Causal Relationship Research Articles

Strategic responses to hostile takeover threats: Empirical evidence on enhancing corporate social engagement

AbstractUnderstanding the dynamics between external corporate governance mechanisms and social engagement offers insights into how corporate strategies towards social capital are shaped. We explore the impact of hostile takeover threats on corporate social engagement by utilizing a measure of takeover vulnerability. This allows for a detailed examination of how exposure to takeover threats influences firms' engagement in social engagement initiatives. Our analysis reveals a positive and causal relation between the degree of takeover threats and the level of social commitments in firms, suggesting that the presence of hostile takeover threats enhances corporate social responsibility as a means of external governance and reduces agency costs. The findings support the hypothesis that the threat of hostile takeovers prompts firms to invest more in social commitments, potentially as a strategic defense mechanism.

#2902 How does IGF-1 levels relate to different stages of chronic kidney disease

Abstract Background and Aims Insulin like growth factor-1 (IGF-I) influences glomerular filtration rate (GFR).Chronic kidney disease (CKD) influences the IGF/GH axis also. Serum IGF-I level is proposed to be a risk factor for renal and cardiovascular complications which is accentuated in CKD. This study is undertaken to observe the level of serum IGF-1 in different stages of chronic kidney disease and its association with cardio-metabolic risk factors Method This cross-sectional pilot study included consecutive 102 CKD patients of advanced renal failure (CKD3-5). Also 52 healthy subjects (free from any known disease) served as healthy referent for IGF-1 level. Stable CKD subjects ( < 15% deviation of eGFR in 3 months) from OPD of study center were included as cases. Information on socio-demographic and clinical characteristics with medical histories was collected from all in a predesigned data collection sheet Urine spot samples and fasting blood samples were collected and stored at −80°C for future analyses. The four-variable Modification of Diet in Renal Disease (MDRD) study equation was used for calculation of the eGFR using enzymatic creatinine. IGF-I was determined by ELISA method. Results Mean age was of CKD subjects was 50.45 ± 10.45 years with male-49% female-51%. Most common primary disease responsible for CKD was GN (53%) followed by DM (21%), HTN (13.0%) and rest others. The IGf-1 level was 297 ± 107 (range 79-588) in CKD patients and in healthy it was 246 ± 82 (114-529) ng/ml, (p < 0.004). The IGF-1 value at 25th percentile of healthy was only present in 12.7% of CKD and above 75th it was present in 42% of CKDs (p < 0.11). Study subjects in CKD 3 was 36%, in 4- 44% and in 5- 24%. The serum IGF-1 levels at CKD 3, 4 and 5 was 246 ± 82, 278 ± 95 and 328 ± 101 ng/ml, (p < 0.014); Hb% 11.1 ± 1.4, 11.3 ± 1.5 and 10.6 ± 1.5 g% (p < 0.001); calcium 9.5 ± 1.4, 9.6 ± 1.4 and 8.9 ± 1.0 mg% (P < 0.014); phosphate 3.6 ± 1.1, 3.6 ± 0.4 and 4.2 ± 1.0 mg%(p < 0.02); iPTH 24 ± 27, 22 ± 16 and 61 ± 82 pg/ml (p < 0.01) and albumin 4.5 ± 1.1, 4.56 ± 1.4 and 8.9 ± 1.0 mg%, (p < 0.04) respectively in 3 stages. The correlation study showed only a negative association of IGF-1 with eGFR (p < 0.01).The logistic regression analysis showed (taking in consideration of cardio-metabolic risk factors) only a significant association of eGFR with IGF-1. Conclusion The Serum IGF-1 level had an increasing value along with alterations in other biochemical parameters at advancing stage of CKD. This increase in IGF-1 value seems to be a reflection of progressing CKD rather than a causal relation to other cardio-metabolic risk markers.

A Wearable Bidirectional Human–Machine Interface: Merging Motion Capture and Vibrotactile Feedback in a Wireless Bracelet

Humans interact with the environment through a variety of senses. Touch in particular contributes to a sense of presence, enhancing perceptual experiences, and establishing causal relations between events. Many human–machine interfaces only allow for one-way communication, which does not do justice to the complexity of the interaction. To address this, we developed a bidirectional human–machine interface featuring a bracelet equipped with linear resonant actuators, controlled via a Robot Operating System (ROS) program, to simulate haptic feedback. Further, the wireless interface includes a motion sensor and a sensor to quantify the tightness of the bracelet. Our functional experiments, which compared stimulation with three and five intensity levels, respectively, were performed by four healthy participants in their twenties and thirties. The participants achieved an average accuracy of 88% estimating three vibration intensity levels. While the estimation accuracy for five intensity levels was only 67%, the results indicated a good performance in perceiving relative vibration changes with an accuracy of 82%. The proposed haptic feedback bracelet will facilitate research investigating the benefits of bidirectional human–machine interfaces and the perception of vibrotactile feedback in general by closing the gap for a versatile device that can provide high-density user feedback in combination with sensors for intent detection.

