Miracle-dz/UniFAR-FTUs huggingface dataset:
Instagram from The Perspective of Media Dependency Theory: A Research on University Students

Bağımlılık olgusu, endüstrileşmiş modern toplumların sosyo-kültürel yaşam formları içinde şekillenen birey yönelimlerinde kendisini belirgin şekilde hissettirmektedir. Bağımlılık, bilimsel çalışmalarda ağırlıklı olarak bireylerin, bedensel, ruhsal ve sosyal sağlığını etkileyen sağlık problemi olarak görülmektedir. Ve son yıllarda artan biçimde, başta çocuklar ve gençler olmak üzere bireylerin sanal dünya araçlarıyla kurdukları bağımlılık temelindeki ilişkilerin yol açtığı sağlık sorunlarına dikkat çekilmektedir. Modern toplumsal kültür içinde bireyler, belirli ihtiyaç ve hedeflere ulaşma adına iletişim araçlarıyla kaçınılmaz bir ilişki sürdürmektedirler. İletişim araştırmaları bu ilişkinin düzeylerini etki ve bağımlılık boyutlarında incelemektedir. İzleyiciye yönelik etkileri çok kapsamlı bir toplumsal yapı bağlamında ele alan Medya Sistem Bağımlılık Kuramına göre, karmaşık toplumsal yapılarda bireyler kendilerini ve etrafındaki dünyayı anlamak ve bu bilgiyi “eylemlerini yönlendirmede” kullanmak ve bazen de eğlenmek amacıyla medyaya daha çok bağımlı hale gelmektedir. Kuram, mikro düzeyde ise bireylerin belirli hedeflere ulaşmak için medya sistemiyle kurduğu ilişkilere dikkat çekmektedir. Çalışma, Instagram’ın bireyler üzerinde ne türden "bağımlılık ilişkileri" yarattığı, temel sorusu üzerine şekillendirilmiştir. Çalışma kapsamında yapılan alan araştırması ile medya bağımlılığı perspektifi ile tanımlanan " bağımlılık tipolojisinin" Instagram açısından geçerliliği sorgulanmış ve yapılan faktör analizi, Instagram’ın “anlama”, “oryantasyon” ve “eğlence/oyun” tipi bağımlılık ilişkilerine kaynaklık ettiğini doğrular nitelikte sonuçlar vermiştir. Ankara Hacı Bayram Veli Üniversitesi İletişim Fakültesi’nde öğrenim gören lisans, yüksek lisans ve doktora düzeyinde 240 öğrenci üzerinde yapılan nicel araştırma, gençlerin sosyal medya kullanımına ilişkin tercih, tutum ve düşünceleri ile geleneksel iletişim biçimlerinin değişimi hususunda da veriler içermektedir.
Lake-land breezes over a small elongated lake (Kozjak, Plitvice Lakes, Croatia)

The temperature of a small elongated lake (2.3 km by 0.1-0.6 km; maximum depth of 46 m) located in a mountaneous area was measured during July-September period over three years (2018-2020) at the depth close to the lake surface (0.2 m). Concurrent standard meteorological variables were measured in the vicinity of the lake. Criteria based on atmospheric and lake conditions were defined to detect the days with persistent lake-land breeze (LLB) events. The results showed that 17.7% of the investigated days were associated with LLB events. These days were accompanied by elevated energy in the normalized wavelet spectra for the wind speed at period of 24 hours. Daytime onshore lake breezes were mainly channeled in the along-lake direction due to the surrounding topography. Accordingly, during the daytime, diurnal wind veering was distorted. However, during the nighttime, a clear clockwise wind rotation was present. In addition, the results exhibited a clear relationship between the lake-land temperature difference and the strength of the LLBs.
Therapy Follows Diagnosis: Old and New Approaches for the Treatment of Acute Porphyrias, What We Know and What We Should Know

