Miracle-dz/UniFAR-FTUs huggingface dataset:
“ASSESSMENT OF SOIL MICROBIAL FUNCTIONAL DIVERSITY: LAND USE AND SOIL PROPERTIES AFFECT CLPP-MICRORESP AND ENZYMES

The assessment of microbial functional diversity is an important indicator of soil quality. Different methodological approaches are currently used; among them are enzyme activities (EA) and CLPP (community level physiological profile) techniques (e.g. MicroRespTM, MR). The aims of the study were: i) to assess the efficacy of both methods in capturing differences among various land use categories when different levels of selected explanatory variables such as land use category, total organic carbon (TOC) and pH are considered, and ii) to explore, through a quantile regression approach, the possible relationships between each of the two methods with land use category, TOC and pH. The Shannon diversity index (H’), calculated from EA and MR data, was chosen as a synthetic index deriving from the same mathematical model. The quantile regression model (QRM), the Kruskal-Wallis and Spearman rank correlation tests were performed. Enzyme activities and MicroResp were reliable ecological indicators to assess soil microbial functional diversity. No correlation was found between the diversity indexes, H’EA and H’MR, it was therefore supposed that the two methods may target complementary components of microbial functional diversity. Both methods were effective in capturing differences among various land use categories, in particular H’MR in soils with low TOC content (<1.5%). Moreover, the QRM approach allowed a more detailed analysis along the distribution of the diversity indexes (H’EA and H’MR) indicating that H’EA was more dependent on the selected variables.
Sharing Risk under Heterogeneity: Participation in Settings of Incomplete Information

Motivated by the emergence of newPeer-to-Peer insurance organizations that rethink how insurance is organized, we have proposed a theoretical model of decision-making in risk-sharing arrangements with risk heterogeneity and incomplete information about the risk distribution as core features. For these new, informal organizations, the available institutional solutions to heterogeneity (e.g., mandatory participation or price differentiation) are either impossible or undesirable. Hence, we need to understand the scope conditions under which individuals aremotivated to participate in a bottom-up risk-sharing setting. Themodel considers participation as a utility-maximizing alternative for agents with higher risk levels, agents who are more risk averse, are driven more by solidarity motives, and less susceptible to cost fluctuations. This basic micro-level model is used to simulate decision-making for agent populations in a dynamic, interdependent setting. Simulation results show that successful risk-sharing arrangements may work if participants are driven by motivations of solidarity or risk aversion, but this is less likely in populations more heterogeneous in risk, as individual motivations can less frequently make up for larger cost deficiencies. At the same time, more heterogeneous groups deal better with uncertainty and temporary cost fluctuations than more homogeneous populations do. In the latter, cascades following temporary peaks in support requests more o en result in complete failure, while under full information about the risk distribution this would not have happened.
Supplementary: Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation

We use the CMU-MoCap [1] dataset to prepare sample set for unpaired 3D poses y and unpaired pose sequences Y . The sample set for the unpaired videos X̃ constitutes of single-person action videos collected from Sports-1M [8]. As compared to the available in-studio datasets (such as Human3.6M and MPI-INF-3DHP) the web-dataset covers a wide diversity in apparel style, background variation, action style, etc. The raw video frames are forwarded through a person-detector [15] to obtain the person-focused image sequences. Note that, the detector pruned video sequences may not have a smooth pixel transition. However, it retains the smooth pose transition at the view-variant root-relative system. In our work, the shared latent pose can be seen as a parametric form to represent plausible 3D poses. And, the image-to-latent model is trained to regress the latent pose parameters with latent being an intermediate 3D pose representation.
Soft Actor-Critic: Deep Reinforcement Learning for Robotics

