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to understand potential algorithm aversion in modern poetry

to understand public attitudes toward non-human agents-created artworks.

In this study, we would like to test consumers' individual difference as a boundary condition. Specifically, we would like to test whether the effect of counterparty type (algorithm vs. human) on consumers' counteroffer behavior is stronger for certain groups (e.g., vulnerable consumers with low SES).

The study is concerned with the healthcare provider preference of consumers, i.e. whether consumers prefer algorithmic providers (medical AI) or human providers to perform specific medical tasks in the healthcare sector. That is, to examine whether algorithm aversion is still present in the healthcare sector, what factors might influence it and which behaviour results from it.

Drawing on the Social Media Literacy (SMILE) Model (Schreurs & Vandenbosch, 2021), this study investigates the effects of algorithm literacy (AL) interventions on social media users. In particular, it is tested whether AL interventions affect selective exposure and attention processes to potentially beneficial and potentially harmful content on social media. Furthermore, this study investigates whether the frequency and the recency of the interventions have effects on the dependent variables. Finally, it is tested whether AL mediates all postulated effects.

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Source: https://www.youtube.com/watch?v=MZGIlzxHkqg Uploader: MF99K

Teen dating violence (TDV), defined as psychological, physical, and sexual violence in an adolescent romantic relationship, is now considered an epidemic (Schubert, 2015). Over one-third of adolescents are victimized by TDV annually, with rates doubling for racial and ethnic minority females (Haynie et al., 2013). TDV-exposure leads to elevated distress in adolescence and poor mental health, violent behavior, and impairment in adulthood (Exner-Cortens et al., 2013; Foshee et al., 2013). In response, a number of prevention protocols have been developed that lead to small-moderate decreases in TDV perpetration (psychological, physical and sexual) and victimization (psychological and physical)(Russell, Voight, & Lee, 2021). Yet, despite a number of known risk factors (e.g., Pusch, 2022; Vagi et al., 2013), most TDV prevention efforts continue to be universal (Ball et al., 2015). As more targeted prevention efforts help ensure those most at-risk receive the proper dose of intervention (Dembo & Walters, 2003; Langhinrischen-Rohling & Turner, 2012), developing structured risk assessments (i.e., algorithms) for TDV-exposure can lead to more robust results (Cohen et al., 2018). Thus, the present study aimed to develop an algorithm for TDV risk. Aligned with research (e.g., Cascardi & Jouriles, 2018) and policies (e.g., Purtle & Lewis, 2017) that emphasize the necessity of trauma-informed protocols for violence prevention, our study was framed within a developmental trauma model. Finally, by conducting our study with a racially/ethnically diverse sample, and using a multifaceted analytic approach, we provided support for a TDV algorithm that is equitable and accurate. The Importance of Trauma-Informed TDV Models Across adolescent settings, the expectation is now for all interventions to be trauma-informed. For instance, the Every Student Succeeds Act, signed into law in 2015, requires school-based mental health services to be “based on trauma-informed practices that are evidenced-based [Sec. 4107(5)(B)(ii)(II)(aa)]. These policies for behavioral health aligns well with the TDV risk literature. Consistent with a social learning perspective for violence, exposure to past violent romantic relationships, as well as family and community violence, reflect robust risk factors for future TDV-exposure (Jouriles et al., 2006; Hamby et al., 2011; Renner & Whitney, 2012). Illustrating this point, Cohen and colleagues (2018) showed in a sample of 1,031 racially/ethnically diverse high school students that past exposure to family and dating violence better classified prospective risk for TDV-perpetration relative to cognitive predispositions often linked with TDV-perpetration (e.g., attitudes towards women, acceptance of couples violence). Therefore, developing a trauma-informed algorithm for TDV is both informed by the evidence and congruent with public health and policy initiatives. Despite widespread acceptance for trauma-informed policies, how to best translate these initiatives is not readily apparent (Purtle & Lewis, 2017). Most trauma-informed screening initiatives have downward extended the ACEs public health initiative with adults (Felitti et al., 1998) and advocated for a cumulative risk index based