Miracle-dz/UniFAR-FTUs huggingface dataset:
TAOAID: Pedestrian Assistance Using Car Motion Detection System

This case study intended to propose an assistive pedestrian system using Arduino UNO as the microcontroller and several electronic components for the actuators and sensors of the prototype. For the sensors, ultrasonic sensors will be used as a proximity sensor to detect if there are pedestrians that need to cross the road and a passive infrared (PIR) is used for motion detection of cards on the road. There are three actuators for this proposed system: speakers, LED, and a micro server motor. These actuators produce sound, light, and motor movements respectively, that intended to alert pedestrians and drivers for a safer pedestrian crossing. This system suits to assist a wide variety of pedestrians including the blind, deaf, and children because of the actuators way of alerting through the sense of hearing, sight, and touch. The internet of things technology proposal in this study includes the circuit design, design illustration, and costs of materials. The speakers, LEDs, and micro servo motor served as actuators that alert the sense of hearing, sight, and touch, respectively, making this proposed project assistive to various pedestrians even to those with disabilities.
Field study: assessing migration and mobility patterns, access to health services and vulnerabilities of female fish traders in the Kafue Flats fishery, Zambia: research design report

The WorldFish Center in Lusaka is implementing a regional collaborative programme entitled "Fisheries and HIVA investing in sustainable solutions". The programme is funded by the Swedish International Development Cooperation Agency (Sida) and the Norwegian Ministry of Foreign Affairs. Under the Zambia component, the technical focus area "Options for reducing vulnerability along the fish marketing chain" focuses on the Kafue Flats fishery in Zambia. This study aims to identify vulnerability factors affecting fisher folk and fish traders in the Kafue Flats, especially those that make them susceptible and vulnerable to HIV&AIDS. The study will provide the analytical basis for pilot interventions to reduce specific vulnerability factors and enhance livelihoods for fisher folk and fish traders in the Kafue Flats fishery. This report describes the methodologies and approaches used for the study in the Kafue Flats fishery, and how the findings and analyses will be used for the further implementation and learning of the programme.
Discrepancy-guided dual-branch semantic segmentation

In order to improve the feature extraction process in the semantic segmentation network and clarify the operating logic of the model, this paper splits the discriminative features according to the pixel's own information and the inter-pixel correlation information. Using two branch networks, relational branch and information branch to focus on the extraction of their own features, and using the fully convolutional segmentation head and the fully-connected segmentation head respectively to make the features more in line with the requirements of the branch itself. In order to make full use of the information contained in the discrepancy between the two branches, a Discrepancy-guided Fusion module is proposed at the feature level, in where the difference information between the features is used as a guide to promote the fusion of the features of the two branches. At the prediction level, with the help of Point-wise Discrepancy Cross Entropy loss, the prediction difference is used to determine the network's attention to each pixel. The effectiveness of the method is verified by extensive experiments on the Cityscapes dataset.
Computer Vision for DC Partial Discharge Diagnostics in Traction Battery Systems

The tendency towards thin insulation layers in traction battery systems presents new challenges regarding insulation quality and service life. Phase-resolved DC partial discharge diagnostics can help to identify defects. Furthermore, different root causes are characterized by different patterns. However, to industrialize the procedure, there is the need for an automatic pattern recognition system. This paper shows how methods from computer vision can be applied to DC partial discharge diagnostics. The derived system is self-learning, needs no tedious manual calibration, and can identify defects within a matter of seconds. Thus, the combination of computer vision and phase-resolved DC partial discharge diagnostics provides an industrializable system for detecting insulation faults and identifying their root causes.
Approximation of three‐dimensional nonlinear wave equations by fundamental solutions and weighted residuals process

In this paper, the localized method of fundamental solutions (LMFS) with coupling the dual reciprocity method (DRM) is applied to simulate three‐space dimensional nonlinear wave equations. First, DRM, which is a popular meshless method based on polyharmonic splines (PhS), is applied to obtain the particular solution. After evaluating the particular solution, 3D‐LMFS method can be employed to evaluate the homogeneous solution. The proposed 3D‐LMFS algorithms construct 3D artificial surfaces where source points and then the collocation procedure propose by using the fundamental solutions in each 3D surface. Straightforwardly, a solution to the 3D wave equation is approximated by a sum of the combination of PhS and fundamental solutions using LMFS. Eventually, the scheme has been prosperously tested with selected examples.
Towards Ethical AI: Mathematics Influences Human Behavior