— Deep reinforcement learning has produced astonishing results in the past, however, most of these stem from purely virtual domains. Many challenges are faced when these methods are applied in real-world robotic scenarios, the most fundamental being the high sample-ineﬃciency, which is typically associated with deep reinforcement learning methods. Thus, this paper presents an extensive review of those challenges and explores the capabilities of the recent soft actor-critic algorithm, which indicates a new state of the art for end-to-end learning of policies. We deeply explore the algorithm ﬁrst from a theoretical, then from a practical perspective and examine how the algorithm relates to the identiﬁed challenges in the joint ﬁeld of robotics and deep reinforcement learning. Finally, we analyze the results attained by the algorithm and compare these to other state of the art methods.
SEAWEED A SUSTAINABLE SOURCE FOR BIOPLASTIC: A REVIEW

Nowadays Petroleum-based plastics have become a part of living and we can find plastics everywhere on earth from oceans to human placenta. Hence it is necessary to find an alternative degradable plastic from renewable sources. In this paper, we have reviewed the biodegradable seaweed-based bioplastic with different seaweed/starch/cellulose proportions along with the plasticizers and its impact on the physical properties and characteristics of the biofilm. The method involved in biofilm synthesis is also studied. Replacement of single use plastic in food packing and also other applications are discussed. Seaweed based bioplastic is eco friendly in nature as it is completely degradable in soil. Besides the great mechanical properties and shorter shelf life, seaweed based bioplastic would be a better chance to overcome the plastic pollution.
Analysis of Book-borrowing Network using Complex Network Analysis

In this study, we analyzed the book-borrowing network that we constructed from the database of the libraries of Ardahan University by using complex network analysis techniques. After the construction of the bipartite reader-book relationship network, we constructed reader-reader and book-book networks via one-mode projection. We performed an exploratory complex network analysis on these networks. We found that both networks revealed scale-free and small-world network properties like most real-world networks from diverse origins and of diverse sizes. In addition, we identified the most central books in the book-book network using several centrality measures. These were generally the works from both Turkish and world literature that were in the reading lists of many readers. We also performed community analysis and identified the communities embedded in the networks visually. We identified and presented the essential genres of the books inside the communities of the book-book network
Prescriptive Cost Management for Lean Supply Chains:

Nowadays, companies belonging to global supply chains should rely on the co-operation from suppliers to achieve their business objectives and the required profit levels. Inter-organizational Cost Management (IOCM) means co-ordinated activities to control and reduce global supply chain costs, which are promoted by buyers, suppliers or both. Ratio Project Planning (RPP) is closely related to IOCM and to Kaizen Costing (KC) and it can be viewed as an extension and a complement of these practices which have a prescriptive nature being focused on “what should we do? ” instead on “what has happened? ” or “what could have happen? ”. This research project was developed in Bosch Car Multimedia located at Braga, Portugal, a world-class manufacturer of electronic products, car radios and car navigation systems. This enterprise has many years of experience on reduction costs, particularly, using RPP.
A New Analysis Framework for Federated Learning on Time-Evolving Heterogeneous Data

Federated Learning (FL) is an emerging learning paradigm that preserves privacy by ensuring client data locality on edge devices. The optimization of FL is challenging in practice, due to the diversity and heterogeneity of the learning system. In spite of recent research efforts on improving the optimization on heterogeneous data, the effects of time-evolving heterogeneous data has not been well studied. In this paper, we propose a uniﬁed data heterogeneity setup by considering time-evolving heterogeneity across edge devices as the ﬁrst step to capture the real-world FL scenarios. We introduce a new theoretical framework, Continual Federated Learning (CFL), to integrate different aspects of data heterogeneity in both FL and conventional Continual Learning (CL). Towards understanding the proposed novel, realistic, yet difﬁcult FL scenario, we design a regularization-based algorithm CFL-R. We show that CFL-R has a better convergence rate than Fed-erated Averaging (FedAvg). Empirically, CFL-R signiﬁcantly outperforms other FL baselines and can alleviate the issue of catastrophic forgetting.
Role in Immunity Wide-Range Microorganism Recognition and Hemolymph: Mechanism of B. mori Multibinding Protein, from the Bombyx mori Characterization of a Novel C-Type Lectin,