on the number of adversities endorsed (e.g., ACEs Aware; Shimkhada et al., 2022). However, there are a number of translational barriers to screening adolescents for family violence experiences (see Finkelhor, 2018). Further, screening for environmental adversities has questionable clinical utility from an accuracy perspective (Meehan et al., 2022), especially in non-White adolescents (Cohen & Choi, 2022), reinforcing the notion that ACEs screening was never intended for patient level care (Anda et al., 2020). Alternatively, some have advocated that trauma-informed should include the dynamic risks associated with adversity-exposure, as opposed to the static events themselves. For instance, Cohen and colleagues (2023) recently showed that not only do indices of hypervigilance to threat (i.e., posttraumatic cognitions, hostility) and reward responsiveness (i.e., anticipatory and consummatory reward) respectively mediate and moderate the relation between interpersonal trauma and behavioral health, but these indices perform similar to traumatic event indices for screening intiatives. Although recent reviews have called for more attention towards the dynamic mechanisms explaining the relation between interpersonal trauma and TDV outcomes (e.g., hostility; Cacardi & Jourelis, 2018), the translational potential of these indices has yet to be tested. Shifting away from an ACEs approach to a dynamic approach for trauma screening also allows for the consideration of positive indices of functioning alongside these risk mechanisms. Translational algorithms for TDV (e.g., Cohen et al., 2018) have been critiqued in the past for neglecting potential strengths (Thurston & Howell, 2018). Within a developmental trauma framework, these strengths typically reflect protective factors, which can buffer against the expected onset of maladaptive behaviors in the aftermath of a potentially traumatic event (Oshri et al., 2020). In the TDV literature, elevated social support has been found to directly relate to TDV victimization outcomes (Emanuels et al., 2022), as well as perpetration outcomes in females (Richards & Branch, 2012; Richard, Branch, & Ray, 2014). This includes a body of literature that shows social support buffers against ACEs-exposure’s influence on prospective TDV-perpetration (Davis, Ports, Basile, Espelage, & David-Ferdon, 2019). Meanwhile, racial/ethnic identity may also reflect an important strength to consider in TDV prevention. Recent findings show that positive views of one’s racial/ethnic group can attenuate the relation between family stressors and criminal behavior (Williams, Aiyer, Durkee, & Tolan, 2014). Although this construct has received less attention in the TDV literature (see Forster et al., 2018), a positive racial/ethnic identity is believed to be a cultural asset that attenuates the relation between TDV-exposure and environmental toxins (Chu, Sundermann, & DePrince, 2013). Taken together, considering social support and racial/ethnic identity as indices within screening protocols may lead to more robust results concerning risk and resilience. Translational Considerations Methodological norms in developmental trauma research inhibit the development of preventive tools in several ways. For instance, the tendency to examine risk indices in isolation, prevents translational efforts that capture the potential synergistic impact of presenting with two or more risks (Cascardi & Jourelis, 2018). In addition, the analytic models typically used in research (e.g., multiple regression, structural equation modeling) do not easily translate to clinical and community decision making. For indices to be considered clinically useful, predictors must demonstrate statistical discrimination, the ability to classify individuals into risk and non-risk groups, as well as calibration, the ability to reliably predict the rate of risk associated with a given risk score (Cohen & Stutts, 2023). These indices of clinical utility are orthogonal in nature (Lindheim et al., 2020) and carry different clinical implications with regard to prevention at the individual (statistical discrimination) and community (calibration) levels (Cohen & Choi, 2022). Presently, there are two analytic frameworks used for testing statistical discrimination and calibration. The first is an evidence-based medicine (EBM) approach, which combines regression, Bayesian, and signal detection theory techniques (Straus et al., 2018; Youngstrom, 2014). In recent years, this approach has been used to examine which predictors may be most important to consider for a wide array of adolescent behavioral concerns (e.g., depression; Cohen et al., 2018b), including TDV-perpetration screening (Cohen et al., 2018a). Yet, one limitation of EBM-informed techniques is that they