Miracle-dz/UniFAR-FTUs huggingface dataset:
N-White Balancing: White Balancing for Multiple Illuminants Including Non-Uniform Illumination

In this paper, we propose a novel white balance adjustment for multi-illuminant scenes, called “N-white balancing,” in which $N$ source white points are mapped into a ground truth one. Most white balance adjustments focus on adjusting single-illuminant scenes. Several state-of-the-art methods for adjusting multi-illuminant scenes have been proposed, but they need to know the number of segments or the number of light sources in advance. Multi-color balance adjustments have been investigated to improve the performance of white balancing, but they also suffer from color distortion due to the rank deficiency problem. In contrast, the proposed method, N-white balancing, can correct multi-illuminant scenes even when we do not know the exact number of segments or light sources in a scene. In an experiment, the proposed method was demonstrated to outperform state-of-the-art methods under various illumination conditions such as single and multiple illuminants including non-uniform light sources.
Molecular circumscription of the blackfly Simulium oyapockense from South America

Problems related to the identity of Simuliidae species are impediments to effective disease control in Amazonia. Some of these species, such as Simulium oyapockense Floch & Abonnenc, 1946 (Diptera: Simuliidae), are vectors of the organisms that cause onchocerciasis and mansonellosis diseases. This blackfly species has a wide distribution in South America, and it is suspected of being a complex of cryptic species. The aim of this study is to characterize the nominal species S. oyapockense using partial COI gene sequences. Seven populations of S. oyapockense (morphologically identified) were analysed, including one from its type‐locality. The other six populations were collected in Brazil and in Argentina. A taxon collected in Amazonas state, Brazil, with adults similar to S. oyapockense but with distinct pupae, was also included in the analysis (Simulium ‘S’). The nominal species S. oyapockense is circumscribed, and its geographical distribution is restricted to areas north of the Amazon River. Populations of S. oyapockense s.l. collected south of the Amazon River comprise a species complex that needs to be evaluated using integrative taxonomy. Simulium ‘S’ represents a species with unique morphological and molecular characteristics. Distinguishing cryptic species is a prerequisite for reducing the taxonomic impediment, especially in medically important taxa.
Selection criteria for patch angioplasty material in carotid endarterectomy

Background: Carotid endarterectomy (CEA) with patch angioplasty has been favored due to its lower reoccurrence of restenosis compared to primary CEA. There are multiple types of patch angioplasty material available. However, selection of patch material is based on uncertain criteria. The aim of this study is to determine the ideal criteria for selecting the best patch material for CEA. Methods: We conducted a comprehensive literature search for studies that describe the ideal criteria for selecting patch material for CEA. We compiled all of the criteria mentioned into one table and selecting the criteria which were most frequently mentioned with a simple scoring system. Results: A total of 65 studies out of 784 studies were assessed for its full-text eligibility. Thus, we found 23 studies that were eligible for analysis. There are 22 ideal criteria that were mentioned in the analyzed studies. We grouped these criteria into physical characteristics, safety, contribution to hemodynamic, contribution in tissue healing, economic aspect, and ability to prevent postsurgical complication. We proposed 10 ideal criteria for guiding vascular surgeon in selecting the best patch angioplasty material. Conclusion: To this day, no material has been discovered which meets all ten criteria. This study’s proposed ideal criteria serve as the foundation for the creation of the best patch angioplasty material.
Disruption of Connectivity Graphs in Uncertain Multi-Agent Systems

Multi-agent systems have become ever-present in modern society, whether as multi-robot teams, sensor networks, or social networks. While ensuring the connectivity and robustness of multi-agent systems has seen extensive research, the problem of disrupting the connectivity of a multi-agent system has remained largely unaddressed. Yet, this capability can be essential in certain applications, such as responding to a hostile multi-robot system or controlling the flow of disinformation in a social network. In this paper we propose a novel method to disrupt the connectivity of a multi-agent system with uncertain relationships. We represent a multi-agent system as a graph, with edges denoting the probability of communication between agents. We introduce the problem of identifying a subgraph which minimizes the overall connectivity of the multi-agent system. We formulate a novel approach to identify optimal sets of vertices to remove by approximating a minimization of the algebraic connectivity, given constraints on the number of vertices to disconnect. We show through evaluation on simulated multi-agent systems that our approach is able to effectively disrupt the connectivity of a multi-agent system, and discuss its comparative complexity to existing approaches while attaining these superior results.
An IoT-based Solution for Military Healthcare Monitoring

