Positive Effect Research Articles

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

Assessing South Indian Ocean tropical cyclone characteristics in HighResMIP simulations

AbstractSeveral damaging tropical cyclones (TCs) have occurred recently over the South Indian Ocean (SIO) region, causing enormous social and economic losses. Yet, while many studies have examined SIO TC characteristics using observations and reanalysis, only a few have assessed these characteristics specifically for this region in climate models, and fewer have investigated their projections under climate change. Here we do this for a historical (1980–2010) and future (2020–2050) period, using multimodel simulations from the High Resolution Model Intercomparison Project, as well as examine biases in the historical period relative to a reanalysis (ERA5). The models have horizontal resolutions of 25–50 km, which has enabled an improved ability to represent tropical cyclones globally in previous studies. TempestExtremes software is employed to detect tropical storm and cyclone tracks. In cases where TempestExtremes cannot be applied due to a lack of requisite variables in a dataset, we instead examine extreme wind speeds in that dataset. For the historical period, we find considerable variation in model biases compared to ERA5, which itself exhibits realistic spatial patterns of tracks and their monthly distribution. Models do at least agree on positive biases in track frequency east of Madagascar and somewhat in the Mozambique Channel. However, the models and ERA5 only produce Category 3 tropical cyclones at best. Wind speeds for 25 km resolution models have much larger positive biases than for 50 km ones, suggesting the former can simulate even higher‐category tropical cyclones. Considerable intermodel variation is also found in track changes between the future and historical periods. No systematic intercategory pattern of change exists, and low signal‐to‐noise may obscure any such patterns in the limited timespan of available data. Thus, no meaningful conclusions can be drawn regarding changes in track intensity. Nevertheless, track frequency broadly decreases across models for the region, as does accumulated cyclone energy. An east‐to‐west shift in track location from east of Madagascar toward the Mozambique Channel is also implied by track frequency and wind speed changes. Our findings provide information to potentially improve storm resiliency in this vulnerable region.

Revisiting Thorpe scale analysis and diapycnal diffusivities in Drake Passage

Ship heave effects in the swell-prevalent Southern Ocean induce frequent false overturns in density profiles, and conventional Thorpe scale analysis leads to unrealistically large diapycnal diffusivities. Another critical factor causing large positive biases in Thorpe scale analysis concerns the Ozmidov to Thorpe scales ratio α often considered constant and equal to 0.8. A revised Thorpe scale analysis is proposed with an innovative segment-by-segment approach to circumvent heave-induced false overturns together with best-fitting α functions to Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES) data, which yields realistic diffusivities in the Drake Passage region. The revised Thorpe scale method applied to a finely-resolved CTD section across the Drake Passage yields typical diffusivities of O (10-5-10-3 m2 s-1), with the largest values being preferentially concentrated close to the bottom at major circumpolar fronts, consistent with nearby DIMES microstructure observations. In the southern Drake Passage the method highlights a middepth (500-1500 m) mixing maximum of 10-4-10-3 m2 s-1 in the Lower Circumpolar Deep Water (LCDW) layer due to large intrusions of Warm Deep Water (a cold/fresh variety of LCDW) of Weddell Gyre origin. The inferred middepth mixing maximum in intrusive regions, which is associated with isopycnal eddy stirring rather than caused by internal waves or double diffusive mixing, supports the short-circuiting paradigm of meridional overturning circulation in the southwestern Scotia Sea region and has important climatic implications in a warming climate.

Enumerative Induction in Mathematics

In my 2007 paper, “Is There a Problem of Induction for Mathematics?” I rejected the idea that enumerative induction has force for mathematical claims. My core argument was based on the fact that we are restricted to examining relatively small numbers, so our samples are always biased, and hence they carry no inductive weight. In recent years, I have come to believe that this argument is flawed. In particular, while arithmetical samples are indeed biased, my new view is that this bias actually strengthens the inductive support that accrues from them. The reason is that small numbers typically provide a more severe test of general arithmetical claims due to the greater frequency of significant properties and boundary cases among such numbers. In this paper, I describe and defend this new view, which a call Positive Bias Pro-Inductivism.

