Miracle-dz/UniFAR-FTUs huggingface dataset:
NH 2 -Terminal Probrain Natriuretic Peptide Is a Stronger Predictor of Cardiovascular Mortality Than C-Reactive Protein and Albumin Excretion Rate in Elderly Patients With Type 2 Diabetes The Casale population-based

OBJECTIVE d To study whether NH 2 -terminal probrain natriuretic peptide (NT-proBNP) is a short-term independent predictor of both all-cause and cardiovascular (CV) mortality in type 2 diabetic patients and to establish whether albuminuria and C-reactive protein (CRP) affect this relationship. RESEARCH DESIGN AND METHODS d The prospective study included 1,825 type 2 diabetic patients from the population-based cohort of the Casale Monferrato study. CV risk factors, preexisting CVD, and NT-proBNP levels were evaluated at baseline. All-cause and CV mortality were assessed 5.5 years after baseline examination. Multivariate Cox proportional hazards modeling was used to estimate mortality hazard ratios (HRs). RESULTS d During the follow-up period, 390 people died (175 for CVD) out of 9,101 person-years of observations. A signi ﬁ cantly increased mortality risk by quartiles of NT-proBNP was observed (test for trend, P , 0.001). NT-proBN P values . 91 pg/mL conferred HRs of 2.05 (95% CI 1.47 – 2.86) for all-cause and 4.47 (2.38 – 8.39) for CV mortality, independently of CV risk factors, including CRP and albumin excretion rate (AER). The association was also signif- icant for modest rises in NT-proBNP levels and in patients without microalbuminuria and CVD at baseline
Project Acronym: BIDAMARK Project title: BIg DAta-driven MARKeting for better competitiveness Conference publication: BIG DATA ANALYTICS USE IN CUSTOMER RELATIONSHIP MANAGEMENT: ANTECEDENTS AND PERFORMANCE IMPLICATIONS

Customer information plays a key role in managing successful relationships with valuable customers. Big data analytics use (BD use), i.e., the extent to which customer information derived from big data analytics guides marketing decisions, helps firms better meet customer needs for competitive advantage. This study aims to (1) determine whether organizational BD use improves customer-centric and financial outcomes, and (2) identify the factors influencing BD use. Drawing primarily from market information use theory, we advance a model to explain how information quality (IQ), customer orientation and big data analytics culture predict BD use, which in turn influences customer relationship and financial performance. Empirical findings from a survey of 301 senior marketing executives, representing large US-based firms in B2C industries, support our conceptualization of the performance outcomes and antecedents of BD use. All seven hypotheses received empirical support. The results highlight that the characteristics of the customer information (IQ) and the characteristics of the user organization (customer orientation and big data analytics culture) strongly predict BD use. The findings also reveal the relative importance of different customer information characteristics to marketing decision-makers. Practitioners may significantly improve firm performance with BD use, but only if certain antecedent factors facilitate BD use in the organization. We offer managers advice how to overcome challenges specific to BD use by managing the quality aspects of customer information, and by fostering shared customer-oriented and analytics-oriented cultures. To the best of our knowledge, this study is the first to examine the role of big data analytics use in managing customer relationships. We hope that this contribution motivates more academic research to address the impact of big data on marketing and customer strategies.
Alkaline Activation and Selective Characterizations of Musa Sapentium Peels Biomass Organic Nano-fertilizer.

:Banana peels of Musa sapentium species contains important phytonutrients that are renewable and can be nanotechnologically modified into useful, cheap and resoucefull bio fertilizers. The natural peels were characterized for Proximate and anti-nutritional analysis prior to Physico-chemical characteristics of the banana peel alkaline activated nano fertilizer and image J analysis of SEM result of the alkaline (sodium hydroxide)activated banana peel nano particles. The fertilizer at the nano scale was estimated to be 1660.20± 823.35nm 2 in average area and 2739nm as the range between the nano irregular particles.The synthesized nanofertilizers was initiated with 0.5kg of Musa sapentium peels with 1000ml distilled water and alkaline modification with 10g Sodium hydroxide for 30 miuntes at 105 o C being stabilized with urea and dispered with citric acid both at the strength of 5% and pH of 5. Banana peels generally should be an excellent and cheap sources of plant macro (NPK),micro and elemental nutrients as compared with the standards from International Center for Soil Fertility and Agricultural Development Alabama, USA.
THERMOMETRY AND COHERENCE PROPERTIES OF A ULTRACOLD QUANTUM GAS OF METASTABLE HELIUM

