Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety assessment of the process ESTERPET, based on Starlinger iV+ technology, used to recycle post‐consumer PET into food contact materials

Abstract The EFSA Panel on Food Contact Materials, Enzymes and Processing Aids (CEP) assessed the safety of the recycling process ESTERPET (EU register number RECYC186), which uses the Starlinger iV+ technology. The input is hot caustic washed and dried poly(ethylene terephthalate) (PET) flakes mainly originating from collected post‐consumer PET containers, with no more than 5% PET from non‐food consumer applications. The flakes are dried and crystallised in a first reactor, then extruded into pellets. These pellets are crystallised, preheated and treated in a solid‐state polycondensation (SSP) reactor. Having examined the challenge test provided, the Panel concluded that the drying and crystallisation (step 2), extrusion and crystallisation (step 3) and SSP (step 4) are critical in determining the decontamination efficiency of the process. The operating parameters to control the performance of these critical steps are temperature, air flow and residence time for the drying and crystallisation step, and temperature, pressure and residence time for the extrusion and crystallisation step as well as the SSP step. It was demonstrated that this recycling process is able to ensure that the level of migration of potential unknown contaminants into food is below the conservatively modelled migration of 0.1 μg/kg food. Therefore, the Panel concluded that the recycled PET obtained from this process is not of safety concern when used at up to 100% for the manufacture of materials and articles for contact with all types of foodstuffs for long‐term storage at room temperature, with or without hotfill. The final articles made of this recycled PET are not intended to be used in microwave and conventional ovens and such uses are not covered by this evaluation.
Safety evaluation of crosslinked polyacrylic acid polymers (carbomer) as a new food additive

Abstract The EFSA Panel on Food Additives and Flavourings (FAF) provides a scientific opinion on the safety of crosslinked polyacrylic acid polymers (carbomer) proposed for use as food additive in solid and liquid food supplements. Carbomer is formed from the monomer, acrylic acid, which is polymerised and crosslinked with allyl pentaerythritol (APE). The polymers are synthesised in ethyl acetate using ■■■■■ as free‐radical polymerisation initiator. In vivo data showed no evidence for systemic availability or biotransformation of carbomer. Carbomer does not raise a concern regarding genotoxicity. Considering the available data set, the Panel derived an acceptable daily intake (ADI) of 190 mg/kg body weight (bw) per day based on a no observed adverse effect level (NOAEL) of 1,500 mg/kg bw per day from a sub‐chronic 13‐week study in rat, applying a compound specific uncertainty factor (UF) of 8. At the proposed maximum use levels, the exposure estimates ranged at the mean from 1.1 to 90.2 mg/kg bw per day and at the p95 from 12.5 to 237.4 mg/kg bw per day. At the proposed typical use level, the exposure estimates ranged at the mean from 0.7 to 60.2 mg/kg bw per day and at the p95 from 10.3 to 159.5 mg/kg bw per day. The Panel noted that the maximum proposed use levels would result in exposure estimates close to or above the ADI. The Panel also noted that level of exposure to carbomer from its proposed use is likely to be an overestimation. Taking a conservative approach, the Panel considered that exposure to carbomer would not give rise to a safety concern if the proposed maximum use level for solid food supplements is lowered to the typical use level reported by the applicant.
Relationship between traditional maternal diet pattern and breastmilk composition of rural lactating women during the first month postpartum in Shigatse, Tibet