Heme, iron protoporphyrin IX, is one of life’s most central molecules. Hence, availability of the enzymatic machinery necessary for its synthesis is crucial for every cell. Consequently, inborn errors of porphyrin metabolism that compromise normal synthesis, namely the family of porphyrias, undermine normal cellular metabolism given that heme has functions in catalytic centers, signal transduction and functional regulation and its synthesis is fully integrated into the center of intermediary metabolism. Very often, diagnosis of porphyrias is difficult and therefore delayed. Therapy can be as complicated. Over the last 50 years, several strategies have been developed: because of its integration with other parts of intermediary metabolism, the infusion of glucose (glucose effect) was one of the first attempts to counterbalance the dysregulation of porphyrin synthesis in porphyrias. Since heme synthesis is impaired, infusional replacement of heme was the next important therapeutic step. Recently, siRNA technology has been introduced in order to downregulate 5-ALA-synthase 1, which contributes to the patho-physiology of these diseases. Moreover, other novel therapies using enzyme protein replacement, mRNA techniques or proteostasis regulators are being developed.
The Diabetic Cardiorenal Nexus

The end-stage of the clinical combination of heart failure and kidney disease has become known as cardiorenal syndrome. Adverse consequences related to diabetes, hyperlipidemia, obesity, hypertension and renal impairment on cardiovascular function, morbidity and mortality are well known. Guidelines for the treatment of these risk factors have led to the improved prognosis of patients with coronary artery disease and reduced ejection fraction. Heart failure hospital admissions and readmission often occur, however, in the presence of metabolic, renal dysfunction and relatively preserved systolic function. In this domain, few advances have been described. Diabetes, kidney and cardiac dysfunction act synergistically to magnify healthcare costs. Current therapy relies on improving hemodynamic factors destructive to both the heart and kidney. We consider that additional hemodynamic solutions may be limited without the use of animal models focusing on the cardiomyocyte, nephron and extracellular matrices. We review herein potential common pathophysiologic targets for treatment to prevent and ameliorate this syndrome.
MENTAL DISORDER COMORBIDITY IN A PUBLIC PHYSICAL AND MENTAL HEALTH COMMUNITY-BASED CARE NETWORK SPECIALIZED IN ALCOHOL AND DRUGS

Mental disorder comorbidity in substance use disorder patients is a common problem with health care implications. This study aimed to investigate mental disorder comorbidity in a physical and mental health community-based care network specialized in alcohol and drug dependence. This multicenter cross-sectional study included 103 substance use disorder patients from four Brazilian Referral Centers for Psycho-Social Care. Data were assessed by Mini International Neuropsychiatric Interview and Addiction Severity Index questionnaires. Unemployed single men between 30-59 years old characterized the sample and showed a significant correlation between alcohol, cocaine or crack , and cannabis dependence with some other serious mental disorders, but not among tobacco smokers. Mental disorder comorbidity is still common in some models of psychosocial care. It suggests that an unambiguous dual-diagnosis and a precise differentiation between primary mental disorders and substance use disorders must be a common clinical staff practice part of the routine in the different models of drug dependence care to obtain an effective mental health intervention for these individuals. especializada em alcoolismo e dependência química. Este estudo transversal multicêntrico incluiu 103 pacientes com transtornos por uso de substâncias de quatro centros brasileiros de referência para atenção psicossocial. Os dados foram avaliados por meio dos questionários Mini International Neuropsychiatric Interview e Addiction Severity Index. Homens solteiros desempregados, de 30 a 59 anos, caracterizaram a amostra e mostraram correlação significativa entre dependência de álcool, cocaína ou crack e cannabis com alguns outros transtornos mentais graves, mas não entre os fumantes de tabaco. A comorbidade transtornos modelos de atenção psicossocial. diagnóstico modelos toxicodependência
Machine Learning for Alzheimer’s Disease Detection Based on Neuroimaging techniques: A Review

