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Early linguistic input is a powerful predictor of children's language outcomes. We investigated two novel questions about this relationship: Does the impact of language input vary over time, and does the impact of time-varying language input on child outcomes differ for vocabulary and for syntax? Using methods from epidemiology to account for baseline and time-varying confounding, we predicted 64 children's outcomes on standardized tests of vocabulary and syntax in kindergarten from their parents' vocabulary and syntax input when the children were 14 and 30 months old. For vocabulary, children whose parents provided diverse input earlier as well as later in development were predicted to have the highest outcomes. For syntax, children whose parents' input substantially increased in syntactic complexity over time were predicted to have the highest outcomes. The optimal sequence of parents' linguistic input for supporting children's language acquisition thus varies for vocabulary and for syntax. [ABSTRACT FROM AUTHOR]

Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks that rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large‐scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational data sets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include (1) a discrete set of treated and untreated units, (2) a known date of treatment intervention, and (3) time series response data that include both pre‐ and post‐treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no‐action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large‐scale brush‐clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long‐term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units. [ABSTRACT FROM AUTHOR]

Dust storms pose a serious threat to air quality and public health through large‐scale, long‐distance transport. In early April 2018, two severe dust storm events occurred in the Taklimakan and Gobi Deserts in northwestern China. The advected air‐masses containing dust traveled eastward over long distances reaching the western Yangtze River Delta region (WYRDR) with local PM10 peak concentrations of 190 and 410 μg/m3 on April 6 and April 11, 2018, respectively. Satellite remote sensing and ground‐based lidar observations showed that the first dust event was transported to WYRDR at an altitude of <5 km, while the second transport height was at an altitude of <3 km. The first one was accompanied with the dry‐cold air mass in the northwest moving eastward and southward. By comparison, in the second event, the dust aerosols were mixed with anthropogenic aerosols from southwestern China. Meanwhile, as southerly warm‐humid air flows prevailed at near‐surface layer over the WYRDR in the second event, the associated higher relative humidity potentially induced hygroscopic growth of polluted dust aerosols, resulting in explosive growth of PM10. In addition, the average depolarization ratio of aerosols over WYRDR on April 6 was 0.3, while on April 11 it was 0.2, indicating that the previous pollution event featured more obvious nonspherical characteristics of aerosols. Our findings provide scientific evidence that transboundary dust events and resultant local PM10 pollution are closely related to various transport trajectories of dust aerosols as well as the local vertical wind profiles and surface humidity conditions. Key Points: The magnitudes of local PM10 pollutions depend on various transport trajectories of dust aerosols as well as local meteorological conditionsDifferent dust transported heights are revealed by integrated observations of satellite remote sensing and ground‐based lidarThe spherical characteristics of dust‐related aerosols are identified by depolarization ratio of aerosols from lidar observation [ABSTRACT FROM AUTHOR]

In response to a comment by Chris Rourk on our article Computing the Integrated Information of a Quantum Mechanism, we briefly (1) consider the role of potential hybrid/classical mechanisms from the perspective of integrated information theory (IIT), (2) discuss whether the (Q)IIT formalism needs to be extended to capture the hypothesized hybrid mechanism, and (3) clarify our motivation for developing a QIIT formalism and its scope of applicability. [ABSTRACT FROM AUTHOR]

The experimental research paradigm lies at the core of empirical psychology. New data analytical and computational tools continually enrich its methodological arsenal, while the paradigm’s mission remains the testing of theoretical predictions and causal explanations. Predictions regarding experimental results necessarily point to the future. Once the data are collected, the causal inferences refer to a hypothesis now lying in the past. The experimental paradigm is not designed to permit strong inferences about particular incidents that occurred before predictions were made. In contrast, historical research and scholarship in other humanities focus on this backward direction of inference. The disconnect between forward-looking experimental psychology and backward-looking historical (i.e., narrative) psychology is a challenge in the postmodern era, which can be addressed. To illustrate this possibility, I discuss three historical case studies in light of theory and research in contemporary psychology.

Managers implement several Green Supply Chain Management (GSCM) practices to improve sustainability and economic performance, such as environmental management systems (EMS), eco-design (ED), source reduction (SR) and attending to external environmental management (EEM) requirements; however, the relationship among them requires a deep study. This paper reports the case of the Mexican maquiladora industry, analyzing the main relationships among GSCM practices with environmental impact (EI) and environmental cost savings (ECS). The analysis reports three structural equation models (SEM) developed as simple, second-order, and mediating models. Those relationships are tested using 160 responses to a survey applied to the Mexican maquiladora industry and with partial least squares algorithms (PLS), where conditional probabilities for different scenarios in latent variables are also reported. Findings indicate that EMS has a direct effect on EI (β = 0.442) and ECS (β = 0.227), indicating that EMS reduces EI and cost associated with the production process; however, ED has no direct effect on EI (β = 0.019) and ECS ((β = 0.006), and it can be due to the maquiladora nature as foreign companies focused on manufacturing and not to product design.