Abstract Maternal nutrition can influence the composition of human breastmilk by altering the components that are sensitive to maternal diet pattern. Traditional Tibetan maternal diet pattern among native rural lactating women possesses distinct characteristics due to its unique geographical environment and dietary habits. This study investigated maternal diet pattern and human breastmilk composition of Tibetan lactating women through different lactation stages during the first month postpartum in Shigatse. The results indicated that Tibetan maternal diet profile was apparently monotonous, mainly sufficient in Zanba, buttered tea, red meat, and fatty soup, yet insufficient in white meat, eggs, leafy vegetables, and fruits, leading to imperfect maternal nutritional intakes with high‐level carbohydrates and deficient proteins. Distinctions of maternal diet profiles in various degrees can be discovered upon different lactation stages, which brings multiple influences to the composition of human milk. There was significantly weak‐to‐medium correlation of protein contents between maternal diet intakes and human milk, while other macronutrients correlated insignificantly. Micronutrient constituents in human milk, involving functional unsaturated fatty acids and free essential amino acids, were also impacted by maternal diet intakes through different lactation stages. These results show that more systematic and profound research is requisite for the clarification and development of Tibetan maternal diet to offer more enhanced and individualized nutritional recommendations for Tibetan lactating women and infants.
Serum semaphorin4C as an auxiliary diagnostic biomarker for breast cancer

Dear Editor, Currently, breast cancer (BC) diagnosis relies on mammography guided by Breast Imaging Reporting and Data System (BI-RADS) and biopsy when necessary.1 Effective blood-based biomarkers for diagnosing BC remain insufficiently unveiled. Semaphorin4C (SEMA4C) was highly expressed in BC-associated lymphatic endothelial cells, and soluble SEMA4C could be obtained when membrane-bound SEMA4C was cleaved by matrix metalloproteinases.2 The biological significance of SEMA4C/PlexinB2 signaling in BC has been highlighted by Gurrapu et al, revealing that SEMA4C is overexpressed in BC cells, and SEMA4C/PlexinB2 signaling is essential for the growth of BC cells.3 Herein, we assessed serum SEMA4C as a diagnostic biomarker for BC, compared it with mammography and ultrasound, and explored the combined diagnosis of the biomarker and imaging. We included consecutive adult women inpatients with pathologic diagnosis for breast lesions (n = 1833) between January 2015 and September 2019 at Tongji Hospital, Hubei Cancer Hospital, and Qilu Hospital. Patients with hepatic or renal diseases were excluded because dysfunction of the two organs could affect the clearance and excretion of serum proteins.4,5 Detailed eligibilities are available in Supporting Information. Pathologic diagnosis, the reference standard, was performed in local centers and confirmed by the consensus of two pathologists in Tongji Hospital. Clinical data were retrospectively extracted from electronic health records by two researchers and assessed by the third investigator. Procedures for serum SEMA4C measurements using enzyme-linked immunosorbent assay are elaborated in Supporting Information. Serum samples were sent to Tongji Hospital for measurements, which were conducted by two researchers independently without knowledge of pathologic diagnosis and imaging assessments, and the average value was used for analysis. Machines utilized to record images and image interpretations are detailed in Supporting Information. Images were recorded by trained
The influence of acute coronary syndrome on levels of clopidogrel active metabolite and platelet inhibition in patients with and without CYP2C19*2(681 G>A), *3(636 G>A) and ABCB1(C3435C> T) gene polymorphisms

Introduction Although ticagrelor and prasugrel remain the standard antiplatelet treatments in acute coronary syndrome (ACS), numerous patients still present with indications for clopidogrel use. Aim We aimed to assess the levels of clopidogrel active metabolite and to evaluate the effect of the drug on platelet inhibition in patients with ACS as compared with those with stable coronary disease. Patients were assessed for the presence of the most common genetic polymorphisms that reduce the absorption (ABCB1) and activation (CYP2C19*2 and CYP2C19*3) of clopidogrel to exclude the effect of genetic variability on drug concentrations and activity. Material and methods This single-center, open-label, prospective study included 199 patients hospitalized due to ST-segment elevation myocardial infarction (STEMI) or non-STEMI (NSTEMI) in Killip class I–III, who underwent percutaneous coronary intervention. The control group included 22 patients with stable coronary artery disease. Results The mean (SD) levels of active clopidogrel were 17.1 (12.3) ng/ml in controls and 16.4 (12.0) ng/ml in the whole study group (p < 0.68). No differences were noted in clopidogrel levels between patients with STEMI and NSTEMI (mean (SD), 17.6 (2.3) ng/ml and 15.1 (11.5) ng/ml; p < 0.45) or between STEMI and NSTEMI groups and controls (p < 0.38 and p < 0.61, respectively). No effect of ABCB1 or CYP2C19 polymorphism was observed in the study subgroups. Conclusions We concluded that ACS does not affect the levels of clopidogrel active metabolite or platelet inhibition in patients in Killip class I-III with or without CYP2C19 or ABCB1 gene polymorphisms.
Integrated Multi-Sensor System for Parallel In-Situ Monitoring of Biotechnological Processes