An Exploration of Causal Relationships Between Behavioural/Emotional Difficulties and Academic Achievement: A Path Model Approach

ABSTRACT As the debate on whether uni-directional and/or bi-directional relationships exists between behavioural/emotional difficulties of school-aged learners, this study employed the path modelling approach to explore if there was a causal direction between behavioural/emotional difficulties (i.e. attention, aggressiveness, depression) and academic achievement (i.e. Korean language arts, mathematics), or vice versa over time using two time points. The final sample contained 1,735 students who attended high schools during 2014–2015 school year. Path modelling estimates showed that (a) behavioural/emotional difficulties caused difficulties in mathematics in a year; (b) difficulties in both Korean language arts and mathematics caused aggressiveness and depression; and (c) there was a bidirectional relation between aggressiveness and depression and mathematics performance. Further, there was a gender difference in reacting to their experiences in behavioural/emotional difficulties and academic difficulties. Overall, findings from the current study provide a significant contribution to the existing literature on the causal relation between behavioural/emotional difficulties and academic achievement, as well as supporting the gender difference in terms of the direction of this relation. Finally, practical implications and future directions for research were discussed.

Quantum Mechanics Based on an Extended Least Action Principle and Information Metrics of Vacuum Fluctuations

We show that the formulations of non-relativistic quantum mechanics can be derived from an extended least action principle. The principle can be considered as an extension of the least action principle from classical mechanics by factoring in two assumptions. First, the Planck constant defines the minimal amount of action a physical system needs to exhibit during its dynamics in order to be observable. Second, there is constant vacuum fluctuation along a classical trajectory. A novel method is introduced to define the information metrics to measure additional observability due to vacuum fluctuations, which is then converted to an additional action through the first assumption. Applying the variational principle to minimize the total actions allows us to recover the basic quantum formulations including the uncertainty relation and the Schrödinger equation in the position representation. In the momentum representation, the same method can be applied to obtain the Schrödinger equation for a free particle while further investigation is still needed for a particle with an external potential. Furthermore, the principle brings in new results on two fronts. At the conceptual level, we find that the information metrics for vacuum fluctuations are responsible for the origin of the Bohm quantum potential. Even though the Bohm potential for a bipartite system is inseparable, the underlying vacuum fluctuations are local. Thus, inseparability of the Bohm potential does not justify a non-local causal relation between the two subsystems. At the mathematical level, quantifying the information metrics for vacuum fluctuations using more general definitions of relative entropy results in a generalized Schrödinger equation that depends on the order of relative entropy. The extended least action principle is a new mathematical tool. It can be applied to derive other quantum formalisms such as quantum scalar field theory.

A physics-and-data co-driven material design strategy for multicomponent diboride ceramics

The vast chemical composition space of multicomponent ceramics provides opportunity for regulating their properties and performances. Other than trial-and-error and screening, a physics-and-data co-driven material design strategy was reported to efficiently explore chemical composition space through establishing prediction model between physical properties and chemical composition via physics-guided data mining method. Firstly, causal and correlative relationships were discriminated according to physicochemical knowledge. Secondly, ensemble features such as chemical formula and chemical bond were digitized by constructing descriptors for crystalline solids with atomic features including atomic weight and radius contained in the Periodic Table of elements. Finally, quantitative causal relations between material structure/properties and ensemble descriptors were mined out of small dataset using a least square regression method, on which physical prediction model established therefore for forward predicting structure/properties and inverse designing chemical composition. Choosing diboride ceramics as a case, those causal analytic relations of composition-structure and composition-property were successfully derived and illustrated to predict the value of structure and properties accompanying with quantitative relation between chemical pressure and chemical bonding ensemble descriptors. This method bridges the connection between atomic features and macroscopic properties via ensemble descriptors, which significantly accelerates the discovery of new materials.

Exploring the causal association between genetically determined circulating metabolome and hemorrhagic stroke.

