Miracle-dz/UniFAR-FTUs huggingface dataset:
INTEGRATED CARE CASE

Introduction: Flexible Assertive Community Treatment (FACT) is a model of integrated care aiming to increase continuity of care for individuals with severe mental illness. FACT teams have been implemented in many countries by merging Assertive Community Treatment (ACT) and standard care from Community Mental Health teams (CMHTs). However, there has been little research on how practitioners experienced the fusion of these teams. Aim: This study explores how former ACT and CMHT practitioners perceived the implementation of FACT. Method: Perceptions of the FACT model, implementation strategies and adaptations of the model were investigated through 17 semi-structured interviews with FACT practitioners. Results: Perceived positive contributions of FACT included the recognition that FACT addressed a service gap for a group of patients who could benefit from increased support and improved integration of outpatient and hospital care. Perceived disadvantages of FACT included the experience that FACT drew away resources from ACT patients with the highest psychiatric needs. The findings also describe barriers to implementation, such as lack of perceived benefit of FACT, the maintenance of traditional work culture and insufficient organisational capacity. These challenges required the negotiation of local implementation strategies and adaptations. Conclusion: FACT increases access to intensive care for a broader group of patients with severe mental illness. However, findings from this study also suggest that the increased caseload in FACT compared with ACT and a changed mindset may not reflect the needs of the smaller group of patients who find it difficult to engage with mental health care services.
Growing Your Own Educational Leaders: Implications for Rural School Districts and Institutions of Higher Education

The purpose of this article is to examine a ‘grow your own’ model of leadership preparation and placement of educational administrators in the state of Tennessee. The growing need for school and district administrators in the rural counties of Tennessee mirrors a nationwide issue, and state policymakers and practitioners must respond appropriately to sustain adequate K-12 educational leadership that is representative of state demographics. Recommendations for policy and practice are provided for state and local education agencies as well as principal preparation programs in higher education.
A bias corrected RMSD item fit statistic. An evaluation and comparison to alternatives

Testing whether items fit the assumptions of an item response theory model is an important step in evaluating a test. In the literature, numerous item fit statistics exist, many of which show severe limitations. The current study investigates the root mean squared deviation (RMSD) item fit statistic, which is used for evaluating item fit in various large-scale assessment studies. The three research questions of this study are (1) whether the empirical RMSD is an unbiased estimator of the population RMSD; (2) if this is not the case, whether this bias can be corrected; and (3) whether the test statistic provides an adequate significance test to detect misfitting items. Using simulation studies, it was found that the empirical RMSD is not an unbiased estimator of the population RMSD, and nonparametric bootstrapping falls short of entirely eliminating this bias. Using parametric bootstrapping, however, the RMSD can be used as a test statistic that outperforms the other approaches—infit and outfit, S (cid:2) X 2 —with respect to both Type I error rate and power. The empirical application showed that parametric bootstrapping of the RMSD results in rather conservative item fit decisions, which suggests more lenient cut-off criteria.
BrainQCNet: a Deep Learning attention-based model for the automated detection of artifacts in brain structural MRI scans

Analyses of structural MRI (sMRI) data depend on robust upstream data quality control (QC). It is also crucial that researchers seek to retain maximal amounts of data to ensure reproducible, generalizable models and to avoid wasted effort, including that of participants. The time-consuming and difficult task of manual QC evaluation has prompted the development of tools for the automatic assessment of brain sMRI scans. Existing tools have proved particularly valuable in this age of Big Data; as datasets continue to grow, reducing execution time for QC evaluation will be of considerable benefit. The development of Deep Learning (DL) models for artifact detection in structural MRI scans offers a promising avenue toward fast, accurate QC evaluation. In this study, we trained an interpretable Deep Learning model, ProtoPNet, to classify minimally preprocessed 2D slices of scans that had been manually annotated with a refined quality assessment (ABIDE 1; n = 980 scans). To evaluate the best model, we applied it to 2141 ABCD scans for which gold-standard manual QC annotations were available. We obtained excellent accuracy: 82.4% for good quality scans (Pass), 91.4% for medium to low quality scans (Fail). Further validation using 799 scans from ABIDE 2 and 750 scans from ADHD-200 confirmed the reliability of our model. Accuracy was comparable to or exceeded that of existing ML models, with fast processing and prediction time (1 min per scan, GPU machine, CUDA-compatible). Our attention model also performs better than traditional DL (i.e., convolutional neural network models) in detecting poor quality scans. To facilitate faster and more accurate QC prediction for the neuroimaging community, we have shared the model that returned the most reliable global quality scores as a BIDS-app (https://github.com/garciaml/BrainQCNet).
Determining of Risk Ranking for Processed Foods in Korea