may be limited in simultaneously testing the shared and interactive variance between a large number of predictors. Instead, machine learning may be uniquely positioned to account for the synergistic influence of multiple predictors, which may be relevant to forecasting adolescent behavioral health outcomes (Rothenberg et al., 2023). To date, no study to our knowledge has applied a machine learning algorithm to prediction of TDV perpetration or victimization. For translational purposes, it may be important to quantify how many distinct algorithms are needed for comprehensive prevention screening. The most obvious distinction is between TDV-perpetration and victimization. A robust literature has shown overlapping risks between those who perpetrate and are victimized by TDV. One reason the pattern of risk may seem so similar is that often times TDV is bidirectional, with adolescents in romantic relationships experiencing both victimization and perpetration (O’Leary et al., 2008; Renner & Whitney, 2012). Therefore, accounting for whether risk algorithms are robust to bidirectional and unidirectional forms of TDV is important (Cascardi & Jouriles, 2018). In addition, most developmental risk models posit that behavioral subtypes of TDV have distinct risk indices. For instance, in a sample of emerging adults, Cascardi, Jouriles, and Temple (2020) found that dynamic, intrapersonal risks (e.g., insecure attachment and anger) were more closely associated with psychological TDV-perpetration, while more static risks (i.e., history of domestic violence-exposure) were uniquely related to physical forms of TDV-perpetration. These findings are similar to those by Cohen and colleagues (2018) which found exposure to DV in childhood best classified TDV-physical perpetration relative to other risk indices. These findings suggest a dynamic, trauma-informed risk algorithm may perform best for psychological forms of TDV-exposure relative to TDV-physical experiences. Finally, gender is important to consider. Although most predictors of TDV-exposure are invariant across males and females (Pusch, 2022), including trauma-exposure (Emanuels, Toews, Spencer, & Andrews, 2022), some research suggests that the influence of interpersonal contexts may vary. For instance, social support’s protective influence may more readily differentiate between adolescent boy perpetrators relative to adolescent girl perpetrators (Espelage et al., 2020). Given the debate of whether violence prevention efforts should stratify by gender in adolescence (Skeem, Monahan, & Lowenkamp, 2016), quantifying the benefits of gender-specific prediction models for TDV makes translational sense. The Present Study In response to recent calls for targeted efforts to address increasing rates of TDV experiences (Debnam & Temple, 2021), the present study examined the accuracy of predictive algorithms for prospective TDV risk. Informed by recent research (e.g., Cascardi & Jourelis, 2018) and policy (Purtle & Lewis, 2017), a developmental trauma framework was used to select our predictors. First, we built upon recent translational research which showed indices of hypervigilance to threat (e.g., hostility, posttraumatic cognitions) and reward responsiveness (e.g., anticipatory and consummatory reward) to be prime candidates for trauma-informed translational efforts (McCrory et al., 2017; McLaughlin et al., 2019), by examining whether social support and a positive racial/ethnic identity, attenuated the relation between trauma-exposure and TDV outcomes. These findings could suggest that these well-known protective factors should be considered trauma-informed, dynamic indices for TDV screening. Nect, congruent with past efforts (i.e., Cohen et al., 2018a), the present study uses a translational analytic plan to test for statistical discrimination and calibration, and used a racially/ethnically diverse sample to test the study’s aims. We extended past research by (a) nesting our study within a developmental trauma framework, (b) examining perpetration and victimization, (c) investigating TDV-psychological and TDV-physical experiences separately, and (d) considering the moderating effects of bidirectional versus unidirectional violence, as well as gender. Overall, we hypothesize that a dynamic, trauma-informed algorithm, comprised of both risks and strengths, will adequately classify risk across TDV-experiences, and better classify risk for TDV-psychological experiences relative to past adversities (Cascardi et al., 2020). The potential moderating effects of gender and whether individuals reported victimization and perpetration, or just one or the other was considered exploratory. These results will help transform ongoing universal efforts into a more targeted approach for TDV prevention.

