Miracle-dz/UniFAR-FTUs huggingface dataset:
Generation and Demand Scheduling in a Micro-grid with Battery-based Storage Systems, Hybrid Renewable Systems and Electric Vehicle Aggregators

This study presents a proposal for generation scheduling and demand response (electric vehicles in this study) of an isolated micro-grid. The system has the following elements: battery-based storage systems, hybrid renewable systems (wind and solar generators with controllable dispatch), traditional generators and electric vehicle aggregators for demand management. In order to reach economic and reliable operation of the system, a multi-objective optimization model considering battery life extension, in a side; and energy generation costs, and recharge price for electric vehicles, on the other side, is established. To address the variability and inherent stochastic nature of renewables, uncertainty cost functions for the hybrid renewable systems are used through probability density functions of the available resources. Additionally, in order to allow balancing the uncertainty and variability of renewable generation, Demand side management (DSM) is introduced into the optimization problem through controllable resources in the grid like plug-in electric vehicles (PEVs). A coordinated charging strategy is developed for PEVs through aggregators to obtain the most economic power dispatch scheme and the lowest charging price. The optimal set of operation parameters of the micro-grid is obtained using the NonDominated Sorting Genetic Algorithm-II (NSGA-II). Results show that the proposed methodology is useful to obtain a market environment able to handle the scheduling of generation resources and operation parameters of the micro-grid system. Key–Words: Economic dispatch models, Mathematical modeling, Genetic Algorithms, Solar energy, Uncertainty cost.
Tailored Randomized-block MCMC Methods for Analysis of DSGE Models∗

In this paper we develop new Markov chain Monte Carlo schemes for Bayesian estimation of DSGE models. The motivation for our work arises from some of the shortcomings of the single block random walk Metropolis Hastings (M-H) algorithm (RW-MH), the sampling method that has been used to date in this context. In our basic tailored randomized block (TaRB-MH) algorithm, the parameters of the model are randomly clustered at every iteration into an arbitrary number of blocks. Then each block is sequentially updated through an M-H step. Furthermore, the proposal density for each block is tailored to the location and curvature of the target density based on the output of simulated annealing, following Chib and Greenberg (1994, 1995) and Chib and Ergashev (2008). We also provide an extension of the TaRB-MH algorithm for sampling multi-modal distributions. In this version, which we refer to as the TaRBMJ-MH algorithm, at a pre-specified mode jumping iteration (say every 100th), a single-block proposal is generated from one of the modal regions using a mixture proposal density, and this proposal is then accepted according to an M-H probability of move. At the non-mode jumping iterations, the draws are obtained by applying the TaRB-MH algorithm. The methodological developments are completed by showing how the approach in Chib (1995) and Chib and Jeliazkov (2001) can be adapted to these sampling schemes for estimating the model marginal likelihood. We illustrate our methods with the aid of stylized problems and three DSGE models that have appeared in the literature. The first is the model in Ireland (2004) where we show that the TaRB-MH algorithm is more reliable and efficient than the RW-MH algorithm. Our second example is the model in An and Schorfheide (2007). The posterior distribution in this model is more challenging to simulate on account of multiple modes. As shown by these authors, the RW-MH algorithm is unable to jump from the low modal region to the high modal region, and vice-versa. The TaRBMJ-MH ∗We gratefully acknowledge the support from Center for Research in Economics and Strategy (CRES), in the Olin Business School, Washington University in St. Louis. †Chib: Olin Business School, Washington University in St. Louis, Campus Box 1133, 1 Bookings Drive, St. Louis, MO 63130 (e-mail: chib@wustl.edu); Ramamurthy: Department of Economics, Washington University in St. Louis, Campus Box 1208, 1 Bookings Drive, St. Louis, MO 63130 (e-mail: sramamur@artsci.wustl.edu).
Developing QoS by Priority Routing for Real Time Data in Internet of Things (IoT) Urban Scenarios

