Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Detection of Ocean Eddy based on Deep Learning Technique with Attention Mechanism

Ocean eddies are a common occurrence in ocean water circulation. They have an enormous impact on the marine ecosystem. One of the most active study topics in physical oceanography is ocean eddy detection. Although using deep learning algorithms to detect eddies is a recent trend, it is still in its infancy. In this paper, an attention mechanism-based ocean eddy detection approach using deep learning is proposed. Attention mechanism has spatial and channel attention modules that are cascaded to convolution blocks-based encoder model to simulate spatial and channel semantic interdependencies. In the spatial attention module, the feature at each point is aggregated selectively by the sum of the features at all positions. The channel attention module aggregates related data from all channel maps to selectively highlight interdependent channel maps. The original feature map and the feature map obtained through the attention mechanism are appended to enhance the feature representation further, resulting in more accurate segmentation results. The findings of the experiments show that adopting an attention-based deep framework improves eddy recognition accuracy significantly.
Developing a similarity measure in biological function space

Supplementary Figure 1. The distribution of Azuaje's ITS scores for the protein pairs that have significant ITS scores (top 5%), but have non-significant total ancestry scores (below 50%). Supplementary Figure 2. A scatter plot to show the correlation between MIPS and GO functional scores calculated by the total ancestry method. Supplementary Figure 3. Comparison of the enrichment of the functionally similar protein pairs between interacting and co-expressed pairs using Azuaje's ITS method and our total ancestry method. Supplementary Figure 4. The cumulative density distribution (F) of the total ancestry similarity scores (P) from different DAG structures all follow power law distributions. We randomly generated these four DAG structures with three parameters: L: the number of layers; M: the number of items in each leaf node; N: the number of daughter nodes from one parent node.
Field investigation‐ and dietary metabarcoding‐based screening of arthropods that prey on primary tea pests

Abstract Predatory natural enemies play key functional roles in biological control. Abundant predatory arthropod species have been recorded in tea plantation ecosystems. However, few studies have comprehensively evaluated the control effect of predatory arthropods on tea pests in the field. We performed a 1‐year field investigation and collected predatory arthropods and pests in the tea canopy. A total of 7931 predatory arthropod individuals were collected, and Coleosoma blandum (Araneae, Theridiidae) was the most abundant species in the studied tea plantation. The population dynamics between C. blandum and four main tea pest species (Aleurocanthus spiniferus, Empoasca onukii, Ectropis grisescens, and Scopula subpunctaria) were established using the individual number of predators and pests in each month. The results showed that C. blandum appeared to co‐occur in the tea canopy with A. spiniferus, Em. onukii, and Ec. grisescens in a longer period. The prey spectrum of C. blandum was further analyzed using DNA metabarcoding. Among prey species, A. spiniferus, Em. onukii, and Ec. grisescens were included, and the relative abundance and positive rates of target DNA fragments of A. spiniferus were greater than that of other two pests. Combined with the high dominance index of C. blandum, co‐occurrence between C. blandum and A. spiniferus in time and space and high positive rate and relative abundance of target DNA fragments of A. spiniferus, C. blandum was identified to prey on A. spiniferus, and C. blandum may be an important predator of A. spiniferus. Thus, C. blandum has potential as a biological control agent of A. spiniferus in an integrated pest management strategy.
The Strange Role of Information Asymmetry in Auctions - Does More Accurate Value Estimation Benefit a Bidder?

We study the second-price auction in which bidders have asymmetric information regarding the item’s value. Each bidder’s value for the item depends on a private component and a public component. While each bidder observes their own private component, they hold different and asymmetric information about the public component. We characterize the equilibrium of this auction game and study how the asymmetric bidder information affects their equilibrium bidding strategies. We also discover multiple surprisingly counter-intuitive equilibrium phenomena. For instance, a bidder may be better off if she is less informed regarding the public component. Conversely, a bidder may sometimes be worse off if she obtains more accurate estimation about the auctioned item. Our results suggest that efforts devoted by bidders to improve their value estimations, as widely seen in today’s online advertising auctions, may not always be to their benefit.
Association between Daily Physical Activity and Locomotive Syndrome in Community-Dwelling Japanese Older Adults: A Cross-Sectional Study