In 2001 metastable Helium (He*) attained Bose-Einstein condensation (BEC). The metastable state has a lifetime of 9000 sec and an internal energy of 20 eV. This energy can be used to detect individual atoms using a micro-channel plate. The extremely good time response and high gain of this detector makes it possible to carry out a density correlation measurement (HBT) with massive particles similar to the pioneering experiment of R. Hanbury Brown and R. Twiss in optics. In addition, inelastic collisions between He* atoms produce a small but detectable flux of ions proportional to the cloud's density. This allows one to follow the evolution of the cloud's density toward BEC, passing through the phase transition, in real time and in a non invasive way. In this dissertation we report on three different experiments: i) the determination of the two- and three-body ionizing rate constants of He*; ii) the determination of a, the He* scattering length; iii) the measure of the intensity correlation function of a falling He* cloud. It has been shown lately that our measure of a was affected by a large systematic error and we propose a possible explanation. We describe methods to determine the temperature and fugacity of a thermal cloud. Finally a major portion of the thesis is devoted to the derivation of an analytical expression for the intensity correlation function of the atomic flux. This theoretical analysis has derived typical values for the transverse and longitudinal atomic coherence length that confirmed the possibility of performing a HBT experiment with our apparatus.
Blockchain Based Platform for Healthcare Information Exchange

Current trend in healthcare industry is to shift its data on the cloud, to increase availability of Electronic Health Records (EHR) e.g. Patient’s medical history in real time, which will allow sharing of EHR with ease. However, this conventional cloud-based data sharing environment has data security and privacy issues. This proposes a distributed solution based on blockchain technology for trusted Health Information Exchange (HIE). In addition to exchange of EHR between patient and doctor, the proposed system is also used in other aspects of healthcare such as improving the insurance claim and making data available for research organizations. Medical data is very sensitive, in both social as well as legal aspects, so permissioned block-chain such as Hyperledger Fabric is used to retain the necessary privacy required in the proposed system. As, this is highly permissioned network where the owner of the network i.e. patient holds all the access rights, so in case of emergency situations the proposed system has a Backup Access System which will allow healthcare professionals to access partial HER. The Process of the Blockchains used architecture depends on the entities participating in the constructed chain network. Although the use of Blockchains may reduce redundancy and provide caregivers with consistent records about their patients, it still comes with few challenges which could infringe patients’ privacy, or potentially compromise the whole network of stakeholders. Blockchain offers the opportunity to enable access to longitudinal, complete, and tamper-aware medical records that are stored in fragmented systems in a secure and pseudo-anonymous fashion.
A Quadrilateral Shaped Fractal Slot Planar Antenna for Ultra-Wide Band Applications

ABSTRACT This paper presents a low-profile quadrilateral-shaped fractal slot planar antenna using a novel fractal geometry where circular, triangular and regular hexagonal motifs are iterated to form antenna elements. It consists of a partial ground plane and a triangular fractal-shaped patch fed by a microstrip line having an I-shape rectangular slot to increase the bandwidth. A compact 32 × 22 mm2 fractal antenna proposed here is investigated to evaluate its performance in ultra-wideband (UWB) applications. The bandwidth of 15.5 GHz ranging from 3.57 GHz to 19.07 GHz with voltage standing wave ratio (VSWR) less than 2 in the entire operating band is achieved. Simulated values of realisable gain (6.8 dBi), impedance bandwidth (15.5 GHz), radiation efficiency (90%), radiation patterns and group delay evaluation indicate that the discussed antenna is favourable for UWB applications such as world interoperability microwave access, wireless local area network, 5 G, X-band, C-band, S-band and Ku-band. The proposed antenna is fabricated, and measured results are compared with simulated results as well as with those reported in the literature.
Enhancer In Vivo d Multiprotein Complex on the TCR in the Assembly and Function of a Factor/Polyoma Enhancer-Binding Protein 2 Distinct Roles for c-Myb and Core Binding

Enhancers and promoters within TCR loci functionally collaborate to modify chromatin structure and to confer accessibility to the transcription and V(D)J recombination machineries during T cell development in the thymus. Two enhancers at the TCR (cid:2)(cid:1) locus, the TCR (cid:2) enhancer and the TCR (cid:1) enhancer (E (cid:1) ), are responsible for orchestrating the distinct developmental programs for V(D)J recombination and transcription of the TCR (cid:2) and (cid:1) genes, respectively. E (cid:1) function depends critically on transcription factors core binding factor (CBF)/polyoma enhancer-binding protein 2 (PEBP2) and c-Myb as measured by transcriptional activation of transiently transfected substrates in Jurkat cells, and by activation of V(D)J recombination within chromatin-integrated substrates in transgenic mice. To understand the molecular mechanisms for synergy between these transcription factors in the context of chromatin, we used in vivo footprinting to study the requirements for protein binding to E (cid:1) within wild-type and mutant versions of a human TCR (cid:1) minilocus in stably transfected Jurkat cells. Our data indicate that CBF/PEBP2 plays primarily a structural role as it induces a conformational change in the enhanceosome that is associated with augmented binding of c-Myb. In contrast, c-Myb has no apparent affect on CBF/PEBP2 binding, but is critical for transcriptional activation. Thus, our data reveal distinct functions for c-Myb and CBF/PEBP2 in the assembly and function of an E (cid:1) enhanceosome in the context of chromatin in vivo. The Journal of Immunology, 2002, 169: 4362–4369.
Interactive Contribution of Observed Mother-Youth Emotional Climate and Youth Physiology: A Biopsychosocial Approach to Understanding Youth Coping With Peer Stress