Hemorrhagic stroke (HS), a leading cause of death and disability worldwide, has not been clarified in terms of the underlying biomolecular mechanisms of its development. Circulating metabolites have been closely associated with HS in recent years. Therefore, we explored the causal association between circulating metabolomes and HS using Mendelian randomization (MR) analysis and identified the molecular mechanisms of effects. We assessed the causal relationship between circulating serum metabolites (CSMs) and HS using a bidirectional two-sample MR method supplemented with five ways: weighted median, MR Egger, simple mode, weighted mode, and MR-PRESSO. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO served for the sensitivity analyses. The Steiger test and reverse MR were used to estimate reverse causality. Metabolic pathway analyses were performed using MetaboAnalyst 5.0, and genetic effects were assessed by linkage disequilibrium score regression. Significant metabolites were further synthesized using meta-analysis, and we used multivariate MR to correct for common confounders. We finally recognized four metabolites, biliverdin (OR 0.62, 95% CI 0.40-0.96, PMVMR = 0.030), linoleate (18. 2n6) (OR 0.20, 95% CI 0.08-0.54, PMVMR = 0.001),1-eicosadienoylglycerophosphocholine* (OR 2.21, 95% CI 1.02-4.76, PMVMR = 0.044),7-alpha-hydroxy-3 -oxo-4-cholestenoate (7-Hoca) (OR 0.27, 95% CI 0.09-0.77, PMVMR = 0.015) with significant causal relation to HS. We demonstrated significant causal associations between circulating serum metabolites and hemorrhagic stroke. Monitoring, diagnosis, and treatment of hemorrhagic stroke by serum metabolites might be a valuable approach.

Causality and modality: A case study on Teochew periphrastic causatives

Many linguistics works have adopted the CAUSE operator to analyze causal relations. However, recent studies have gradually converged on the idea that a denotation like CAUSE(e, e') is not sophisticated enough to capture complex causalities encoded in linguistic structures, echoing long-time discussions on causation in the field of philosophy. This study supports this view by working on the plural instantiations of causation encoded in five periphrastic causative constructions in Teochew, an understudied Southern Min language. I demonstrated causality notions encoded in Teochew causatives differ in four dimensions: (i) direct vs. indirect (temporal, spatial, intermediary agent), (ii) deterministic vs. probabilistic (in terms of the actuality entailment of the caused event), (iii) attitude-neutral vs. attitude-bearing (benefactive/adversative) and (iv) permissive vs. non-permissive. I provide a sublexical modal analysis paired with event semantics to capture these complexities, aiming to replace the monolithic CAUSE event linker and to show most of the causal complexities result from different flavors of sublexical modality encoded in the causative verbs.

Exploration of materials fatigue influence factors using interpretable machine learning

AbstractData‐driven fatigue strength predictions are gaining popularity. Nevertheless, many machine learning models lack trustworthiness due to their limited decision‐making transparency which often hinders their practical application. In this investigation, we assess the expressiveness of the model‐agnostic explainable AI method known as SHapley Additive exPlanations (SHAP) for data‐driven fatigue strength prediction. Our study demonstrates that the SHAP feature sensitivity analysis underpins known physical relations from materials processing and fatigue theory. This even applies in view of the high‐dimensional, cross‐correlated fatigue feature space and despite data heterogeneity (different steels, component designs, and loads). For instance, SHAP indicates a fatigue strength increase with higher solid solution‐strengthening element concentrations, such as chromium and nickel. SHAP identifies correlations rather than causality. Thus, data science and domain knowledge should be closely linked during the SHAP assessment. If this is satisfied, plausible causal relations can be inferred, and spurious ones arising from confounding variables can be discarded.

Application of advanced causal analyses to identify processes governing secondary organic aerosols

Understanding how different physical and chemical atmospheric processes affect the formation of fine particles has been a persistent challenge. Inferring causal relations between the various measured features affecting the formation of secondary organic aerosol (SOA) particles is complicated since correlations between variables do not necessarily imply causality. Here, we apply a state-of-the-art information transfer measure coupled with the Koopman operator framework to infer causal relations between isoprene epoxydiol SOA (IEPOX-SOA) and different chemistry and meteorological variables derived from detailed regional model predictions over the Amazon rainforest. IEPOX-SOA represents one of the most complex SOA formation pathways and is formed by the interactions between natural biogenic isoprene emissions and anthropogenic emissions affecting sulfate, acidity and particle water. Since the regional model captures the known relations of IEPOX-SOA with different chemistry and meteorological features, their simulated time series implicitly include their causal relations. We show that our causal model successfully infers the known major causal relations between total particle phase 2-methyl tetrols (the dominant component of IEPOX-SOA over the Amazon) and input features. We provide the first proof of concept that the application of our causal model better identifies causal relations compared to correlation and random forest analyses performed over the same dataset. Our work has tremendous implications, as our methodology of causal discovery could be used to identify unknown processes and features affecting fine particles and atmospheric chemistry in the Earth’s atmosphere.