Can transparency promote citizen compliance in China? The role of justice and social identification

Although public administration has advocated transparency for achieving citizen compliance, mixed results suggest that the mechanisms behind transparency need further clarification. In this paper, it is examined how transparency promotes citizen compliance in the Chinese context using urban renewal as example and data from interviews and a questionnaire survey. The results show that government transparency can enhance citizen compliance by promoting perceived justice. However, the positive effect of transparency on citizen compliance through justice is only significant in conditions of low social identification. Specifically, the effect of perceived justice decreases with increasing social identification, ultimately leading to a diminished effect of transparency. This study contributes conditional evidence regarding the role of transparency in promoting citizen compliance, which has practical implications for public administration.

Empagliflozin significantly prevents QTc prolongation due to amitriptyline intoxication.

Empagliflozin (EMPA) is a sodium-glucose transporter-2 inhibitor used in the treatment of type 2 diabetes and has positive effects on cardiovascular outcomes. Amitriptyline (AMT) can be used in many clinical indications but leads to cardiotoxicity by causing QT prolongation. Our aim in this study was to determine how the effects of the concomitant use of empagliflozin and amitriptyline, which have been shown to have effects on sodium and calcium metabolism in cardiomyocytes, would cause an effect on QT and QTc intervals in clinical practice. Twenty-four male Wistar albino rats were randomised into four groups. The control group received only physiological serum (1 ml) via orogastric gavage (OG). The EMPA group received empagliflozin (10 mg/kg) via OG. The AMT group received amitriptyline (100 mg/kg) via OG. The AMT + EMPA group (n = 6) received amitriptyline (100 mg/kg) and empagliflozin (10 mg/kg). Under anaesthesia, QT and QTc intervals were measured at baseline, and in the first and second hours. In the AMT group, QT intervals and QTc values were found to be statistically longer than in the control group (p ≤ 0.001). Empagliflozin significantly ameliorated amitriptyline-induced QT and QTc prolongation. In the AMT + EMPA group, QT and QTc intervals were significantly lower compared to that in the AMT group (p < 0.01). In this study, we determined that empagliflozin significantly ameliorated amitriptyline-induced QT and QTc prolongation. This effect was probably due to the opposite effects of these two agents in the intracellular calcium balance. With more clinical trials, the routine use of empagliflozin may be suggested to prevent QT and QTc prolongation in diabetic patients receiving amitriptyline.

Bipolar topological spin diode and topological spin transistor in finite size quantum spin Hall insulators

Abstract The miniaturization and stability of electronic devices are becoming increasingly important today. We attempt to provide the theoretical support for designing spintronic devices by numerically investigating spin transport in finite size quantum spin Hall insulators (QSHI) under a perpendicular weak magnetic field. By modifying magnetic field strength, we find the gapped spin up and spin down bands are split to realize a half-metal phase which is a promising candidate for designing an efficient spin filter. Moreover, one of the two energy gaps becomes larger and the other smaller due to the weakened or enhanced coupling between two edge states. Here we propose and demonstrate the spin filter based on a finite size QSHI junction under a magnetic field and the polarity can be inverted by a bias voltage or magnetic field. Interestingly, we find a bipolar spin diode effect that only one spin channel is opened and the other spin channel is closed at positive bias, and the opposite spin electron can be transmitted with negative bias. Two spin filters in series can be a spin transistor, the on and off states can be controlled by spin polarization of one spin filter. We show that the topological spin transistor can be controlled by the gate voltage, and it survives in moderate disorder.

“I’m both smoking and vaping”: a longitudinal qualitative study of US young adults who tried to quit smoking cigarettes by using electronic cigarettes

ObjectiveTo describe how young adults use electronic cigarettes (electronic nicotine delivery systems (ENDS)) for smoking cessation and reasons why they may or may not successfully quit smoking.MethodsLongitudinal qualitative data were...