We examined the prospective associations between two indicators of mother-youth emotional climate (i.e., maternal affect, dyadic cohesiveness) during a peer problem discussion and youth coping with peer stress following entry into middle school. We also investigated youth baseline respiratory sinus arrhythmia (RSA) as a moderator of these associations given its role in physiological stress regulation. Participants were 100 mother-youth dyads (53% boys; Mage = 11.05 years; 43% ethnic minorities). More positive maternal affect at Time 1 (spring of 5th grade) was associated with more primary and secondary control engagement coping and advice-seeking at Time 2 (fall of 6th grade); greater dyadic cohesiveness was prospectively linked with more advice-seeking only. Further, the positive association between maternal affect and primary control coping was only evident for youth who showed lower (not higher) baseline RSA. We discuss implications for jointly considering the mother-youth relational context and youth physiological regulatory capacity towards understanding coping socialization.
Uncoupled Nonnegative Matrix Factorization with Pairwise Comparison Data

In this paper, we propose a new method called uncoupled nonnegative matrix factorization (UNMF). UNMF enables us to analyze data that cannot be represented by a matrix, due to the lack of correspondence between the index and values of the matrix elements caused by e.g., data collection under the constraint of privacy protection. We derive the multiplicative update rules for parameter estimation and confirm the effectiveness of UNMF by numerical experiments.
Evaluating molecular representations in machine learning models for drug response prediction and interpretability

Abstract Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a post hoc feature attribution method can boost the explainability of the DL models.
Performance-driven Wire Sizing for Analog Integrated Circuits

Analog IC performance has a strong dependence on interconnect RC parasitics, which are significantly affected by wire sizes in recent technologies, where minimum-width wires have high resistance. However, performance-driven wire sizing for analog ICs has received very little research attention. In order to fill this void, we develop several techniques to facilitate an end-to-end automatic wire sizing approach. They include a circuit performance model based on customized graph neural network (GNN) and two optimization techniques: one using Bayesian optimization accelerated by the GNN model, and the other based on TensorFlow training. Experimental results show that our technique can achieve 11% circuit performance improvement or 8.7× speedup compared to a conventional Bayesian optimization method.
Uncertainty Analysis Based Attention Network for Lung Nodule Segmentation from CT Images

In recent years, with the development of computer science and technology, computer aided diagnosis (CAD) systems have played important role in clinical practice. Using deep learning technology, existing CAD systems can predict whether CT images contain lung nodule automatically. However, for the precise segmentation of lung nodule and nodule boundary, further diagnosis by doctors is required. The current widely used segmentation networks still have segmentation uncertainty in the lung nodules boundary, which will interfere the accuracy of segmentation results. To solve this problem, this paper propose a UAA-UNet (Uncertainty Analysis Based Attention UNet) based on the uncertainty analysis of edge regions. The network structure is divided into two stages. In the first stage, the initial segmentation map of the lung nodule is obtained, and the second stage focuses on the uncertainty region of the initial segmentation map. By learning the features of the uncertainty region, the uncertainty is reduced and the segmentation accuracy is improved. The second stage includes two modules, the uncertainty attention module and the uncertainty elimination module. In uncertainty attention module, the entropy map of the initial segmentation map of the lung nodule is input into the network as attention information to improve the network's ability to understand uncertainty. In uncertainty elimination module, by using EWCE (entropy map weighted cross entropy loss function), the entropy map of the prediction result is fed back to the network as a weight factor to further improve the network's learning ability of the uncertain region. We selected lung nodule slices from 1012 patients in the Lung Image Database Consortium (LIDC) to validate the feasibility and effectiveness of the proposed method. The experiment result shows that, in the lung nodule segmentation task, by leveraging uncertainty analysis, the network achieves significant improvements over the baseline network.
Product configurators for additively manufactured products: exploring their peculiar characteristics

