Miracle-dz/UniFAR-FTUs huggingface dataset:
The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented.
New Pulse Recognition System for Coronary Heart Disease Patients Based on Pressure Sensors

The Coronary Heart Disease (CHD) is the most common cardiovascular disease due to the risk of heart-related complications. Currently, pulse diagnosis in Chinese medicine is an important method to recognition tool recognition of CHD. Here we designed pulse signal acquisition and recognition system based on the pressure sensors. The sensing units of the pulse signal utilized the ionic gel-based pressure sensors. Combined with the acquisition circuit board and convolution neural networks, the pulse signal acquisition and recognition system was established to identify CHD. This developed system provides an idea for the diagnosis of CHD in the homes of middle-aged and elderly people, and accelerated the development of the modernization of Chinese medicine.
In-Path Oracles for Road Networks

Many spatial applications benefit from the fast answering to a seemingly simple spatial query: “Is a point of interest (POI) ‘in-path’ to the shortest path between a source and a destination? ” In this context, an in-path POI is one that is either on the shortest path or can be reached within a bounded yet small detour from the shortest path. The fast answering of the in-path queries is contingent on being able to determine without having to actually compute the shortest paths during runtime. Thus, this requires a precomputation solution. The key contribution of the paper is the development of an in-path oracle that is based on precomputation of which pairs of sources and destinations are in-path with respect to the given POI. For a given road network with n nodes and m POIs, an O(m×n)-sized oracle is envisioned based on the reduction of the well-separated pairs (WSP) decomposition of the road network. Furthermore, an oracle can be indexed in a database using a B-tree that can answer queries at very high throughput. Experimental results on the real road network POI dataset illustrate the superiority of this technique compared to a baseline algorithm. The proposed approach can answer ≈ 1.5 million in-path queries per second compared to a few hundred per second using a suitable baseline approach.
Simulating from the Polya posterior

The Polya posterior is an objective Bayesian approach to finite population sampling. In its simplest form it assumes little prior information is available about the population and that the sample, however chosen, is “representative. ” That is the unobserved or unseen units in the population are assumed to be similar to the observed or seen units in the sample. It is appropriate when a classical survey sampler would be willing to use simple random sampling as their sampling design. Let ysamp = (y1, . . . , yn) be the vector of observed values in the sample of size n from a population of size N . Given the data, the Polya posterior is a predictive joint distribution for the unobserved units in the population conditioned on ysamp, the values in the sample. For a sample ysamp we now define this distribution for the unseen units. Consider two urns where the first urn contains the n observed ysamp values while the second urn contains the N − n unsampled units. We begin by choosing one unit at random from each of the two urns. We then assign the observed y value of the unit selected from the first urn to the unit selected from the second urn and then place them both in the first urn. The urns now contain n + 1 and N − n − 1 items respectively. This process is repeated until all the units have been moved from the second urn to the first and have been assigned a value. At each step in the process all the units in the first urn have the same probability of being selected. That is, unobserved units which have been assigned a value are treated just like the ones that actually appeared in the sample. Once this is done, we have generated one realization of the complete population from the Polya posterior distribution. This simulated, completed copy contains the n observed values along with the N − n simulated values for the unobserved members of the population. Hence, simple Polya sampling yields a predictive distribution for the unobserved given the observed. One can use the Polya posterior in the usual Bayesian manner to find point and interval estimates of various population parameters. In practice such estimates are usually found approximately by considering many simulated copies of the completed population. A good reference for Polya sampling is Feller (1968). The Polya posterior is related to the Bayesian bootstrap of Rubin (1981). See also Lo (1988). For more discussion on the Polya posterior see Ghosh and Meeden (1997).
A novel approach for length scale control in structural topology optimization

ABSTRACT This article proposes a novel method for topology optimization with length scale control. In this method, the structural skeleton based on the level set framework is employed. On this basis, the concept of the skeleton feature is proposed. The skeleton feature is defined as a circle, of which the centre is the skeleton point and the radius is the length scale. The signed distance function based on the skeleton feature is applied to achieve minimum and maximum length scale control. The length scale constraint is determined according to the location relationship between the structure and the skeleton feature. An increasing Lagrange multiplier is applied for length scale constraints. Several simple examples are presented to demonstrate the effectiveness of the skeleton features in length scale control.
From Labels to Decisions: A Mapping-Aware Annotator Model