To investigate the system used by insects to recognize invading microorganisms, we examined proteins from the larval hemolymph of Bombyx mori that bind to the cell surface of microorganisms. Two hemolymph proteins that bound to the cell surfaces of Micrococcus luteus and Saccharomyces cerevisiae were shown to be identical. This protein bound to all 11 microorganisms ex-amined–5 Gram-negative bacteria, 3 Gram-positive bacteria, and 3 yeasts–and was consequently designated B . mori multibinding protein (BmMBP). The sequence of the cDNA encoding BmMBP revealed that it was a C-type lectin with two dissimilar carbo-hydrate-recognition domains (CRD1 and CRD2) distantly related to known insect C-type lectins. CRD1 and CRD2 were prepared as recombinant proteins and their binding properties were investigated using inhibition assays. Each domain had wide, dissimilar binding spectra to sugars. These properties enable BmMBP to bind to two sites on a microorganism, facilitating high-afﬁnity binding to many types of microorganisms. The dissociation constants of BmMBP with M . luteus cells and S . cerevisiae were 1.23 (cid:1) 10 (cid:2) 8 and 1.00 (cid:1) 10 (cid:2) 11 M, respectively. rBmMBP triggered the aggregation of hemocytes from B . mori larvae in vitro and microorganisms recognized by BmMBP were surrounded by aggregated hemocytes in vivo, forming a nodule, which is the typical cellular reaction in insect immune responses. These observations suggest that BmMBP functions as a trigger for the nodule reaction and that the multirecognition characteristic of BmMBP plays an important role in
SERUM AND SYNOVIAL ONCOSTATIN M AND LEUKEMIA INHIBITORY FACTOR IN RHEUMATOID AND OSTEOARTHRITIS

Objective: To measure levels of oncostatin M (OSM) and leukemia inhibitory factors (LIF) in the serum and synovial fluid of rheumatoid arthritis (RA) and osteoarthritis (OA) patients. Also, to correlate their levels with the parameters of disease activity in RA and severity in both RA and OA. Methods: This study was conducted on 20 RA patients, 10 OA patients and 10 healthy controls. Serum and synovial fluid levels of both OSM and LIF were measured using quantitative sandwich enzyme immunoassay technique. Results: The mean levels of serum and synovial OSM as well as LIF were significantly higher in RA patients than controls (30.3 ± 14.5, 252 ± 117, 23.5 ± 5.2, 41.8 ± 7.2 pg/ml respectively, p<0.001). Similar findings were also detected only in synovial OSM and LIF of OA patients (39.5 ± 4.8, 22.4 ± 4.5 pg/ml respectively, p <0.001). On comparing RA and OA patients, there was a highly significant increase in the serum and synovial OSM as well as LIF in RA patients (p <0.001). Also, there was a highly significant correlation between the serum and synovial OSM in RA and OA patients. Similar findings were found between synovial OSM and LIF as regards the activity score, total number of WBCs and ESR. Stepwise multiregression Conclusion: Our results confirm the role of both OSM and LIF as important factors that mediate the pathophysiologic events in RA, besides their role in detection of activity of RA and severity of both RA and OA.
Knowledge diversity under socio-environmental pressure

Experimental cultural evolution has been convincingly applied to the evolution of natural language and we aim at applying it to knowledge. Indeed, knowledge can be thought of as a shared artefact among a population inﬂuenced through communication with others. It can be seen as resulting from contradictory forces: internal consistency, i.e., pressure exerted by logical constraints, against environmental and social pressure, i.e., the pressure exerted by the world and the society agents live in. However, adapting to environmental and social pressure may lead agents to adopt the same knowledge. From an ecological perspective, this is not particularly appealing: species can resist changes in their environment because of the diversity of the solutions that they can offer. This problem may be approached by involving diversity as an internal constraint resisting external pressure towards uniformity.
On one algorithm for reconstruction of an disturbance in a linear system of ordinary differential equations