The risk ranking of food groups included the Korea Food Code is a potentially powerful means to prioritize food safety management strategies. Although the interest in risk ranking of food groups has been increasing worldwide, there was, until recently, no standard system in Korea for the risk ranking of food groups. This study was conducted to rank food groups using theoretically estimated comparative risk scores of 101 food groups included the Korea Food Code. These scores were estimated using the risk evaluation model, which focuses on 3 aspects, namely, exposure assessment, severity assessment, and consumption part. The results of this study revealed that the risk was the highest in the case of ready-to-eat (RTE) food items, followed by fish products and breads. Using this ranking system, we can identify the food with high risk scores and design risk management strategies targeted specifically at these items.
Five New World Primates

Launching a new series, Monographs in Behavior and Ecology, this work is an intensive study of five species of New World monkeys--all omnivores with a diet of fruit and small prey. Notwithstanding their common diet, they differ widely in group size, social system, ranging patterns, and degree of territorialityOriginally published in 1984.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These paperback editions preserve the original texts of these important books while presenting them in durable paperback editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
Modeling orientation % elds of % ngerprints with rational complex functions

In this paper, a novel model is proposed for the orientation %eld of %ngerprints, which can be expressed as the argument of a rational complex function. It is suitable for all types of %ngerprints. Experimental results show that it performs much better than the previous works. 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
An Empirical Performance Evaluation of Relational Keyword Search Systems

In the past decade, extending the keyword search paradigm to relational data has been an active area of research within the database and information retrieval (IR) community. A large number of approaches have been proposed and im- plemented, but despite numerous publications, there remains a severe lack of standardization for system evaluations. This lack of standardization has resulted in contradictory results from different evaluations, and the numerous discrepancies muddle what advantages are proffered by different approaches. In this paper, we present a thorough empirical performance evaluation of relational keyword search systems. Our results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption precludes many search techniques from scaling beyond small datasets with tens of thousands of vertices. We also explore the relationship between execution time and factors varied in previous evaluations; our analysis indicates that these factors have relatively little impact on performance. In summary, our work confirms previous claims regarding the unacceptable performance of these systems and underscores the need for standardization—as exemplified by the IR community—when evaluating these retrieval systems.
Agroforestería en fincas sobre planicie inundable en el área de influencia de la reserva forestal Caparo

Lo presentado corresponde a parte de un trabajo de maestría realizado en el área de influencia de la Unidad Experimental de la Reserva Forestal Caparo, ubicada en los llanos occidentales de Venezuela al suroeste del estado Barinas y con una superficie total de 174.370 ha; de éstas, 7.000 ha son de la Unidad Experimental, que es administrada por la Universidad de Los Andes (ULA) bajo la modalidad de un Comodato con el Ministerio del Ambiente y de los Recursos Naturales (MARN). El área donde se desarrolló el estudio cubre una superficie de unas 20.000 ha y se dividió en cinco sectores con el fin de abarcar la mayor diversidad en los diferentes niveles de producción de las fincas. De acuerdo con su importancia y representatividad, se analizaron las prácticas agroforestales utilizadas tradicionalmente por los campesinos y agricultores. Los aspectos sociales, económicos, de producción y la opinión personal de los productores se levantaron mediante encuestas, observaciones directas en el campo y análisis de la información recabada. Con los principales resultados obtenidos se construyó un cuadro resumen, discriminado por sectores para facilitar su interpretación. Los mismos se analizan en función del tamaño de las fincas (muy grandes: >200 ha, grandes: 50–200 ha, medianas: 25–50 ha y pequeñas: <25 ha), frecuencia de uso agroforestal y de cercas vivas, uso principal (agricultura, ganadería, forestería) y la existencia de reforestaciones y/o relictos de bosque natural. El componente arbóreo tiene una gran receptividad como parte de los sistemas de producción practicados, detectándose alguna forma de combinación agroforestal en el 100% de las fincas encuestadas. Con este estudio se busca contribuir al conocimiento del uso y manejo actual de la tierra con un enfoque agroforestal en el área de influencia de la Reserva Forestal Caparo, concluyéndose que las prácticas agroforestales se erigen como alternativas de uso de la tierra viables para la zona, sobre todo desde los puntos de vista económico, social y ecológico.
A Appendix

We prove that sufficiently wide and deep neural networks with supervised feedback and local learning rules can approximate any learning algorithm. We borrow some of the notation and proof techniques from Finn et al. [1]. We suppose the network propagates an input x forward, receives a target signal y from a supervisor, propagates a function of y back to its neural activations (feedback), and undergoes synaptic plasticity according to a local learning role dependent on these activations. We let {(xk,yk)} denote the training data, observed in that order, and x denote the test input.
Guidelines for the Surgical Management of Laryngeal Cancer: Korean Society of Thyroid-Head and Neck Surgery