Synopsis Mathematics plays an important role in the linguistic structure of artificial intelligence (AI). We describe the linguistic process as a unique structure present both in human cognition and in cognitive computing. The close relationship with both AI and human cognition is due to this unique structure, which paves the way for AI to interfere with the behavior of those who interact with it. We highlight the role of mathematicians in designing algorithms—the core of the AI linguistic process—and in defining steps and instructions for AI. Because algorithms, through AI, interfere with the thought of those who interact with AI by providing anticipated solutions that prevent users from making free choices, we investigate how we can apply ethical principles to guide interactions between users and intelligent systems in order to address this issue. We contend that by integrating ethical principles in the mathematical modeling of algorithms, we can avoid manipulation, inequality, and black boxes in the protection of individual rights. As such, ethical considerations are highly important for those working with algorithms, which highlights the humanistic side of mathematics.
VI-HSO: Hybrid Sparse Monocular Visual-Inertial Odometry

In this letter, we present VI-HSO, a hybrid sparse monocular visual-inertial odometry system based on two innovative techniques called adaptive interframe alignment (AIA) and dynamic inverse distance filter (DIDF). Although the sparse image alignment algorithm appears efficient for calculating frame-to-frame motion, it tends to fail in case of significant intensity changes and motion blur. To overcome these limitations, we propose an adaptive interframe alignment method that allows for an adaptive selection between the original Lucas-Kanade (LK) method and the inverse compositional method when constructing photometric errors, along with the addition of inertial information in the process. This approach enables the tracking phase to utilize the full image and inertial information. During intense motion, the inverse distance of the new candidate point often fails to converge, leading to either scale drift or tracking failure. We present a dynamic inverse distance filter that can adjust the convergence range to update candidate points' inverse distance. This adjustment is based on the convergence ratio of the inverse distance of keyframes, which enables more convergent map points aiding in robust tracking in regions lacking texture and during rapid rotation. We evaluate the performance of VI-HSO on public datasets and real-world experiments, and our system outperforms state-of-the-art algorithms.
Reforming the Financial Health Care System： The Case of the Republic of Albania

Italy  The Albanian health care system is currently in a period of deep transformation as well as the country is reflecting the future reforms after the turbulent development of the most recent years. The admission of Albania among future members of the European Union requires also an innovation in the health care system in order to build a model more compliant with the European performance and standards. These innovations are required also in the managerial approach to the health care and in its financing system. The aim of this paper is to analyze the actual financing model of the Albanian health care sector while highlighting the possible future managerial development. First of all, this article presents a history review of the Albanian health-care system, analyzing the current governance model. The goal of this approach is to describe the starting point of the reform paths for the future policy makers. Afterwards the research underlines the transition from a financing model based on historical public expenditure to a system based on the performance as one of the main innovation in the managerial approach to the health care. The introduction of management thinking will then allow developing a cost-based financing model, an accounting system in the teaching hospital and, finally, a financing system able to pay for services provided by private health care entities. The article then offers also a contribution to policy makers in order to define the “paths” of the Alba n ian health care system in the
Mathematical properties of Hand Incremental Effect Additivity and other synergy theories

Synergy theories for multi‐component agent combinations use 1‐agent dose‐effect relations (DERs), known from analyzing previous 1‐agent experiments, to calculate neither‐synergy‐nor‐antagonism combination DERs. Synergy theories are widely used in pharmacology, toxicology, and radiation biology. This article analyzes mathematical properties of one important synergy theory, proposed by John Hand in a little‐known article published in 2000 and here called Hand Incremental Effect Additivity (HIEA). In 2018, Hand's approach was reinvented in a radiobiology study that inadvertently overlooked his paper. We carefully state the assumptions required for mathematically rigorous development of HIEA and other synergy theories. Under these assumptions, studying synergy/antagonism between any number of agents can be done on a single 2d plot. We show that HIEA combination DER is in general not well‐defined in that it can “blow up” at finite total dose. We also formulate necessary and sufficient conditions on 1‐agent DERs preventing such pathology. Using weighted harmonic means, we study the betweenness property of HIEA synergy theory and demonstrate that Hand's combination DER has a systematic tendency for betweenness violation. On the positive side, we introduce the concept of a strongly dominant agent and show that the presence of such an agent ensures the betweenness property. We show also that the betweenness property holds for another major synergy theory, Loewe–Berenbaum Additivity. Our emphasis in this article on synergy theories that can handle any number of agents was motivated by applications to the study of toxic effects of galactic cosmic rays on astronauts during interplanetary travel.
Forecasting stock prices with CNN and LSTM model

: Utilizing historical data from the Quandl API, this experiment investigates the use of Convolutional Neural Networks (CNNs) in stock price prediction. The information for Microsoft Corporation (MSFT) spans the dates of January 1, 2013, and May 18, 2018. The high, low, open, and close prices are scaled, and input sequences of length 6 are created in order to capture temporal dependencies. Dense layers, 1D convolutional layers, max pooling, and dropout regularization are all components of the CNN architecture. Mean squared error (MSE) loss and the Adam optimizer are used to train the model. Mean absolute error (MAE) and root mean square error (RMSE) are used to assess performance. To evaluate the model's convergence and generalization, the losses and errors from training and validation are examined. For visual purposes, the anticipated and actual stock prices are contrasted. The results shed light on how well CNNs anticipate stock prices, assisting investors and financial institutions in making wise choices.
Measurements of drop size distribution in the southwestern Amazon basin : Large-scale biosphere-atmosphere experiment in Amazonia (LBA)