The problem of reconstructing an unknown disturbance under measuring a part of phase coordinates of a system of linear diﬀerential equations is considered. Solving algorithm is designed. The algorithm is based on the combination of ideas from the theory of dynamical inversion and the theory of guaranteed control. The algorithm consists of two blocks: the block of dynamical reconstruction of unmeasured coordinates and the block of dynamical reconstruction of an input.
Preconditioned elastic full waveform inversion using approximated Hessian matrix

We describe a simple way of estimating the elements of the Hessian matrix for elastic full waveform inversion (FWI) problems by applying it to spikes in the model space. We explain how to use this estimated matrix to precondition an elastic isotropic FWI problem and show results from a simple one layer model and a more complex subsurface elastic earth. We observe that already a main diagonal approximation is able to increase the inverse problem’s convergence rate. This gradient preconditioning allows us to obtain a meaningful inversion result for the deeper part of the model in fewer iterations.
THE DEVELOPMENT STRATEGY OF FASHARKAN LANTAMAL IX XYZ FROM CLASS C FASHARKAN TYPE TO FASHARKAN CLASS A KOARMADA III WITH SWOT AND ISM APPROACH

The formation of Koarmada III resulted in a shift and an increase in the number the KRI in its working area, so it must be balanced with the ability of maintenance and repair facilities (fasharkan). Fasharkan XYZ is a fasharkan with class C status so it needs to be upgraded to class A in order to support its main task. The purpose of this research is to determine the alternative development strategy of Fasharkan XYZ. This study uses an integrated SWOT analysis and Interpretative Structural Modeling (ISM). SWOT analysis to formulate alternative strategies developed, namely the SO strategy consisting of 6 (six) strategic steps, the ST Strategy consisting of 6 (six) strategic steps, the WO Strategy consisting of 9 (nine) strategic steps and the WT strategy consisting of 4 (four) strategy move. From the weighting of the EFAS and IFAS matrices the WO strategy was selected. By using ISM, it is obtained 5 (five) levels of hierarchical structure from the classification of elements in the WO strategy, including the strategy (WO5) at level V. Then the sub strategies (WO1) and (WO4) are at level IV. At level III it consists of 4 (four) sub strategies, namely (W O2); (WO6); (WO7); (WO8). At level II, namely the sub strategy (WO3). At level I sub strategy (WO9).
An Optimized Discrete Data Classification Method in N -Dimensional

We propose a discrete data classi ﬁ cation method of scattered data in N -dimensional by solving the minimax problem for a set of points. The current research is extended from 2-dimensional and 3-dimensional to N -dimensional. The problem can be applied to arti ﬁ cial intelligence classi ﬁ cation problems (machine learning, deep learning), point data analysis problems (data science problem), the optimized design of nanoscale circuits, and the location of facility problems, circle detection on 2D image, or sphere detection on depth image. We generalized the discrete data classi ﬁ cation methodology in N -dimensional. Finally, we resolved to ﬁ nd an exact solution of the location of a manifold for our suggested problem in N -dimensional.
Dance Performance in New Rural Areas Based on 3D Image Reconstruction Technology

Because of the special ecological environment and humanistic atmosphere in new rural areas, excellent regional dance art has been created. Through computer-aided technology, the essence of dance art in rural areas can be reconstructed and displayed. Therefore, based on 3D image reconstruction technology, this paper obtains the dance data of southeast Guangxi and puts forward the dance display scheme of new rural areas. Through acquisition of image information and image matching algorithm, the dance pose is estimated, and the extracted dance sequence is simplified by 3D reconstruction and mapped by texture. In addition, extraction effect of data set, comparison of dance similarity, and user authenticity score were used to test the five types of dance, which provides ideas for the inheritance and development of traditional folk dance culture.
PERFORMANCE EVALUATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF EEG SIGNALS BASED ON ATTENTION TASK