Korean Society of Thyroid-Head and Neck Surgery appointed a Task Force to develop clinical practice guidelines for the surgical treatment of laryngeal cancer. This Task Force conducted a systematic search of the EMBASE, MEDLINE, Cochrane Library, and KoreaMed databases to identify relevant articles, using search terms selected according to the key questions. Evidence-based recommendations were then created on the basis of these articles. An external expert review and Delphi questionnaire were applied to reach consensus regarding the recommendations. The resulting guidelines focus on the surgical treatment of laryngeal cancer with the assumption that surgery is the selected treatment modality after a multidisciplinary discussion in any context. These guidelines do not, therefore, address non-surgical treatment such as radiation therapy or chemotherapy. The committee developed 62 evidence-based recommendations in 32 categories intended to assist clinicians during management of patients with laryngeal cancer and patients with laryngeal cancer, and counselors and health policy-makers.
Slovakia: A Catching Up Euro Area Member In and Out of the Crisis

The Slovak economy experienced a strong but short recession in 2009. The recovery afterwards was driven by exports and investment. While GDP growth was one of the strongest in OECD, employment did not reach the pre-crisis level and unemployment remains stubbornly high. This paper argues that Slovakia joined the euro area after a period of unprecedented real appreciation, which generated a threat for competitiveness of its export-oriented manufacturing industry. The response combined internal devaluation with productivity increasing measures, including capital deepening and laying off low productivity workers. While this strategy was successfully restoring an external equilibrium, its consequences for domestic demand and employment are less positive. This development is compared with Estonia and Slovenia, two other small and very open economies, recently entering the euro area.
The predictive significance of ratio between white blood cells count and serum albumin in assessing the functional outcome of acute ischemic stroke

Introduction: The acute ischemic infarction is associated with significant damage to brain parenchyma, where the high degree of acute inflammation is found. This work aimed to study the predictive value of ratio between blood WBCs and serum albumin levels in assessing the functional outcome in subjects with acute ischemic stroke. Materials and methods: A prospective observational study was conducted on 223 subjects from February 2022 to August 2022 at K.R. Hospital, Mysore. The data collected from patients of acute ischemic stroke, with the onset of stroke within 24 hours. Severity of stroke was assessed by using National Institute of Health Stroke Scale (NIHSS) at the time of admission. Data was collected using a pretested proforma meeting the objectives of the study. Results: The study has 124(55.6%) male and 99(44.4%) of female patients. Most of the patients (65%) were in the age group of more than 66 years. Most of the patients i.e., 66 (29.6%) had moderate to severe stroke at presentation as assessed by NIHSS stroke scale. At the end of third month of follow up, the acute ischemic stroke patients with MRS of >3 had WBCs count/serum albumin ratio of 4361.1 (p value - 0.001). Conclusion: The significant acute inflammation is found in acute ischemic stroke and it may be one of the causes of poor outcome and disability in so many ischemic stroke patients. This study once again endorses the use of high intensive statins and adequate dose of antiplatelet agents as early as possible in acute ischemic stroke patients. Keywords: acute ischemic stroke; WBCs; Modified Rankin Scale; NIHSS; serum albumin
Multi-Point path planning for robots based on deep reinforcement learning

Motion Planning is a key technology for mobile robots, which decomposes a Motion task that cannot be completed by a single action into multiple discrete actions that can be performed. This paper aims to design a robot motion planning algorithm based on reinforcement learning and make a robot carry out continuous multi-objective point motion planning. Motion planning network is a planning algorithm based on a neural network, and DQN is a classical algorithm in the field of reinforcement learning. Based on the two kinds of algorithm for motion planning, the Deep Q - learning algorithm chooses the robot’s next target, and then through the motion planning of the network between the current coordinates to the next target path planning. This paper analyzes the performance of the multi-point motion planning algorithm, and the results show that the algorithm is able to a higher success rate of successful completion of the task planning, but the reward strategy derived from the experiment still has the possibility of optimization.
The Effect of Biochar on Root Growth in Sustainable Agriculture