Disease detection became one of the most important applications, especially with the rapid development of artificial intelligence techniques in the medical field. Alzheimer’s disease is considered as one of the irreversible disorders that infect the human brain, where cognitive performance declined, gradually. This paper present and discuss machine learning approaches for Alzheimer’s disease detection based on the neuroimaging modalities. Based on the revision, it shows that the utilization of different modalities, the availability of the scans, and the optimization of machine learning architectures played the main role to devise an accurate detection method for Alzheimer’s disease.
Prediction of Contralateral Lower-Limb Joint Angles Using Vibroarthrography and Surface Electromyography Signals in Time-Series Network

Multisource biosignals are being increasingly used in human–machine interaction applications. In particular, a critical problem in exoskeleton-assisted rehabilitation for patients with hemiplegia is the generation of rhythmic and symmetrical locomotion. To support lower limb rehabilitation, we propose a model for predicting contralateral joint angles using multisource biosignals. First, a vibroarthrography (VAG) sensor is attached to the affected leg, and surface electromyography sensors are attached to the sound leg. The corresponding signals are used to estimate the hip, knee, and ankle joint angles of the affected leg. Second, an algorithm based on a temporal convolution network (TCN) is introduced to predict the contralateral lower-limb joint angles during human locomotion. The TCN is compared with a long short-term memory (LSTM) network and a convolutional neural network. Experiments were conducted by 10 healthy participants. The results of the proposed model were compared with measurements from encoders in three joints mounted on an exoskeleton to verify the applicability of the proposed model. In addition, by using the outputs of a motion capture system as the ground truth, the experimental results validated the model prediction performance. The prediction root mean square error (RMSE) of the TCN was 52%–70% lower than that of the LSTM network and CNN at different paces. The prediction RMSE using VAG was 20%–24% lower than that without using VAG. Note to Practitioners—We propose a model for predicting the contralateral lower-limb joint angles of an exoskeleton. The model uses measurements from multiple biosignal sensors to support exoskeleton-assisted hemiplegia rehabilitation. A sensing system based on acoustic signals and bioelectric signals is developed to improve the joint angle prediction in the affected leg of patients with hemiplegia. A temporal convolution network is implemented to estimate joint angles, and training considering the root mean square error and Pearson correlation coefficient as evaluation indicators is performed. Unlike similar methods that include no sensory feedback from the affected leg, the proposed method incorporates VAG signals from the affected leg and sEMG signals from the sound leg to handle different degrees of hemiplegia and support the generation of rhythmic and symmetrical locomotion for gait rehabilitation using an exoskeleton.
Towards a Computational Cognitive Neuroscience Model of Creativity

Recent progress in AI has expanded the boundaries of the cognitive functions that can be simulated, but creativity remains a challenge. Neuroscience sheds light on its mechanisms and its tight relationship with episodic memory and cognitive control, while machine learning provides preliminary models of these mechanisms. We present these lines of research and explain how they can be exploited in the domain of computational creativity in order to further expand the capabilities of AI.
Application of the Chimera Method to Poisson’s Equation with the Homogeneous Dirichlet Boundary Condition

Establishing variational formulation is an effective way to study the existence and uniqueness of the solution of certain elliptic partial differential equation with boundary condition. For the solution of certain elliptic partial differential equation with boundary condition, we know that the numerical solution obtained by the finite element method approximates the solution of this equation. Moreover, to avoid gridding overly complex domains, we can use the Chimera method to decompose the domain into several overlapping sub-domains. In this paper, we study Poisson’s equation with the homogeneous Dirichlet boundary condition. By analyzing the existence and uniqueness of the solution of the corresponding variational formulation, we know the existence and uniqueness of the solution of Poisson’s equation with the homogeneous Dirichlet boundary condition. We use the Chimera method and the finite element method to deal with Poisson’s equation with the homogeneous Dirichlet boundary condition by constructing two iterative sequences and analyzing their properties.
DevianceNet: Learning to Predict Deviance from a Large-Scale Geo-Tagged Dataset

