Positive Effect Research Articles

Charge trapping gate stack enabled non-recessed normally off AlGaN/GaN HEMT with high threshold voltage stability

This paper reports a non-recessed normally off AlGaN/GaN high-electron-mobility transistor (HEMT) with a charge trapping gate dielectric stack. The charge trapping gate dielectric stack featuring a low density of fixed positive charges consists of an O3-based Al2O3 tunneling layer and an O3-based HfO2 blocking layer, both deposited via atomic layer deposition. For HEMT with a 15 nm AlGaN barrier layer, a threshold voltage of 2.57 V and an on-resistance of 8.50 Ω × mm are achieved. For positive bias temperature instability (PBTI) testing, the electric field provided by the 15 nm AlGaN layer serves as a barrier to channel carriers, significantly enhancing the PBTI test stability. The optimized gate dielectric stack enables the fabrication of the non-recessed normally off metal–insulator–semiconductor-HEMT with enhanced threshold voltage (Vth) stability.

Teachers' non‐response bias in questionnaires about their attitude towards inclusive education

AbstractThe attitude of teachers towards inclusive education is a key issue for the success of inclusive education. Many studies have been designed to assess teachers' attitudes, but none have looked at the bias caused by teachers' non‐response to questionnaires on their attitudes. Non‐response biases are difficult to identify because it is impossible to access the responses of individuals who do not wish to reply. It is therefore necessary to use other data. This article examines the non‐response biases of teachers in questionnaires about their attitude towards inclusive education by cross‐checking the responses of parents of children with a disability (N = 382) regarding the quality of their child's inclusion and the responses or non‐responses of teachers to questionnaires about their attitude (N = 48 responses). This study shows that the non‐responses of teachers are not random and are associated with poorer well‐being and social inclusion at school, a lower sense of belonging and higher levels of bullying. This significant non‐response bias of teachers leads us to question the existence of a potential positivity bias in previous studies.

Adoption of CKD-EPI (2021) for GFR estimation - implications for UK practice.

Recommendations to move to a race free estimating equation for glomerular filtration rate have gained increasing prominence since 2021. We wished to determine the impact of any future adoption upon the CKD patient population of a large teaching hospital, with a population breakdown largely similar to that of England as a whole. We compared four estimating equations (MDRD, CKD-EPI (2009), CKD-EPI (2021) and EKFC) using the Bland-Altman method. Bias and precision were calculated (in both figures and percentages) for all patients with CKD and specific subgroups determined by age, ethnic group, CKD stage and sex. CKD stage was assessed using all four equations. All equations studied had a positive bias in South Asian patients and a negative bias in Black patients compared to CKD-EPI (2021). Similarly, there was a positive bias in White patients across all equations studied. Comparing CKD-EPI (2009) and EKFC this positive bias increased as patients aged, the opposite was seen with MDRD. Between 10% and 28% of patients in our dataset changed their CKD staging depending upon estimating equation used. Our work confirms previous findings that the MDRD equation overestimates eGFR in South Asians and underestimates eGFR in Blacks. The alternative equations also demonstrated similar bias. This may, in part, explain the health inequalities seen in ethnic minority patients in the UK. Applying our findings to the UK CKD population as a whole would result in anywhere from 260,000 - 730,000 patients having their CKD stage reclassified, which in turn will impact secondary care services.

Perceptions of Academic Performance, Impairment, and Mental Health in University Students With and Without ADHD.