Online platforms regularly rely on human annotators to make real-time operational decisions for tasks such as content moderation. While crowdsourcing models have been proposed for aggregating noisy labels, they do not generalize well when annotators produce a labels in a large space, e.g., generated from complex review trees. We study a novel crowdsourcing setting with D possible operational decisions or outcomes, but annotators produce labels in a larger space of size L > D which are mapped to decisions through a known mapping function. For content moderation, such labels can correspond to violation reasons (e.g. nudity, violence), while the space of decisions is binary: remove the content or keep it up. In this setting, it is more important to make the right decision rather than estimating the correct underlying label. Existing methods typically separate out the labels to decisions mapping from the modeling of annotators, leading to sub-optimal statistical inference efficiency and excessive computation complexity. We propose a novel confusion matrix model for each annotator that leverages this mapping. Our model is parameterized in a hierarchical manner with both population parameters shared across annotators to model shared confusions and individual parameters to admit heterogeneity among annotators. With extensive numerical experiments, we demonstrate that the proposed model substantially improves accuracy over existing methods and scales well for moderate and large L. In a real-world application on content moderation at Meta, the proposed method offers a 13% improvement in AUC over prior methods, including Meta's existing model in production.
Bayesian modeling of abandonments in ticket queues

Ticket queues (TQs) issue tickets to customers upon arrival, and are often used in the public and private sectors. Abandonment data collected by TQs is interval censored, which makes predicting customer abandonments a challenging problem. In this paper, we build a Bayesian framework for predicting abandonment counts in TQs to assist managers in workforce planning. In doing so, we propose parametric and semiparametric modulated Poisson process models and develop their Bayesian analyses using Markov chain Monte Carlo methods. We implement our models using actual abandonment data from a bank's TQ, and illustrate how we can provide managerial insights related to abandonment counts and server allocation policies.
High-Performance Relative Localization Based on Key-Node Seeking Considering Aerial Drags Using Range and Odometry Measurements

Using an inertial measurement unit and a single ultrawideband radio can provide effective relative localization only if the system observability is guaranteed. To ensure observability, recent studies utilize extended sliding window filter for state augmentation, selecting key-nodes as replacements for continuous states. However, as the velocity measurements are obtained indirectly through preintegration, integrating over key-nodes far apart could lead to velocity divergence. To tackle this issue, this article proposes the reformulated key-node seeking approach considering aerial drag effects, which inherently reflects the dissipative nature and enhances system observability and estimation precision. Considering the potential velocity divergence, aerial drag effect is extended to reformulate the process model and observability matrix. Then, to further eliminate a possible ill-conditioned observability matrix, an index naturally similar to the condition number of the reconstructed matrix is proposed to measure the degree of observability. Furthermore, the key-node selection strategy is developed, selecting the optimal measurements regarding the least ill-conditioned requirement as optimization. Thus, the selected key-nodes can achieve maximum observability within each sliding window. Finally, validated in single-agent, homogeneous, and heterogeneous multiple-agent systems, the proposed method steadily reduces estimation rmse and the condition number compared with the state-of-the-art methods, showing its effectiveness.
Large kernel convolution YOLO for ship detection in surveillance video.

At present, ship detectors have many problems, such as too many hyperparameter, poor recognition accuracy and imprecise regression boundary. In this article, we designed a large kernel convolutional YOLO (Lk-YOLO) detection model based on Anchor free for one-stage ship detection. First, we discuss the introduction of large size convolution kernel in the residual module of the backbone network, so that the backbone network has a stronger feature extraction capability. Second, in order to solve the problem of conflict regression and classification fusion under the coupling of detection heads, we split the detection head into two branches, so that the detection head has better representation ability for different branches of the task and improves the accuracy of the model in regression tasks. Finally, in order to solve the problem of complex and computationally intensive anchor hyperparameter design of ship data sets, we use anchor free algorithm to predict ships. Moreover, the model adopts an improved sampling matching strategy for both positive and negative samples to expand the number of positive samples in GT (Ground Truth) while achieving high-quality sample data and reducing the imbalance between positive and negative samples caused by anchor. We used NVIDIA 1080Ti GPU as the experimental environment, and the results showed that the mAP@50 Reaching 97.7%, mAP@. 5:. 95 achieved 78.4%, achieving the best accuracy among all models. Therefore, the proposed method does not need to design the parameters of the anchor, and achieves better detection efficiency and robustness without hyperparameter input.
Simple Obesity in Children