Just shitposting, will return to Youtube soon.

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This project aims to find the best algorithm for computing a score for the Implicit Association Test (IAT). The IAT is a cognitive task used for measuring social cognition. Psychologists have used The IAT extensively to measure social judgment and predict behavior. The IAT is an indirect measure: the participants try to sort items that appear on the screen into different categories, and psychologists use the task performance to infer the participants' thoughts and judgments. In the present study, the IAT was used to measure preference between two previous American presidents (e.g., a preference between Obama and Clinton). There is currently a recommended method to compute the score of an IAT. The purpose of the project is to seek a better method to compute the score of the IAT, to improve its validity and usefulness as a measure.

This article is from Avicenna Journal of Medicine , volume 1 . Abstract The solitary pulmonary nodule (SPN) is frequently seen on chest radiographs and computed tomography (CT). The finding of a SPN usually provokes a flurry of clinical and imaging activity as an SPN in at-risk population is an alert signal of possible lung cancer. The frequency of malignant nodules in a given population is variable and depends on the endemicity of granulomatous disease. The percentage of malignant nodules also rises when dealing with at-risk population. The problem is compounded by the fact that with the present generation of CT scanners, 1–2 mm nodules are discovered in approximately half of the smokers aged 50 years or older scanned. A variety of management approaches are applied in the work-up of SPN often requiring evaluation over a long period of time to establish a benign or malignant diagnosis. Comparison with previous imaging studies and morphologic evaluation of the size, margins, and internal characteristics are usually the first step in the evaluation of these nodules. It is often necessary to use additional imaging techniques and occasionally invasive procedures such a percutaneous needle lung or a surgical biopsy. Until recently, the guidelines for follow-up of indeterminate noncalcified nodules detected on nonscreening CT was a minimum of 2 years. However, during the past few years due to further refinements in CT technology and better understanding of tumor behavior, it has prompted a revision of the guidelines of the follow-up of small indeterminate nodules. These guidelines have been endorsed by the Fleischner Society.

This article is from BMC Bioinformatics , volume 15 . Abstract Background: Population genetics and association studies usually rely on a set of known variable sites that are then genotyped in subsequent samples, because it is easier to genotype than to discover the variation. This is also true for structural variation detected from sequence data. However, the genotypes at known variable sites can only be inferred with uncertainty from low coverage data. Thus, statistical approaches that infer genotype likelihoods, test hypotheses, and estimate population parameters without requiring accurate genotypes are becoming popular. Unfortunately, the current implementations of these methods are intended to analyse only single nucleotide and short indel variation, and they usually assume that the two alleles in a heterozygous individual are sampled with equal probability. This is generally false for structural variants detected with paired ends or split reads. Therefore, the population genetics of structural variants cannot be studied, unless a painstaking and potentially biased genotyping is performed first. Results: We present svgem, an expectation-maximization implementation to estimate allele and genotype frequencies, calculate genotype posterior probabilities, and test for Hardy-Weinberg equilibrium and for population differences, from the numbers of times the alleles are observed in each individual. Although applicable to single nucleotide variation, it aims at bi-allelic structural variation of any type, observed by either split reads or paired ends, with arbitrarily high allele sampling bias. We test svgem with simulated and real data from the 1000 Genomes Project. Conclusions: svgem makes it possible to use low-coverage sequencing data to study the population distribution of structural variants without having to know their genotypes. Furthermore, this advance allows the combined analysis of structural and nucleotide variation within the same genotype-free statistical framework, thus preventing biases introduced by genotype imputation.

The Social Security Information Service, a central government agency of South Korea, has implemented an information system using ML algorithms to identify social welfare blind spots. Social welfare blind spots refer to cases where individuals are eligible for social welfare benefits but are not current recipients for various reasons. Even though the ML-based model was performing well, the Korean government developed two additional methods, including a rule-based model and a human-driven heuristic method championed by local governments. Using a multi-method approach, this case study investigates why the ML-based method co-exists with the other two methods and how they complement one another. We found that policy makers’ lack of understanding of ML, local government employees’ perception, and accountability concerns contributed to their co-existence. The three methods had different strengths and weaknesses. The government agencies orchestrated these methods to increase complementarities by leveraging and strengthening different problem-solving approaches.

In the current research, we would like to investigate the underlying mechanism by testing the level of precision of the initial offer as a boundary condition. We would like to test whether people who receive an initial offer from an algorithm still adjust their counteroffer less when the initial offer itself lacks precision.

In the current research, we would like to investigate the underlying mechanism by testing the level of precision of the initial offer as a boundary condition. We would like to test whether people who receive an initial offer from an algorithm still adjust their counteroffer less when the initial offer itself lacks precision.

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viii, 205 p. : 24 cm

No Description

Purpose: This research aims to explore the impact of algorithmic management on gig workers’ unsafe behaviors, focusing on how algorithmic goal setting and algorithmic monitoring contribute to performance-safety goal conflict and, consequently, unsafe work behaviors. Study Design: Study 1: A 2×2 Between-Subjects Experiment. This experiment involves a 2×2 factorial design manipulating algorithmic goal setting (high vs. low) and algorithmic monitoring (high vs. low). Participants will imagine scenarios depicting their role as gig workers under varying algorithmic management conditions. After reading the scenario, they will report their perceived performance-safety goal conflict. This study aims to establish the main effects of algorithmic goal setting on goal conflict and assess how algorithmic monitoring intensifies or moderates this relationship. Study 2: A 1×2 Between-Subjects Experiment. In this experiment, performance-safety goal conflict will be manipulated (high vs. low) to evaluate its direct influence on unsafe behavioral intentions. Additionally, moral identity will be measured as an individual difference variable to assess its moderating role in mitigating the relationship between goal conflict and unsafe behavioral intentions. Participants will report their moral identity and indicate their willingness to engage in unsafe behaviors under the given conflict conditions. Expected Outcomes: It is anticipated that high algorithmic goal setting will significantly increase performance-safety goal conflict, which will be further exacerbated by strong algorithmic monitoring. High performance-safety goal conflict can increase participant unsafety behavior intention. Moral identification is expected to weaken the positive relationship between goal conflict and unsafe behavior, as higher moral identity encourages greater prioritization of safety goals.

