Miracle-dz/UniFAR-FTUs huggingface dataset:
D_n-geometry and singularities of tangent surfaces

The geometric model for D n -Dynkin diagram is explicitly constructed and associated generic singularities of tangent surfaces are classiﬁed up to local diﬀeomorphisms. We observe, as well as the triality in D 4 case, the diﬀerence of the classiﬁcation for D 3 ; D 4 ; D 5 and D n ( n (cid:21) 6), and a kind of stability of the classiﬁcation in D n for n ! 1 . Also we present the classiﬁcations of singularities of tangent surfaces for the cases B 3 ; A 3 = D 3 ; G 2 ; C 2 = B 2 and A 2 arising from D 4 by the processes of foldings and removings.
Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers’ mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.
PREDICTION OF INSULIN LEVEL OF DIABETES PATIENT USING MACHINE LEARNING APPROACHES

: In today's world, there are so many patients with Diabetes having varying insulin levels and blood glucose levels in the human body. So, there is a requirement to constantly monitor the blood glucose level and improve the patient's condition with an adjusted insulin dose. Before each meal, they must take a dose of insulin. Doctors must calculate insulin dosages for each patient based on previous dose data and regular sugar levels. Our study uses a Machine Learning approach to develop the model, which uses an RNN (LSTM) and ANN algorithm to predict a patient's insulin chart. The algorithm was trained using the patient's 36-month chart, and the extended series of following insulin predictions are based on that data. The predictive model is used in this investigation.
Available online at Journal Website Plasma Leptin and Adiponectin Levels and Disease Severity in Primary Knee Osteoarthritis

and Levels and Disease Severity in ABSTRACT Introduction and aim: Osteoarthritis (OA) is the commonest degenerative and painful articular disease. It is a significant cause of disability. Early diagnosis is crucial and searching for biomarkers is continuing. The current work aimed to assessment the plasma leptin and adiponectin concentrations in patients with primary knee osteoarthritis (OA), and to explore the relationship of leptin and adiponectin with OA severity Methodology: Forty patients with primary knee osteoarthritis from both sexes who fulfilled ACR criteria for diagnosis of knee OA were enrolled. Other healthy subjects were included as a control group. Patients were sassed by history taking, clinical examination, radiological and laboratory investigations. WOMAC questionnaire was used to evaluate a patient’s functions. Results: Both study and control groups were comparable as regards to patient demographics, except their weight, height and body mass index (BMI). Obesity, hypertension, tenderness, pain grade, limitation of joint movements and WOMAC score, Kellgren Lawrence radiological criteria, leptin and adiponectin were increased in study when compared to control group. There was significant, positive, moderate correlation between leptin and BMI (r=0.557, p < 0.001). Higher leptin was associated with obesity and diabetes mellitus. Leptin serum levels revealed proportional, moderate, significant correlation with each of pain, stiffness and K-L criteria, while this correlation was powerful with physical function and total WOMAC score. Conclusion: Leptin and adiponectin were increased in patients with osteoarthritis. Leptin alone is significantly associated with disease severity indices.
Digital resource granting in new education policy and socioeconomic in small industry development in India

The economy of India cannot accomplish the objective of sustainable economic development until or unless it can put a generous sum into human resources. Education is a fundamental factor in development. It does not just improve the personal satisfaction of an individual, yet, addition, it improves the social advantage of the public. The motivation behind this examination is to discover how public expenditures on education can improve the economic and social state of the society in India. Regardless, the issue is whether further developed levels of education come about on the ground of education to craft skill artisans in small industries like giving digital training or digital education to Indian traditional artisans that may compete with the global handicraft sector. For the development of the handicraft sector, a separate global handicraft index has also been developed by the Indian scholar to give positive competition and advance new education to an artisan of global craft, and that more education spending can advance economic growth is yet sketchy. A few economic and social analysts and researchers have maintained the two-sided connectivity between these two factors, yet some are recommending that economic growth essentially animates the government to spend more on education, not the substitute way. The research question of this study is “do education and expenditures on education get economic and social advancement? ” Considering this research issue, the current study utilizes Granger causality methods to conclude the causal connection between the government’s education spending and socioeconomic development in India for the period 1972–2021. Results uncovered that there is no short-run causality between government expenditure on education and socioeconomic growth, yet economic growth influences public spending on education. This provides guidelines to the new education policy (NEP) makers that they should prioritize their attention toward allocation of development and non-development expenditures of the government’s budget to enhance the quality of education and welfare of individuals in the society. Special focus should be given to compulsory primary education, which is already a part of sustainable development goals.
Trust the test: Score-user perspectives on the roles of language tests in professional registration and skilled migration