Due to increasing complexity of bioprocesses in production and research, e.g. in the fabrication of antibodies and vaccines, and high efficiency and product safety requirements, precise monitoring and control in biotechnology becomes increasingly important. In this work a multi-sensor system for the parallel in-situ monitoring of the four important process parameters glucose, lactate, cell density and pH in bioprocesses is developed. The fabrication process of the sensor chip, based on a CMOS process flow, is suitable for integration into standard processes and mass production of the system. Thanks to the continuous, parallel measurement directly inside the bioreactor the system is considerably simpler, smaller, more cost-effective and easier to sterilize than the state-of-the-art measurement approach, which involves a separate sensor for every parameter and sampling and external measurement for glucose and lactate. The focus of this thesis is on the development of amperometric enzyme sensors for glucose and lactate that cover the wide concentration ranges found in bioprocesses. These range from a few millimoles per liter (mM) to concentrations above 1000 mM. To achieve such wide measurement ranges diffusion-limiting membranes are employed. Two membrane materials, poly(dimethylsiloxane) (PDMS) and polyurethane (PU), are assessed for this purpose, examining the surface morphology, permeability and long-term performance of fabricated membranes. Using PDMS membranes glucose is measured up to 200 mM, and with sensors covered with PU membranes concentrations up to 600 mM glucose and 900 mM lactate are measured. These ranges include a significant part of the concentrations found in bioprocesses. The glucose sensors are employed in long-term measurements and show good performance in the continuous monitoring of a yeast culture over 13 days and weekly calibration measurements over 30 days. The cell density sensor is realized as interdigitated platinum electrodes, and cell density is determined via conductivity changes of a cell suspension. Measurements are performed up to 20 g/l dry cell weight, covering a significant part of the cell densities found in bioprocesses. The pH is determined in C-V measurements via threshold voltage shifts at an electrolyte-insulator-semiconductor structure. Ta2O5 is employed as a pH-sensitive material, deposited on the chip surface by an atomic layer deposition (ALD) process. The sensor is able to measure pH in a range from pH 3 to pH 12, being suitable for any medium used in cell cultures. Both sensors show good performance and tight correlation with reference measurements when used in the monitoring of yeast culture. The glucose and cell density sensor in parallel are successfully employed in the monitoring of a yeast culture, showing good correlation with reference measurements. The feasibility of the parallel operation of multiple sensors on one chip is proven, and the advantage of the multi-sensor system in comparison to single sensors is demonstrated.
Algorithmic correspondence for relevance logics, bunched implication logics, and relation algebras: the algorithm PEARL and its implementation (Technical Report)

The non-deterministic algorithmic procedure PEARL (an acronym for `Propositional variables Elimination Algorithm for Relevance Logic') has been recently developed for computing first-order equivalents of formulas of the language of relevance logics RL in terms of the standard Routley-Meyer relational semantics. It succeeds on a large class of axioms of relevance logics, including all so-called inductive formulas. In the present work we re-interpret PEARL from an algebraic perspective, with its rewrite rules seen as manipulating quasi-inequalities interpreted over Urquhart's relevant algebras, and report on its recent Python implementation. We also show that all formulae on which PEARL succeeds are canonical, i.e., preserved under canonical extensions of relevant algebras. This generalizes the"canonicity via correspondence"result in Urquhart's 1996 paper. We also indicate that, with minor modifications, PEARL can be applied to bunched implication algebras and relation algebras.
Task-Sensitive Concept Drift Detector with Metric Learning