Understanding how a city’s physical appearance and environmental surroundings impact society traits, such as safety, is an essential issue in social artificial intelligence. To demonstrate the relationship, most existing studies utilize subjective human perceptual attributes, categorization only for a few violent crimes, and images taken from still shot images. These lead to difficulty in identifying location-specific characteristics for urban safety. In this work, to address this problem, we propose a large-scale dataset and a novel method by adopting a concept of “Deviance" which explains behaviors violating social norms, both formally (e.g. crime) and informally (e.g. civil complaints). We first collect a geo-tagged dataset consisting of incident report data for seven metropolitan cities, with corresponding sequential images around incident sites obtained from Google street view. We also design a convolutional neural network that learns spatio-temporal visual attributes of deviant streets. Experimental results show that our framework can reliably recognize real-world deviance in various cities. Furthermore, we analyze which visual attribute is important for deviance identification and severity estimation. We have released our dataset and source codes at our project page: https://deviance-project.github. io/DevianceNet/
Performance analysis of air curtains on indoor environment and dehumidification energy consumption in a low humidity plant

ABSTRACT The purpose of this study is to evaluate the effect of air curtains on indoor environment and dehumidification energy consumption for different air supply angle and velocity. The numerical investigation is conducted in a low-humidity plant located in the atmospheric boundary layer. The standard k-epsilon turbulence model is used in the simulation study. The optimum air supply angle and velocity are evaluated based on temperature and humidity distribution. The simulation cases range in air supply angle from 0° to 20° and velocity from 6 m/s to 14 m/s, which is studied by means of orthogonal experiments. Simulation results show that air curtain can reduce the average indoor temperature of the plant by up to 3.9°C and reduce the average moisture content by up to 86.6%. The energy saving assessment of the dehumidification rotor system shows a parabolic variation in energy consumption with increasing air velocity at a certain air supply angle. The optimum energy consumption occurs in the range of 9–11 m/s for air supply velocity. When air supply velocity is over 11 m/s, the airflow exchange is enhanced by air curtain at the plant opening, resulting in higher energy consumption from air leakage for dehumidification. GRAPHICAL ABSTRACT
What’s pitch got to do with it? – The connection between speech prosody and investor-pitch success

German investor-pitch presentations were acoustically analyzed based on five tonal and five non-tonal prosodic features. Results show that both feature sets significantly predicted the placements of these presentations in the ‘Rheinland Pitch’ competition series. We conclude in line with previous studies on speaker charisma that prosody plays a considerable role for the success of presentations and their speakers.
An Iron Refractory Phenotype in Obese Adipose Tissue Macrophages Leads to Adipocyte Iron Overload

Adipocyte iron overload is a maladaptation associated with obesity and insulin resistance. The objective of the current study was to determine whether and how adipose tissue macrophages (ATMs) regulate adipocyte iron concentrations and whether this is impacted by obesity. Using bone marrow-derived macrophages (BMDMs) polarized to M0, M1, M2, or metabolically activated (MMe) phenotypes, we showed that MMe BMDMs and ATMs from obese mice have reduced expression of several iron-related proteins. Furthermore, the bioenergetic response to iron in obese ATMs was hampered. ATMs from iron-injected lean mice increased their glycolytic and respiratory capacities, thus maintaining metabolic flexibility, while ATMs from obese mice did not. Using an isotope-based system, we found that iron exchange between BMDMs and adipocytes was regulated by macrophage phenotype. At the end of the co-culture, MMe macrophages transferred and received more iron from adipocytes than M0, M1, and M2 macrophages. This culminated in a decrease in total iron in MMe macrophages and an increase in total iron in adipocytes compared with M2 macrophages. Taken together, in the MMe condition, the redistribution of iron is biased toward macrophage iron deficiency and simultaneous adipocyte iron overload. These data suggest that obesity changes the communication of iron between adipocytes and macrophages and that rectifying this iron communication channel may be a novel therapeutic target to alleviate insulin resistance.
Allele-specific expression analyses reveal immune divergences between ibex and goat species

