Miracle-dz/UniFAR-FTUs huggingface dataset:
DHA-Provoked Reduction in Adipogenesis and Glucose Uptake Could Be Mediated by Gps2 Upregulation in Immature 3T3-L1 Cells

The signaling pathway of fatty acids in the context of obesity is an extensively explored topic, yet their primary mechanism of action remains incompletely understood. This study aims to examine the effect of docosahexaenoic acid (DHA) on some crucial aspects of adipogenesis in differentiating 3T3-L1 cells, using palmitic acid-treated (PA), standard differentiated, and undifferentiated adipocytes as controls. Employing 60 µM DHA or PA, 3T3-L1 preadipocytes were treated from the onset of adipogenesis, with negative and positive controls included. After eight days, we performed microscopic observations, cell viability assays, the determination of adiponectin concentration, intracellular lipid accumulation, and gene expression analysis. Our findings demonstrated that DHA inhibits adipogenesis, lipolysis, and glucose uptake by suppressing peroxisome proliferator-activated receptor gamma (Pparg) and G-protein coupled receptor 120 (Gpr120) gene expression. Cell cytotoxicity was ruled out as a causative factor, and β-oxidation involvement was suspected. These results challenge the conventional belief that omega-3 fatty acids, acting as Pparg and Gpr120 agonists, promote adipogenesis and enhance insulin-dependent glucose cell flux. Moreover, we propose a novel hypothesis suggesting the key role of the co-repressor G protein pathway suppressor 2 in mediating this process. Additional investigations are required to elucidate the molecular mechanisms driving DHA’s anti-adipogenic effect and its broader health implications.
Enabling the Evaluation of Driver Physiology Via Vehicle Dynamics

Driving is a daily routine for many individuals across the globe. This paper presents the configuration and methodologies used to transform a vehicle into a connected ecosystem capable of assessing driver physiology. We integrated an array of commercial sensors from the automotive and digital health sectors along with driver inputs from the vehicle itself. This amalgamation of sensors allows for meticulous recording of the external conditions and driving maneuvers. These data streams are processed to extract key parameters, providing insights into driver behavior in relation to their external environment and illuminating vital physiological responses. This innovative driver evaluation system holds the potential to amplify road safety. Moreover, when paired with data from conventional health settings, it may enhance early detection of health-related complications.
Atomic Clusters : A Unique Building Motif for Future Smart Nanomaterials

The fundamental issue in understanding the origin and growth mechanism of nanomaterials, from a fundamental unit is a big challenging problem to the scientists. Recently, an immense attention is generated to the researchers for prediction of exceptionally stable atomic cluster units as the building units for future smart materials. The present study is a systematic investigation on the stability and electronic properties of a series of bimetallic (semiconductor-alkaline earth) clusters, viz., BxMg3 (x=1-5) is performed, in search for exceptional and/ or unusual stable motifs. A very popular hybrid exchange-correlation functional, B3LYP along with a higher basis set, viz., 6-31+G[d,p] is employed for this purpose under the density functional formalism. The magic stability among the concerned clusters is explained using the jellium model. It is evident from the present study that the magic stability of B4Mg3 cluster arises due to the jellium shell closure. Keywords—Atomic Clusters, Density Functional Theory, Jellium Model, Magic Clusters, Smart Nanomaterials.
2010 2nd International Conference on Industrial Mechatronics and Automation Sequential Speaker Clustering Based on Second Order Statistical Measures

This paper presents an investigation on the sequential clustering, based on second order statistical measures, for the task of speaker diarization. This clustering technique, which represents the second part of a global system of speaker indexing, is made for gathering the homogeneous segments obtained during the segmentation step. Most existing clustering systems are based on hierarchical clustering as agglomerative techniques. Such systems present two problems: finding the stopping criterion and choosing the threshold of clustering decision. In this research work, we try to resolve the first problem (stopping criterion) by proposing a sequential clustering approach based on second order statistical measures in order to gather the similar homogeneous segments (obtained by the segmentation process). At the end, each class will contain the global intervention of only one speaker in the entire audio document. Our sequential clustering approach uses a mono gaussian measure, called IlGp, which is able to assess the degree of similarity between the different homogeneous segments. Experiments are done on a subset of Hub4 Broadcast-News database and the corresponding results show that the implemented algorithms are interesting for the task of speaker clustering.
2010 2nd International Conference on Industrial and Information Systems Liuchen Software School

A model of XML document is extended by considering both path and frequency information, namely the frequency-path model. Based on this model, a structural similarity calculation algorithm with position and frequency weight by longest common subsequence (PFWLCS) is proposed, which is fast and has high precision. Furthermore the selection of the position and frequency factors are discussed in depth. Experiments show that the PFWLCS has higher recall ratio and accuracy than existing similarity calculation methods, especially on XML with different Structures.
Conduct problems among middle adolescents in the community settings: A concept analysis