Otyłość prosta u dzieci jest jednym z najważniejszych, globalnych problemów zdrowia publicznego. Jest to przewlekła, ogólnoustrojowa choroba spowodowana zaburzeniem homeostazy energetycznej organizmu. Charakterystyczne jest patalogiczne zwiększenie ilości tkanki tłuszczowej, które prowadzi do powikłań ze strony wszystkich układów i narządów. Podstawową przyczyną otyłości prostej u dzieci jest zaburzenie homeostazy energetycznej między spożywanymi, a użytkowanymi kaloriami w ciągu doby - dodatni bilans energetyczny. Leczenie otyłości prostej u dzieci obejmuje ustrukturyzowany program redukcji masy ciała, dostosowany indywidualnie do każdego dziecka, obejmujący zmianę nawyków żywieniowych i stylu życia uwzględniając wysiłek fizyczny. Leki przeciw otyłości prostej mają ograniczone zastosowanie u dzieci. Natomiast chirurgia bariatryczna jest zarezerwowana dla nastolatków cierpiących na otyłość olbrzymią. Należy jednak pamiętać, że istnieje niewiele dowodów dotyczących długoterminowego bezpieczeństwa tej metody w leczeniu otyłości prostej u dzieci. Przeprowadzono przegląd literatury korzystając z baz danych PubMed/MEDLINE, Cochrane Library, Science Direct, w latach 2002-2023 dla anglojęzycznych i polskojęzycznych metaanaliz, przeglądów systematycznych, randomizowanych badań klinicznych i badań obserwacyjnych z całego świata. Uwzględniono również strony internetowe organizacji naukowych, m.in. WHO. Celem pracy jest przedstawienie otyłości prostej jako choroby cywilizacyjnej, analiza głównych czynników wpływających na jej powstawanie, identyfikacja długoterminowych konsekwencji zdrowotnych oraz stosowanych metod prewencji i leczenia otyłości prostej u dzieci. Stale rosnący odsetek dzieci z otyłością prostą, dowodzi, że jest to jeden z podstawowych problemów epidemiologicznych współczesnej ludzkości.
Factors that influence food choices in secondary school canteens: a qualitative study of pupil and staff perspectives

Background Adolescence is recognised as a period of nutritional vulnerability, with evidence indicating that United Kingdom adolescents have suboptimal dietary intakes with many failing to meet dietary recommendations. Additionally, adolescence is a time of transition when they become more independent in their dietary choices and begin to develop their own sense of autonomy and are less reliant on their parent’s guidance, which is reported to lead to less favourable dietary behaviours. Reducing the prevalence of poor dietary intakes and the associated negative health consequences among this population is a public health priority and schools represent an important setting to promote positive dietary behaviours. The aim of this school-based study was to explore the factors and barriers which influence food choices within the school canteen and to identify feasible strategies to promote positive dietary behaviours within this setting. Methods Thirteen focus groups with 86 pupils in Year 8 (n = 37; aged 11–12 years) and Year 9 (n = 49; aged 12–13 years) in six secondary schools across Northern Ireland, United Kingdom were conducted. Additionally, one-to-one virtual interviews were conducted with 29 school staff [principals/vice-principals (n = 4); teachers (n = 17); and caterers (n = 7)] across 17 secondary schools and an Education Authority (EA) senior staff member (n = 1). Focus groups and interviews were audio-recorded, transcribed, and analysed following an inductive thematic approach. Results Using the ecological framework, multiple factors were identified which influenced pupils’ selection of food in the school canteen at the individual (e.g., time/convenience), social (e.g., peer influence), physical (e.g., food/beverage placement), and macro environment (e.g., food provision) level. Suggestions for improvement of food choices were also identified at each ecological level: individual (e.g., rewards), social (e.g., pupil-led initiatives), physical (e.g., labelling), and macro environment (e.g., whole-school approaches). Conclusion Low-cost and non-labour intensive practical strategies could be employed, including menu and labelling strategies, placement of foods, reviewing pricing policies and whole-school initiatives in developing future dietary interventions to positively enhance adolescents’ food choices in secondary schools.
Numerical and Experimental Investigation on a “Tai Chi”-Shaped Planar Passive Micromixer

(1) Background: Microfluidic chips have found extensive applications in multiple fields due to their excellent analytical performance. As an important platform for micro-mixing, the performance of micromixers has a significant impact on analysis accuracy and rate. However, existing micromixers with high mixing efficiency are accompanied by high pressure drop, which is not conducive to the integration of micro-reaction systems; (2) Methods: This paper proposed a novel “Tai Chi”-shaped planar passive micromixer with high efficiency and low pressure drop. The effect of different structural parameters was investigated, and an optimal structure was obtained. Simulations on the proposed micromixer and two other micromixers were carried out while mixing experiments on the proposed micromixer were performed. The experimental and simulation results were compared; (3) Results: The optimized values of the parameters were that the straight channel width w, ratio K of the outer and inner walls of the circular cavity, width ratio w1/w2 of the arc channel, and number N of mixing units were 200 μm, 2.9, 1/2, and 6, respectively. Moreover, the excellent performance of the proposed micromixer was verified when compared with the other two micromixers; (4) Conclusions: The mixing efficiency M at all Re studied was more than 50%, and at most Re, the M was nearly 100%. Moreover, the pressure drop was less than 18,000 Pa.
A Gated Graph Neural Network With Attention for Text Classification Based on Coupled P Systems