- On the battlefield, troops face a range of hurdles, including sickness, loss of track, and communication issues with the base station, all of which can endanger their lives. To assist soldiers in overcoming these problems, we have created a system that would allow us to offer army members with safety measures. For the suggested system, we are utilising Internet of Things technology. This technology allows GPS tracking of the soldier's location, as well as communications containing temperature, pulse, and heart rate, which can be detected by sensors and transmitted to the base station. This paper suggests the methodology and implementation behind the development of this system.
Early divergence of translation initiation and elongation factors

Protein translation is a foundational attribute of all living cells. The translation function carried out by the ribosome critically depends on an assortment of protein interaction partners, collectively referred to as the translation machinery. Various studies suggest that the diversification of the translation machinery occurred prior to the last universal common ancestor, yet it is unclear whether the predecessors of the extant translation machinery factors were functionally distinct from their modern counterparts. Here we reconstructed the shared ancestral trajectory and subsequent evolution of essential translation factor GTPases, elongation factor EF‐Tu (aEF‐1A/eEF‐1A), and initiation factor IF2 (aIF5B/eIF5B). Based upon their similar functions and structural homologies, it has been proposed that EF‐Tu and IF2 emerged from an ancient common ancestor. We generated the phylogenetic tree of IF2 and EF‐Tu proteins and reconstructed ancestral sequences corresponding to the deepest nodes in their shared evolutionary history, including the last common IF2 and EF‐Tu ancestor. By identifying the residue and domain substitutions, as well as structural changes along the phylogenetic history, we developed an evolutionary scenario for the origins, divergence and functional refinement of EF‐Tu and IF2 proteins. Our analyses suggest that the common ancestor of IF2 and EF‐Tu was an IF2‐like GTPase protein. Given the central importance of the translation machinery to all cellular life, its earliest evolutionary constraints and trajectories are key to characterizing the universal constraints and capabilities of cellular evolution.
Session hijacking vulnerabilities and prevention algorithms in the use of internet.

The concept of Internet security is studied by computer science as a safe medium for exchanging data while minimizing the likelihood of online threats. The extensive use of advanced web-based software in different industries such as education, retail, medical care, and payment systems, represents a security challenge for the programmers and an opportunity for the hackers to attack through session hijacking. This paper aims to present vulnerability with the respective control mechanisms and to propose an approach for avoiding hijacking threats by using one-time cookies along with other prevention strategies. The study uses a la review of literature, by analyzing resources from existing literature. Based on recent OWASP guidelines, session hijacking of attack is indeed one of the most frequent attacks that happens lately. Session hijacking happens as a result of poorly designed websites and a lack of security mechanisms, where the user's identity and session data are exposed. Keywords: Cookies; internet; security; session hijacking, vulnerability.
Dietary Intakes and Nutritional Status of Mother-Child (6-23 Months Old) Pair Targeted through the "Organic Residual Products for Biofortified Foods for Africa Project" in Rural Area in Senegal

Background: Despite micronutrient supplementation and food fortification strategies carried out for decades, micronutrient deficiencies remain prevalent among children under 5 years old in rural area in Senegal. The OR4FOOD project was implemented as a preventive and long-term approach to reduce malnutrition through biofortification. Objective: We aimed to assess the baseline dietary intakes and nutritional status of the mother-child (6-23 months old) pair in a rural community in Senegal. Methods: Dietary intakes were assessed using dietary recall questionnaires and weight food records. All foods and beverages consumed from waking to bedtime were quantified, and nutrient intakes were calculated. The nutritional status was measured by anthropometry. Results: Results showed that 77.2% of children had low dietary diversity score. Only 18% of them received an appropriate complementary feeding according to the minimum acceptable diet. Cereals and legumes were among the most consumed food groups, whereas orange-fleshed sweet potato (OFSP) and animal food products were rarely consumed. Median dietary intakes of iron, zinc, and vitamin A were lower than the recommended dietary allowances. Acute malnutrition and stunting affected 14.6% and 16.9% of children, respectively. Overall, 20.8% of mothers were underweighted, and overweight/obesity affected 23.1% of them. Conclusion: Malnutrition remains prevalent in rural areas of Senegal and affects both mothers and children. Furthermore, their nutrient requirements were not covered by the diet. Millet and cowpea being widely consumed, optimizing their iron and zinc content through biofortification and the introduction of OFSP might improve micronutrient intakes and would be promising strategies to prevent child malnutrition.
A Survey of Intelligent Chip Design Research Based on Spiking Neural Networks

