Miracle-dz/UniFAR-FTUs huggingface dataset:
Görme Yetersizliği Olan ve Gören Çocukların Gelişimsel Oyun Seviyeleri ile Zihin Kuramının Karşılaştırmalı İncelenmesi

Giris: Bu arastirmada okul oncesi donemde gorme yetersizligi olan ve goren cocuklarin gelisimsel oyun seviyeleri ile Zihin Kurami becerileri arasindaki iliskinin karsilastirilarak incelenmesi amaclanmistir. Yontem: Arastirmanin calisma grubunu, 48-72 ay araliginda olan gorme yetersizligi olan 30 cocuk ve ayni ay diliminde olan goren 30 cocuk olusturmustur. Katilimci cocuklar dil ve bilissel gelisim duzeylerinde de eslestirilmislerdir. Arastirmada nedensel karsilastirma deseni kullanilmistir. Arastirma kapsaminda Gelisimsel Oyun Degerlendirmesi araciligiyla katilimci cocuklarin oyun seviyeleri incelenmis ve Zihin Kurami Birinci Derece Yanlis Kani Atfi gorevleri uygulanmistir. Bulgular: Arastirma bulgulari gorme yetersizligi olan ve goren cocuklarin gelisimsel oyun seviyelerinde iki grup arasinda anlamli farkliliklarin oldugunu gostermistir. Ek olarak arastirmanin bulgulari gorme yetersizligi olan ve goren cocuklarin Yanlis Kani Atfi ve Gelisimsel Oyun Degerlendirmesi puanlari arasinda anlamli iliskiler oldugunu gostermistir. Tartisma: Arastirmadan elde edilen bulgular gorme becerileri, oyun ve zihin kurami gelisimini anlama acisindan onemlidir. Arastirma bulgulari tartisilmis ve ileri arastirmalara yonelik onerilere yer verilmistir.
Cosmological constraints from unWISE and Planck CMB lensing tomography

A number of recent, low-redshift, lensing measurements hint at a universe in which the amplitude of lensing is lower than that predicted from the ΛCDM model fit to the data of the Planck CMB mission. Here we use the auto- and cross-correlation signal of unWISE galaxies and Planck CMB lensing maps to infer cosmological parameters at low redshift. In particular, we consider three unWISE samples (denoted as "blue", "green" and "red") at median redshifts z ∼ 0.6, 1.1 and 1.5, which fully cover the Dark Energy dominated era. Our cross-correlation measurements, with combined significance S/N ∼ 80, are used to infer the amplitude of low-redshift fluctuations, σ8; the fraction of matter in the Universe, Ω m ; and the combination S8 ≡ σ8 (Ω m /0.3)0.5 to which these low-redshift lensing measurements are most sensitive. The combination of blue, green and red samples gives a value S m = 0.784 ± 0.015, that is fully consistent with other low-redshift lensing measurements and in 2. 4σ tension with the CMB predictions from Planck. This is noteworthy, because CMB lensing probes the same physics as previous galaxy lensing measurements, but with very different systematics, thus providing an excellent complement to previous measurements.
Mediating role of post-traumatic growth in the relationship between inadequate disaster recovery and mental health outcomes: long-term evidence from the Wenchuan earthquake