In networks, many application protocols such as CoAP, REST, XMPP ,AMQP have been proposed for IoT communication which includes p2p or S2S. In MANET Network convergence does the way for improvements in Internet of Things (IoT) communication with high potential for a wide range of applications. Each protocol focuses on some aspects of communication in the IoT. Hence, these application protocols have indicated of how IoT has integrated to enhanced and developed of a new service that require to guarantees the wide range offered by the quality of services. In this paper, we will introduce a smart pathway that can be bridge the gap between IoT services with its real data traffic. Therefore, we enhanced the MANET routing protocol for computing two or more paths to pass the more that one high priority real traffic data via these paths to improve the gloomy picture of this protocol in the context of IoT. In particular, the good services with high timely delivery of urgent data such as real time data environmental monitoring. After surveying the published and available protocol interoperability given for urban sensing. In this research, we have proposed a novel solution to integrate MANET overlays, and collaboratively formed over MANET, to boost urban data in IoT. Overlays are used to dynamic differentiate and fasten the delivery of high priority real application time data over low-latency MANET paths by integrating with the original specifications. Our experimental results showed the effectiveness on the network such as the overhead and network congestion. In addition, the initial results of the light-weight improved the routing protocol over the baseline protocols in terms of the delay of reciveing the packets between nodes which lead to increase the throughput by reducing loss packets.
Practical Chromatic Aberration Correction in Virtual Reality Displays Enabled by Cost‐Effective Ultra‐Broadband Liquid Crystal Polymer Lenses

Chromatic aberration (CA) is a critical issue in immersive virtual reality displays. The current digital compensation method can reduce the CA by pre‐processing the image contents but at the cost of extra memory space, processing time, and power consumption. Moreover, it is not possible to digitally compensate the CA within each color channel. Here, a practical optical approach is presented to correct the CAs, including the sub‐channel ones, in the virtual reality system, whose functionality is verified by both ray‐tracing analysis and experimental results. In this device, a compact ultra‐broadband Pancharatnam‐Berry phase lens (PBL) with complex axial molecular structure is fabricated and then hybridized with a refractive Fresnel lens. Due to their opposite chromatic dispersion, the system's CA is significantly reduced. To eliminate ghost image from the zero‐order leakage of PBL, a broadband circular polarizer is implemented to block the stray light. As a result, clear image within the entire 100° field‐of‐view is achieved. The proposed large‐size cost‐efficient broadband wide‐view flat optics can practically benefit not only virtual reality displays but also general imaging systems for practical applications and scientific research.
Being at the Bottom Rung of the Ladder in an Unequal Society: A Qualitative Analysis of Stories of People without a Home

Background: Homelessness is rising in the United Kingdom, despite investment in measures to eradicate it made by the government and charity organisations. Aim: The aim is to examine the stories of homeless people in order to document their perceptions of their social status, the reasons that led to their homelessness, and propose a conceptual explanation. Method: We conducted 26 semi-structured interviews in three centres for homeless people in Cheshire, North West of England. Results: Three categories—education, employment, and health—emerged from the data and provided a theoretical explanation for the reasons that led to their homelessness. These are vital not only for the successful negotiation of one’s way out of homelessness, but also for achieving other social goods, including social connections, social mobility, and engaging in positive social relationships. Conclusion: Participants catalogued the adverse childhood experiences, which they believe limited their capacity to meaningfully engage with the social institution for social goods, such as education, social services, and institutions of employment. Since not all people who have misfortunes of poor education, poor health, and loss of job end up being homeless, we contend that a combination of these with multiple adverse childhood experiences may have weakened their resilience to traumatic life changes, such as loss of job and poor health.
Making a Difference in PE Lessons: Using a Low Organized Games Approach to Teach Fundamental Motor Skills in China