Background Adolescent problem behavior in research and practice has been traditionally categorized as Oppositional Deviant Disorder and Conduct Disorder. However, a significant number of adolescents remain underdiagnosed. To address this issue, the term “Conduct Problem” has emerged as a commonly used descriptor for those who have not yet received a formal diagnosis, particularly within the community. It is crucial for nurses to comprehend the characteristics of these conduct problems to address them effectively. Objective This concept analysis aimed to clarify the concept of conduct problems among adolescents aged 14 to 16, specifically within community settings. Methods The concept analysis followed Walker and Avant’s approach. The usage of the concept was examined in five databases (PsyINFO, ProQuest, PubMed, ScienceDirect, Scopus), which yielded 41 relevant studies for comprehensive analysis. Results The identified attributes of conduct problems in adolescents included oppositional problems, antisocial problems, and criminal-related problems. These conduct problems were found to have antecedents stemming from personal, parental, and environmental factors. Furthermore, the consequences of conduct problems significantly impacted both middle adolescents and their parents. Conclusion The findings of this concept analysis contribute to a better comprehension of the concept of conduct problems among middle adolescents in community settings. The insights gained from this analysis will assist in using this term more effectively in research and nursing practice, ultimately leading to improved care and support for affected adolescents and their families.
The List Length Effect and Immediate Free Recall of Visually Displayed Information in Various Forms

This paper investigated four main phenomena connected with the free immediate recall and visual representation of the information: list-length effect, serial position effect, horizontal versus vertical presentation of the information, expression of the information in the form of the symbol, text, and symbol with text. The number of examined respondents was 4140. The experiment revealed a different memory recall of various forms of visually displayed information and studied a dependency of list length effect on primacy and recency effect. The results showed that people recall better the combination of symbols and text than only text or symbols. The other finding indicated a significant difference between the number of recalled items in the tests of 5, 7, and 9 items. The respondents recalled the highest number of items in 5-item tests and the lowest number in 9-item tests. There was investigated that the proportions of the respondents recalling the items from the beginning is higher than those of the respondents recalling the items from the end. The results showed that the primacy effect was higher than the recency effect regardless of the list length (5, 7, or 9 items tests). This article's findings should help design visually optimized websites and applications.
Dietary Vitamin E Isoforms Intake: Development of a New Tool to Assess Tocopherols and Tocotrienols Intake in Adults

Due to the documented health benefits of tocopherols and tocotrienols as bioactive compounds, it seems important to assess their intake. The aim of this study was to develop a new tool and its application for assessment of tocopherol and tocotrienol intake in adults. Dietary data were collected by semiquantitative FFQ (VitE-FFQ) and by a 1-day dietary record in a group of 447 subjects. The database of the US Department of Agriculture (USDA) was used to calculate the individual isoforms of vitamin E and develop the tool—VIT_E. CAL. The assessment of measuring agreement between the two methods was conducted by analysis of the correlations and Bland–Altman plots. The average α-tocopherol intake was 11.3 mg/day for the data obtained using the FFQ method and 12.8 mg/day for the results obtained using the 1-day dietary record. Depending on the adopted recommendation, only 40–57% of the subjects had adequate vitamin E intake. The intake of α-tocopherol did not exceed the UL value in any of the respondents. The dominant forms of vitamin E in the diet of the studied group were α- and γ- forms (55% and 38% of the total sum) among tocopherols and β- and γ- forms (49% and 24% of the total sum) among tocotrienols. VIT_E. CAL allows us to calculate not only the total amount of vitamin E but also its eight isoforms. It can be a useful tool to assess individual and group intake of various forms of vitamin E in the diet. The use of VIT_E. CAL enables the proper assessment of vitamin E (as α-tocopherol and not α-tocopherol equivalent) in the diet of Poles, and most likely also in the European diet. The obtained results indicate the need to take into account the content of individual forms of vitamin E in food/diet, which will allow for a reliable assessment of its consumption. It also seems necessary to standardize the nomenclature regarding the name of vitamin E and its use for correct nutritional assessment.
Genetically Optimized UFLANN for Uncovering Clusters