—Detecting drifts in data is essential for machine learning applications, as changes in the statistics of processed data typically has a profound inﬂuence on the performance of trained models. Most of the available drift detection methods require access to true labels during inference time. In a real-world scenario, true labels usually available only during model training. In this work, we propose a novel task-sensitive drift detection framework, which is able to detect drifts without access to true labels during inference. It utilizes metric learning of a constrained low-dimensional embedding representation of the input data, which is best suited for the classiﬁcation task. It is able to detect real drift, where the drift affects the classiﬁcation performance, while it properly ignores virtual drift, where the classiﬁcation performance is not affected by the drift. In the proposed framework, the actual method to detect a change in the statistics of incoming data samples can be chosen freely. We also propose the two change detection methods, which are based on the exponential moving average and a modiﬁed z -score, respectively. We evaluate the performance of the proposed framework with a novel metric, which accumulates the standard metrics of detection accuracy, false positive rate and detection delay into one value. Experimental evaluation on nine benchmarks datasets, with different types of drift, demonstrates that the proposed framework can reliably detect drifts, and outperforms state-of-the-art unsupervised drift detection approaches.
Distributed Control of Truss Robots Using Consensus Alternating Direction Method of Multipliers

Truss robots, or robots that consist of extensible links connected at universal joints, are often designed with modular physical components but require centralized control techniques. This paper presents a distributed control technique for truss robots. The truss robot is viewed as a collective, where each individual node of the robot is capable of measuring the lengths of the neighboring edges, communicating with a subset of the other nodes, and computing and executing its own control actions with its connected edges. Through an iterative distributed optimization, the individual members utilize local information to converge on a global estimate of the robot’s state, and then coordinate their planned motion to achieve desired global behavior. This distributed optimization is based on a consensus alternating direction method of multipliers framework. This distributed algorithm is then adapted to control an isoperimetric truss robot, and the distributed algorithm is used in an experimental demonstration. The demonstration allows a user to broadcast commands to a single node of the robot, which then ensures the coordinated motion of all other nodes to achieve the desired global motion.
Decision Making in Project Groups Formation: Students' Perception and Reconciliation

Academics is a huge repository of research avenue. Students tend to behave and adapt to the classroom based on their peer influences. Peers help in the increase of communication skills. Research shows group study is more effective than individual study. Group formation is influenced by several factors, like friends, demographic and linguistically similar. Toppers are more considerate but again based on comfort level friends are chosen. In this paper we analyze the change in preferences of group mates, and inferences are drawn over their reconciliations.
Feature Identification and Matching for Hand Hygiene Pose

Three popular feature descriptors of computer vision such as SIFT, SURF, and ORB are compared and evaluated. The number of correct features were extracted and matched for the original hand hygiene pose-“Rub hands palm to palm” image and rotated image. An accuracy score is calculated based on the total number of matches and the correct number of matches that were produced. The experiment demonstrated that ORB algorithm outperforms by giving the high number of correct matches in less amount of time. ORB feature detection technique will be applied over handwashing video recordings for the purpose of feature extraction and hand hygiene pose classification as a future work. OpenCV utilized to apply the algorithms within python scripts.
On the cuspidal locus in the dual varieties of Segre quartic surface

Motivated by a kind of Penrose correspondence, we investigate the space of hyperplane sections of Segre quartic surfaces which have an ordinary cusp. We show that the space of such hyperplane sections is empty for two kinds of Segre surfaces, and it is a connected surface for all other kinds of Segre surfaces. We also show that when it is non-empty, the closure of the space is either birational to the surface itself or birational to a double covering of the surface, whose branch divisor consists of some specific lines on the surface.
Hurdles for Developers in Cryptography