This study aims to investigate the impact of positive illusory bias (PIB) on the relationship between ADHD symptoms and functioning in college students, with a focus on gender differences. The sample consisted of 195 college students, including 148 with ADHD and 47 without ADHD. Measures of ADHD symptomatology, life satisfaction, affect, gender identity, and impairment were collected, along with cumulative grade point average. ADHD symptomatology was inversely linked to subjective well-being, with PIB acting as a significant moderator. Functional impairment was predicted by ADHD symptoms and subjective well-being, while social impairment and academic functioning were predicted by PIB and well-being. Significant gender differences were found, particularly in the interaction between PIB and ADHD symptoms for non-binary individuals. This study suggests that PIB is relevant for emerging adults with ADHD enrolled in higher education. Subjective well-being and PIB act as buffers against the detrimental functional and social effects of ADHD symptoms in emerging adult college students. The study highlights the importance of considering gender-specific approaches in understanding and supporting the mental health of this population.

Enhanced Stability Under Positive Bias Temperature Stress in Oxide Thin Film Transistors with Double Gate Structure By Improving Bottom Channel Interface

In this paper, we discuss the causes of reliability degradation in oxide TFTs with a double gate structure and propose methods for improvement. Reducing IGZO deposition power significantly improves PBTS (Positive Bias Temperature Stability) reliability in double gate structures. This improvement is attributed to reduction of excess oxygen defects accumulated at the bottom channel interface, as inferred from the difference in PBTS results for the top single gate and double gate structures

Quantitative Dynamic Evolution of Unoccupied States in Hydrogen Diffused InGaZnSnO TFT under Positive Bias Temperature Stress

Quantitative Dynamic Evolution of Unoccupied States in Hydrogen Diffused InGaZnSnO TFT under Positive Bias Temperature Stress

Dynamically Tunable Localized Surface Plasmon Resonance in Self‐Assembled SrCoOx‐Au Vertically Aligned Nanocomposite Thin Films

AbstractWhile the physical principles for regulating localized surface plasmon resonance (LSPR) are well established, dynamically tuning the LSPR in a given material post synthesis remains challenging. Herein, this study demonstrates a strategy for dynamically tuning the LSPR of Au nanostructures by selectively altering the dielectric environment. Au is integrated with an electrochemically gatable oxide, i.e., SrCoOx (SCO), into vertically aligned nanocomposite thin films via a self‐assembly growth mechanism, where Au develops into nanopillars embedded in the SCO matrix. By selectively controlling the tri‐state phase transitions of SCO matrix via varying the bias voltage polarity in ionic liquid gating (ILG), the LSPR behavior of Au nanopillars can be dynamically tuned. Specifically, gating with a negative bias fully suppresses the LSPR, while a positive bias leads to a continuous blueshift of the LSPR wavelength upon increasing the ILG duration. This work not only opens new directions for the dynamic control of LSPR of noble metal nanostructures, but also offers insight to the voltage control of multifunctionalities via structural and physical intercoupling between different phases in self‐assembled nanocomposites.

Boron-Doped Single-Molecule van der Waals Diode.

Single-molecule diode was the first proposed device in molecular electronics. Despite the great efforts and advances over 50 years, the reported rectification ratios, the most critical parameter of a diode, remain moderate for the single-molecule diode. Herein, we report an approach to achieve a larger rectification ratio by adopting the combined strategies of p-type boron doping, the single-layer graphene nodes, and the van der Waals layer-by-layer architecture. Measured current-voltage curves showed one of the as-fabricated single-molecule diodes hit an unprecedented large rectification ratio of 457 at ±1 V. Break junction operations and spectroscopic measurements revealed the three-atom-thick configuration of the single-molecule diodes. With the experimental and theoretical calculation results, we demonstrated the doped boron atoms induced holes to redistribute the electron density, making the asymmetric coupling at positive and negative biases, and the van der Waals interaction promoted asymmetric coupling and significantly boosted diode performance.