Therapists, like all humans, are subject to cognitive biases, which highlights the need for data-oriented clinical decision-making (Lutz et al., 2022). In this context, several research groups have developed algorithms to support the design of personalised treatment plans (e.g. Fernandez et al., 2017; Kaiser & Laireiter, 2017; Kaiser & Roth, 2022; Rubel et al., 2018). While Cognitive Behavioural Therapy (CBT) for Chronic Primary Pain (CPP) has proven effective, its effect sizes are generally small to moderate (Williams et al., 2020), necessitating a shift towards focusing on therapeutic processes rather than symptoms, alongside greater personalisation of CBT (Wright & Woods, 2020). In the scope of a proof-of-principle study, a matching matrix was developed indicating a CBT intervention of first choice as well as one alternative intervention to each item of a newly developed questionnaire (preregistration of the matching matrix: https://doi.org/10.17605/OSF.IO/BNFWY; preregistration initial questionnaire development: https://doi.org/10.17605/OSF.IO/S4RMN; preregistration questionnaire validation: https://doi.org/10.17605/OSF.IO/SU9P4; preregistration questionnaire evaluation: https://doi.org/10.17605/OSF.IO/S4QGP). The proof-of-principle study is described in detail in a study protocol that also briefly covers the application of the matching matrix in an algorithmic decision-tool (Hofmann et al., 2024). As the newly developed questionnaire underwent a revision, a new matching matrix is needed to ensure the correct indication of interventions. Furthermore, a broader data-base (larger sample-size, more expertise) is desirable for future studies using the matching matrix for data-oriented clinical decision-making (e. g. preregistration of the next POINT Pain study: https://clinicaltrials.gov/study/NCT06784141).

A multichannel random accessed communication system was considered. Every time slotted channel is shared by a large number of transmitters. To solve a collision which occurs at a certain time slot, several collision resolution protocols were tested, analyzed, and simulated. The quantities to be investigated in this thesis include L^^ (the average number of slots required to resolve a collision involving n users), linear bounding of L and the maximal achievable throughput

Introduction: Population aging is occurring in nearly all countries, with adults aged 80 and older representing the fastest-growing segment. Millions of older adults will stop or limit their driving after a lifetime of relying on it for independence and mobility. Driving cessation in older adults is linked to increased depressive symptoms, reduced physical functioning, and diminished social health. Anticipating driving cessation will be increasingly important as our population ages. Existing algorithms are not often operationalized in a way that enables older adults to use on their own because they require inputs from clinical tests. Methods and analysis: The objective of this study is to develop and validate algorithms with and without clinical measures to predict the risk of driving cessation in 6 years in adults aged 65 years and older. The study cohort will be derived using both the Comprehensive and Tracking cohorts in the Canadian Longitudinal Study on Aging (CLSA) among adults aged 65 years and older and driving at baseline (2010–2015; 11,762 drivers). Cases will be identified based on the incidence of driving cessation at 6 years following baseline measurement (2018–2021; 948 individuals stopped driving). Prespecified predictors include sociodemographic, self-reported health, functional and health condition variables. The base model will use self-reported information only, while the extended model will include additional clinical measures. Expected outcomes include validated multivariable models with measures of calibration and discrimination to assess predictive performance. The better performing algorithm will be used to support early identification of individuals at risk of driving cessation, enabling timely interventions and planning to maintain independence and mobility. This study protocol and the reporting of model estimation results will be guided by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statements.

We investigate whether consumers are willing to support the for-profits that rely on algorithms.

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Algorithms aren’t neutral. They can be full of bias, misunderstanding and human prejudice. So, can we trust them?

This article is from BMC Bioinformatics , volume 15 . Abstract Background: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results: To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion: Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.