The assessment of comprehensiveness of soil conservation measures (SCMs), along with economic, social, and environmental assessments of these projects, is a prerequisite for good governance in a watershed. This study was conducted using the cause-and-effect framework of DPSIR (Driver-Pressure-State-Impact-Response) to assess the comprehensiveness of SCMs in reducing the soil erosion potential of the Kond watershed area and its adverse impacts. Horticultural, mining, and ranching activities; population growth; and road network development were identified as the most important driving forces of the watershed. After determining the indicators, the integrated index was calculated based on weight calculation and standardization of values to detect changes before and after the implementation of SCMs. The results showed a decrease in soil erosion and the corresponding adverse impacts in 2019 compared with the base year, 1997, so that according to the integrated index, the soil erosion status and related impacts have decreased by 16 and 35%, respectively. Despite this decline, the watershed still has a high rate of soil erosion (26.27 t ha−1 year−1). This is because SCMs are more focused on improving the state and impacts, and there are no necessary managerial responses to the components of the driving forces and pressures. Given that the proactive approach has less contribution than the reactive approach in SCMs, in addition to the reactive approach, it is necessary to pay more attention to the proactive approach to reduce the soil erosion rate of the watershed and decrease the relevant negative impacts. [ABSTRACT FROM AUTHOR]

Objectives: Previous studies have revealed a correlation between eosinophils and allergic rhinitis, but the causal relationship has not been fully confirmed. This study aims to evaluate the causal link between blood eosinophils and allergic rhinitis using the Mendelian randomization (MR) method., Methods: Summary data from the Genome-Wide Association Study Catalog (GWAS) for eosinophil count (exposure variable) and allergic rhinitis (outcome variable) were collected. GWAS data for the exposure variable were obtained from the IEU Open GWAS Project developed by the Integrative Epidemiology Unit at the University of Bristol, while data for the outcome variable were sourced from the FinnGen Biobank (Finland) database. The causal relationship between eosinophils and allergic rhinitis was analyzed using the two-sample MR method with inverse variance weighted (IVW) analysis. Sensitivity analyses were conducted using the weighted median method, MR-Egger regression, leave-one-out analysis, and funnel plots., Results: An increase in blood eosinophil count showed a potential causal relationship with an increased risk of allergic rhinitis ( OR =1.187, 95% CI 1.051 to 1.341, P =0.006). This finding was consistent across the weighted median method and MR-Egger regression. Leave-one-out analysis indicated that no single nucleotide polymorphism significantly influenced the causal inference., Conclusions: There is a causal association between increased eosinophil count and a higher risk or worsening of allergic rhinitis.

The northwest China is a climate change-sensitive and ecologically vulnerable area. Under the backdrop of global warming, this region exhibits clear characteristics of warming and wetting. In recent years, the causes of climate change in the northwest China has become a widely-focused topic. Previous research has mainly attributed the increase in precipitation to changes in the westerly belt and enhanced local convective activity caused by global warming. South Asia, beside the Tibetan Plateau from the northwest China, is one of the regions with the fastest growth in global atmospheric pollutant emissions. This study utilizes reanalysis data such as ERA5 and MERRA-2. Statistical methods, including Theil-Sen Median trend analysis and Singular Value Decomposition analysis, are employed to analyze the spatiotemporal characteristics of South Asian aerosols, extreme precipitation in the northwest China, and the correlation between the two. The study reveals the existence of two key aerosol-precipitation response areas. Synthetic analysis of the meteorological elements of the response events in both regions is conducted to explore the possible physical mechanisms behind the correlation between South Asian aerosols and precipitation in the northwest China. The result of this study is to provide a new perspective on the causes of extreme precipitation in the arid region of northwest China., Competing Interests: The authors declared that they have no conflicts of interest in connection with the manuscript submitted., (© 2024 The Authors.)

Microbial nitrogen metabolism is a complicated and key process in mediating environmental pollution and greenhouse gas emissions in rivers. However, the interactive drivers of microbial nitrogen metabolism in rivers have not been identified. Here, we analyze the microbial nitrogen metabolism patterns in 105 rivers in China driven by 26 environmental and socioeconomic factors using an interpretable causal machine learning (ICML) framework. ICML better recognizes the complex relationships between factors and microbial nitrogen metabolism than traditional linear regression models. Furthermore, tipping points and concentration windows were proposed to precisely regulate microbial nitrogen metabolism. For example, concentrations of dissolved organic carbon (DOC) below tipping points of 6.2 and 4.2 mg/L easily reduce bacterial denitrification and nitrification, respectively. The concentration windows for NO 3 - -N (15.9-18.0 mg/L) and DOC (9.1-10.8 mg/L) enabled the highest abundance of denitrifying bacteria on a national scale. The integration of ICML models and field data clarifies the important drivers of microbial nitrogen metabolism, supporting the precise regulation of nitrogen pollution and river ecological management.

