Miracle-dz/UniFAR-FTUs huggingface dataset:
Listening and Processing Skills in Young School Children with a History of Developmental Phonological Disorder

There is a lack of longitudinal studies on the broad-based outcomes in children with Developmental Phonological Disorder (DPD). The aim of this study was to investigate listening and processing skills in a clinical sample of 7-to-10-year-old children diagnosed with DPD in their preschool years and compare these to same-aged typically developing (TD) children. The Evaluation of Children’s Listening and Processing Skills (ECLiPS) was completed by parents of 115 children with DPD and by parents of 46 TD children. The total ECLiPS mean score, and the five subscale mean scores, the proportion of children with clinically significant difficulties (≤10th percentile), and the proportion of children with co-occurrence of clinically significant difficulties on more than one subscale, were calculated. Results showed that the ECLiPS mean scores did not differ between the groups. There was no difference between groups regarding language and literacy, but a higher proportion of children with DPD than TD had difficulties in the total score, speech, and auditory processing, environmental and auditory sensitivity, and pragmatic and social skills. In addition, 33.9% of children with DPD had clinically significant difficulties in two or more subscales compared to 10.9% of TD children.
Evaluation of five models for constructing forest NPP–age relationships in China based on 3121 field survey samples

Abstract. Forest net primary productivity (NPP), representing the net carbon gain from the atmosphere, varies significantly with forest age. Reliable forest NPP–age relationships are essential for forest carbon cycle modeling and prediction. These relationships can be derived from forest inventory or field survey data, but it is unclear which model is the most effective in simulating forest NPP variation with age. Here, we aim to establish NPP–age relationships for China's forests based on 3121 field survey samples. Five models, including the semi-empirical mathematical (SEM) function, the second-degree polynomial (SDP) function, the logarithmic (L) function, the Michaelis–Menten (M) function, and the Γ function, were compared against field data. Results of the comparison showed that the SEM and Γ functions performed much better than the other three models, but due to the limited field survey samples at old ages, the Γ function showed a sharp decrease in NPP (decreased to almost zero) at old ages when building some forest NPP–age curves, while SEM could capture the variations in forest NPP at old ages reasonably well. Considering the overall performance with currently available forest field survey samples, SEM was regarded as the optimal NPP–age model. The finalized forest NPP–age curves for five forest types in six regions of China can facilitate forest carbon cycle modeling and future projection by using the process-based Integrated Terrestrial Ecosystem Carbon (InTEC) model in China and may also be useful for other regions.
Benign Paroxysmal Positional Vertigo Following Electroconvulsive Therapy

212 Erciyes Med J 2019; 41(2): 212–4 • DOI: 10.14744/etd.2019. 26937 CASE REPORT – OPEN ACCESS This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Leyla Kansu Benign Paroxysmal Positional Vertigo Following Electroconvulsive Therapy Patients with severe major depression most commonly undergo electroconvulsive therapy (ECT). Although this procedure is usually safe and the complication rates are very low, some side-effects and medical complications are observed. The reported adverse effects are generally minor in severity, and vertigo is one of these complications. We presented a case of benign paroxysmal positional vertigo (BPPV) occurring after ECT and aimed to discuss the pathophysiological mechanism of this condition. To the best of our knowledge, there are no other case reports regarding the co-occurrence.
Physically Meaningful Surrogate Data for COPD

The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are “data hungry” whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV1/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV1/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.
Low-Complex AI-Empowered Receiver for Spatial Media-Based Modulation MIMO Systems

Next-generation communication systems are expected to integrate artificial intelligence (AI) techniques into multi-antenna setups to improve system performance. One of the most important use cases of AI in wireless communications is multiple-input multiple-output (MIMO) systems, due to the superior AI capability of learning the best possible decision-making in complex transceiver structures. In this study, we propose a deep neural network (DNN) receiver structure for spatial media-based modulation (SMBM)-MIMO systems, which detects the symbols in an end-to-end manner. Instead of a conventional two-stage approach, which handles channel estimation and symbol detection separately, the proposed DNN receiver recovers the transmitted symbols directly, utilizing an offline training process. It is demonstrated that while the proposed DNN receiver structure and conventional maximum likelihood (ML) receiver utilizing linear minimum mean-square error (LMMSE)-based channel estimation, perform similarly for a single receive antenna case, DNN is superior for multiple receive antenna cases. We conclude that using DNNs in SMBM with multi-antenna receivers can provide higher performance and thus permits higher data transmission rates in addition to the reduced receiver complexity due to the removing channel estimation process.
A systematic literature survey on recent trends in stock market prediction

Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.
Relational catamorphisms

Abstrnct This paper reports ongoing resea.rch into a theory of datatypes based on the calculus of relations. A fundamental concept introduced here is the notion of "relator" which is an ada.ption of the categorical notion of functor. Relational catamorphisms are then introduced and shown to satisfy a unique extension property. Several further properties are discussed including so-called fusion properties. The paper is concluded by showing how new relators' ca.n be constructed by an appropriate choice of relational catamorphism
Anthocyanins: Plant-based flavonoid pigments with diverse biological activities

Anthocyanins are flavonoid containing polyphenolic phytochemicals. They are widely present in plants and accounts for different color shades displayed by the plant organs. A broad range of health-revitalizing effects is attributed to anthocyanins, constituting a vital part of the human diet. They are also accountable for ameliorating the detrimental effects of various lifestyle diseases, including cancer, cardiovascular disorders, neurological disorders, etc. These beneficial impacts highly depend on the bioavailability of anthocyanins, governed by their absorption and metabolism in the human body. The primary goal of this review is to summarize the latest anthocyanin knowledge while focusing on the chemistry, pharmacokinetics, and various biological advantages with anti-cancer, neuroprotective, antidiabetic, antioxidant, cardiovascular protective, vision improvement, antiviral, and antimicrobial effects.
The potential of rotational woodlot and ngitiri systems in carbon sequestration and soil fertility improvement in Meatu District, Tanzania

Rotational woodlots and Ngitiri fallows have considerable amount of carbon storage and potential for soil fertility improvement though not well quantified. The objectives of this study were to develop allometric models for determination of above-ground carbon, quantify above and below-ground carbon, estimate soil organic carbon stocks and assess soil fertility status in rotational woodlots and Ngitiri in Meatu District, Tanzania. In each system, the experiment comprised 3 treatments, replicated 3 times in 8 and 10 year-old Acacia fallows. Ninety sample trees were measured for diameter at breast height (DBH) and height, and used in destructive sampling for development of allometric models. Several models were developed and the best fit model based on higher coefficient of determination (R) and low standard error was used to predict above-ground carbon for all tree components. Soil samples at 20 cm intervals were taken from 100 cm profile at each plot, standard routine soil analysis for organic carbon, nitrogen, phosphorus and potassium was done. The best fit models for A. tortilis and A. polyacantha were those which used DBH as predictor variable for different tree species. The highest total tree carbon storage under woodlots and Ngitiri was 22.0 ± 3.14 and 17.18 ± 2.13 t ha respectively. In two years Ngitiri and woodlots captured between 6.4 to 9.16 and 1.41 to 5.02 t ha respectively of above-ground carbon. Ngitiri and woodlots captured 91.3 and 60.7 t ha of soil organic carbon respectively in two years. Total nitrogen, inorganic nitrogen and Olsen phoshorus was higher in woodlots than Ngitiri while potassium was low in woodlots than Ngitiri.The best fit models for predicting above-ground carbon in Acacia species are those which used DBH as predictor variables, though height may as well be used in some cases. Rotational woodlots and Ngitiri have the potential for carbon storage and soil fertility improvement. Though, Ngitiri can sequester more carbon than woodlots while woodlots have higher potential for soil fertility improvement. Therefore, ii
MiSSNet: Memory-Inspired Semantic Segmentation Augmentation Network for Class-Incremental Learning in Remote Sensing Images