Validation of Cool-Season Snowfall Forecasts at a High-Elevation Site in Utah’s Little Cottonwood Canyon

Abstract Producing a quantitative snowfall forecast (QSF) typically requires a model quantitative precipitation forecast (QPF) and snow-to-liquid ratio (SLR) estimate. QPF and SLR can vary significantly in space and time over complex terrain, necessitating fine-scale or point-specific forecasts of each component. Little Cottonwood Canyon (LCC) in Utah’s Wasatch Range frequently experiences high-impact winter storms and avalanche closures that result in substantial transportation and economic disruptions, making it an excellent testbed for evaluating snowfall forecasts. In this study, we validate QPFs, SLR forecasts, and QSFs produced by or derived from the Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) using liquid precipitation equivalent (LPE) and snowfall observations collected during the 2019/20–2022/23 cool seasons at the Alta–Collins snow-study site (2945 m MSL) in upper LCC. The 12-h QPFs produced by the GFS and HRRR underpredict the total LPE during the four cool seasons by 33% and 29%, respectively, and underpredict 50th, 75th, and 90th percentile event frequencies. Current operational SLR methods exhibit mean absolute errors of 4.5–7.7. In contrast, a locally trained random forest algorithm reduces SLR mean absolute errors to 3.7. Despite the random forest producing more accurate SLR forecasts, QSFs derived from operational SLR methods produce higher critical success indices since they exhibit positive SLR biases that offset negative QPF biases. These results indicate an overall underprediction of LPE by operational models in upper LCC and illustrate the need to identify sources of QSF bias to enhance QSF performance. Significance Statement Winter storms in mountainous terrain can disrupt transportation and threaten life and property due to road snow and avalanche hazards. Snow-to-liquid ratio (SLR) is an important variable for snowfall and avalanche forecasts. Using high-quality historical snowfall observations and atmospheric analyses, we developed a machine learning technique for predicting SLR at a high mountain site in Utah’s Little Cottonwood Canyon that is prone to closure due to winter storms. This technique produces improved SLR forecasts for use by weather forecasters and snow-safety personnel. We also show that current operational models and SLR techniques underforecast liquid precipitation amounts and overforecast SLRs, respectively, which has implications for future model development.

Parenthood and gender inequality on labor market outcomes: Evidence from South Korea

AbstractThis study estimates the long‐term effect of parenthood on labor market outcomes and explores the variations within this effect by gender and education. We use the event study method to track changes in a range of labor market outcomes from 3 years before to 8 years after the first birth, with a sample of 556 married women and 528 married men from the Korea Labor and Income Panel Study (KLIPS). We find that women experience an immediate drop in wages following birth, whereas men experience continuous wage growth even before birth. The decline in women's wages arises largely from their withdrawal from the labor market, while the increase in men's wages could be explained by a positive selection bias among fathers. In addition, mothers' wage trajectories do not vary significantly by education level, whereas other labor market outcomes show discernible trends. These patterns are the opposite of those observed for men. The findings indicate that severe gender inequality in the Korean labor market lowers the wages of mothers, regardless of their education, while the wages of fathers are more likely to be affected by education. Based on the findings, we make policy suggestions for improving the work‐family balance for all genders.

Evaluating Drawings’ Creativity: Synchrony Effects on Rater Bias and the Mediating Role of Emotional Arousal

ABSTRACT Creativity evaluation, particularly rating the creativity of products, is an unstable construct subjective to the influence of various individual states and characteristics. However, little research has been conducted on whether and how participants’ physiological states affect rating products. This experimental study aimed to explore how synchrony (i.e. whether individuals’ evaluation time synchronizes with their peak circadian arousal periods during a day) affects the rating of creativity in drawings, whether these effects vary according to drawings’ different creativity levels, and the influence of emotional state on synchrony and creativity rating. A sample of 191 university students rated several drawings of low, medium, and high creativity levels at their optimal or non-optimal times of day, and their subjective emotional valence and arousal were recorded. The results showed that participants exhibited a more positive rater bias when rating low- and medium-creativity-level drawings during their optimal times of day. Emotional arousal, but not valence, mediated the relationship between synchrony and rater bias. These findings highlight the role of synchrony and emotional arousal in rating creative products, contributing to our understanding of how raters’ synchrony states influence creativity evaluation.