Text classification is a classical task in natural language processing. Prior traditional text classification methods rely on manually extracted features to a great extent, which are easily influenced by human subjectivity. Some existing text classification methods based on the artificial neural networks sometimes neglect the contextual semantic relationships of discontinuous word sequences, resulting in poor learning results. To alleviate these problems, we propose an attention-based gated graph neural network in the framework of coupled P systems (CPGANN) to automatically extract feature representations of nodes. The gating unit with attention is introduced to aggregate neighbor information to capture context semantic relations, and effectively alleviate the long-term dependence on discontinuous words. In order to obtain more discriminative nodes for classification, the attention mechanism is employed in CPGANN to extract keyword nodes before readout to aggregate subgraph representations. Extensive experiments on four real-world datasets demonstrate that CPGANN outperforms all other state-of-the-art baseline algorithms.
Analysis of Performance and Failure Modes of the IROC Proton Liver Phantom

Purpose To analyze trends in institutional performance and failure modes for the Imaging and Radiation Oncology Core’s (IROC’s) proton liver phantom. Materials and Methods Results of 66 phantom irradiations from 28 institutions between 2015 and 2020 were retrospectively analyzed. Univariate analysis and random forest models were used to associate irradiation conditions with phantom results. Phantom results included pass/fail classification, average thermoluminescent dosimeter (TLD) ratio of both targets, and percentage of pixels passing gamma of both targets. The following categories were evaluated in terms of how they predicted these outcomes: irradiation year, treatment planning system (TPS), TPS algorithm, treatment machine, number of irradiations, treatment technique, motion management technique, number of isocenters, and superior-inferior extent (in cm) of the 90% TPS isodose line for primary target 1 (PTV1) and primary target 2 (PTV2). In addition, failures were categorized by failure mode. Results Average pass rate was approximately 52% and average TLD ratio for both targets had slightly improved. As the treatment field increased to cover the target, the pass rate statistically significantly fell. Lower pass rates were observed for Mevion machines, scattered irradiation techniques, and gating and internal target volume (ITV) motion management techniques. Overall, the accuracy of the random forest modeling of the phantom results was approximately 73% ± 14%. The most important predictor was the superior-inferior extent for both targets and irradiation year. Three failure modes dominated the failures of the phantom: (1) systematic underdosing, (2) poor localization in the superior-inferior direction, and (3) range error. Only 44% of failures have similar failure modes between the 2 targets. Conclusion Improvement of the proton liver phantom has been observed; however, the pass rate remains the lowest among all IROC phantoms. Through various analysis techniques, range uncertainty, motion management, and underdosing are the main culprits of failures of the proton liver phantom. Clinically, careful consideration of the influences of liver proton therapy is needed to improve phantom performance and patient outcome.
Forests and pattern-avoiding permutations modulo pure descents

Abstract We investigate an equivalence relation on permutations based on the pure descent statistic. Generating functions are given for the number of equivalence classes for the set of all permutations, and the sets of permutations avoiding exactly one pattern of length three. As a byproduct, we exhibit a permutation set in one-to-one correspondence with forests of ordered binary trees, which provides a new combinatorial class enumerated by the single-source directed animals on the square lattice. Furthermore, bivariate generating functions for these sets are given according to various statistics.
Efficient Robust Matrix Factorization with Nonconvex Penalties

Robust matrix factorization (RMF) is a fundamental tool with lots of applications. The state-of-art is robust matrix factorization by majorization and minimization (RMF-MM) algorithm. It iteratively constructs and minimizes a novel surrogate function. Besides, it is also the only RMF algorithm with convergence guarantee. However, it can only deal with the convex $\ell_1$-loss and does not utilize sparsity when matrix is sparsely observed. In this paper, we proposed robust matrix factorization by nonconvex penalties (RMF-NP) algorithm addressing these two problems. RMF-NP enables nonconvex penalties as the loss, which makes it more robust to outliers. As the surrogate function from RMF-MM no longer applies, we construct a new one and solve it in its dual. This makes the runtime and memory cost of RMF-NP only depends on nonzero elements. Convergence analysis based on the new surrogate function is also established, which shows RMF-NP is guaranteed to produce a critical point. Finally, experiments on both synthetic and real-world data sets demonstrate the superiority of RMF-NP over existing algorithms in terms of recovery performance and runtime.