Background: Fundamental motor skills (FMS) is the foundational movement for children’s physical development. Physical Education (PE) lessons provide a unique opportunity for children to acquire these skills. The purposes of this study were to: (1) to describe the FMS performance of primary school children in China; and (2) to examine the effect of a PE-based intervention on children’s FMS. Methods: The 12-week PE-based intervention was conducted using a low organized games (LOG) approach. Children’s FMS was measured by the Test of Gross Motor Development-2 (TGMD-2). Participants were 560 children aged between 6–9 years old (grade one to three) from two primary schools in Chongqing, China. They were grouped into intervention group (with LOG program PE; n = 282) or control group (with regular PE; n = 278). Results: The results revealed significant interaction effect between group and grade on the locomotor skill score changes (F(1,554) = 22.31, p < 0.000), and object control score change (F(1,554) = 627.1, p < 0.000). There was no significant interaction effect between the intervention group and gender on locomotor skill (F(1,554) = 1.49, p = 0.223) and object control skill significant (F(1,554) = 743, p = 0.389). Conclusions: The present study supported the application of LOG approach in PE lesson as an effective intervention to enhance children’s FMS in China.
Genetic Spectrum of ABCA4-Associated Retinal Degeneration in Poland

Mutations in retina-specific ATP-binding cassette transporter 4 (ABCA4) are responsible for over 95% of cases of Stargardt disease (STGD), as well as a minor proportion of retinitis pigmentosa (RP) and cone-rod dystrophy cases (CRD). Since the knowledge of the genetic causes of inherited retinal diseases (IRDs) in Poland is still scarce, the purpose of this study was to identify pathogenic ABCA4 variants in a subgroup of Polish IRD patients. We recruited 67 families with IRDs as a part of a larger study. The patients were screened with next generation sequencing using a molecular inversion probes (MIPs)-based technique targeting 108 genes involved in the pathogenesis of IRDs. All identified mutations were validated and their familial segregation was tested using Sanger sequencing. In the case of the most frequent complex allele, consisting of two variants in exon 12 and 21, familial segregation was tested using restriction fragment length polymorphism (RFLP). The most prevalent variant, a complex change c. [1622T>C;3113C>T], p.[Leu541Pro;Ala1038Val], was found in this cohort in 54% of all solved ABCA4-associated disorder cases, which is the highest frequency reported thus far. Additionally, we identified nine families displaying a pseudo-dominant mode of inheritance, indicating a high frequency of pathogenic variants within this population.
Beyond child maltreatment: The incremental value of household dysfunction in the prediction of negative beliefs and internalizing symptoms in women

Abstract Child maltreatment is associated with negative mental health outcomes including depression, posttraumatic stress disorder (PTSD), and anxiety. Maltreatment experiences can result in negative beliefs about safety and wellbeing, which may maintain negative mental health symptoms. Household dysfunction (eg, impaired caregiver) is considered less severe than maltreatment but more problematic than well-functioning family environments. Objective: This study investigates the incremental value household dysfunction contributes, above effects of maltreatment, to the prediction of internalizing symptoms (PTSD, depression, anxiety), and the extent to which negative beliefs might maintain these symptoms. Participants/methods: Female college students (N = 252; Mage = 19.2) electronically reported childhood exposure to maltreatment and household dysfunction, current maladaptive beliefs and internalizing symptoms. Results: Structural equation modeling showed household dysfunction significantly predicted internalizing symptoms after controlling for maltreatment experiences; this association was mediated through negative beliefs. Conclusions: Results suggest the additional importance of considering elements of household dysfunction when exploring traumatic histories.
Simulation of Ti-6Al-4V Additive Manufacturing Using Coupled Physically Based Flow Stress and Metallurgical Model

Simulating the additive manufacturing process of Ti-6Al-4V is very complex due to the microstructural changes and allotropic transformation occurring during its thermomechanical processing. The α-phase with a hexagonal close pack structure is present in three different forms—Widmanstatten, grain boundary and Martensite. A metallurgical model that computes the formation and dissolution of each of these phases was used here. Furthermore, a physically based flow-stress model coupled with the metallurgical model was applied in the simulation of an additive manufacturing case using the directed energy-deposition method. The result from the metallurgical model explicitly affects the mechanical properties in the flow-stress model. Validation of the thermal and mechanical model was performed by comparing the simulation results with measurements available in the literature, which showed good agreement.
Model-Based Event-Triggered Tracking Control of Underactuated Surface Vessels With Minimum Learning Parameters