ABSTRACT Background: The mental health outcomes from disasters have aroused great concern worldwide, yet few studies incorporate a long-term disaster recovery perspective. Evidence has also emerged about the importance of the social determinants of mental health, but aspects of recovery including disruptions to livelihoods and social networks remain relatively understudied. Objective: This study utilizes cognitive adaptation theory to examine the long-term relationship between inadequate disaster recovery and mental health outcomes while considering domains of post-traumatic growth (PTG) as mediators. Methods: A cross-sectional study was conducted among 1369 adult survivors of the 2008 Wenchuan earthquake. Correlation analyses and multivariable regression analyses examined the association between inadequate disaster recovery and mental health outcomes, while parallel multiple mediator models and structural equation model explored the mediating role of PTG among these relationships. Results: 52.2% and 8.1% of the respondents reported inadequate recovery on livelihood and social ties, respectively. Inadequate disaster recovery was associated with higher levels of PTSD and depressive symptoms, and this association was partially mediated by PTG. Lower reported PTG on the interpersonal relationship and new possibilities domains mediated the association between inadequate livelihood recovery on PTSD and depressive symptom severity, and enhanced personal strength mediated the association between inadequate social ties recovery on these symptoms. Conclusion: Enhancing disaster recovery is essential for mental health protection, and PTG may be a valuable starting point in cognitive therapy to protect against stress responses after trauma. HIGHLIGHTS • Disaster recovery is a long-term issue, which should be enhanced in further disaster reconstruction programmes. • Poor disaster recovery associates with higher levels of mental health symptoms. • PTG mediates the relationship between disaster recovery and mental health symptoms.
An inter-disciplinary review of the literature on mental illness disclosure in the workplace: implications for human resource management

Abstract The purpose of this review is to consolidate the stock of empirical research relating to the disclosure or concealment of mental illness in the workplace. We present a comprehensive framework for understanding the challenges that employees and job applicants face surrounding mental illness and identity management. This review aims to synthesize the findings of studies pertaining to the decision to disclose (or not) mental illnesses in the workplace and to channel the findings into a comprehensive model that can serve as a stepping-stone for future research in the field of human resource management (HRM). From the review, we found that organizational support, social support, and individual differences were key antecedents of the decision to disclose. Disclosure also affects outcomes such as access to accommodations and support programs, helping behaviors, psychological wellbeing, and employment success. Practically speaking, this research contributes to the development of a safe and diverse organizational climate in which all employees, regardless of mental illness, feel included in their work environment.
Six-gene Signature Based on Metabolism Is Closely Related to the Prognosis and Immune Infiltration of Patients With Sepsis

Background Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Current studies indicated that metabolism associated genes (MAGs) have critical roles in sepsis T his study aim s to construct a gene signature for sepsis and explore its possible mechanism ss. Methods: Differentially expressed metabolism associated genes (DEMAGs) were identified in sepsis patients based on the Gene Expression Omnibus (GEO) database. Univariate and multivariate Cox regression analyses were performed to identify and construct the prognostic gene signature. Quantitative real time PCR was applied to examine the mRNA level of sig nature genes in the sepsis mice model established by cecalligation and puncture (CLP). Next, Gene S et Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms of gene signature We then assessed the abundance of infiltrating immune cell s in each sample to explore the immune microenvironment of sepsis patient s Finally, the Tumor Immune Dysfunction and Exclusion (TIDE ) and SubMap algorithms were used to explore the immunotherapy response of sepsis patients . Results: A novel six gene signature (including ELANE , NQO2 , TLR2 , PTGDS , SMAD3 , CD3E ) was established for sepsis prognosis prediction , which could accurately predict the overall survival ( of sepsis patients. In the sepsis mice model, except PTGDS, the mRNA expression of the other five genes was consistent with that of sepsis patients. T he result s of GSEA analysis indicate that the prognostic signature w as closely related to immunity function Besides , we found that the risk score was strikingly negatively correlated with the tumor microenvironment (TME) immunecells infiltration and expression of critical immune checkpoints The patients in the low risk group were more likely to benefit from immune therapy through the TIDE and SubMap analysis Conclusion : In conclusion, our signature can predict the OS of sepsis patients and provide potential guidance for exploring patients who may benefit from immunotherapy.
Influence of Rayleigh-Bénard convection on electrokinetic instability in overlimiting current conditions

We investigate the influence of buoyancy on electroconvection at an ion-exchange membrane in an aqueous electrolyte solution. Electrokinetic instabilities (EKIs) and Rayleigh-Benard (RB) convection are both known to mix the appearing concentration gradient layer and overcome the limiting current arising from diffusional limitations. The different physics, as well as the interplay between them, are investigated by electrical, flow, and concentration characterization. In the buoyancy stable orientation, an EKI mixing layer, having a low concentration, grows till saturated size. In the buoyancy unstable orientation, RB occurs and dominates the advective transport due to the large system size. When current density i 5ilim EKI starts before RB and hastens the onset of RB. Upon onset of RB, EKI is suppressed while the overall resistance is still decreased. The onset times of EKI and RB could be predicted using a simple diffusion-migration model based on Fick's second law.
A2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation

Learning pyramidal feature representations is crucial for recognizing object instances at different scales. Feature Pyramid Network (FPN) is the classic architecture to build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion inhibit FPN from further aggregating more discriminative features. In this work, we propose Attention Aggregation based Feature Pyramid Network (A2-FPN), to improve multi-scale feature learning through attention-guided feature aggregation. In feature extraction, it extracts discriminative features by collecting-distributing multi-level global context features, and mitigates the semantic information loss due to drastically reduced channels. In feature fusion, it aggregates complementary information from adjacent features to generate location-wise reassembly kernels for content-aware sampling, and employs channel-wise reweighting to enhance the semantic consistency before element-wise addition. A2-FPN shows consistent gains on different instance segmentation frameworks. By replacing FPN with A2-FPN in Mask R-CNN, our model boosts the performance by 2.1% and 1.6% mask AP when using ResNet-50 and ResNet-101 as backbone, respectively. Moreover, A2-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade.
Seismic Performance Evaluation of Buried Sewage Collection Pipelines

One of the systems which is damaged because of earthquake is an urban wastewater network. In this paper, it is tried to study the damages occurred on urban wastewater networks in a selected network. The most important definitions by the network about some branches of the wastewater network and relevant reformations are studied. Then, the function of pipeline networks buried by the previous earthquake is studied and also it is proceeded about the research studies done regarding the relationships dominant on susceptibility in which some of the mentioned relationships are studied. The statistical society of some methods used in this research is studied, and the software applied in this part is introduced. Finally, the data of the output results are analysed in which the results obtained by this research are including the output drawings of the software GIS that demonstrate the occurred damages in applied and clear way. Some suggestions for using the aforesaid results by the researchers and authorities, especially disaster headquarter, have been presented. The results of this study showed that the assumptions and also the modelling process have handled the simulation well and this process is an effective method in decision making.
Initial exploration in workplace coaching: coaches’ thematic and methodological approach

ABSTRACT Coaching is increasingly used as an HRD intervention. Initial research suggests the importance of coaching behaviour, especially at the beginning of a coaching engagement, for coaching success. However, findings are scarce as to how coaches proceed during the initial phase in coaching and recent reviews therefore call for research that investigates how coaches determine the focus of the coaching intervention. Therefore, our study aimed to answer the questions of what and how regarding coaches’ approach to the ‘initial exploration’. We conducted 20 semi-structured interviews with workplace coaches. Qualitative content analysis revealed a broad range of contents addressed, methods applied, and principles that guide coaches during the initial exploration. Moreover, we identified specific patterns for categorising coaches’ approaches. Concerning the content coaches address, we identified three dimensions: coachees’ areas of life (private vs. professional, with the latter including individual vs. social vs. organisational aspects), temporal focus (past, present, future), and perspective (solution- vs. problem-orientation). We integrate our findings into a taxonomy of the initial exploration in coaching and thereby provide a basis for future research as well as a guide for reflection and decision-making for coaches, coachees, and organisational sponsors.
Retrieving Data Permutations from Noisy Observations: High and Low Noise Asymptotics

This paper considers the problem of recovering the permutation of an n-dimensional random vector X observed in Gaussian noise. First, a general expression for the probability of error is derived when a linear decoder (i.e., linear estimator followed by a sorting operation) is used. The derived expression holds with minimal assumptions on the distribution of X and when the noise has memory. Second, for the case of isotropic noise (i.e., noise with a diagonal scalar covariance matrix), the rates of convergence of the probability of error are characterized in the high and low noise regimes. In the low noise regime, for every dimension $n$, the probability of error is shown to behave proportionally to $\sigma$, where $\sigma$ is the noise standard deviation. Moreover, the slope is computed exactly for several distributions and it is shown to behave quadratically in $n$. In the high noise regime, for every dimension $n$, the probability of correctness is shown to behave as $1/\sigma$, and the exact expression for the rate of convergence is also provided.
Soft-Attention Improves Skin Cancer Classification Performance