Samples were taken from 13 female ibex-goat hybrids (Capra sibirica and Capra hircus) (4–5 years old) from the Xinjiang Tianshan Wildlife Park, Karamay, China. All animals were born in the park and were in good health when the blood samples were taken. All samples were compared based on fixed divergent sites (FDSs) between ibex and goat to identify those fragments with ibex descent/lineage from the bin map (see “Lineage/descent/generation identification”).
Small and Medium-sized Enterprises Credit Risk Assessment Based on Temporal Knowledge Graphs

Credit Risk Assessment (CRA) is a challenging task in the financial field. Previous studies mainly focus on large firms with more comprehensive data especially financial data, annual reports, but for Small and Medium-sized Enterprises (SMEs), there is only public data to utilize, such as news, cases, etc. To better assess risk for SMEs, we constructed a temporal knowledge graph by using public data and proposed a credit risk assessment model (short for TKG-CRA) which comprehensively considers the topological structure of the temporal enterprise knowledge graph with the spread of risks and the neighbor node sequence. Experiments on real-world datasets prove that our model has a larger performance improvement than other traditional methods.
Reported Injuries from Sharp Objects among Healthcare Workers in Central Greece

Sharp injuries (SIs) are incidents or accidents caused by a needle, blades (such as scalpels) or other medical instruments which penetrate the skin. They are among the major work-related injuries in healthcare professionals. The purpose of this study is to estimate SIs in healthcare workers (HCWs) in Central Greece. Method: A cross-sectional descriptive study through an online survey in healthcare facilities in Central Greece was conducted. Snowball sampling contributed to further dissemination of the survey among the target population. The modified version of the EPINet questionnaire was used with self-reported answers of the participants via electronic Google form. Results: Analysis of collected data indicated that 74.1% of the participants had at least one injury, with the highest number of injuries occurring in nursing staff at 65.1% and 62.3% of injuries recorded in the morning shift. With respect to the site of the injury, participants reported 33.1% of the injuries in the patient’s room, 11.8% in the nurse’s station, 9.6% in the Emergency Department (ED), 9.2% in the Intensive Care Unit (ICU), 8.4% in blood sampling, 8.4% in surgery, and only 7.8% in laboratories or other places. Additionally, hands were the most frequently affected body part (96%), while 69.6% of the workers did not report the injury and 53% of them did not apply the procedures and guidelines defined by the healthcare organization (employer). Relative factors to the injury are age, level of education, shifts, and possibly sex. Conclusions: SIs are the “Achilles heel” of health workers. The high incidence and low reporting rate of SIs highlights the need for specialized training and education. Age, work experience, and shift appear to significantly affect the incidence of injury.
MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction

Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture user' preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memory-enhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.
Robust Tests in Online Decision-Making

Bandit algorithms are widely used in sequential decision problems to maximize the cumulative reward. One potential application is mobile health, where the goal is to promote the user's health through personalized interventions based on user specific information acquired through wearable devices. Important considerations include the type of, and frequency with which data is collected (e.g. GPS, or continuous monitoring), as such factors can severely impact app performance and users’ adherence. In order to balance the need to collect data that is useful with the constraint of impacting app performance, one needs to be able to assess the usefulness of variables. Bandit feedback data are sequentially correlated, so traditional testing procedures developed for independent data cannot apply. Recently, a statistical testing procedure was developed for the actor-critic bandit algorithm. An actor-critic algorithm maintains two separate models, one for the actor, the action selection policy, and the other for the critic, the reward model. The performance of the algorithm as well as the validity of the test are guaranteed only when the critic model is correctly specified. However, misspecification is frequent in practice due to incorrect functional form or missing covariates. In this work, we propose a modified actor-critic algorithm which is robust to critic misspecification and derive a novel testing procedure for the actor parameters in this case.