Background: Previous observational researchers have found an inverse bidirectional link between Alzheimer's disease (AD) and prostate cancer (PCa); yet, the causative nature of this link remains unclear. To investigate the causal interactions between AD and PCa, a bidirectional Mendelian randomization (MR) analysis was conducted., Methods: This study comprised two Genome-Wide Association Study (GWAS) summary statistics for AD (17,008 cases and 37,154 controls) and PCa (79,148 cases and 61,106 controls) in individuals of European ancestry. The inverse-variance weighted (IVW) method was employed as the primary approach, while MR-Egger, weighted median, weighted mode, and simple mode served as supplementary methods for estimating the causal effect. To assess pleiotropy, the MR-PRESSO global test and MR-Egger regression were used. Cochran's Q test was adopted to check heterogeneity, MR Steiger test and the leave-one-out analysis was performed to confirm the robustness and reliability of the results., Results: The causal association genetically inferred of AD on PCa was found using IVW (OR = 0.974, 95% CI = 0.958-0.991, p = 0.003) in forward MR analysis and the causal association genetically inferred of PCa on AD was not found using IVW (OR = 1.000, 95% CI: 0.954-1.049, P = 0.988) in reverse MR analysis. The sensitivity analysis showed that no pleiotropy and heterogeneity was observed. The leave-one-out analysis showed that the findings were not inordinately affected by any instrumental variables., Conclusion: The results of this study demonstrated an absence of bidirectional causality between AD and PCa among the European population, suggested that a genetically predicted possibility of decreased PCa risk in AD patients, and no significant genetically predicted causal effect of PCa on AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Peng, Deng, Li and Wu.)

This study aimed to analyse the mediating role of farmers' time perspective in water resources exploitation behaviour/s (WREB). The study was descriptive correlational and causal relational and was carried out using a survey. The statistical population consisted of farmers in the eastern area of Lake Urmia (N = 232 295), of whom 386 persons were selected using a stratified random sampling method. The instrument was a questionnaire, whose validity was confirmed by a panel of experts. Its reliability was also verified by performing the Cronbach's alpha test (0.60 ≥ α ≥ 0.94). The causal framework is presented based on the findings. In such a condition, self‐efficacy, psychological needs and time perspective had the highest total causal effect on farmers' WREB. Moreover, the results confirmed the mediating role of time perspective (as a personal variable) and time norms (as a social variable) in WREB. Therefore, the proper conduct of farmers' WREB needs to provide a futuristic perspective, which is also confirmed by social norms. [ABSTRACT FROM AUTHOR]

• We evaluate the time-varying safety effects of speed cameras. • Speed cameras have significantly reduced the number of road accidents. • Safety effects of speed cameras experienced a sharp decrease after the installation. • Appropriate management is important to maintain the effectiveness of speed cameras. Introduction: Speed limit enforcement cameras provide an effective approach to reduce vehicle speeds and the number of road accidents. However, it is still unclear whether the safety effects of speed cameras show durability over long periods of time. This paper analyses how the effects of speed cameras on road accidents change over time. A total number of 771 camera sites and 4787 potential control sites are observed for a period of 18 years (1999–2016) across England. Method : Covariates such as road class, crash history, speed limit, and annual average daily traffic (AADT) are included in the data set. A difference in difference (DID) based propensity score matching (PSM) method is employed to select proper control sites and estimate the treatment effects. The safety effects of speed cameras are then evaluated from a long-term perspective. The post-treatment period is divided into four equal-length periods: early, medium 1 and 2, and late. Results and Conclusions : The results show that speed cameras have significantly reduced the number of road accidents near the camera sites. However, the effects vary across different time periods. The safety effects of speed cameras experienced a sharp decrease during the medium periods after an initial period of highly reduced accidents (medium 1: −53.1%, medium 2: −40.7%) and recovered slightly during the late period. In addition, to evaluate the criteria for selecting camera sites in the UK, we further investigated whether speed cameras at high risk sites have better safety performance. The results show that while safety effects at high risk camera sites also decreased during the medium periods, the reduction was smaller (medium 1: −20.8%, medium 2: −2.1%). Practical Applications : Appropriate road traffic regulations and management, as well as proper camera sites selection criterion, are important to maintain the effectiveness of speed cameras. [ABSTRACT FROM AUTHOR]