This study aimed to evaluate the association between locomotive syndrome (LS) and daily physical activity (PA) in community-dwelling older adults. This cross-sectional study included 80 healthy Japanese older adults (40 men and 40 women; age: 60–79 years). Habitual daily PA was evaluated using a triaxial wrist accelerometer. Participants were divided into two groups based on the results of the two-step test, stand-up test, and 25-question geriatric locomotive function scale. Binomial logistic regression analysis was conducted to examine the statistical relationships between daily PA and category of LS, adjusting for age from adjusted odds ratio (adjusted OR) with the 95 percent confidence intervals (95%CI) and bootstrap 95%CI. The mean step count and time spent on moderate to vigorous physical activity (MVPA) were significantly higher among non-LS participants than among LS participants in women, but not in men. Logistic regression analyses indicated that spending longer than 28 min/day on MVPA was significantly associated with a lower likelihood of LS relative to short time category under 28 min/day in women (adjusted OR = 0.12, 95%CI = 0.02–0.59, bootstrap 95%CI = 0.01–0.43), but not in men. This study suggests that in community-dwelling older women, those with higher MVPA had lower odds of LS, and daily MVPA was associated with LS, but not in men. Therefore, the associations between LS and daily physical activity were partly dependent on sex differences.
Implementation of Linear Quadratic Regulator in an Isolated Microgrid System

This paper elaborates a linear–quadratic–regulator (LQR) technique for an isolated microgrid in presence of electric vehicle (EV) and renewable power system (RPS) participation. Generally, in control theory the state feed-back control can place the poles in the desired locations in order to improve stability but many a time, it is observed that the steady state error is appropriately not achieved as well as the overall cost is compromised. In this connection, LQR control theory helps to obtain the feedback gain optimally using quadratic cost function. The weight adjustment matrices in LQR control theory requires to adjust in order to achieve desired system response. Hence, the weight matrices are adjusted to achieve optimal operating condition based on the proposed flow chart. Numerous, test cases are carried out considering different system configurations to validate performance and efficacy of the controller under MATLAB / Simulink environment.
KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction

Molecular interaction prediction is essential in various applications including drug discovery and material science. The problem becomes quite challenging when the interaction is represented by unmapped relationships in molecular networks, namely molecular interaction, because it easily suffers from (i) insufficient labeled data with many false-positive samples, and (ii) ignoring a large number of biological entities with rich information in the knowledge graph. Most of the existing methods cannot properly exploit the information of knowledge graph and molecule graph simultaneously. In this paper, we propose a large-scale Knowledge Graph enhanced Multi-Task Learning model, namely KG-MTL, which extracts the features from both knowledge graph and molecular graph in a synergistic way. Moreover, we design an effective Shared Unit that helps the model to jointly preserve the semantic relations of drug entity and the neighbor structures of the compound in both knowledge graph and molecular graph. Extensive experiments on four real-world datasets demonstrate that our proposed KG-MTL outperforms the state-of-the-art methods on two representative molecular interaction prediction tasks: drug-target interaction prediction and compound-protein interaction prediction. The source code of KG-MTL is available at https://github.com/xzenglab/KG-MTL.
Elastic-Link for Binarized Neural Network

Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to reduce this accuracy gap has thus far largely focused on specific network architectures with few or no 1 × 1 convolutional layers, for which standard binarization methods do not work well. Because 1 × 1 convolutions are common in the design of modern architectures (e.g. GoogleNet, ResNet, DenseNet), it is crucial to develop a method to binarize them effectively for BNNs to be more widely adopted. In this work, we propose an “Elastic-Link” (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. The proposed EL module is easily implemented and can be used in conjunction with other methods for BNNs. We demonstrate that adding EL to BNNs produces a significant improvement on the challenging large-scale ImageNet dataset. For example, we raise the top-1 accuracy of binarized ResNet26 from 57.9% to 64.0%. EL also aids con-vergence in the training of binarized MobileNet, for which a top-1 accuracy of 56.4% is achieved. Finally, with the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy.
Versatile multiphysics model for thermal runaway estimation of a lithium‐ion battery

This study proposes a fast yet accurate multiphysics model for a lithium‐ion battery (LIB) that estimates multiphysics responses under operational and abuse conditions. The proposed multiphysics model couples a novel electrochemical model and a thermodynamic‐chemical model. The electrochemical model modifies a conventional streamlined model by accounting for the state of charge dependency. The thermodynamic‐chemical model accounts for entropic, ohmic, and chemical reaction heating originating from thermal runaway, thereby replicating the chemical reaction of the main components in a LIB under abuse conditions. The proposed multiphysics model was calibrated through intensive experiments under operational and thermal abuse conditions. A comprehensive analysis revealed that the proposed model accurately estimated the electrochemical‐thermal characteristics of both cells, confirming the accuracy and robustness. Moreover, the proposed method is seven to eight times faster than a pseudo‐two‐dimensional model, implying that the proposed model is effective for elucidating multiphysics thermal runaway phenomena in a three‐dimensional LIB domain. Hence, the proposed model replicates thermal propagation inside a cell under electric abuse conditions. The versatility of the proposed fast yet accurate multiphysics model of LIBs was demonstrated with applications of an optimal cell design and a battery thermal management system.
Refractive Index Biosensor Using Metamaterial Perfect Absorber Based on Graphene in Near-Infrared for Disease Diagnosis