The traditional neural network Intelligent chip has the problem of high power consumption due to classical computing architecture, limiting the development of neural network Intelligent chips. Stochastic computing (SC) encodes binary numbers into stochastic pulse sequences in operation, taking advantage of low power consumption and high performance. The application of SC in spiking neural networks (SNNs) Intelligent chips is beneficial to solving the high power consumption of traditional neural network chips. This article first summarizes the basic elements of SNNs and the basic principles of SC. Then, we review the development trends of the stochastic computation-based neural network chips and existing SNN chips under research at home and abroad, respectively, and analyze the current problems. Finally, a review of SNN chips based on SC is highlighted. This paper aims to provide new research directions and to learn ideas for the field of SNN chips through systematic summaries.
Finite Element Analysis of Fluid Flows

Abstract : The finite element method is applied to several simple cases of steady flow of a perfect, incompressible fluid. It is shown that the finite element representation accurately reflects the behavior of the classical flow equations. Finite elements form the basis for a versatile analysis procedure applicable to problems in several different fields. The earliest applications were to problems in structural mechanics. In recent years, nonstructural problems also have been treated by this method. The finite element method represents an approximate procedure for satisfying the problem in terms of its variational formulation. In structural mechanics this is generally accomplished by determining displacement fields based on satisfying the minimum potential energy theorem. Consequently, finite elements furnish a useful alternative scheme for applying the well-known Ritz method. For nonstructural problems, it is essential that the appropriate variational expressions be known beforehand. For the flow problems taken up in this paper, such expressions are well known. The governing matrix equation for the assemblage of elements is based on the properties derived for a single typical element. These properties, in turn, depend on assuming a mathematical form for the primary unknown of the problem and then satisfying the variational principle. For the elasticity problem, the unknowns are the displacements, while for the perfect incompressible fluid, either the velocity potential or the stream function may be used. Of great interest is that structural and nonstructural elements may often be identical in shape and may be represented by similar mathematical expressions. By way of illustration, the problems taken up in this paper were solved using the new ASTRA structural program developed at The Boeing Company. Finally, it should be pointed out that the major difference between the elasticity and fluid flow problems lies in the boundary conditions to be satisfied.
Optimal Latency-Oriented Coding and Scheduling in Parallel Queuing Systems

The evolution of 5G and Beyond networks has enabled new applications with stringent end-to-end latency requirements, but providing reliable low-latency service with high throughput over public wireless networks is still a significant challenge. One of the possible ways to solve this is to exploit path diversity, encoding the information flow over multiple streams across parallel links. The challenge presented by this approach is the design of joint coding and scheduling algorithms that adapt to the state of links to take full advantage of path diversity. In this paper, we address this problem for a synchronous traffic source that generates data blocks at regular time intervals (e.g., a video with constant frame rate) and needs to deliver each block within a predetermined deadline. We first develop a closed-form performance analysis in the simple case of two parallel servers without any buffering and single-packet blocks, and propose a model for the general problem based on a Markov Decision Process (MDP). We apply policy iteration to obtain the coding and scheduling policy that maximizes the fraction of source blocks delivered within the deadline: our simulations show the drawbacks of different commonly applied heuristic solutions, drawing general design insights on the optimal policy.
User Willingness-Based participant selection strategy of Crowdsensing

Mobile crowd sensing network has become a new type of perceptual datas collection method, and the quality of datas will have a great impact on the subsequent use of perceptual datas. The quality of datas upload is closely related to the willingness of participants, but the existing participant selection method ignores the user's willingness to a certain extent, which may cause users to quit the task midway and maliciously upload false datas. In order to select the appropriate task participants, this paper combines the actual scenario, considers multiple attributes such as user, task, and surrounding environment, and establishes a regression model based on user willingness using a fully connected deep neural network to quantitatively evaluate the user's willingness to perform the perceptual task. After that, a participant selection strategy that takes into account user willingness and user utility is designed for perceptual scenarios with different requirements. Through simulation experiments, this method is verified to be superior to the baseline method. Its rationality and effectiveness are illustrated by simulating two application scenarios with different degrees of urgency,.
Construction and Evolution of Fault Diagnosis Knowledge Graph in Industrial Process

The steel industry production line is complicated and contains a substantial amount of equipment, leading to serious problems in fault diagnosis (FD) such as wrong inspection strategies, fault location, and maintenance. To realize accurate and efficient equipment FD, this article proposes a steel production line equipment FD knowledge graph (SPLEFD-KG) based on a novel relation-oriented model with global context information for jointly extracting overlapping relations and entities (ROMGCJE). A low-level self-learning SPLEFD-KG is first constructed using the triples extracted by ROMGCJE. However, this low-level SPLEFD-KG is incomplete, and only contains sparse paths for fault reasoning. To overcome this problem, a reinforcement learning (RL) framework is applied to mine hidden semantic knowledge to complete the missing relation. Besides, the graph neural networks (Graph-NNs) are introduced to compute the embedding vector of new entities outside of the SPLEFD-KG for continuously completing missing entities. Finally, the low-level self-learning SPLEFD-KG evolves to one high-level SPLEFD-KG, which can provide information-rich and accurate fault-related knowledge. Extensive experiments conducted on the steel production line equipment failure (SPLEF) dataset indicate that the novel SPLEFD-KG significantly improves FD results and provides effective maintenance programs.
Ambiguity, responsibility and political action in the UK daily COVID-19 briefings