Behavioural change techniques are currently used by many global organisations and public institutions. The amassing evidence base is used to answer practical and scientific questions regarding what cognitive, affective, and environment factors lead to successful behavioural change in the laboratory and in the field. In this piece we show that there is also value to examining interventions that inadvertently fail in achieving their desired behavioural change (e.g., backfiring effects). We identify the underlying causal pathways that characterise different types of failure, and show how a taxonomy of causal interactions that result in failure exposes new insights that can advance theory and practice. The behavioural change enterprise disproportionately focuses on promoting successes at the expense of examining the failures of behavioural change interventions. We review the literature across different fields through a causal explanatory approach to identify structural relations that impede (or promote) the success of interventions. Based on this analysis we present a taxonomy of failures of behavioural change that catalogues different types of failures and backfiring effects. Our analyses and classification offer guidance for practitioners and researchers alike, and provide critical insights for establishing a more robust foundation for evidence-based policy. [ABSTRACT FROM AUTHOR]

The experimental research paradigm lies at the core of empirical psychology. New data analytical and computational tools continually enrich its methodological arsenal, while the paradigm's mission remains the testing of theoretical predictions and causal explanations. Predictions regarding experimental results necessarily point to the future. Once the data are collected, the causal inferences refer to a hypothesis now lying in the past. The experimental paradigm is not designed to permit strong inferences about particular incidents that occurred before predictions were made. In contrast, historical research and scholarship in other humanities focus on this backward direction of inference. The disconnect between forward-looking experimental psychology and backward-looking historical (i.e., narrative) psychology is a challenge in the postmodern era, which can be addressed. To illustrate this possibility, I discuss three historical case studies in light of theory and research in contemporary psychology. [ABSTRACT FROM AUTHOR]

Internet public opinion, as a reflection of social public opinion in the internet space, in turn affects the development process of social entity events. Improving the ability to deal with online public opinion has become an important task for party committees and government departments at all levels. The rules and characteristics of the spread of online public opinion were analyzed, system dynamics was used to analyze the driving factors of the ecological spread of online public opinion, the relationship between factors was analyzed through causal loop diagrams, and a model of the driving force of online public opinion was established. Finally, the model was simulated and analyzed, in order to put forward suggestions for actively managing the spread of online public opinion. [ABSTRACT FROM AUTHOR]

This new 3-page publication in the Conducting the Needs Assessment series provides Extension educators and other service providers with an introduction to two techniques that can easily be used when seeking information about relationships between causes and needs: fishboning and cause and consequence analysis. Written by Amy Harder and published by the UF/IFAS Department of Agricultural Education and Communication. https://edis.ifas.ufl.edu/wc352

According to the stages of the banking system stability monitoring, the analysis of caus¬al links is used to identify the causes of the crisis trends spreading and the rationale for the most effective levers of regulatory influence on the banking system parameters by the central bank.The research is based on the use of the canonical correlation method for structuring causal links between the indicators for the assessment of the banking system stability, which are grouped into four sub-indices (assessing the intensity of credit and financial interaction in the interbank market, the effectiveness of the banking system functions, structural changes and financial disproportions in the banking system, activities of systemically important banks); the method of regression analysis and the calculation of elasticity coefficients is also used to assess the sensitivity of the banking system stability to changes in parameters that characterize the banking regulation instruments.The article analyzes the results of quantitative and qualitative assessment of the banking system stability (comparison of actual results of the evaluation with the data for previous years and comparison of values of stability indicators with critical values). The causes of detected deviations are determined taking into account the results of applying the canonical correlations method. Regression models have been constructed to confirm the dependence of the banking system stability index on the change in parameters that characterize banking regulation instruments, and to determine the most effective of them. Practical testing of submitted proposals is realized based on the Ukrainian banking system indicators for 2007–2016.

Abstract An electric vehicle (EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users’ travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can be used to analyze the behaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EE-based simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users’ travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users’ travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine (ICE) vehicle users.

We sought to analyze the clinical data and imaging features from a rare case presenting an intravertebral mobile nerve sheath tumor of the lumbar spine, review the relevant literature, discuss the imaging features and possible causes of the tumor, and propose preventive measures and solutions. The clinical data and imaging data of a patient with a lumbar spinal canal mobile nerve sheath tumor were retrospectively analyzed in conjunction with the relevant literature. The first preoperative lumbar spine magnetic resonance imaging (MRI) showed the tumor located at level L1-2. Further lumbar spine MRI, which was performed 5 days later, showed the tumor was at level L3-4, with a range of motion of 8 cm. End spinal resection of the tumor was performed under general anesthesia, and a tumor, which was cystic solid, was found to be located at level L3-4. The tumor originated from a distinctly twisted and elongated posterior root of the spinal cord, with complete fusion of the tumor-bearing nerve. Both the tumor and tumor-carrying nerve were removed. Postoperative pathologic examination confirmed that the tumor was a nerve sheath tumor. Lumbar MRI on postoperative day 10 showed complete resection of the tumor in the L3-4 spinal canal. The patient was discharged with normal urination and defecation, normal sensation in both lower extremities, grade 5 muscle strength, normal muscle tone, and normal reflexes in both knee and Achilles tendons. Intravertebral mobile nerve sheath tumors are rare, and the marked distortion and elongation of the carrier nerve seen on MRI are important imaging features of this disease. The possible causes of tumor movement include tumor texture, location, positional changes, and altered cerebrospinal fluid dynamics. Acute changes in intraabdominal pressure caused by forceful defecation may be a high-risk factor for tumor migration. Multiple preoperative MRIs to localize the tumor are particularly important. [ABSTRACT FROM AUTHOR]