This article studies the model-based event-triggered control (ETC) for the tracking activity of the underactuated surface vessel (USV). Following this ideology, the continuous acquisition of states is no longer needed, and the communication traffic is reduced in the channel of sensor to controller. The control laws are fabricated in the frame of an adaptive model, which is renewed with the states of the original system whenever the triggering condition is violated. In the scheme, both internal and external uncertainties are approximated by the neural networks (NNs). To decrease the computing complexity, the minimum learning parameters (MLPs) are involved both in the adaptive model and the derived controller. The adaptive laws of only two MLPs are devised, and their updating only happens at triggering instants. Using the MLPs, an adaptive triggering condition is further derived. To avoid the “Zeno” phenomenon in small tracking errors, a dead-zone operator is designed for the triggering condition. Furthermore, we incorporate the dynamic surface control (DSC) into the controller design, such that the jumping of virtual control laws at triggering instants is smoothed and the problem of “complexity explosion” is circumvented. Through the techniques of the impulsive dynamic system and the direct Lyapunov function, the parameter setting for the DSC is derived to guarantee the semiglobal uniformly ultimate boundedness (SGUUB) of all the error signals in the closed-loop system. Finally, the effectiveness of the proposed scheme is validated through the simulation.
Thermal Psychophysics and Associated Brain Activation Patterns Along a Continuum of Healthy Aging

Abstract Objective To examine psychophysical and brain activation patterns to innocuous and painful thermal stimulation along a continuum of healthy older adults. Design Single center, cross-sectional, within-subjects design. Methods Thermal perceptual psychophysics (warmth, mild, and moderate pain) were tested in 37 healthy older adults (65–97 years, median = 73 years). Percept thresholds (oC) and unpleasantness ratings (0–20 scale) were obtained and then applied during functional magnetic resonance imaging scanning. General linear modeling assessed effects of age on psychophysical results. Multiple linear regressions were used to test the main and interaction effects of brain activation against age and psychophysical reports. Specifically, differential age effects were examined by comparing percent-signal change slopes between those above/below age 73 (a median split). Results Advancing age was associated with greater thresholds for thermal perception (z = 2.09, P = 0.037), which was driven by age and warmth detection correlation (r = 0.33, P = 0.048). Greater warmth detection thresholds were associated with reduced hippocampal activation in “older” vs “younger” individuals (>/<73 years; beta < 0.40, P < 0.01). Advancing age, in general, was correlated with greater activation of the middle cingulate gyrus (beta > 0.44, P < 0.01) during mild pain. Differential age effects were found for prefrontal activation during moderate pain. In “older” individuals, higher moderate pain thresholds and greater degrees of moderate pain unpleasantness correlated with lesser prefrontal activation (anterolateral prefrontal cortex and middle–frontal operculum; beta < –0.39, P < 0.009); the opposite pattern was found in “younger” individuals. Conclusions Advancing age may lead to altered thermal sensation and (in some circumstances) altered pain perception secondary to age-related changes in attention/novelty detection and cognitive functions.
First Nations people with diabetes in Ontario: methods for a longitudinal population-based cohort study.

BACKGROUND To improve diabetes care, First Nations leaders and others need access to population-level health data. We provide details of the collaborative methods we used to describe the prevalence and incidence of diabetes in First Nations people in Ontario and present demographic data for this population compared to the rest of the Ontario population. METHODS To identify the population of First Nations people and other people in Ontario, we created annual cohorts of the Ontario population for each year between Apr. 1, 1995, and Mar. 31, 2015. Through a partnership between First Nations and academic researchers, we linked provincial population-based health administrative data stored at ICES with the Indian Register, which identifies all Status First Nations people. Our collaborative process was guided by the First Nations principles of ownership, control, access and possession (OCAP). RESULTS Demographic characteristics for the 2014/15 cohort (n = 13 406 684) are presented here. The cohort includes 158 241 Status First Nations people and 13 248 443 other people living in Ontario. Using postal codes, we were able to identify virtually all (99.9%) First Nations people in Ontario as living in (n = 55 311) or outside (n =102 889) a First Nations community. First Nations people were younger and more likely to live in semiurban or rural areas than the rest of Ontario's population. INTERPRETATION The collaborative methodology used in this study is applicable to many jurisdictions working with Indigenous groups who have access to similar data. The Ontario cohort defined here is being used to conduct analyses of health outcomes and use of health care services among First Nations people with diabetes in Ontario.
Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: A multivariable radiomics model