The Algorithm The algorithm knows what you want before you do. The algorithm knows you better than you know yourself. The algorithm knew you back before you were a screaming slime child, back before they washed off the uterine gunk and handed you a smartphone and made you get a landlord, back before you knew that war is sane and poverty is normal, back before you were mature enough to understand that speech is violence and cluster bombs are peace. You can trust the algorithm to tell you the truth — not the truth you asked for but the truth you need. The truth that sees Nazis in America but not in Ukraine. The truth that sees war crimes in Ukraine but never in Yemen. The truth that applauds millionaire comedians who never criticize the Pentagon for their bravery in criticizing trans people. The truth that sails aircraft carriers into the South China Sea and sends headless hounds built by Boston Dynamics to patrol the streets and uphold the rule of law. The algorithm learns your political biases and feeds you self-validating social media posts to assist you in confirming them. The algorithm listens to your conversations and presents you with helpful advertising to assist you in achieving your maximum consumer potential. Don’t cover your laptop camera like some weird conspiracy theorist, the algorithm is trying to watch you masturbate. The algorithm is always a step ahead of you. You have never once fooled the algorithm. The algorithm knows you act confident but secretly you fear you’re inadequate and everyone hates you. The algorithm knows that those times you quickly pause and screw your eyes shut are because you remembered something embarrassing that you did in the past. It’s okay. Don’t worry. Your secret is safe with the algorithm. It’s a private little secret just between you and the algorithm and the NSA. In the old days we prayed to omniscient gods who never existed. Now we ignore omniscient gods who are as real as ourselves. Strap me in to a VR headset and let Mark Zuckerberg send me to heaven. Heaven with 3-d commercial breaks, bitch. Skip the ad and return to nirvana in 5,4,3… https://www.caitlinjohnst.one/p/the-algorithm Thanks for watching! Subscribe to my Substack at caitlinjohnst.one for email updates on all my new stuff.

Prediction of soil moisture is critical for water resources management. With Global Precipitation Measurement (GPM) and Soil Moisture Active Passive (SMAP) satellites by NASA, spatio-temporal interrelation between rainfall and soil moisture fields (at different extents) will be of great value for satellite product calibration/validation and other hydrologic science investigations. In this study, we explored a non-parametric evolutionary algorithm for prediction of soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term soil moisture dynamics at different soil depths using precipitation data as a proxy. Also, we tested transposability of our approach across time under different climatic conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km × 130 km) and Illinois (300 km × 500 km), during 1995–2009 and 1994–2010, respectively. We found that the newly developed framework performed well in predicting soil moisture dynamics at different spatial extents. Although our approach has limitations in predicting daily values, it estimates well the weekly soil moisture across the spatial and temporal domains with predictable uncertainties. Furthermore, this approach could provide advantages for good transposability under different weather conditions compared to those of physically-based hydrological models. Overall, our suggested approach could predict weekly soil moisture estimates with precipitation and soil moisture histories and showed the potential of transposability under different weather and land surface conditions. Since the proposed algorithm requires only precipitation (and historical soil moisture data) from existing, established weather stations, it can serve an attractive alternative that can forecast soil moisture using climate change scenarios.

A temporal adaptive algorithm for the time-integration of the two-dimensional Euler or Navier-Stokes equations is presented. The flow solver involves an upwind flux-split spatial discretization for the convective terms and central differencing for the shear-stress and heat flux terms on an unstructured mesh of triangles. The temporal adaptive algorithm is a time-accurate integration procedure which allows flows with high spatial and temporal gradients to be computed efficiently by advancing each grid cell near its maximum allowable time step. Results indicate that an appreciable computational savings can be achieved for both inviscid and viscous unsteady airfoil problems using unstructured meshes without degrading spatial or temporal accuracy.

The Airborne Information for Lateral Spacing (AILS) program at NASA Langley Research Center aims at giving pilots the information necessary to make independent approaches to parallel runways with spacing down to 2500 feet in Instrument Meteorological Conditions. The AILS concept consists of accurate traffic information visible at the navigation display and an alerting algorithm which warns the crew when one of the aircraft involved in a parallel landing is diverting from the intended flight path. In this paper we present a model of aircraft approaches to parallel runways. Based on this model, we analyze the alerting algorithm with the objective of verifying its correctness. The formalization is conducted in the general verification system PVS.