English language proficiency is a deciding factor in the life opportunities of many thousands of applicants for Australian skilled migration every year. This paper focuses on the perspectives of professional bodies that use English language tests in their decisions. Taking an interpretative approach, we explore the meanings that policy makers from these organisations ascribe (as score users) to test standards (cut-scores) so that we can better understand the uses of test scores in migration policy. The policy narratives we observed around the use of test scores describe the need to manage large numbers of applicants, to assure a level of English proficiency for high-risk professional communications, to provide an objective assessment that is separate from any assessment of professional competence and to maintain consistency of standards with other bodies. These views are contextualised with other relevant information, particularly that available from test providers, who are key players in the test-using interpretive community. We observe that particular tests and their standards become trusted and entrenched in policy, using the apparently simple semiotics of scores. Concomitantly, trust in tests is nurtured by test marketing. These tendencies warrant attention from test researchers, providers and score users.
Magnetic-Actuated Cyber-Physical System for Interventional Surgery

At present, interventional surgery has been successfully applied in the treatment of many diseases, since due to the significant dosage of ionizing radiation, surgeons and staff are susceptible to serious health problems. Nowadays guidewires and catheters are not capable of active steering by themselves remotely, so achieving precise control of the movement of the guidewire in interventional surgery is a hot issue at the forefront of research in related fields. In this paper, we propose a cyber-physical system that consists of a magnetically actuated patient module and operator module with a virtual surgical environment. Unlike the existing magnetic-actuated systems, our system adopts dynamically changing currents through coils to excite a dynamic magnetic field, which results in more flexible and rapid control. The experimental result shows that our method can control the direction of a magnetic field vector within an average absolute error of 2° and an average error of – 0.127°, and the single-round calculation time of an electric current configuration is only 0.2 ms, which meets the requirement of real-time guidance for catheter intervention.
Efficient Shadow Generation of Massive Crowds Based on the Area of Screen Space

Shadows are critical for realistic and visually appealing images. In particular, for interactive applications such as video games, the shadows of objects significantly increase the realism of the virtual environment. However, for scenes in which a large number of characters are animated, it is not easy to generate shadows in real time because the shadow of each character must be rendered individually. This study introduces a simple yet efficient technique for generating shadows for large crowds. This technique considers the space occupied by each character in the final image. If the space is significantly small, the algorithm applies a technique that uses a simple and fast yet physically correct projection shadow matrix to generate shadows. If the space is relatively large, which means that the character is close to the camera, the algorithm applies a widely known shadow mapping technique. This technique is slower than the projection shadow matrix but is more physically practical. The rendering of large crowds is achieved using the GPU instancing technique. Because the algorithm adaptively choses shadow generation techniques according to their visual saliency, the overall performance improves. Through several experiments, we demonstrated that our algorithm can generate shadows for more than 20,000 characters, all of which have a full-skinned mesh, at a real-time rate while maintaining the visually plausible shadow quality, with the rate being approximately 54% faster than the traditional shadow mapping method.
Comparison of Lifestyle Based to Traditional CVD Prediction in a Multiethnic Cohort of Non-Smoking Women

Background — Healthy levels of lifestyle factors can reduce risk of CVD. However, except for smoking status, often considered a traditional risk factor, their effect on cardiovascular risk prediction is unclear. Methods and Results — We used a case-cohort design of post-menopausal non-smokers in the multiethnic Women’s Health Initiative Observational Study (1587 cases and 1808 sub-cohort participants) with a median follow-up of 10 years in non-cases. Compared to non-smokers with no other healthy lifestyle factors (healthy diet, recreational physical activity, moderate alcohol use, and low adiposity), the risk of cardiovascular disease was lower for each additional factor (hazard ratio for trend 0.82; 95% CI 0.76, 0.89), with a 45% reduction in risk with all factors (95% CI 0.36, 0.84). When lifestyle factors were added to traditional risk factor models (variables from the Pooled Cohort and Reynolds risk scores), only recreational physical activity remained independently associated with risk of cardiovascular disease. The addition of detailed lifestyle measures to traditional models showed a change in the Integrated Discrimination Improvement and continuous Net Reclassification Index (p < 0.01 for both), but had little impact on more clinically relevant risk stratification measures. Conclusions — While lifestyle factors have important effects on CVD risk factors and subsequent risk, their addition to established CVD risk models does not result in clear improvement in overall prediction. cacacasese cccohohhoorrt aactctivivititieies:s: AAnn upupdadatete ooff acactitivivityty ccododeses aandnd ScScii SpSporortsts
Application of Artificial Intelligence Techniques to Traffic Prediction and Route Planning: the vision of TIMON project