Greater social network complexity mitigates pandemic-related negativity

Having strong, diverse social relationships is tightly linked to myriad physical and mental health benefits by mitigating the negative impacts of adverse events. The COVID-19 pandemic brought increased uncertainty and changes to our social lives that reduced the spread of COVID-19 (e.g., social distancing), but also likely impacted emotional well-being. We investigated how one of these changes – changing social networks – relates to emotional bias (a proxy for well-being), specifically one’s tendency to interpret ambiguous stimuli as positive or negative (i.e., valence bias). Participants ( N = 614) categorized the valence of clearly (e.g., angry and happy expressions) and ambiguously valenced stimuli (e.g., surprised expressions), and were asked about their social network before and during the pandemic. Compared to before the pandemic, participants reported a decrease in overall network size ( p < .001) and number of embedded networks (i.e., a measure of network complexity represented by number of areas of social activity; p < .001). In a model with all three social network dimensions (size, complexity, diversity), network complexity uniquely predicted valence bias ( p = .02). Specifically, participants with greater social network complexity during the pandemic showed a more positive bias. Further, participants that did not experience a pandemic-related reduction in network complexity were protected from pandemic-related increases in negativity ( p < .001). In other words, having a more complex social network could act as a protective factor against adverse outcomes in times of uncertainty.

The effects of time and anticoagulant type on hematologic values in cockatiels (Nymphicus hollandicus)

BackgroundAvian hematologic characteristics complicate evaluation; therefore, complete blood cell counts require manual analysis for best accuracy. Furthermore, the quality of samples may be affected by prolonged shipment. MethodsBlood was collected from the right jugular vein of twenty-four, 7-year-old (14 male, 10 female), wild-type cockatiels (Nymphicus hollandicus). Smears were prepared using fresh, untreated blood. The remainder was divided into dipotassium ethylenediaminetetraacetic acid (EDTA) and lithium heparin microtainers. A hematocrit tube and blood smear were prepared at baseline, 24, 48, and 72 hours after venipuncture. Packed cell volume (PCV), total solids (TS), estimated white blood cell (WBC) count, WBC differential (heterophils, lymphocytes, monocytes, eosinophils, basophils), hemolysis index (HI), and preparation quality were assessed and compared to baseline. After log transformation, the effects of anticoagulants across time points were compared. ResultsHeparinized samples had significant differences in WBC, heterophil, and lymphocyte counts at 48 hours, and PCV and TS at 72 hours compared to baseline. There was a positive proportional bias in PCV and TS from EDTA anticoagulant samples at 24, 48, and 72 hours, and from heparinized samples at 48 and 72 hours compared to baseline. The EDTA samples had increased PCV and TS compared to heparin at all time points, and higher WBC and heterophil counts at 48 hours compared to baseline. A type II methodological error cannot be excluded. ConclusionThese results suggest that the anticoagulant used and time spent in anticoagulant alters hematologic values in cockatiels, which may have significant clinical implications. The importance of sample processing within 24 hours and anticoagulant-specific reference intervals for hematologic variables should be considered.

AI-based prediction of academic success: Support for many, disadvantage for some?

The use of computational tools to predict academic success has become increasingly popular. Machine learning algorithms, trained on past study histories, have been shown to provide valid predictions. However, knowing about biases and unfairness in algorithms, one should take a closer look at these predictions. This paper explores the extent to which the predictive accuracy of academic success varies between specific groups of students, focusing on traditional and non-traditional students (NTS), who have not acquired a higher education entrance qualification at school. In a case study the study compares several popular algorithms and their prediction quality, and investigates whether misclassified NTS show positive or negative biases. Results revealed that the accuracy of predicting academic success for NTS was significantly lower than when considering all students as a whole. The direction of the distortion cannot be determined exactly due to small case numbers. The study emphasizes that the possibility of bias always has to be considered when predicting study success, and the use of such tools must ensure there are no undesirable biases that could affect certain students.

Resective epilepsy surgery and its impact on depression in adults: a systematic review, meta-analysis, and implications for future research

BackgroundHow epilepsy surgery influences the bidirectional relationship of epilepsy and depression remains poorly defined.MethodFor a better understanding of this question, we conducted a systematic review and meta-analysis of risk ratio...