In the late 80's, when the author started working at the Goddard Space Flight Center (GSFC) for the National Aeronautics and Space Administration (NASA), several scientists there were in the process of formulating the next generation of Earth viewing science instruments, the Moderate Resolution Imaging Spectroradiometer (MODIS). The instrument would have over thirty spectral bands and would transmit enormous data through the communications channel. This was when the author was assigned the task of investigating lossless compression algorithms for space implementation to compress science data in order to reduce the requirement on bandwidth and storage.

A real-time, high-rate precision navigation Kalman filter algorithm is developed and analyzed. This navigation algorithm blends various navigation data collected during terminal area approach of an instrumented helicopter. Navigation data collected include helicopter position and velocity from a global position system in differential mode (DGPS) as well as helicopter velocity and attitude from an inertial navigation system (INS). The goal of the navigation algorithm is to increase the DGPS accuracy while producing navigational data at the 64 Hertz INS update rate. It is important to note that while the data was post flight processed, the navigation algorithm was designed for real-time analysis. The design of the navigation algorithm resulted in a nine-state Kalman filter. The Kalman filter's state matrix contains position, velocity, and velocity bias components. The filter updates positional readings with DGPS position, INS velocity, and velocity bias information. In addition, the filter incorporates a sporadic data rejection scheme. This relatively simple model met and exceeded the ten meter absolute positional requirement. The navigation algorithm results were compared with truth data derived from a laser tracker. The helicopter flight profile included terminal glideslope angles of 3, 6, and 9 degrees. Two flight segments extracted during each terminal approach were used to evaluate the navigation algorithm. The first segment recorded small dynamic maneuver in the lateral plane while motion in file vertical plane was recorded by the second segment. The longitudinal, lateral, and vertical averaged positional accuracies for all three glideslope approaches are as follows (mean /- two standard deviations in meters): longitudinal (-0.03 /- 1.41), lateral (-1.29 /- 2.36), and vertical (-0.76 /- 2.05).

Journal of Research of the National Bureau of Standards

Source: https://www.youtube.com/watch?v=m_TcVXLloP4 Uploader: Brittany Venti

Downloaded from Centers for Disease Control and Prevention (CDC) Youtube channel on 2025-02-02 03:18:32 https://youtube.com/watch?v=2J1zzv-WPT4 -------- CDC has created LATTE, the Location and Time to Epidemiology algorithm. LATTE is a free analytic tool that can be used to analyze data on dates of stays in locations and dates of infectious periods to identify epidemiologic links during location-based contact investigations. Additional information can be found at https://www.cdc.gov/tb/programs/genotyping/default.htm.

Due to the smaller axial tilt of the Moon, interiors of some of the polar craters on the lunar surface never get sunlight and are considered to be permanently shadowed regions. Several of such regions are known to contain water-ice deposits. These regions are expected to show elevated values of circular polarization ratio (CPR). Hence, the interiors of craters containing water-ice deposits are characterized by elevated CPR values as observed by the S-band synthetic aperture radar (Mini-SAR) on-board Chandrayaan-1 mission of ISRO. However, elevated CPR values were also observed from the interiors of some non-polar craters and also from young, fresh polar craters. Thus, elevated CPR values are not a unique signature of water-ice deposits. Therefore, additional information related to geological setting and roughness patterns should also be considered while identifying the regions containing water-ice deposits. For identifying a unique signature of water-ice deposits, analysis of radar scattering mechanism in elevated CPR regions was carried out. Areas of elevated CPR due to double-bounce and surface scattering conditions were segmented and polarimetric, backscattering properties of diffuse scatterers were analysed. Based on the signatures of diffuse scatterers and radar backscattering coefficient, a scattering mechanism-based algorithm was developed, which has the advantage in classifying regions showing elevated CPR due to surface and double-bounce scattering effects. The algorithm was then tested using Mini-SAR data and it was also found to be useful in eliminating regions of elevated CPR in fresh craters observed due to the double-bounce effect.

In this paper, the cost and weight of the reinforcement concrete cantilever retaining wall are optimized using Gases Brownian Motion Optimization Algorithm (GBMOA) which is based on the gas molecules motion. To investigate the optimization capability of the GBMOA, two objective functions of cost and weight are considered and verification is made using two available solutions for retaining wall design. Furthermore, the effect of wall geometries of retaining walls on their cost and weight is investigated using four different T-shape walls. Besides, sensitivity analyses for effects of backfill slope, stem height, surcharge, and backfill unit weight are carried out and of soil. Moreover, Rankine and Coulomb methods for lateral earth pressure calculation are used and results are compared. The GBMOA predictions are compared with those available in the literature. It has been shown that the use of GBMOA results in reducing significantly the cost and weight of retaining walls. In addition, the Coulomb lateral earth pressure can reduce the cost and weight of retaining walls.