In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network to achieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performance of VGG, ResNet, InceptionResNetv2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline[14] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline[28] and achieves 91.6% on ISIC-2017 dataset. The code is publicly available at github.
Molecular docking and dynamics simulations reveal the potential of anti-HCV drugs to inhibit COVID-19 main protease

Background: Drug repurposing is the fastest effective method to provide treatment for coronavirus disease (COVID-19). Drugs that targeting a closely related virus with similar genetic material such as hepatitis C virus (HCV) and more specifically targeting a similar viral protease would be an excellent choice. Methods: In this study, we carried out a virtual screening for fifteen anti HCV drugs against COVID-19 main protease using computational molecular docking techniques. Moreover, Velpatasvir (4) and Sofosbuvir (13) drugs were further evaluated through molecular dynamics simulations followed by calculating the binding free energy using the molecular mechanics generalised born/solvent accessibility (MM-GBSA) approach. Results: The binding affinity descending order was N3 natural inhibitor (1), Velpatasvir (4), Sofosbuvir (13), Ombitasvir (3), Glecaprevir (2), Asunaprevir (8), Paritaprevir (10), Grazoprevir (11), Elbasvir (6), Ledipasvir (5), Daclatasvir (7), Pibrentasvir (9), Simeprevir (12), Dasabuvir (14), Taribavirin (16) and finally Ribavirin (15). Molecular dynamics simulation reveals that Sofosbuvir (13) has exciting properties and it was stable within the active site; it also showed good MM-GBSA compared to the natural inhibitor N3. Conclusion: Our results could be auspicious for fast repurposing of the examined drugs either alone or in combinations with each other for the treatment of the COVID-19. Furthermore, this work provides a clear spot on the structure-activity relationship (SAR) for targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease and helps the design and synthesis of new drugs in the future targeting it as well.
Understanding Catastrophic Overfitting in Adversarial Training

Recently, FGSM adversarial training is found to be able to train a robust model which is comparable to the one trained by PGD but an order of magnitude faster. However, there is a failure mode called catastrophic overfitting (CO) that the classifier loses its robustness suddenly during the training and hardly recovers by itself. In this paper, we find CO is not only limited to FGSM, but also happens in $\mbox{DF}^{\infty}$-1 adversarial training. Then, we analyze the geometric properties for both FGSM and $\mbox{DF}^{\infty}$-1 and find they have totally different decision boundaries after CO. For FGSM, a new decision boundary is generated along the direction of perturbation and makes the small perturbation more effective than the large one. While for $\mbox{DF}^{\infty}$-1, there is no new decision boundary generated along the direction of perturbation, instead the perturbation generated by $\mbox{DF}^{\infty}$-1 becomes smaller after CO and thus loses its effectiveness. We also experimentally analyze three hypotheses on potential factors causing CO. And then based on the empirical analysis, we modify the RS-FGSM by not projecting perturbation back to the $l_\infty$ ball. By this small modification, we could achieve $47.56 \pm 0.37\% $ PGD-50-10 accuracy on CIFAR10 with $\epsilon=8/255$ in contrast to $43.57 \pm 0.30\% $ by RS-FGSM and also further extend the working range of $\epsilon$ from 8/255 to 11/255 on CIFAR10 without CO occurring.
Design of an Android Wear Smartwatch Application as a Wearable Interface to the Diabetes Diary Application

In this study, an application was developed for Android-based smartwatches which has the capacity of monitoring the state of diabetes mellitus and indicating the data concerning the physical activities and cardiac rhythm. Android Studio was used to develop and design the application. The application consists of five pages (glucose, insulin, carbohydrate, physical activity
Application of Nomarski interference system in supersonic gas-jet target diagnosis