• Estimated the causal economic impact of an off-peak fare discount in Hong Kong. • Applied the difference-in-difference method to control for confounding bias. • Found the aggregate effect to be small but statistically significant and heterogeneous. • Responsiveness was governed by travel costs and crowding. • Produced novel causal estimates of trip rescheduling elasticities. This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-in-difference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy. [ABSTRACT FROM AUTHOR]

This paper discusses approaches to evaluating the quality of intelligent data analysis results in diagnostic tasks. The reliability (indisputability) of empirical dependencies established during training (interpolation–extrapolation) on precedents is evaluated using a special mathematical tool, that is, characteristic functions. Characteristic functions are generated on the available sample of empirical data based on similarity analysis of precedent descriptions, formalized as a binary algebraic operation. Some estimates of the computational complexity of applying the proposed mathematical technique of characteristic functions to predicting (diagnosing) the properties of newly studied precedents are presented. [ABSTRACT FROM AUTHOR]

The need for shared freight and passenger rail corridors in the United States is increasing due to the growing demand for regional and intercity passenger transport. Several researches have been conducted on reducing the risk of freight train accidents, but little research has been done on the risk of passenger train accidents. The accident rates of passenger trains have declined in the past two decades; however, faster and more frequent passenger train services require even higher safety standards, and therefore further reduction to the risk of passenger train accidents is needed. The research presented in this paper analyzed the passenger train accidents in the United States using the Federal Railroad Administration train accident database to understand the trend of passenger train accident rates, the frequency and severity of different types of accidents, and to explore the major factors that cause them. Derailments and collisions were identified as the most significant types of passenger train accidents, and track failures and human factors, respectively, were the primary causes of those accidents. Accidents caused due to human factors and train operations such as train speed violations and failure to obey signals are often high-consequence accidents and therefore pose the greatest risk. Higher risk infrastructure-related factors include track geometry defects and broken rails or welds. This study on passenger train accidents provides a solid foundation for further research on improving the safety of passenger rail and shared-use rail corridors. [ABSTRACT FROM AUTHOR]

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers: how to convey the explanatory content of a hypothesized model or set of competing models clearly. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed, and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create "well‐formed hypotheses," as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that. [ABSTRACT FROM AUTHOR]

Measuring the performance-related factors of a unit within a supply-chain is a challenging problem, mainly because of the complex interactions among the members governed by the supply chain strategy employed. Synergistic use of discrete-event simulation and structural equation modeling allows researchers and practitioners to analyze causal relationships between order-fulfillment characteristics of a supply-chain and retailers' performance metrics. In this study, we model, simulate, and analyze a two-level supply-chain with seasonal linear demand, and using the information therein, develop a causal model to measure the links/relationships among the order-fulfillment factors and the retailer's performance. According to the findings, of all the order-fulfillment characteristics of a supply-chain, the forecast inaccuracy was found to be the most important in mitigating the bullwhip effect. Concerning the total inventory cost and fill-rate as performance indicators of retailers, the desired service level had the highest priority, followed by the lead-time and forecast inaccuracy, respectively. To reduce the total inventory cost, the bullwhip effect seems to have the lowest priority for the retailers, as it does not appear to have a significant impact on the fill rate. Although seasonality (to some extent) influences the retailer's performance, it does not seem to have a significant impact on the ranking of the factors affecting retailers' supply-chain performance; except for the case where the backorder cost is overestimated. [ABSTRACT FROM AUTHOR]

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The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim is to identify the main factors associated with the desertion of 2018 residents from a drug addicts rehabilitation center, with telephone interviews with deserters and face-to-face with managers. It has a phenomenological design that tries to understand a specific situation of the group of deserters. It is theorized in the stream of dynamic psychology. Semi-structured interviews and group activities were used. The target population is the 2018 drug addicts deserters from the rehabilitation center in the township of San Antonio de Prado. The information obtained through the interviews is exposed in a category sheet that groups the most common responses. In the analysis of the responses, analysis categories emerge that intersect with the categories resulting from the interviews with current residents. In conclusion, 43% of defectors in 2018 abandoned treatment for having an erroneous belief in their mental wellbeing. [ABSTRACT FROM AUTHOR]