Prior research has shown that cryptography is hard to use for developers. We aim to understand what cryptography issues developers face in practice. We clustered 91 954 cryptography-related questions on the Stack Overflow website, and manually analyzed a significant sample (i.e., 383) of the questions to comprehend the crypto challenges developers commonly face in this domain. We found that either developers have a distinct lack of knowledge in understanding the fundamental concepts, e.g., OpenSSL, public-key cryptography or password hashing, or the usability of crypto libraries undermined developer performance to correctly realize a crypto scenario. This is alarming and indicates the need for dedicated research to improve the design of crypto APIs.
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models

How to explore efficiently is a central problem in multi-armed bandits. In this paper, we introduce the metadata-based multi-task bandit problem, where the agent needs to solve a large number of related multi-armed bandit tasks and can leverage some task-specific features (i.e., metadata) to share knowledge across tasks. As a general framework, we propose to capture task relations through the lens of Bayesian hierarchical models, upon which a Thompson sampling algorithm is designed to efficiently learn task relations, share information, and minimize the cumulative regrets. Two concrete examples for Gaussian bandits and Bernoulli bandits are carefully analyzed. The Bayes regret for Gaussian bandits clearly demonstrates the benefits of information sharing with our algorithm. The proposed method is further supported by extensive experiments.
Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data

Bregman proximal point algorithm (BPPA) has witnessed emerging machine learning applications, yet its theoretical understanding has been largely unexplored. We study the computational properties of BPPA through learning linear classifiers with separable data, and demonstrate provable algorithmic regularization of BPPA. For any BPPA instantiated with a fixed Bregman divergence, we provide a lower bound of the margin obtained by BPPA with respect to an arbitrarily chosen norm. The obtained margin lower bound differs from the maximal margin by a multiplicative factor, which inversely depends on the condition number of the distance-generating function measured in the dual norm. We show that the dependence on the condition number is tight, thus demonstrating the importance of divergence in affecting the quality of the learned classifiers. We then extend our findings to mirror descent, for which we establish similar connections between the margin and Bregman divergence, together with a non-asymptotic analysis. Numerical experiments on both synthetic and real-world datasets are provided to support our theoretical findings. To the best of our knowledge, the aforementioned findings appear to be new in the literature of algorithmic regularization.
U-mesh: Human Correspondence Matching with Mesh Convolutional Networks

The proliferation of 3D scanning technology has driven a need for methods to interpret geometric data, particularly for human subjects. In this paper we propose an elegant fusion of regression (bottom-up) and generative (top-down) methods to fit a parametric template model to raw scan meshes. Our first major contribution is an intrinsic convolutional mesh U-net architecture that predicts pointwise correspondence to a template surface. Soft-correspondence is formulated as coordinates in a newly-constructed Cartesian space. Modeling correspondence as Euclidean proximity enables efficient optimization, both for network training and for the next step of the algorithm. Our second contribution is a generative optimization algorithm that uses the U-net correspondence predictions to guide a parametric Iterative Closest Point registration. By employing pre-trained human surface parametric models we maximally leverage domain-specific prior knowledge. The pairing of a mesh-convolutional network with generative model fitting enables us to predict correspondence for real human surface scans including occlusions, partialities, and varying genus (e.g. from self-contact). We evaluate the proposed method on the FAUST correspondence challenge where we achieve 20% (33%) improvement over state of the art methods for inter- (intra-) subject correspondence.
A New Entity Extraction Method Based on Machine Reading Comprehension

Entity extraction is a key technology for obtaining information from massive texts in natural language processing. The further interaction between them does not meet the standards of human reading comprehension, thus limiting the understanding of the model, and also the omission or misjudgment of the answer (ie the target entity) due to the reasoning question. An effective MRC-based entity extraction model-MRC-I2DP, which uses the proposed gated attention-attracting mechanism to adjust the restoration of each part of the text pair, creating problems and thinking for multi-level interactive attention calculations to increase the target entity It also uses the proposed 2D probability coding module, TALU function and mask mechanism to strengthen the detection of all possible targets of the target, thereby improving the probability and accuracy of prediction. Experiments have proved that MRC-I2DP represents an overall state-of-the-art model in 7 from the scientific and public domains, achieving a performance improvement of up to compared to the model model in F1.