This paper aims to present the Convolutional Neural Network (CNN) model to differentiate attention from non-attention conditions using spontaneous electroencephalogram (EEG) signals. The CNN model was constructed to acquire a general concept to classify attention conditions. A total of 30 subjects were recruited voluntarily for the data acquisition purpose. The experimental performance was benchmarked with the commonly used non-convolution learning algorithms, the support vector machine (SVM). The coherence feature extraction method was used to generate the training data for non-convolution model. The experimental results show that the proposed CNN model has accurately classify 63.89% of the test cases. It has outperformed the SVM model with 4.45% of improvement. In summary, the CNN model is able to create a decent attention classification model using spontaneous EEG signals.
Kerberos based Enhanced Authentication Protocol for Cloud Computing Environment

Cloud Computing is one of the most famous field in the world of computer science research. The layered architecture forms a pyramid with categorization SaaS, PaaS&IaaS laid top to bottom. The user of this service model goes pay-per-use basis to the service provider. Like the other popular field, cloud computing too attracts those who likes to find loopholes to benefit themselves illegally. In this paper, we are using Kerberos at the server end, to avoid third-party dependency. To achieve confidentiality, cryptography is also taken in use along with the elliptic curve.
Registration of 3D Point Clouds with Low Overlap Master Thesis

Point cloud registration serves as a key component to a wide range of applications include 3D reconstruction and LiDAR odometry and mapping. Existing approaches focus on registration of point clouds with over 30% overlap, lowering this bound is of great benefit to real world applications. For example, under a 3D reconstruction setting, this would allow for reduced acquisition time and fine-grained local geometry reconstruction by leveraging more fragment pairs. In this thesis, we take the first attempt to study registration of low overlapping point clouds. This is challenging because: i). fully convolutional local geometric features are contaminated by irrelevant global context; ii). the inlier ratio of the over-complete correspondence set is upperbounded by the low overlap ratio p. To mitigate such challenges, we propose a pruning module, which works by first aggregating local features to global patch-wise descriptors, then solving a partial assignment problem with a differentiable optimal transport layer. It directly attends the deep point cloud registration model to the overlap region and explicitly reduces the search space of feature matching. Our pruning module is light and efficient that can be easily plugged into deep point cloud registration models. We demonstrate improved performances from two deep learning models on two benchmarks, the improvement is significant in low overlap regions.
UVM ScholarWorks UVM ScholarWorks Validation of a modified rat model for erectile function evaluation Validation of a modified rat model for erectile function evaluation

The in vivo model for evaluation of erectile function in rats and mice has been widely used to investigate pathophysiology and treatment modalities of erectile function. The model is technically challenging which limits its broad availability. We have recently introduced a simplified surgical technique for dissection of corporal bodies and developed a new method to achieve stable contact between the cavernous nerve and the stimulating electrode without the need to manipulate the nerve between stimulations using 2-component silicone glue. The goal of this study was to validate this new technique and describe in detail the technical aspects of the procedure so that researchers with basic microsurgery skills can adopt it.
THE RELATIONSHIP BETWEEN GOVERNMENT EXPENDITURE ON SELECTED SECTORS TOWARDS ECONOMIC GROWTH IN MALAYSIA

Government expenditure plays an important role in the country’s economic growth. Budget allocations through the annual budget presented have their strategies to ensure that the allocated budget can improve the growth and development in Malaysia and also can be enjoyed by all Malaysian. The government’s expenditure has contributed to the expansion of this country’s development from various sectors. However, there are various problems involving government expenditure such as low level of health, homeless poor people, weak public transportation and illegal immigrants. The purpose of this study is to examine the relationship between government expenditure on health, education, housing, transportation and defence sectors towards economic growth. The data used is secondary data from 1980-2017 for 38 years from the Department of Statistics, Malaysia and World Bank Data. The method used in this study is Descriptive Analysis and Autoregressive Distributed Lagged Model (ARDL). The result showed that economic growth and government expenditure on health, education, transportation, and defence has a long-run relationship. Therefore, the government should increase more expenditure on crucial sectors such as education, transportation, defence and health that will strengthen economic growth in the long run.