Naive Realism and Its Relationship to Self-Awareness

The current research targets naive realism among university students, self-awareness among university students, and the relationship between naive realism and self-awareness among university students. The researcher suggests the following studies: Like the current research on other samples such as (employees in senior positions, professors, returnees, and displaced persons), and addressing other demographic variables such as (age, education, culture, and social status). It addresses the relationship of naive realism with a number of psychological variables such as (positive work, joint rumination, confirmation bias, cognitive biases, and attentional bias). It addresses the relationship of self-awareness and its relationship with a number of psychological variables such as (reflective thinking, emotional creativity, psychological adjustment, cognitive styles, and parental treatment styles).

Artificial Intelligence-Generated Editorials in Radiology: Can Expert Editors Detect Them?

We aimed to evaluate GPT-4's ability to write radiology editorials and to compare these with human-written counterparts, thereby determining their real-world applicability for scientific writing. Sixteen editorials from eight journals were included. To generate the AI-written editorials, the summary of 16 human-written editorials was fed into GPT-4. Six experienced editors reviewed the articles. First, an unpaired approach was used. The raters were asked to evaluate the content of each article using a 1-5 Likert scale across specified metrics. Then, they determined whether the editorials were written by humans or AI. The articles were then evaluated in pairs to determine which article was generated by AI and which should be published. Finally, the articles were analyzed with an AI detector and for plagiarism. The human-written articles had a median AI probability score of 2.0%, whereas the AI-written articles had 58%. The median similarity score among AI-written articles was 3%. 58% of unpaired articles were correctly classified regarding authorship. Rating accuracy was increased to 70% in the paired setting. AI-written articles received slightly higher scores in most metrics. When stratified by perception, human-written perceived articles were rated higher in most categories. In the paired setting, raters strongly preferred publishing the article they perceived as human-written (82%). GPT-4 can write high-quality articles that iThenticate does not flag as plagiarized, which may go undetected by editors, and that detection tools can detect to a limited extent. Editors showed a positive bias toward human-written articles. AI = Artificial intelligence; LLM = large language model; SD = standard deviation.

Inflation’s Reduction of the Real Minimum Wage and Unemployment in the USA: 1987 to 2021

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">Hundreds of articles have been written that include empirical estimates of the dis-employment effects of minimum wages; however, many of these articles find statistically insignificant effects, some find significant negative effects, and a few find significant positive effects. Most of these studies use multivariate analyses which can be criticized for omitting key variables. The omitted variables problem ruins all statistics and estimates. This paper uses reiterative truncated projected least squares (RTPLS), a solution to the omitted variables problem, to estimate the percentage increase in unemployment due to a one percent increase in the real minimum wage using monthly data for the 50 states of the USA from 1987 to 2021. RTPLS produces a separate elasticity for every observation where differences in these estimates are due to omitted variables. We argue that RTPLS solves most of the econometric problems that David Neumark identified in his keynote address at a minimum wage conference in Berlin in 2018. We find that the percentage change in the unemployment rate due to a one percent change in the minimum wage ranges between 1.156 and 3.389, that the elasticities for different states tend to move together over time, and that all these elasticities are statistically significant at a 95 percent confidence level.</span></p>

Understanding the Biases in Global Monsoon Simulations from the Perspective of Atmospheric Energy Transport