This paper presents a metamaterial perfect absorber as a biosensor in the near-infrared region for biomedical molecular detection, such as urine concentration, malaria infection, bacillus bacteria, and cancer cell detection. In the proposed biosensor, an Au layer with a bracket-shaped cavity is utilized to boost electric field enhancement intensity in the vicinity of the nanohole. According to the capacitance of the bracket-shaped nanohole, changing the length of the hole controls the resonance wavelength. Due to the significant confinement of the electromagnetic field in the sharp edges and gaps, which excites Localized Surface Plasmon Resonances (LSPRs) in the metal-dielectric interface, near-unity absorption is obtained. Through evaluating the optical responses of the structure, via the finite element method (FEM), for different biomedical samples, high sensitivity of 2120.86 nm/RIU is acquired. In order to increase the sensitivity of the biosensor, a graphene layer is added between the dielectric layer and the gold nanohole layer. Adding graphene increases the LSPRs excitation and losses which yields a maximum sensitivity of 2500 nm/RIU. The presented biosensor with high sensitivity and compact size is a good candidate for biomedical purposes.
Dynamic Offloading and Trajectory Control for UAV-Enabled Mobile Edge Computing System With Energy Harvesting Devices

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computation service to distributed devices in the absence of terrestrial infrastructure. In this paper, we consider a UAV-enabled MEC system serving multiple energy harvesting (EH) devices, where the energy and task data arrive at the users stochastically. Without any future knowledge of task data and energy arrivals, our objective is to design an online algorithm to jointly optimize the UAV energy and task processing rate, meanwhile satisfying the long-term data queue stability. We formulate the problem as a multi-stage stochastic programming and propose an online algorithm, named PLOT, based on perturbed Lyapunov optimization technique. In particular, PLOT resolves the coupling effect of sequential control actions, and converts the stochastic problem into per-slot deterministic optimization problem. For each per-slot problem, we design a low-complexity algorithm to solve it. We show that the PLOT algorithm can derive a feasible solution to the original problem and achieve an $[O(1/V),O(V)]$ trade-off between the system cost and the data queue length. Simulation results justify our analysis and demonstrate that the PLOT algorithm achieves better performance in terms of system utility and maintains queue stability that is not achieved by other benchmark methods.
Modeling forest cover dynamics in Bangladesh using multilayer perceptron neural network with Markov chain

Abstract. Raghunandan Hills Reserve is an important protected area in Bangladesh that supports some remnant patches of natural forest and is the habitat of several globally threatened primates including Western Hoolock Gibbon, Northern Pig-tailed Macaque, and Capped Langur. However, deforestation and forest degradation due to anthropogenic factors, such as illegal logging and fuelwood collection are age-old problems at Raghunandan. The areas of the reserve vulnerable to future conversions due to the possible proximate or underlaying causes were unknown. This study analyzed the historical trend of forest and land-use/landcover transitions at Raghunandan Hills Reserve from 1995 until 2015 at a 10-year interval using Monte Carlo spectral unmixing and knowledge-based classification approaches to Landsat satellite images in Claslite and ArcGIS software. Based on the past trend, it then predicted the future trend of forest land-use/landcover transitions for 2025 and 2035 using an artificial multi-layer perceptron neural network with Markov Chain machine learning algorithm integrated into the land change modeler module of IDRISI/TerrSet software. Results indicated that ∼30 % to 35% of the total area of the reserve was covered by forest, which included patches of natural forest and plantations, whereas the remaining area was occupied by non-forest categories like scattered degraded forests, grasses, and shrubs. Forest cover declined during 1995–2005, and then increased slightly during 2005–2015 due to afforestation activities. This trend is likely to continue in the future with forest cover occupying nearly 40% of the reserve by 2025 and 2035. Along with identifying the areas where the forest is likely to be expanded, the areas of the reserve vulnerable to deforestation (hotspots) were also highlighted and quantified in the form of maps and statistics. The findings have useful implications for any forest conservation initiatives including the global climate change mitigation program reducing emissions from deforestation and forest degradation+, which requires identifying at-risk areas of planned and unplanned deforestation.
Interrelation between α-Cardiac Actin Treadmilling and Myocardin-Related Transcription Factor-A Nuclear Shuttling in Cardiomyocytes