Open data are becoming increasingly available in various domains, and many organizations rely on making decisions according to data. Such decision making requires care to distinguish between correlations and causal relationships. Among data analysis tasks, causal relationship analysis is especially complex because of unobserved confounders. For example, to correctly analyze the causal relationship between two variables, the possible confounding effect of a third variable should be considered. In the open-data environment, however, it is difficult to consider all possible confounders in advance. In this paper, we propose a framework for exploratory causal analysis of open data, in which possible confounding variables are collected and incrementally tested from a large volume of open data. To the extent of the authors' knowledge, no framework has been proposed to incorporate data for possible confounders in causal analysis process. This paper shows an original way to expand causal structures and generate reasonable causal relationships. The proposed framework accounts for the effect of possible confounding in causal analysis by first using a crowdsourcing platform to collect explanations of the correlation between variables. Keywords are then extracted using natural language processing methods. The framework searches the related open data according to the extracted keywords. Finally, the collected explanations are tested using several automated causal analysis methods. We conducted experiments using open data from the World Bank and the Japanese government. The experimental results confirmed that the proposed framework enables causal analysis while considering the effects of possible confounders. [ABSTRACT FROM AUTHOR]

• We estimate the effects of the dockless bike-sharing systems on the London Cycle Hire. • A propensity score matching method is employed to make causal inferences. • The average weekly usage of the London Cycle Hire is reduced by 5.93%. • Dockless bikes replace a part of short duration London Cycle Hire trips. • The reductions in the cycle hire usage vary across time. This paper evaluates the effects of dockless bike-sharing systems on the usage of the London Cycle Hire (LCH). A total number of 707 LCH docking stations are included for a period of 36 weeks. Covariates such as socio-demographic characteristics, traffic conditions, and the built environment are included in the data set. A difference in difference (DID) based propensity score matching (PSM) method is applied to select control docking stations and estimate the effects of the dockless bike-sharing systems on the LCH usage. The results suggest that a significant reduction of 22.42 (5.93%) in the average weekly usage of each LCH docking station is caused by the dockless bike-sharing system after its initial launch. We also investigate the effects of dockless bike-sharing systems on the characteristics of the LCH trips, including travel duration, travel distance, and departure time. The majority of trips replaced by the dockless bikes are short duration (0–15 min) and middle distance (1–3 km) trips. The reduction in the average weekly short duration trips of each station is 16.91 (7.16%), while the figure is 15.11 (6.74%) for middle distance trips. The average travel duration is increased by 0.25 min, but the travel distance shows no significant change due to data restriction. Furthermore, the LCH usage is significantly reduced by 6.85% during the weekday commuting peak, and 10.47% during the weekend leisure peak. As to weekday off-peak and other times, reductions are not statistically significant. [ABSTRACT FROM AUTHOR]

In this paper, a safety analysis method based on system theoretic process analysis (STPA) is proposed for the submarine torpedo launch control system. One typical action of torpedo launching process, releasing the weapon brake is taken as an example for analysis with XSTAMPP safety engineering platform. Traditional STPA causal scenario analysis model is improved; refined system safety requirements are generated and the descriptions are standardized by linear temporal logic (LTL), the limitations of natural language descriptions used by traditional STPA analysis have been avoided, which provides theoretical support for further safety model verification and decision-making assistant for system operators. [ABSTRACT FROM AUTHOR]

In the age of big data, identifying causality has become the focus of different scientific disciplines. This importance is particularly acute in geography, where understanding complex systems is a tough task. This study provides an in-depth review of causal analysis techniques, assessing their strengths, assumptions, and limitations in geographic research. Using case studies of precipitation impacts on vegetation and runoff, we compare three key approaches: granger causality, the PC algorithm, and LiNGAM. Our findings reveal that (1) causal analysis is evolving from linear to nonlinear, and from bivariate to multivariate, despite challenges such as uncertainty testing. (2) Establishing causal direction is crucial, but distinguishing between direct and indirect causation is equally important. (3) While detailed assumptions enhance the refinement of causal approaches, they may limit their generalizability. Our results support the broader use of causal analysis in geographic research. • We offer a comprehensive theoretical review for causal analysis techniques, assessing their key issues, assumptions, merits, and limitations in geographic research. • The causal analysis method shows a trend from linear to nonlinear and bivariate to multivariate but may bring some problems, such as incomplete significant tests. • In the process of developing causal analysis methods, in addition to determining the causal direction between variables, it is also necessary to distinguish between direct causality and indirect causality among variables • The more detailed the assumptions of the causal analysis methods are, the finer and richer the ex-pressive ability is, but the degree of generalization may be restricted. • It is important for promoting the application and practice of causal analysis in geographic research. [ABSTRACT FROM AUTHOR]