Sustainable agriculture can be used to achieve food security by continuously producing agriculture while taking safety, soil health, and environmental and economic considerations into account. Reduced agricultural land for agricultural production could stifle the growth of sustainable agriculture. This study made use of biochar, which is made from organic waste and serves to temporarily hold soil nutrients in the root zone of plants. The focus of this research was to find new ways to expand coastal land use by using biochar made from organic waste as a soil amendment for agriculture, particularly vegetable production. The study employed a two-factor design: the first was the size of the biochar material, which was coarse and fine, and the second was the biochar dosage, which included 10, 15 and 20 tons per hectare. Coconut shell waste was used to make the biochar. According to the results, the application of course-sized biochar at a dose of 15 tons per hectare resulted in lateral root distribution. Vegetable root development tended to be reduced when fine biochar was used. We can conclude that one of the solutions for sustainable agricultural production is to use biochar ameliorant materials made from environmentally friendly organic waste.
Defining Non-Valvular Atrial Fibrillation : a Quest for Clarification

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Defining nonvalvular atrial fibrillation: A quest for clarification R.P. Martins, V. Galand, E. Colette, N. Behar, D. Pavin, C. Leclercq, J.-C. Daubert, P. Mabo
Sistema de navegación monocular para robots móviles en ambientes interiores/exteriores

Uno de los desafios actuales de la robotica movil es alcanzar el mayor grado de autonomia, es decir, lograr que un robot desarrolle sus tareas sin la necesidad de un operador humano. El objetivo principal de este trabajo es el desarrollo de un nuevo sistema de navegacion autonomo basado en vision para robot moviles en entornos interiores/exteriores. El sistema propuesto utiliza solo una camara y sensores de odometria, no depende de ningun sistema de localizacion externo o infraestructura similar. Ademas, es capaz de tratar con variaciones en el ambiente (por ejemplo, cambios de iluminacion o estaciones del ano) y satisface las restricciones para guiar al robot en tiempo real. Para alcanzar el objetivo de este trabajo, se propone un enfoque hibrido que hace uso de dos tecnicas de navegacion visual: una basada en segmentacion de imagenes y otra basada en marcas visuales. Para representar el ambiente se construye un mapa topologico que puede ser interpretado como un grafo, donde las aristas corresponden a caminos navegables y los nodos a espacios abiertos. Para recorrer los caminos (aristas) se desarrollo un metodo original basado en segmentacion y para navegar por los espacios abiertos (nodos) se realizo una mejora y adaptacion de un metodo basado en marcas visuales. Se evaluaron diversos algoritmos de extraccion de caracteristicas distintivas de las imagenes para determinar cual representa la mejor solucion para el caso de la navegacion basada en marcas visuales, en terminos de performance y repetibilidad. El sistema desarrollado es robusto y no requiere de la calibracion de los sensores. La convergencia del metodo de navegacion se ha demostrado tanto desde el punto de vista teorico como practico. Su complejidad computacional es independiente del tamano del entorno. Para validar el metodo realizamos experiencias tanto con sets de datos como con el robot movil ExaBot, que se presenta como parte de este trabajo. Los resultados obtenidos demuestran la viabilidad del enfoque hibrido para abordar el problema de la navegacion basa en vision en entornos complejos interiores/exteriores.
RF and AMS

The paper shows four basic circuit functions which are RF transceiver, AMS, power amplifier (PA) and power management (PM), and digital signal processor (DSP). In this article, the authors emphasize the first three circuit functions, which drive analog and RF technology needs. Each of those three major parts in a RF front-end for a wireless system are discussed in a separate section with special emphasis on the device needs and technology choices for those blocks and with main focus on the frequency range from 0.8 to 10 GHz. A section on millimeter wave circuits and devices cover device and technology integration issues for applications in the frequency range starting from 10-100 GHz. Finally, we discuss the evolution of technology choices, integration issues, and potentially new emerging devices, all within the time-frame for the 2003 ITRS roadmap (2003-2018).
DigitalCommons@USU DigitalCommons@USU

—This paper applies non-orthogonal multiple access (NOMA) and relaying schemes in a mmWave based wireless heterogeneous system that aims to support Internet of Things (IoT) applications. The system consists of high power base stations, low-power relays, and low-power IoT devices. Due to the ad hoc deployment nature of low-power relays, they have very limited access to wireline power charging facilities. Furthermore, IoT devices normally have limited power and short battery life. The study assumes low-power relays and IoT devices are capable of energy harvest functionality. With the help of relays or IoT devices, downlink NOMA transmission consists of two phases. In the ﬁrst phase, the BS sends a composite signal to a UE and a selected relay simultaneously by applying NOMA. After receiving the signal, relay or the IoT device split the signal into two parts. One part is for information decoding and the other part is for energy harvesting. In the second phase, the BS sends another message to UE 1 while the relay sends the decoded message to UE 2 by using the harvested energy in phase 1. The outage problem of the proposed scheme is analyzed and simulations results are presented to verify the theoretical results.