Supersonic gas-jet target performs an important role in laser wakefield acceleration, and its density diagnosis is a significant part of target characteristic study. In this paper, a Mach–Zehnder and Nomarski interference system is set up and used for gas-jet target density diagnosis. We have investigated and compared the performance of the Mach–Zehnder part and Nomarski part. The feasibility of the Nomarski interferometer with vertical fringes has been verified. Moreover, it shows better stability and has a more compact structure, beneficial for obtaining more accurate and effective target density characterization in laser wakefield acceleration.
Bacterial Profile, Antimicrobial Susceptibility Pattern, and Associated Factors of Community- and Hospital-Acquired Urinary Tract Infection at Dessie Referral Hospital, Dessie, Northeast Ethiopia

Background Bacterial urinary tract infection is among the most common community and hospital-acquired infections. Therefore, to know the status of the community and hospital-acquired urinary tract infection, antimicrobial susceptibility patterns, and associated factors among urinary tract infection profiles are essential to physicians and health workers to implement appropriate intervention. Methods An institution-based cross-sectional study was conducted among 422 urinary tract infection suspected patients. All isolates were identified by standard microbiological techniques, and their antibiotic susceptibility was done by the Kirby–Bauer disc diffusion method. Data were entered using EpiData version 3.1 and analyzed by SPSS software version 20. P value < 0.05 at 95% CI was considered statistically significant. Result Of 422 urine samples processed, 100 (23.7%) yielded bacterial isolates. About 50(30.7%) and 50(19.3%) were bacterial isolates from the community and hospitalized patients, respectively. E. coli 44/103(42.7%) predominated across the two groups, followed by S. aureus 25/103(24.3%), CONs, 14/103(13.5%), Klebsiella spp. 7/103(6.78), Proteus spp. 3/103(2.91), and Enterococcus spp. 3/103 (2.91%). Pseudomonas spp. 3/103 (2.91), Citrobacter spp. 2/103(1.94%), and Acinetobacter spp. 1/103(0.999), which were isolated from only the hospitalized patients. Meropenem susceptibly was 100% in both study groups and Ampicillin resistance was documented as 83.3% to 100% and 76.9% to 100% in hospitalized and community-acquired samples, respectively. Conclusion This study found a high prevalence of bacterial urinary tract infection in the study area and a high rate of bacterial resistance was observed to most antimicrobial drugs tested. Meropenem and nitrofurantoin were the most active drugs for urinary tract infections. Therefore, expanding routine bacterial culture and identification with antimicrobial susceptibility testing and strengthening regular surveillance systems are essential for appropriate patient care.
Evaluating the Impact of In-Home Behavioral Health Services on Housing First Residents’ Emergency Room and Inpatient Utilization

Individuals experiencing chronic homelessness are highly marginalized and frequently access acute healthcare services. This program evaluation used secondary data collected from adults experiencing chronic homelessness (n=133) who participated in a Housing First program offering in-home behavioral health services within a large Midwestern city. Participant demographics (e.g., gender, race, age) and data on health factors (e.g., substance misuse and mental health symptoms, and ER visits and inpatient hospitalizations) were collected at program enrollment and at 6-month and 12-month follow-ups. High proportions of missing data led the evaluators to exclude 12-month follow-up and in-home behavioral health data from the analyses. Neither inpatient nights nor ER visits changed significantly between enrollment and 6-months. Males were disproportionately hospitalized throughout the study, suggesting a need for gendertargeted services. Higher rates of hospitalization among African Americans at enrollment subsided by follow-up. Future evaluation should examine if in-home behavioral health services reduced racial health disparities. Acute care use was low overall, likely because of the stabilizing impact of housing. Data limitations suggest a need for more robust study designs to identify causal factors and to enrich our understanding of the role of behavioral health intervention within the Housing First paradigm. Results underscore the importance of using empirically-supported assessment tools to evaluate consumers' individualized needs and responsively allocate supportive services.