[1] Simultaneous observations of an optical and an impact type disdrometer and their application in radar rainfall estimation are evaluated. The disdrometers and two collocated rain gauges were operated in the southwest Amazon region of Brazil in 1999 as part of a NASA Tropical Rainfall Measuring Mission (TRMM) field campaign and the hydrometeorological component of the Large Scale Biosphere-Atmosphere Experiment (LBA). During the experiment, we observed large drops with diameters greater than 5 mm. These large drops were not adequately detected by the impact disdrometer and resulted in differences in drop size distribution and integral rain parameters derived from the two sensors. Considering coincident observations, we calculated that the impact disdrometer recorded about 11% lower rainfall accumulations than the optical disdrometer. In addition, radar rainfall algorithms, which we derived from the impact and optical disdrometer measurements, showed instrument dependency. Out of four radar rainfall algorithms that we considered, rain rate derived from specific differential phase has the least dependency, while the rain rate derived from reflectivity at horizontal polarization and differential reflectivity combined exhibited the largest. We also observed the characteristics of rainfall and drop size distribution in two distinct wind regimes present during the TRMM - LBA field campaign. Rain was heavier in the easterly regime, with more large drops being present.
Interactively Defining Examples to be Generalized

In a programming by demonstration system, examples provided by the user are generalized to form abstractions that are used by the system. There are several reasons why the system and the user should collaborate to annotate examples with knowledge that guides generalization. These include dealing with inconsistencies among examples or the underlying domain theory, making queries in order to speed generalization (e.g., active learning), and making suggestions (perhaps based on data mining) to change the outcome of generalization. This paper presents the design of a system that interacts with users via a set of “guessers”: algorithms for suggesting possible annotations to the user, in the context of learning hierarchical task models.
Morphogenetic forces planar polarize LGN/Pins in the embryonic head during Drosophila gastrulation

Atypical deployment of social gaze is present early on in toddlers with autism spectrum disorders (ASDs). Yet, studies characterizing the developmental dynamic behind it are scarce. Here we used a data-driven method to delineate the developmental change in visual exploration of social interaction over childhood years in autism. Longitudinal eye-tracking data were acquired as children with ASD and their typically developing (TD) peers freely explored a short cartoon movie. We found divergent moment-to-moment gaze patterns in children with ASD compared to their TD peers. This divergence was particularly evident in sequences that displayed social interactions between characters and even more so in children with lower developmental and functional levels. The basic visual properties of the animated scene did not account for the enhanced divergence. Over childhood years, these differences dramatically increased to become more idiosyncratic. These findings suggest that social attention should be targeted early in clinical treatments.
Increased television viewing is associated with elevated body fatness but not with lower total energy expenditure in children 1 – 3

Background: Television (TV) viewing in children is associated with a higher body mass index, but it is unknown whether this reflects body fatness, and, if it does, why. Objective: The objective was to investigate whether TV viewing is associated with body fatness, physical activity, and total energy expenditure in preschool children. Design: Eighty-nine children were recruited into a cross-sectional study. Total daily energy expenditure (TEE) was measured by doubly labeled water, body composition by dual-energy X-ray absorptiometry, and physical activity by accelerometry. Results: There was a significant positive association between fat mass (corrected for fat-free body mass) and TV viewing (F 1⁄4 9.05, P 1⁄4 0.004). Each extra hour of watching TV was associated with an extra 1 kg of body fat. Children who watched more TV were also significantly less physically active (F 1⁄4 5.16, P 1⁄4 0.026). Independent of body composition and sex, children with greater physical activity levels had higher levels of TEE (F 1⁄4 5.15, P 1⁄4 0.029); however, physical activity did not mediate the relation between TV viewing and adiposity (P . 0.05). Conclusions: Preschool children who watch more TV are fatter and are less active, and activity influences TEE. However, despite TV viewing being linked to lower physical activity, the relation between TV viewing and fatness is not mediated by physical activity. The results suggest that a relation between TV viewing and fatness is more likely to be due to an effect on food intake. Am J Clin Nutr
Application of cold atmospheric plasma for mold inactivation