TIMON is an European research project under the Horizon 2020 programme. The main objective of this project is to provide real-time services through a web based platform and a mobile APP for drivers, Vulnerable Road Users (VRUs) and businesses. These services will contribute to increasing drivers and VRUs assistance and safety. To provide these services, one of the core technologies developed inside TIMON will be the design and development of Artificial Intelligence (AI) techniques for traffic prediction and route planning. The DeustoTech-Mobility research group is in charge of this part of the project. The objective of this technical paper is to describe the approach followed in TIMON to develop traffic congestion prediction and route planning services based on AI techniques and the progress done so far. Additionally, the deployment and the result obtained in the first test done is also detailed in this study.
Gene-Speciﬁc Effects of Antisense Phosphorodiamidate Morpholino Oligomer-Peptide Conjugates on Escherichia coli and Salmonella enterica Serovar Typhimurium in Pure Culture and in Tissue Culture

The objective was to improve efﬁcacy of antisense phosphorodiamidate morpholino oligomers (PMOs) by improving their uptake into bacterial cells. Four different bacterium-permeating peptides, RFFRFFRFFXB, RTRTRFLRRTXB, RXXRXXRXXB, and KFFKFFKFFKXB (X is 6-aminohexanoic acid and B is (cid:1) -alanine), were separately coupled to two different PMOs that are complementary to regions near the start codons of a luciferase reporter gene ( luc ) and a gene required for viability ( acpP ). Luc peptide-PMOs targeted to luc inhibited luciferase activity 23 to 80% in growing cultures of Escherichia coli . In cell-free translation reactions, Luc RTRTRFLRRTXB-PMO inhibited luciferase synthesis signiﬁcantly more than the other Luc peptide- PMOs or the Luc PMO not coupled to peptide. AcpP peptide-PMOs targeted to acpP inhibited growth of E. coli or Salmonella enterica serovar Typhimurium to various extents, depending on the strain. The concentrations of AcpP RFFRFFRFFXB-PMO, AcpP RTRTRFLRRTXB-PMO, AcpP KFFKFFKFFKXB-PMO, and ampicillin that reduced CFU/ml by 50% after 8 h ofgrowth (50% inhibitory concentration [IC 50 ]) were 3.6, 10.8, 9.5, and 7.5 (cid:2) M, respectively, in E. coli W3110. Sequence-speciﬁc effects of AcpP peptide-PMOs were shown by rescuing growth of a merodiploid strain that expressed acpP with silent mutations in the region targeted by AcpP peptide-PMO. In Caco-2 cultures infected with enteropathogenic E. coli (EPEC), 10 (cid:2) M AcpP RTRTRFLRR TXB-PMO or AcpP RFFRFFRFFXB-PMO essentially cleared the infection. The IC 50 of either AcpP RTRTR FLRRTXB-PMO or AcpP RFFRFFRFFXB-PMO in EPEC-infected Caco-2 culture was 3 (cid:2) M. In summary, RFFRFFRFFXB, RTRTRFLRRTXB, or KFFKFFKFFXB, when covalently bonded to PMO, signiﬁcantly in- creased inhibition of expression of targeted genes compared to PMOs without attached peptide. the stopped and the was for puriﬁcation with a fourfold Puriﬁcation deprotected (KFF) KXB-PMO conjugate with The eluate was and HPLC MS. desalted through Amberchrom CG300M resin, of the product lyophilization. The product was as described (2.5 mg/ml in EDTA, 15 min, 37°C) and combined with the culture supernatant on ice. Control experiments showed that the same trypsin treatment of EPEC had no effect on bacterial viability. An aliquot of the Caco-2 cells prior to being combined with the culture supernatant was measured for viable cells by hemacytometer using
New Directions In Sparse Sampling and Estimation For Underdetermined Systems