In this work, we present a novel clustering approach which is inheriting the best characteristics of Unsupervised Functional Link Artificial Neural Network (UFLANN) and Genetic Algorithms (GAs) for uncovering clusters embedded in dataset represented through <inline-formula> <tex-math notation="LaTeX">$(X)_{Nxd}$ </tex-math></inline-formula>, where X consists of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> data points with <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensions. With an aim to realize natural clusters in a linear space UFLANN mapped the input vectors from lower dimension to higher dimension with a greater hope to achieve linearity in higher dimensional space. As a result, UFLANN introduces the problem of curse of dimensionality in the given datasets. However, it has been realized that the problems like sparse data and distance concentration associated with curse of dimensionality cast this problem to again a very complex problem. Hence to address some of the issues of curse of dimensionality, we have used GAs for selecting optimal number of features in the higher dimension for UFLANN to discover clusters embedded in the dataset. The proposed approach herein after named as GAUFLANN has been experimentally evaluated by using the metrics like (i) Davies-Bouldin Index, ii) Silhouette Score, and iii) Completeness score on different synthetic and real datasets. Our experimental study confirms that GAUFLANN is evidently scoring better in DB-index, Silhouette score, and Completeness score than the clustering methods like K-means, Hierarchical-Agglomerative (Average Linkage), and UFLANN across the datasets like Circles, Moons, Iris, and CORD-19.
The Application of Social Media in Knowledge Management

The social networking sites are being used predominantly at the age of Information and communication technology. The sites are now regarded as one of the prime disseminator and consolidator of information. The study is directed to unveil the implications of social networking software, especially Facebook, over the knowledge management. A methodology of reviewing related literaturesand a quantitative analysis of the postings under the Facebook group ‘LIS Group in West Bengal’ have been followed to establish the theoretical propositions on the factual ground. Finally, some conclusions are drawn on the basis of the findings. It was found that the use of the social networking sites in knowledge management is gaining popularity day by day. The job oriented information was published in most of the cases and the male group members participate more in the group communication.
Clinical, Kinematic and Muscle Assessment of Bilateral Coordinated Upper-Limb Movements Following Cervical Spinal Cord Injury

Cervical spinal cord injury (cSCI) often results in bilateral impairment of the arms, leading to difficulties in performing daily activities. However, little is known about the neuromotor alterations that affect the ability of individuals with cSCI to perform coordinated movements with both arms. To address this issue, we developed and tested a functional assessment that integrates clinical, kinematic, and muscle activity measures, including the evaluation of bilateral arm movements. Twelve subjects with a C5-C7 spinal lesion and six unimpaired subjects underwent an evaluation that included three tests: the Manual Muscle Test, Range Of Motion test and Arm stabilisation test, a subsection of the “Van Lieshout arm/hand function test”. During the latter, we recorded kinematic and muscle activity data from the upper-body during the execution of a set of movements that required participants to stabilize both arms against gravity at different configurations. Analytical methods, including muscle synergies, spinal maps, and Principal Component Analysis, were used to analyse the data. Clinical tests detected limitations in shoulder abduction-flexion of cSCI participants and alterations in elbows-wrists motor function. The instrumented assessment provided insight into how these limitations impacted the ability of cSCI participants to perform bilateral movements. They exhibited severe difficulty in performing movements involving over-the-shoulder motion and shoulder internal rotation due to altered patterns of activity of the scapular stabilizer muscles, latissimus dorsi, pectoralis, and triceps. Our findings shed light on the bilateral neuromotor changes that occur post-cSCI addressing not only motor deficits, but also the underlying abnormal, weak, or silent muscle activations.
Towards DDoS-aware Routing in SDN using Cross-layer Knowledge Transfer

Routing packets efficiently is the main purpose of today’s networking. Mainly, efficiency deals with how securely are packets delivered taking in consideration the exponential growth of network threats, particularly the Distributed Denial of Service (DDoS) Attacks. However, security and routing are separately addressed in literature and little work was devoted to security-aware routing.In this paper, we point out the need to merge security and routing in SDN instead of addressing them separately. We propose a new DDoS-aware routing scheme using a cross-layer knowledge transfer between flows and switches. This scheme consists of learning features from flow-based DDoS attack datasets then transferring knowledge to switches. Transferred knowledge is used to establish reliable paths between end-points that avoid suspicious switches. Mainly, evaluation of a switch risk is based on a computation of a Suspicion Risk Factor (SRF) using a set of features learned from an ML-based classification of flows. Finally, SRFs of switches are used to seek for DDoS-aware paths. Evaluation results and analysis show that the proposed scheme outperforms the baseline protocol OSPF in terms of finding the most secure path and offered throughput.
Towards Privacy-Preserving Depression Detection: Experiments on Passive Sensor Signal Data