With remote sensing images constantly being collected rapidly, the class-incremental semantic segmentation (CISS) task has attracted increasing attention. However, the semantic distribution shift problem of the background class in remote sensing images, which is a case of catastrophic forgetting, continues to limit available CISS algorithms. To address this challenge, we present a new memory-inspired semantic segmentation augmentation network (MiSSNet) for class-incremental learning in remote sensing images. The MiSSNet mainly includes two modules: the local semantic distillation (LSD) module and the class-specific regularization (CSR) module. LSD is a distillation structure that employs the local semantic features in retained memory to maintain correlation between pixels throughout the training process of incremental learning. It constructs a series of pixel-level correlation matrices and implicitly adjusts the semantic distribution shift problem of the background class. CSR is a classwise regularization term that utilizes the class-specific portion of the preserved memory to help the model keep repeating the learning of the old categories. It alleviates the background class shift problem by generating countless pixel-level instances of old classes. LSD and CSR work together to tackle the semantic distribution shift problem of background class from semantic information and class information aspects, respectively. In particular, MiSSNet only needs an additional single inference process for memory extraction and storage, and the whole algorithm does not add any new training parameters. Experimental results on three semantic segmentation datasets indicate the advantage of the proposed method.
The MPEG7 Visual Search Solution for image recognition based positioning using 3D models

This paper describes a location algorithm for mobile phones based on image recognition. The use of image recognition based (IRB) positioning in mobile applications is characterized by the availability of a single camera; under this constraint, to estimate the camera position and orientation, a prior knowledge of 3D environment is needed in the form of a database of images with associated spatial information; this database can be built projecting the 3D model, acquired for instance with a LiDAR (Light Detection And Ranging), on a set of synthetic images. The herein proposed procedure to locate the camera can be divided in two steps, a first step is the selection from a database of the most similar image to the query image used to locate the camera, and a second step for estimation of the position and orientation of the camera based on available 3D information on the reference image. In designing the proposed location procedure, we have reused as much as possible the MPEG standard Compact Descriptors for Visual Search. For processing load optimization, similarly to the retrieval procedure defined by MPEG, we have introduced also in the position estimation step a preliminary statistical geometric check for coarse rejection of wrong matches (where a match represents two views in the respective images of the same point). We present the position and orientation accuracy results of the location methodology, for indoor and outdoor environment, that reaches respectively few decimeters and tenth of radians of precision
Time‐varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

A time‐varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time‐varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time‐varying joint return period for multivariate time‐dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme‐climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long‐term decision making strategies should be updated based on the anomalies of the nonstationary environment.
Seizure: European Journal of Epilepsy

The pathophysiology leading to the development of status epilepticus (SE) remains a topic of significant scientific interest and clinical relevance. The use of multiple experimental and computational models has shown that SE relies on a complex interaction between mechanisms that operate at both a cellular and network level. In this narrative review, we will summarise the current knowledge on the factors that play a key role in allowing SE to develop and persist. These include pathological adaptations to changing ion dynamics, neuroenergetics, receptor expression and neurotransmission, which enable the brain to meet the extensive demands required to maintain ongoing synchronous hyperexcitability. We will examine how these processes converge to enable synapses to support seizure perpetuation. Lastly, we will use the concept of a perpetuating network to highlight how connections between brain regions can provide positive feedback loops that can serve to propagate seizure activity. We hope this review will collate the findings of previous research and help fuel further studies into the mechanisms that underlie how the brain can make the transition to SE.
First report of eclogites from the Himalayan belt, Kaghan Valley (northern Pakistan)

Ec10gites characterized by the omphacite gamet quartz rutile ± phengite assemblage are reported for the first time in the Himalayan belt. They occur in the High Himalayan Crystallines (HHC) and Tethyan metamorphic units of northern Pakistan. The ec10gites formed at T = 650 ± 50üC and P = 13 18 kbar, and represent the deepest rocks derived from the Indian plate known so faT. They show an incipient decompressio­ nal re-equilibration which probably occurred at decreasing temperatures, in contrast with the exhumation path at constant or increasing temperatures of the HHC in India and Nepal. Key-words : ec1ogite, high-pressure metamorphism, metamorphic evolution, Himalayas.
Brain tumor classification: a novel approach integrating GLCM, LBP and composite features