Efforts to educate for wisdom are sorely needed in order to solve humanity’s most pressing problems, as explained by philosopher Nicholas Maxwell and psychologist Robert Sternberg, among others. This paper explains that the handful of wisdom pedagogies that have been put forward to date, however, are inadequate for developing the sort of wisdom that can solve our major problems, because they fail to identify and target for development four cognitive functions necessary for wise decision-making. These functions are causal analysis, prospection, social cognition, and metacognition. I show how adequately performing these cognitive functions, which constitute the core of systems thinking, is necessary for solving our most serious global problems, as various systems-thinking experts have also argued. Drawing on recent research on learning and the development of expertise, I explain how the capabilities to perform these functions can be developed by pedagogical methods that help students construct more adequate cognitive models of (i) natural, social, and psychological systems of cause and effect and (ii) the cognitive procedures required to comprehend and effectively intervene in these systems. The basic principles for implementing this wisdom/systems-thinking pedagogy across academic disciplines are explained, and examples from different disciplines are provided.

Very Small Entities (VSE) dedicated to software development lack of enough resources to adopt causal analysis practices, defined in models as CMMI, due to their complexity and costs. It is important to detect the generated defects in the development process, and to make a systematic analysis aimed at determining its causes. However, identifying those root causes is an arduous task, and failing to do so leads to wrong decisions that either fail to solve the problem or even make it worse. On this basis, this paper proposes a causal analysis procedure focused on small organizations PAC-DS (according to its initials in Spanish), which includes activities aimed at identifying the defects causes. After its evaluation in a preliminary case study, the utility of the procedure was evidenced.

Purpose: We investigated the connectivity of neural signals from movement-related cortical areas to the primary motor area (M1) in the hemisphere contralateral to the movement side during the period of movement-related magnetic fields before movement., Materials and Methods: Participants were 13 healthy adults, and nerual signals were recorded using magnetoencephalography. Spontaneous extension of the right wrist was performed at the participant's own pace and following a visual cue in internal (IC) and external (EC) cue tasks. The connectivity of neural signals to M1 from each movement-related motor area was assessed by Granger causality analysis (GCA). The GCA was performed on the neural activity elicited in a frequency band between 7.8 and 46.9 Hz during the pre-movement periods, which occurred durng the readiness field (RF) and the negative slope prime (NSp). F-values, as connectivity values obtained by GCA, were compared between the EC and IC cue tasks., Results: For NSp periods, the connectivity of neural signals from the left superior frontal area (SF-L) to M1 was dominant in the IC task, whereas that from the left superior parietal area (SP-L) to M1 was dominant in the EC task. The F value in the GCA from SP-L to M1 was greater in the EC task during RF than in the IC task during equivalent periods., Conslusions: In the present study, there were differences in the connectivity of neural signals to M1 between IC and EC tasks. The present results suggested that the pattern of pre-movement neural activity that resulted in a movement was not uniform but differed between movement tasks just before the movement.

Background: The COVID-19 pandemic has highlighted gaps in the current handling of medical resource demand surges and the need for prioritizing scarce medical resources to mitigate the risk of health care facilities becoming overwhelmed., Objective: During a health care emergency, such as the COVID-19 pandemic, the public often uses social media to express negative sentiment (eg, urgency, fear, and frustration) as a real-time response to the evolving crisis. The sentiment expressed in COVID-19 posts may provide valuable real-time information about the relative severity of medical resource demand in different regions of a country. In this study, Twitter (subsequently rebranded as X) sentiment analysis was used to investigate whether an increase in negative sentiment COVID-19 tweets corresponded to a greater demand for hospital intensive care unit (ICU) beds in specific regions of the United States, Brazil, and India., Methods: Tweets were collected from a publicly available data set containing COVID-19 tweets with sentiment labels and geolocation information posted between February 1, 2020, and March 31, 2021. Regional medical resource shortage data were gathered from publicly available data sets reporting a time series of ICU bed demand across each country. Negative sentiment tweets were analyzed using the Granger causality test and convergent cross-mapping (CCM) analysis to assess the utility of the time series of negative sentiment tweets in forecasting ICU bed shortages., Results: For the United States (30,742,934 negative sentiment tweets), the results of the Granger causality test (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a stochastic system) were significant (P<.05) for 14 (28%) of the 50 states that passed the augmented Dickey-Fuller test at lag 2, and the results of the CCM analysis (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a dynamic system) were significant (P<.05) for 46 (92%) of the 50 states. For Brazil (3,004,039 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (22%) of the 27 federative units, and the results of the CCM analysis were significant (P<.05) for 26 (96%) of the 27 federative units. For India (4,199,151 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (23%) of the 26 included regions (25 states and the national capital region of Delhi), and the results of the CCM analysis were significant (P<.05) for 26 (100%) of the 26 included regions., Conclusions: This study provides a novel approach for identifying the regions of high hospital bed demand during a health care emergency scenario by analyzing Twitter sentiment data. Leveraging analyses that take advantage of natural language processing-driven tweet extraction systems has the potential to be an effective method for the early detection of medical resource demand surges., (©Mahakprit Kaur, Taylor Cargill, Kevin Hui, Minh Vu, Nicola Luigi Bragazzi, Jude Dzevela Kong. Originally published in JMIR Formative Research (https://formative.jmir.org), 29.01.2024.)