Background Osteosarcoma (OS) is the most common primary bone tumor affecting humans and it has extreme heterogeneity. Despite modern therapy, it recurs in approximately 30–40% of patients initially diagnosed with no metastatic disease, with the long-term survival rates of patients with recurrent OS being generally 20%. Thus, early prediction of metastases in OS management plans is crucial for better-adapted treatments and survival rates. In this study, a radiomics model for metastasis risk prediction in OS was developed and evaluated using metabolic imaging phenotypes. Methods and findings The subjects were eighty-three patients with OS, and all were treated with surgery and chemotherapy for local control. All patients underwent a pretreatment 18F-FDG-PET scan. Forty-five features were extracted from the tumor region. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved cross validation in the following four steps leading to final prediction model construction: (1) feature set reduction and selection; (2) model coefficients computation through train and validation processing; and (3) prediction performance estimation. The multivariable logistic regression model was developed using two radiomics features, SUVmax and GLZLM-SZLGE. The trained and validated multivariable logistic model based on probability of endpoint (P) = 1/ (1+exp (-Z)) was Z = -1.23 + 1.53*SUVmax + 1.68*GLZLM-SZLGE with significant p-values (SUVmax: 0.0462 and GLZLM_SZLGE: 0.0154). The final multivariable logistic model achieved an area under the curve (AUC) receiver operating characteristics (ROC) curve of 0.80, a sensitivity of 0.66, and a specificity of 0.88 in cross validation. Conclusions The SUVmax and GLZLM-SZLGE from metabolic imaging phenotypes are independent predictors of metastasis risk assessment. They show the association between 18F-FDG-PET and metastatic colonization knowledge. The multivariable model developed using them could improve patient outcomes by allowing aggressive treatment in patients with high metastasis risk.
Knowledge-Powered Deep Breast Tumor Classification With Multiple Medical Reports

Breast tumor classification with multiple medical reports such as B-ultrasound, Mammography (X-ray) and Nuclear Magnetic Resonance Imaging (MRI) is crucial to the intelligent cancer diagnosis system. Unlike the other domain texts, the medical reports have latent hierarchical syntactic structures and have hidden rich semantic information about the entities and relationships, which poses a great challenge of breast cancer classification. In this article, we proposed a Knowledge-powered Deep Breast Tumor Classification model (KDBTC), which takes the semantic information as a kind of prior knowledge and incorporated it into deep neural networks. Specially, our proposed model first uses Hierarchical Attention Bidirectional Recurrent Neural Networks (HA-BiRNNs) to encode the syntax-aware representation of medical reports in a hierarchical way. In the HA-BiRNN, a hierarchical neural network structure, consisting in two encoder layers of BiRNN (Bidirectional Recurrent Neural Networks), mirrors the hierarchical structure of medical reports, and a hierarchical attention mechanism, consisting of two levels attentions, attends to important elements within clinical report with word-level attention and sentence-level attention. Secondly, our model obtains the semantic information relevant to the medical reports from the clinical domain semantic tree, and encodes the semantic representation of medical reports by using Tree Structured Recurrent Neural Network with gated recursive units (Tree-GRUs). Finally, we classify breast tumors by combining both the syntax and semantic representations of medical reports. We evaluate our method on the real-world breast cancer medical reports, and results show that our method achieves higher performance on breast cancer classification.