Identifying and classifying tumors are critical in-patient care and treatment planning within the medical domain. Nevertheless, the conventional approach of manually examining tumor images is characterized by its lengthy duration and subjective nature. In response to this challenge, a novel method is proposed that integrates the capabilities of Gray-Level Co-Occurrence Matrix (GLCM) features and Local Binary Pattern (LBP) features to conduct a quantitative analysis of tumor images (Glioma, Meningioma, Pituitary Tumor). The key contribution of this study pertains to the development of interaction features, which are obtained through the outer product of the GLCM and LBP feature vectors. The utilization of this approach greatly enhances the discriminative capability of the extracted features. Furthermore, the methodology incorporates aggregated, statistical, and non-linear features in addition to the interaction features. The GLCM feature vectors are utilized to compute these values, encompassing a range of statistical characteristics and effectively modifying the feature space. The effectiveness of this methodology has been demonstrated on image datasets that include tumors. Integrating GLCM (Gray-Level Co-occurrence Matrix) and LBP (Local Binary Patterns) features offers a comprehensive representation of texture characteristics, enhancing tumor detection and classification precision. The introduced interaction features, a distinctive element of this methodology, provide enhanced discriminative capability, resulting in improved performance. Incorporating aggregated, statistical, and non-linear features enables a more precise representation of crucial tumor image characteristics. When utilized with a linear support vector machine classifier, the approach showcases a better accuracy rate of 99.84%, highlighting its efficacy and promising prospects. The proposed improvement in feature extraction techniques for brain tumor classification has the potential to enhance the precision of medical image processing significantly. The methodology exhibits substantial potential in facilitating clinicians to provide more accurate diagnoses and treatments for brain tumors in forthcoming times.
Two centuries of nitrogen dynamics: Legacy sources and sinks in the Mississippi and Susquehanna River Basins

Global flows of reactive nitrogen (N) have increased significantly over the last century in response to agricultural intensification and elevated levels of atmospheric deposition. Despite widespread implementation conservation measures, N concentrations in surface waters are often remaining steady or continuing to increase. Although such lack of response has been attributed to time lags associated with legacy N stores in subsurface reservoirs, it is unclear what the magnitudes of such stores are and how they are partitioned between shallow soil and deeper groundwater reservoirs. Here we have synthesized data to develop a 214 year (1800–2014) trajectory of N inputs to the land surface of the continental U.S. We have concurrently developed a parsimonious, process‐based model, Exploration of Long‐tErM Nutrient Trajectories (ELEMeNT) that pairs this input trajectory with a travel time‐based approach to simulate transport and retention along subsurface pathways. Using the model, we have reconstructed historic nitrate yields at the outlets of two major U.S. watersheds, the Mississippi River Basin (MRB) and Susquehanna River Basin (SRB). Our results show significant N loading above baseline levels in both watersheds before the widespread use of commercial N fertilizers, largely due to the conversion of forest and grassland to row crop agriculture. Model results also allow us to quantify the magnitudes of legacy N in soil and groundwater pools and to highlight the dominance of soil legacies in MRB and groundwater legacies in SRB. Approximately 55% and 18% of the current annual N loads in the MRB and SRB were found to be older than 10 years of age.
Tourism destination competitiveness: Proposing a framework for measurement

The measurement of the qualitative elements of Tourism Destination Competitiveness such as climate, scenery and quality of service has been a significant endeavor of tourism scholars in recent decades. Within these measurement efforts, there is a clear divide regarding how these subjective elements are assessed. Tourism scholars have made a choice between a demand perspective, in which they survey tourists about the list of competitiveness indicators, and a supply perspective where they turned to the supply side stakeholders with the same intention. The main purpose of this study is to challenge this notion of a forced choice dichotomy between a demand and a supply approach in the measurement of Tourism Destination Competitiveness. It is argued that rather than alternate, these perspectives are complementary and that competitiveness of tourism destination's assessments can and should be performed using a combination of both. Ultimately, this paper proposes a framework that combines both perspectives encouraging a more holistic account of Tourism Destination Competitiveness.