ABSTRACT This paper investigates how pronouns were used by UK government speakers to allocate responsibility to themselves and others in all 92 daily televised COVID-19 briefings that were held between March and June 2020. We identified the referent for every use of the first-person plural pronoun (1PL) as ‘inclusive’, ‘exclusive’, or 'ambiguous' and analysed the transitivity patterns in which these pronouns act as Participants. We argue that the UK government uses the inherent ambiguity of this pronoun to strategically mitigate their own responsibility for controlling the spread of the virus, while increasing the amount of responsibility to the general public. In doing so, we propose a transparent and replicable systematic method for identifying the referents of pronouns, which may be useful to other discourse analysts faced with the challenging task of pronoun resolution.
Ramp Rate Exaggeration during Wind Speed to Wind Power Conversion

Wind turbine power curves are frequently used within the wind power industry and academic research to convert wind speed to wind power. In assessing power curve skill, the focus is often placed on a power curve's ability to model mean power as opposed to variability. This paper aims to assess the skill of a number of power curves - including regular curve fitting, machine learning techniques and the IEC recommended binning method - in modeling wind power variability for both a single turbine and a wind farm. It was found that all power curve methodologies tested yielded high accuracy in modeling mean wind power over long periods. However, a marked exaggeration in wind power ramps was observed in the modelled time series - typically on the order of 10-14% for a single turbine and 25-60% for a wind farm. It was found that with post-processing, such as 11 trend filtering, observed ramp exaggerations can be minimized. The findings of this study should be considered by researchers working on wind power variability assessments, forecasting and ramp predictions, so as not to overestimate wind power variability.
Postnatal Growth Trajectories and Neurodevelopmental Outcomes Following Bevacizumab Treatment for Retinopathy of Prematurity

Purpose To investigate the postnatal growth and neurodevelopment of infants with retinopathy of prematurity (ROP) treated with intravitreal bevacizumab (IVB). Patients and Methods This was a retrospective comparative study. A total of 262 infants were divided among three study groups: 22 treated with intravitreal bevacizumab, 55 treated with laser, and 185 with ROP that resolved without treatment. Infants with nonviable course or hydrocephalus, a source of non-physiologic weight gain, were excluded. Neurodevelopment was assessed with Bayley III scores at 17–28 months if available and presence of hearing loss or cerebral palsy. Weekly weight, height, and head circumference from birth through 50 weeks postmenstrual age (PMA) were modeled to determine differences in growth trajectories following treatment. Results Comparison of postnatal growth curves from the time of treatment to 50 weeks PMA showed no significant differences in growth trajectories between groups after adjusting for the corresponding growth parameters at birth. Comparison of Bayley scores in patients with available data (n = 120) showed no significant differences. There was an increased risk of cerebral palsy in the IVB group after logistic regression adjusting for baseline confounders, but this did not retain statistical significance after applying the false discovery rate correction for multiple testing. Conclusion To our knowledge, this is the first large retrospective study to examine longitudinal growth in infants treated with IVB compared to controls. There were no significant differences in postnatal growth or neurodevelopmental outcomes between groups, which overall continue to support the safety of bevacizumab treatment for ROP.
The Role of Local Administrations in the Energy Retrofit of Public Buildings: a Case Study

Recent regulations, and related incentives, concerning urban sustainability issued at European and national level place particular attention on improving the energy and environmental performance of buildings. In this sense, a particular focus has been placed on public buildings (offices, schools, hospitals, etc.), being these centers of gravitation of social life. Among the EU strategies on this matter is an initiative called Sustainable Energy and Climate Action Plan (SECAP), through which local administrators, based on the knowledge of their territory, are called to establish appropriate energy strategies and commit to achieving their targets within the timeframe set by the EU. The present paper contribute to this specific topic by means of a case study concerning the retrofit of a Sicilian office building, for which a comparison between the actual energy consumption and CO2 emissions, based on bill reading, and those consequent to the implementation of some SECAP selected energy improvement intervention, estimated through building dynamic simulation, was performed, taking into account also the economic aspects.