Myocardin-related transcription factors (MRTFs) play a central role in the regulation of actin expression and cytoskeletal dynamics that are controlled by Rho GTPases. SRF is a ubiquitous transcription factor strongly expressed in muscular tissues. The depletion of SRF in the adult mouse heart leads to severe dilated cardiomyopathy associated with the down-regulation of target genes encoding sarcomeric proteins including α-cardiac actin. The regulatory triad, composed of SRF, its cofactor MRTFA and actin, plays a major role in the coordination of the nuclear transcriptional response to adapt actin filament dynamics associated with changes in cell shape, and contractile and migratory activities. Most of the knowledge on the regulation of the SRF–MRTF–Actin axis has been obtained in non-muscle cells with α-actin and smooth muscle cells with α-smooth actin. Here, we visualized for the first time by a time-lapse video, the nucleocytoplasmic shuttling of MRTFA induced by serum or pro-hypertrophic agonists such as angiotensin II, phenylephrine and endothelin-1, using an MRTFA-GFP adenovirus in cultures of neonatal rat cardiomyocytes. We showed that an inhibitor of the RhoA/ROCK signaling pathway leads to an α-cardiac actin polymerization disruption and inhibition of MRTFA nucleocytoplasmic shuttling. Moreover, inhibition of the PI3K/Akt signaling pathway also prevents the entry of MRTFA into the nuclei. Our findings point out a central role of the SRF–MRTFA–actin axis in cardiac remodeling.
Nutritional status associated with metabolic syndrome

Aim: The purpose of this study was to determine the prevalence of metabolic syndrome in patients of different diet habits, life style. Method: 260 participants age ranging from 17 to 80 years(180 control and 180 cases) were included in this study.From qeustionnarie we took the diet chart of respondents like what they had in breakfast, lunch and dinner. Blood sampleswere taken for the routine parameters i.e., FBS and Lipid profile.Metabolic syndrome patients wereidentified according to the criteria of National Cholesterol Education Program’s Adult Treatment Panel III(ATP III). Paired t-test was performed to evaluate the association between various parameters. Result: According to theNational Cholesterol Education Program’s Adult Treatment Panel III (ATP III) guidelines people who regularly consume high-calorie, high-fat foods like samosas, badas, aloo paranthas, puri, pakoda, and jalebi were more prone to metabolic syndrome. The waist circumference in 89.4% cases was above >90cm. The amount of cholesterol was also high in90% cases (>150 mg/dl) as compared to control.
Generation Z and their gender impact in the structural elements of cause related marketing

Companies have started asking a question themselves everyday: Do we have a purpose for our business or we should stickwith the traditional marketing. Generations have totally changed and their commercial lives differ entirely from the previous ones; they are socially responsible and pour out their minds, hearts and spirits in it. Their search for value contributes more to the Cause related Marketing. Research suggests outcomes of CRM campaigns are helpful for firms as well as for the causes. Causes, receive greater funding and publicity with which the cause can be ended or reduced.With regard to firms it enhances corporate image and increase sales, but effects may vary depending on the audience. In this light, this paper investigates the significance of gender in the structural elements of Cause Related Marketing.This study surveyed the various dimensions of Cause Related Marketing in the union territory of Puducherry considering Generation Z as their respondents.The researcher generated primary data using questionnaire as the research instrument. Student t test was used to test the hypothesis.The results indicate that gender does influence with few of the structural elements of CRM and not with all. Based on these results, implications for CRM campaign managers are highlighted.
Cross-component Sample Adaptive Offset

This paper proposes one new In-loop filtering technique cross-component sample adaptive offset (CCSAO) for further coding efficiency improvement beyond Versatile Video Coding (VVC). The CCSAO reduces the sample distortion by 1) utilizing the strong correlation between luma and chroma components to classify the reconstructed samples into different categories and 2) deriving one offset for each category and adding the offset to the samples in the category. The offset of each category is properly derived at encoder and signaled to decoder. To keep the design at low complexity, only band information of reconstructed samples is considered for the sample classification of the CCSAO. To verify the performance, the proposed CCSAO is implemented on top of the enhanced compression model (ECM) for the joint video exploration team (JVET)'s exploratory work of future video coding technologies beyond VVC. Simulation results show that the CCSAO achieves average {0.20%, 2.83%, 2.98%} and {0.41%, 7.36%, 7.36%} Bjentegaard delta (BD)-rate savings for {Y, U, V} components under the Random Access and Low Delay B configuration, with negligible complexity impacts on encoding and decoding complexity. The proposed CCSAO scheme has been adopted to the ECM-2.0 software platform.