Introduction: In recent years, there has been a national push to incorporate high-fidelity quality improvement and patient safety (QIPS) education into physician training programs. In fact, integration of robust patient safety education became an Accreditation Council for Graduate Medical Education (ACGME) Common Program Requirement for residency programs in 2017. We developed a curriculum to not only fulfill the ACGME's requirement but also provide PGY 1 internal medicine residents with the skills needed to become active participants in ongoing patient safety work throughout their training and careers., Methods: Our patient safety curriculum was woven into residents' existing protected educational time and supported by a standardized facilitator guide and participant workbook. It combined didactic prework with the review of recent near-miss or low-harm patient safety events, empowering residents to identify root causes and propose interventions., Results: We successfully delivered our patient safety curriculum to 80 PGY 1 residents over the course of 2 academic years. Residents rated the curriculum as a valuable educational experience, and the event reviews they completed met most of the criteria for high-quality patient safety reviews according to the Strong String Assessment., Discussion: Implementation of this standardized curriculum has allowed us to reliably and consistently incorporate experiential patient safety education into the first year of training for internal medicine residents. Unlike purely didactic sessions, our curriculum encourages active learning, building muscle memory for event reviews that enables future engagement in patient safety activities., (© 2024 Perkons et al.)

Background: Aspirin, as one of the most commonly used drugs, possesses a broad spectrum of therapeutic applications. Presently, the potential association between aspirin usage and the risk elevation of erectile dysfunction (ED) remains inconclusive. The objective of this study employing two-sample Mendelian randomization (MR) was to clarify the causal impact of aspirin use on the risk of ED., Methods: This study incorporated two sets of Genome-Wide Association Study (GWAS) summary statistics, one for aspirin use (46,946 cases and 286,635 controls) and another for ED (6,175 cases and 217,630 controls) in individuals of European ancestry. The inverse-variance weighted (IVW) method was employed as the primary approach, supplemented by MR-Egger, weighted median, weighted mode, and simple mode to estimate the causal effect of aspirin usage on the risk of ED development. To assess pleiotropy, the MR-PRESSO global test and MR-Egger regression were used. Cochran's Q test was adopted to check heterogeneity, and the leave-one-out analysis was performed to confirm the robustness and reliability of the results., Results: The causal association between genetically inferred aspirin use and ED was found by using inverse variance weighted (OR = 20.896, 95% confidence interval = 2.077-2.102E+2, P = 0.010). The sensitivity analysis showed that no pleiotropy and heterogeneity was observed. Furthermore, the leave-one-out analysis demonstrated that the findings were not significantly affected by any instrumental variables., Conclusion: The results of this study highlighted the significance of aspirin use as a predisposing factor for ED and provided further evidence supporting the causal association between aspirin utilization and ED within European populations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Peng, Deng, Li and Wu.)

Objectives: To assess the indications, timing, and clinical outcomes that result from the early tracheostomy (ET) administration, by causal inference models., Design: A retrospective observational study., Setting: Multiinstitutional intensive care unit in the United States PARTICIPANTS: The study comprised 626 trauma patients., Interventions: An ET versus late tracheostomy (LT)., Measurements and Main Results: Trauma patients with tracheostomy were identified from 2 public databases named Medical Information Mart for the Intensive Care-IV and eICU Collaborative Research Database. Tracheostomy was defined as early (≤7 days) or late (>7 days) from intensive care unit admission. A marginal structural Cox model (MSCM) with inverse probability weighting was employed. For comparison, the authors also used time-dependent propensity-score matching (PSM) to account for differences in the probability of receiving an ET or LT. A total of 626 eligible patients were enrolled in the study, of whom 321 (51%) received a ET. The MSCM and time-dependent PSM indicated that the ET group was associated with reduced ventilation-associated pneumonia (VAP) and a shorter mechanical ventilation (MV) duration than the LT group. Yet, mortality did not show any difference between the two groups., Conclusions: The authors' study observed that ET was not associated with reduced mortality in trauma patients, but it was associated with reduced VAP risk and MV duration. The results warrant further validation in randomized controlled trials., Competing Interests: Conflict of Interest None., (Copyright © 2022 Elsevier Inc. All rights reserved.)