A central objective in signal processing is to infer meaningful information from a set of measurements or data. While most signal models have an overdetermined structure (the number of unknowns less than the number of equations), traditionally very few statistical estimation problems have considered a data model which is underdetermined (number of unknowns more than the number of equations). However, in recent times, an explosion of theoretical and computational methods have been developed primarily to study underdetermined systems by imposing sparsity on the unknown variables. This is motivated by the observation that inspite of the huge volume of data that arises in sensor networks, genomics, imaging, particle physics, web search etc., their information content is often much smaller compared to the number of raw measurements. This has given rise to the possibility of reducing the number of measurements by down sampling the data, which automatically gives rise to underdetermined systems. In this thesis, we provide new directions for estimation in an underdetermined system, both for a class of parameter estimation problems and also for the problem of sparse recovery in compressive sensing. There are two main contributions of the thesis: design of new sampling and statistical estimation algorithms for array processing, and development of improved guarantees for sparse reconstruction by introducing a statistical framework to the recovery problem. We consider underdetermined observation models in array processing where the number of unknown sources simultaneously received by the array can be considerably larger than the number of physical sensors. We study new sparse spatial sampling schemes (array geometries) as well as propose new recovery algorithms that can exploit priors on the unknown signals and unambiguously identify all the sources. The proposed sampling structure is generic enough to be extended to multiple dimensions as well as to exploit different kinds of priors in the model such as correlation, higher order moments, etc. Recognizing the role of correlation priors and suitable sampling schemes for underdetermined estimation in array processing, we introduce a correlation aware framework for recovering sparse support in compressive sensing. We show that it is possible to strictly increase the size of the recoverable sparse support using this framework provided the measurement matrix is suitably designed. The proposed nested and coprime arrays are shown to be appropriate candidates in this regard. We also provide new guarantees for convex and greedy formulations of the support recovery problem and demonstrate that it is possible to strictly improve upon existing guarantees. This new paradigm of underdetermined estimation that explicitly establishes the fundamental interplay between sampling, statistical priors and the underlying sparsity, leads to exciting future research directions in a variety of application areas, and also gives rise to new questions that can lead to stand-alone theoretical results in their own right.
Predicting Loneliness in Older Age by Two Measures of Loneliness

Older people are especially vulnerable to loneliness that has become a major public health concern for people in later life. In this paper, we propose a machine learning based approach to predict loneliness probability using two gradient boosting algorithms, XGBoost and LightGBM. The predictive models are built on a large nationally representative sample from the English Longitudinal Study of Ageing (ELSA) with 7 successive waves (2002 ~ 2015). Two measures of loneliness are applied to investigate the impact of different measure strategies on loneliness prediction. The system achieves good performance with a high AUC and a low LogLoss on the test data, AUC (0.88) and LogLoss (0.24) in the single-item direct measure of loneliness, and AUC (0.84) and LogLoss (0.31) in the multi-item indirect measure of loneliness. Moreover, we investigate a wide range of variables to identify significant risk factors associated with loneliness. Specific categories associated with important variables are also recognized by the model. Such information will further enrich our understanding and knowledge of loneliness causes in the elder.
RESEARCH Occurrence of a bivalve-inhabiting marine hydrozoan (Hydrozoa: Hydroidolina: Leptothecata) in the amber pen-shell Pinna carnea Gmelin, 1791 Panama

Hydroidomedusan polyps are involved in associations ranging from simple epibiosis to strict symbiosis (mutualism and parasit- ism) with organisms belonging to many animal phyla, including sponges, cnidarians, molluscs, annelids, arthropods (crusta-ceans), bryozoans, echinoderms, tunicates and vertebrates (ﬁshes) (Gili & Hughes, 1995). Many known symbiotic hydroids are found on mollusc shells, as are the cases of Neoturris pileata (Forsska˚l, 1775) on Nucula spp. (see Edwards, 1965); Hydractinia angusta Hartlaub, 1904 on Adamussium colbecki (E. A. Smith, 1902) (Cerrano et al. Zanclea costata Gegenbaur, 1857 on the three bivalve species Chamelea gallina (Linnaeus, 1758), Cardium sp. and Spisula subtruncata (da Costa, 1778) (Gravili, Boero Bouillon, Cerrano, Amoretti Bavestrello, 1997) and Monobrachium parasitum Mereschkowsky, 1877 on the shells of some Antarctic bivalves (Jarms & Mu¨hlenhardt-Siegel, 1998). Two genera of hydroids have been observed inhabiting the mantle cavity of bivalves during their benthic phase, Eutima McCrady, 1859 and Eugymnanthea Palombi, 1935; both in Leptothecata, Eirenidae (Brinckmann-voss, Bouillon,
Oriented 2-hop Forwarding Approach on Voids Boundaries in Wireless Sensor Networks

— Scalable geographical routing protocols suffer from voids that appear in Wireless Sensor Networks (WSNs). Several techniques are proposed in literature to handle this problem, but they present some limits, particularly in time-critical applications. Consequently, we propose in this paper a new 2-hop forwarding approach that orients any packet which arrives at a boundary node in the shortest path towards the sink. The handled voids can be either closed within a deployed WSN or open on the network boundary. To keep unchanged the actual size of a void for a long time, the use of a 2-hop forwarding mode is privileged to preserve the limited energy of boundary nodes. The information needed for our approach is provided by simple and reactive algorithms that we propose in this paper to discover and maintain the boundaries of voids. Associated with the SPEED real-time routing protocol, our proposal performs very well in terms of packet delivery ratio, control packet overhead, network and boundary nodes energy consumption.