Mental health such as depression issues are on an unfortunate rise. Analyzing signals generated from sensors using machine learning (ML) techniques has shown promise as an objective, holistic, and cost-effective tool for assessing human mental conditions. However, many sensors often expose individuals’ Personally Identifiable Information (PII). In this work-in-progress study, we explore the performance of ML-based models for depression detection using only non-PII-releasing sensors. We compared the performance of prevailing ML models on the StudentLife dataset, which contains sensor signal data from smartphones for 48 students across 65 days. Our findings suggest that Decision Tree, Gradient Boosting, and Logistic Regression show higher predictive power even with limited non-PII-releasing sensor data. Additionally, we found that certain combinations of non-PII releasing sensors, such as surrounding conversation and phone lock frequency, help ML classifiers attain almost perfect performance across accuracy, recall, precision, and F1. This study has implications for a privacy-preserving approach for detecting depression.
Optical Beamforming Concepts for Wide-Swath Synthetic Aperture Radar Systems

High-resolution wide-swath (HRWS) imaging using scan-on-receive (SCORE) synthetic aperture radar (SAR) with one or multiple simultaneous beams is being heavily investigated using digital beamforming paradigms. However, a contender is analogue beamforming using photonics. In this work, the feasibility of photonics enhanced beamforming in SAR systems in higher frequency bands and bandwidths is investigated using a state-of-the-art SAR system as a comparison. The most common performance indicators are derived and an example SAR system is devised, with a discussion on potential antenna configurations. The integrated microwave photonics (IMWP) platforms available and most suited to SAR systems is discussed, including architectures and materials. Additionally, an investigation into system noise and dynamic range performance reveals a number of essential challenges that need to be addressed, including the reduction of losses an thereby the required number of semiconductor optical amplifiers (SOA) and the noise performance of the laser(s). The reconfiguration speed of beamforming phase shifters and true-time delay (TTD) elements are essential for SCORE operation with a large number of beams, which limits the viable materials used for these elements. The current state of IMWP indicate that these advanced systems with many beams and channels is very difficult to realize for SAR and implementations in simpler systems are more viable. However, the rapid development of IMWP indicate that these beamforming systems will soon be viable for SAR.
Uncertainty during migration: the role of predictable resources in unpredictable conditions in determining movement in Woodland Caribou (Rangifer tarandus)

Environmental and climatic variation drive animal migration. Animals must adjust their behavioral strategies, for example, habitat selection, to match best variation in resources whose value likely varies with conditions such as weather. For example, climate change makes processes such as snowmelt and the emergence of vegetation less predictable at the scale of months or weeks. Furthermore, climate change makes meteorological conditions unpredictable—or dynamic—at the scale of days and hours. The profitability of selecting any particular resource may vary according to local meteorological condition. We studied the impact of dynamic weather conditions on fine-scale movement strategies and resource selection during spring migration of adult female Woodland Caribou (Rangifer tarandus). We assessed the role played by static resources such as open, lichen, and closed forest habitats and their role in a context-dependent of dynamic weather conditions, including temperature, precipitation, and changing snow water equivalent. We tested the role of static resources and dynamic conditions in three contexts: (1) whether caribou were encamped or moving using hidden Markov models; state-specific habitat selection for, and the interaction between, resources and conditions while (2) encamped and (3) moving. Weather conditions influenced the probability of moving or staying encamped, and the probability of selecting for a resource, which likely reflects the profitability of a given resource in a particular condition. The probability of staying encamped in the forest increases as temperature increases, and the probability of selecting an open area is higher when precipitation is lower. We highlight how meteorological conditions modify the selection of a static resource and likely the profitability of a given resource. The resource utility to a consumer is increasingly susceptible to climate change-induced effects.
Deep Learning Modeling of Cardiac Arrhythmia Classification on Information Feature Fusion Image with Attention Mechanism

The electrocardiogram (ECG) is a crucial tool for assessing cardiac health in humans. Aiming to enhance the accuracy of ECG signal classification, a novel approach is proposed based on relative position matrix and deep learning network information features for the classification task in this paper. The approach improves the feature extraction capability and classification accuracy via techniques of image conversion and attention mechanism. In terms of the recognition strategy, this paper presents an image conversion using relative position matrix information. This information is utilized to describe the relative spatial relationships between different waveforms, and the image identification is successfully applied to the Gam-Resnet18 deep learning network model with a transfer learning concept for classification. Ultimately, this model achieved a total accuracy of 99.30%, an average positive prediction rate of 98.76%, a sensitivity of 98.90%, and a specificity of 99.84% with the relative position matrix approach. To evaluate the effectiveness of the proposed method, different image conversion techniques are compared on the test set. The experimental results demonstrate that the relative position matrix information can better reflect the differences between various types of arrhythmias, thereby improving the accuracy and stability of classification.