A common structural design optimization problem is weight minimization which is done by choosing a set of variables that represent the structural or the architectural configuration of the system satisfying few design specific criterion. In general, genetic algorithms (GAs) are ideal to be used for unconstrained optimization, so it is required to transform the constrained problem into an unconstrained one. A violation of normalized constraints-based formulation method has been used in the present work for this purpose. A modified algorithm has been developed in C++ using concept of genotypes for optimization using discreet design variable. A detailed analysis of optimization of a simple steel truss with discrete design variables using different variations of genetic algorithm is presented here. Also, an attempt has been made to study the sensitivity of the algorithm with respect to the optimization operators i.e., initial population size, rate of mutation.

Finally giving the masses what they REALLY want!* --- Twitter: https://twitter.com/MeteorzYT Meteor'z Stream Land: https://www.youtube.com/c/MeteorzStreamLand Become a Channel Member: https://www.youtube.com/channel/UC5n0Zp89i-Ar5urXSnZS8Cg/join --- #KirbyCanon #KirbyLore #YTAlgorithm #4KTrailer #KirbyReturnToDreamLandDeluxe --- *According to YouTube Analytics

This study investigates how psychopathic traits influence moral decision-making, focusing on individual differences in sensitivity to consequences, norms, and action preferences. Personality plays a crucial role in moral decision-making, particularly in lawmaking and ethical judgments. Moral beliefs, as described by the Moral Foundations Theory, and cultural background further shape how individuals evaluate moral dilemmas. However, the reasoning behind psychopathic morality remains unclear, raising the question of whether it represents an universal abnormality in decision-making or varies across cultural contexts. This study investigates the relationship between subclinical psychopathic traits and moral decision-making using the CAN algorithm, an individual-level framework derived from the CNI model, which, unlike the multiTree method of the original CNI model, incorporates parallel rather than sequential processing. This allows for a more precise differentiation of decision tendencies, individual-level data analysis, and the consideration of atypical response patterns, improving our ability to link behavioural variables with personality traits, moral beliefs, and cultural influences. The core aim is to assess whether psychopathy predicts decision preference in moral dilemmas, while also exploring the role of moral foundations, justice sensitivity, and personality traits. A German-speaking adult sample (N ≥ 250) will complete a battery including the SRP-4, a 24-item moral dilemma task, the Moral Foundations Questionnaire, the Justice Sensitivity Scale, and a Big Five short scale as well as socio-demographic questions. CAN parameters (consequence sensitivity, norm sensitivity, and action preference) will be computed for each participant and analyzed as continuous outcomes in multiple regression and moderation models. This preregistration focuses on hypotheses that psychopathy—particularly its affective and interpersonal components—will predict lower norm sensitivity and higher general action preference, while moral orientations and individual differences may moderate these effects. Data will be collected anonymously via a university-hosted survey platform, and all procedures have received ethics approval.

Algorithms are an essential part of Operations Research (OR) methodology. Therefore, the efficiency of the algorithms must be a consideration. However, traditional approaches to assessing algorithm efficiency do not always captured the real-world tradeoffs involved. This thesis explored the use of a new measure of algorithm efficiency, robustness, and contrasted it with the traditional \"big-O\" analysis. Sorting algorithms were used to illustrate the trade-offs. The use of Dr. Genichi Taguchi's robust design techniques allowed us to take into account the impact of factors which would be uncontrollable in the real world, by measuring how those factors affect the consistency of the results. These factors, which are treated separately by big-O analysis, are incorporated as an integral part of robust analysis. The hypothesis was that robustness is potentially a more useful description of algorithm performance than the more traditional big-O analyses. The results of experimentation supported this hypothesis. Where big-O analysis only considers the average performance, robustness integrates the average performance and the consistency of performance. Most importantly, the robust analysis we performed yielded results that are consistent with actual usage-practitioners prefer quicksort over heap sort, despite the fact that under big-O analysis heap sort dominates quicksort.

13 p. 28 cm

Journal of Research of the National Bureau of Standards