Mold growth in indoor interiors is an increasing problem that has adverse effects on both occupants and building materials. The aim of this study was to present the new environmentally friendly method (cold atmospheric plasma treatment) for decontamination of indoor environments contaminated by mold spores, with the potential to become an effective replacement for conventional methods, such as ultraviolet-C (UV-C) germicidal irradiation. The spores of Aspergillus species on malt extract agar plates were exposed to plasma produced by diffuse coplanar surface barrier discharge. The surface coverage of mold was evaluated using image analysis. Cold plasma treatment inactivated spores on the surface of the culture medium in 0.5 min, whereas a weak growth delay was observed after 30 minute exposure to UV-C irradiation. This study demonstrated that cold atmospheric plasma is a more effective method for reducing mold spores on agar compared with germicidal UV-C irradiation.
Predictors of All-Cause Mortality in Patients With Severe COPD and Major Depression Admitted to a Rehabilitation Hospital.

BACKGROUND COPD is a major cause of all-cause mortality. We examined predictors of 1-year mortality in patients with severe COPD and major depression after inpatient treatment in a rehabilitation hospital. METHODS We screened 898 consecutively admitted patients. Of these, 138 patients received the diagnoses of COPD according to American Thoracic Society Guidelines and major depression by Diagnostic and Statistical Manual of Mental Disorders, 4th edition and signed consent; 67 were randomized to a treatment adherence enhancement intervention and 71 to usual care. We assessed history of falls, dyspnea-related disability, severity of depression, medical burden, and cognitive functioning. Following discharge from inpatient rehabilitation, participants were prospectively followed, and mortality was ascertained over 52 weeks from hospital notes and reports of primary care physicians and relatives. RESULTS One-year, all-cause mortality was 22% (31 of 138). Multivariate Cox regression analysis showed that history of falls in the 6 months preceding hospital admission was the strongest predictor of mortality (OR, 3.05; 95% CI, 1.40-6.66; P < .005). Dyspnea during activities (Pulmonary Functional Status and Dyspnea Questionnaire-Modified domain) was also associated with mortality (OR, 1.05; 95% CI, 1.02-1.08; P < .002). Depression severity, medical burden, and cognitive impairment were not predictors of mortality. CONCLUSIONS Recent falls and dyspnea during activities identify subgroups of depressed patients with COPD at increased risk for all-cause mortality. These subgroups are in need of clinical attention and follow-up and can serve as targets for prevention research aiming to inform clinical strategies and public health planning.
Curcumin Inhibits Rift Valley Fever Virus Replication in Human Cells*

Background: Rift Valley fever virus is a single-stranded RNA virus that causes disease in humans and livestock. Results: Rift Valley fever virus infection activates the host NFκB signaling cascade. Conclusion: NFκB inhibitors, particularly curcumin, down-regulate virus in both in vitro and in vivo models. Significance: Novel versions of host components resulting from an infection make them ideal therapeutic targets. Rift Valley fever virus (RVFV) is an arbovirus that is classified as a select agent, an emerging infectious virus, and an agricultural pathogen. Understanding RVFV-host interactions is imperative to the design of novel therapeutics. Here, we report that an infection by the MP-12 strain of RVFV induces phosphorylation of the p65 component of the NFκB cascade. We demonstrate that phosphorylation of p65 (serine 536) involves phosphorylation of IκBα and occurs through the classical NFκB cascade. A unique, low molecular weight complex of the IKK-β subunit can be observed in MP-12-infected cells, which we have labeled IKK-β2. The IKK-β2 complex retains kinase activity and phosphorylates an IκBα substrate. Inhibition of the IKK complex using inhibitors impairs viral replication, thus alluding to the requirement of an active IKK complex to the viral life cycle. Curcumin strongly down-regulates levels of extracellular infectious virus. Our data demonstrated that curcumin binds to and inhibits kinase activity of the IKK-β2 complex in infected cells. Curcumin partially exerts its inhibitory influence on RVFV replication by interfering with IKK-β2-mediated phosphorylation of the viral protein NSs and by altering the cell cycle of treated cells. Curcumin also demonstrated efficacy against ZH501, the fully virulent version of RVFV. Curcumin treatment down-regulated viral replication in the liver of infected animals. Our data point to the possibility that RVFV infection may result in the generation of novel versions of host components (such as IKK-β2) that, by virtue of altered protein interaction and function, qualify as unique therapeutic targets.
Research on the Identification Method of Radionuclide Based on the Lightweight Model

Traditional CNN has problems with significant parameters and is difficult to deploy in a resource-constrained portable spectrometer for radionuclide identification. We propose a lightweight neural network model based on GhostNet to address these problems. First, the Ghost-CS module is built by adding the channel mixing operation in the Ghost module to enhance the information exchange between different channels; Secondly, using the ECA module reduces the noise information in the spectrum. The results showed that the algorithm maintains a high recognition accuracy while the number of model parameters is greatly reduced, making it easier to implement in portable spectrometers.