The capability of realizing individually customized products with complex geometries makes additive manufacturing (AM) ever more considered by companies engaged in mass customized manufacturing. In order to be exploited in the market, the AM allowed geometry freedom has to be transferred to the customers for the customer-specific customization. Notably, this is a new request posed to product configurators (PC). So, in this research we ask: How is this request being answered by pioneers who engage in this challenge? Are there other new requests that AM poses to configurators? The present paper aims at answering these exploratory questions by looking at how these issues have been considered in existing literature and by providing some examples. We hope that considerations derived from this investigation will open a discussion on this topic in the product configuration research community with the goal to identify peculiar PC capabilities needed to customize additively manufactured products using PCs.
Ethical deception

The use of images of students from traditionally underrepresented racial and ethnic backgrounds in college recruitment materials presents a seemingly difficult dilemma. Should colleges and universities use diversity in recruitment materials to try and attract students from underrepresented racial and ethnic backgrounds even if those images do not accurately represent the amount of diversity at the university? To discover student perceptions relating to this question, I used a mixed-methods approach in which I surveyed 117 students and then interviewed 10 survey participants. Survey and interview questions were based on utilitarian versus deontological ethics with an emphasis on whether exaggerating diversity in recruitment materials is ethl. The results of this exploratory study showed that most students believe using a disproportionate amount of diversity in recruitment materials is unethical. Student participants who identified as a person from an underrepresented racial/ethnic group indicated that it is unethical to exaggerate diversity in recruitment materials at a higher percentage than their white counterparts. This is likely because people from underrepresented backgrounds face a much higher risk of harm from misleading recruitment materials than their white peers.
Urban Digital Twins: Decision-Making Models for Transportation Network Simulation

Digital twin technology has been applied in multiple fields recently by providing modelling platform, information of built environment, or real-time data. It was created to improve generation process and efficiency of industrial products at first. Today, digital twins start to create scenarios of smart cities with information technology and computing. This paper explores the digital twin implementation in design progress of transportation network. A case study of Wellington Digital Twin has been analyzed. This public and accessible model allows for communities and neighborhoods’ participation in transportation network design progress with components to tag and report comments in urban areas. It is found that compared with conventional transportation simulation, the digital twins have the characteristics of accurate, implemented, and digitalized. Meanwhile, digital twins have the capability to present the exact counterpart of physical world and predict how it perform in the future. This research also argues that digital twins are not a panacea although they can assist interactive transportation network simulation to a certain extent.
DEVELOPING ENGAGEMENT STRATEGIES IN THE BLENDED LEARNING TRIANGLE: THE CASE OF I&E EDUCATION IN THE EIT DIGITAL

Digitalised education is already a consolidated practice in Higher Education. Budget constraints and the need to target larger students cohorts show the benefit of leveraging educational content without increasing delivery costs. However, if on the one hand this “economy of scale” based rationale is the main foundation of the digitalised education narrative, on the other, such a view clashes with the need to address a series of novel social dynamics which are enacted by technological mediation . This claim is rooted in a change of focus from just economies of scale to that of scope. When knowledge sources are diverse, fast-changing and interconnected, Blended Learning (BL) offers an opportunity to provide learners with a wide mix of contents which can be hardly owned by a single knowledge provider. However, in BL, the online part tends to drive its main value proposition, which turns out to be based on an efficiency-based view. On the other hand, positioning ICT as a means to produce complex educational content delivered to students interactively by trainers, transforms the traditional learner/trainer relationship into a three-dimensional learning environment made of content producers (CPs), class trainers (CTs) and learners (CLs), here referred to as the Blended Learning Triangle (BLT). In this contribution we claim that these actors are traditionally seen as, somehow, disjoint. The question is whether such an interactional independence can be effective when dealing with complex knowledges instead of requiring a deeper engagement between them. This implies a problematization of the BLT, whereby these actors need to interact, cooperate and, plausibly, handle conflicts over demands of flexibility, adaptability and knowledge absorption capacity. This paper explores issues related to this problematization and, relying on experiences developed in the context of the EIT Digital, a pan-european network of HEIs, suggests possible recommendations to address these novel interactional demands.
On Satisfiability of Polynomial Equations over Large Prime Fields

The importance of zkSNARKs [1] is ever-increasing in the cryptocurrency and smart contracts ecosystem. Due to the significant threat of financial loss a bug represents in such applications, there is also a surge of interest in the formal verification of zkSNARK programs. These programs are ultimately encoded as a system of polynomial equations over large finite prime fields, and to prove statements about such a program is to prove statements about its system of equations. In this ongoing work, we investigate algebraic techniques with the goal of writing a mixed algebraic-SMT decision procedure to compute satisfiability of a new theory of polynomials over large prime fields. Ideally, the new theory and decision procedure could be implemented in existing SMT solvers as well as a standalone tool, in order to perform verification tasks over real world applications of zkSNARKs.