Abstract Understanding global monsoon (GM) variability and projecting its future changes rely heavily on climate models. However, climate models generally show pronounced biases in GM simulations, and the reasons for this remain unclear. Here, we evaluate the performance of 20 pairs of climate models that participated in both phase 5 of the Coupled Model Intercomparison Project (CMIP5) and phase 6 of CMIP (CMIP6) and identify the sources of their GM simulation biases from an energy transport perspective. The multimodel mean improvement in CMIP6 compared to CMIP5 is demonstrated by the increasing skill scores for various GM metrics from 0.20–0.79 to 0.48–0.83. More specifically, the dry biases in the Northern Hemisphere Summer Monsoon (NHSM) precipitation in CMIP5 [root-mean-square error (RMSE): 1.85 mm day−1] are reduced in CMIP6 (RMSE: 1.66 mm day−1). This higher simulation skill is associated with higher skill in simulating the precipitation-solstitial mode, monsoon intensity, and monsoon domains. The improvement in the NHSM precipitation simulation results from that in the meridional transport of atmospheric energy. Atmospheric energy budget analysis shows that the negative biases in downward surface longwave radiation and northward energy transport are smaller in CMIP6 than in CMIP5 in the boreal summer, resulting in a more realistic interhemispheric thermal contrast and meridional gradient of moist static energy. However, a major weakness of the CMIP6 models is found in the Southern Hemisphere Summer Monsoon precipitation simulation due to the positive bias in the top-of-the-atmosphere downward longwave radiation. This study shows that reasonably reproducing the meridional global atmospheric energy transportation is necessary for skillful GM simulation.

User Sentiment Analysis Based on Securities Application Elements.

Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like "update", "screen", "chart", "login", "access", "authentication", "account", and "transaction", revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.

Coupled Thermal and Power Transport of Optical Waveguide Arrays: Photonic Wiedemann-Franz Law and Rectification Effect.

In isolated nonlinear optical waveguide arrays, simultaneous conservation of longitudinal momentum flow ("internal energy") and optical power ("particle number") of the optical modes enables study of coupled thermal and particle transport in the negative temperature regime. Based on exact numerical simulation and rationale from Landauer formalism, we predict generic photonic version of the Wiedemann-Franz law in such systems, with the Lorenz number L∝|T|^{-2}. This is rooted in the spectral decoupling of thermal and particle current, and their different temperature dependence. In addition, in asymmetric junctions, relaxation of the system toward equilibrium shows apparent asymmetry for positive and negative biases, indicating rectification behavior. This Letter illustrates the possibility of simulate nonequilibrium transport processes using optical networks, in parameter regimes difficult to reach in natural condensed matter or atomic gas systems. It also provides new insights in manipulating power and momentum flow of optical waves in artificial waveguide arrays.

High-density integration of uniform sub-22 nm silicon nanowires for transparent thin film transistors on glass

Ultrathin catalytical silicon nanowires (SiNWs), grown as orderly arrays upon glass substrates, are ideal 1D channels for the construction of high-performance field effect transistors for low-power and high aperture/transparency electronics. In this work, a rather uniform growth integration of high-density ultrathin SiNWs has been accomplished directly on glass substrate at 290 °C, via an in-plane solid–liquid-solid mechanism. The indium (In) catalyst thickness deposited on the dense guiding terrace steps was identified as a key parameter to suppress the size dispersion of the In droplets, and thus enable a uniform growth of ultrathin SiNWs with diameters down to DNW=21.8 ± 1.8 nm, a close-to-unity guided growth rate and an excellent transparency > 90 % in spectrum ranging from 350 nm to 950 nm. Based on a convenient dry plasma etching and low-temperature passivation procedure, high-performance SiNW transistors have been successfully fabricated upon a dense array of parallel SiNWs as channels with a narrow NW-to-NW spacing of 100 nm. A high current ratio Ion/Ioff > 107, a low off-state current down to 0.1 pA, a steep subthreshold swing of SS∼120 mV dec-1 and excellent positive and negative bias stabilities have been demonstrated, as well as SiNW-based inverter logics that achieve a gain of 14.6 V/V under a drive voltage of 2.25 V. These results highlight the potential of catalytic SiNWs to serve as advantageous 1D channels for large-area display or transparent electronics.

Training to Increase Processing of Positive Content Paradoxically Decreases Positive Memory Bias in High Levels of Depression

Training to Increase Processing of Positive Content Paradoxically Decreases Positive Memory Bias in High Levels of Depression