in PTC in vitro . In addition, fibronectin 1 (FN1) was identified as a direct target of miR‑139 in PTC. Furthermore, FN1 was highly expressed in PTC tissues and negatively associated with miR-139 expression. Moreover, the tumour-suppressive effects of miR-139 overexpression on PTC cells were ameliorated by ectopic FN1 expression. To the best of our knowledge, the present study is the first to demonstrate that miR‑139 may serve as a tumour suppressor and serve important roles in inhibiting tumourigenesis by targeting FN1 in PTC cells.