Anomaly stands

Donald Davidson’s anomalous monism has been repeatedly criticized since its initial defense in the paper Mental Events, which was published in 1970. Despite the widespread rejection, there seems to be no agreement on why anomalous monism fails. This paper systematizes two strong objections to anomalous monism. First, Davidson’s argument for monism requires the problematic assumption that physics can provide strict causal laws for causal relations in general. Second, Davidson’s monism requires an ontology of events for which no satisfactory criterion of identity has been provided. Despite these problems, the paper argues that the theses on the anomalism and irreducibility of the mental remain acceptable, despite the difficulty of reconstructing precisely the arguments Davidson uses to defend them.

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

Blameworthiness and Causal Outcomes

AbstractIt is widely held that whether a person is morally responsible for an outcome partly depends on whether certain causal relations obtain between that person and the outcome. This paper argues that, regardless of whether the preceding claim about moral responsibility is true, moral blameworthiness is independent of such causal considerations. This conclusion is motivated by considering cases from Carolina Sartorio and Sara Bernstein. The causal structures of these cases are complex. Sartorio and Bernstein believe that reaching conclusions about moral responsibility and blameworthiness requires wading through this complexity. But it is argued here that getting clear on these causal structures does not help us assess blameworthiness (regardless of the relevance of such considerations for drawing conclusions about moral responsibility). Moral blameworthiness simply does not turn on the considerations relevant to figuring out whether an agent causally contributed to an outcome.

The impact of Industry 4.0 on occupational health and safety: A systematic literature review

Introduction: Industry 4.0 has brought new paradigms to businesses based on high levels of automation and interconnectivity and the use of technologies. This new context has an impact on the work environment and workers. Nevertheless, these impacts are still inconclusive and controversial, requiring new investigative perspectives. This study aimed to investigate the requirements sought, the risk factors identified, and the adverse effects on workers caused by the characteristics of I4.0. Method: The methodology was based on a systematic literature review utilizing the PRISMA protocol, and 30 articles were found eligible. A descriptive and bibliometric analysis of these studies was performed. Results: The results identified the main topics that emerged and have implications for workers' Occupational Health and Safety (OHS) and divided them into categories. The requirements are related mainly to cognitive, organizational, and technological demands. The most significant risk factors generated were associated with the psychosocial ones, but organizational, technological, and occupational factors were also identified. The adverse effects cited were categorized as psychic, cognitive, physical, and organizational; stress was the most cited effect. An explanatory theoretical model of interaction was proposed to represent the pathway of causal relations between the requirements and risk factors for the effects caused by I4.0. Conclusions and practical applications: This review has found just how complex the relationships between the principles of Industry 4.0 are (e.g., requirements, risk factors, and effects) and the human factors. It also suggests a pathway for how these relationships occur, bridging the gap left by the limited studies focused on connecting these topics. These results can help organizational managers understand the impacts of I4.0 on workers' safety and health.

Managerial risk data analytics applications using grey influence analysis (GINA)

We observe and analyze the causal relations among risk factors in a system, considering the manufacturing supply chains. Seven major categories of risks were identified and scrutinized and the detailed analysis of causal relations using the grey influence analysis (GINA) methodology is outlined. With expert response based survey, we conduct an initial analysis of the risks using risk matrix analysis (RMA) and the risks under high priority are identified. Later, the GINA is implemented to understand the causal relations among various categories of risks, which is particularly useful in group decision-making environments. The results from RMA concludes that the capacity risks (CR) and delays (DL) are in the category of very high priority risks. GINA results also ratify the conclusions from RMA and observes that managers need to control and manage capacity risks (CR) and delays (DL) with high priorities. Additionally from the results of GINA, the causal factors disruptions (DS) and forecast risks (FR) appear to be primary importance and if unattended can lead to the initiation of several other risks in supply chains. Managers are recommended to identify disruptions at an early stage in supply chains and reduce the forecast errors to avoid bullwhips in supply chains.

Causal emergent principles and relations for mechanical properties of covalent and ionic crystals

A knowledge and data-synergized intelligent computation architecture for materials was proposed within the data science paradigm. As a vital operation, two digital ensemble descriptors implying chemical composition and structural trend for crystals were created using the features contained in the Periodic Table of elements without a priori assumption, which affords causal emergence and regulation principles for the mechanical response of covalent and ionic solids. In addition to a linear correlation among structural state/mechanical response parameters, causal analytic relations in an exponential form between structural and thermodynamic state/mechanical response parameters and a digital ensemble descriptor were unveiled through least squares regression, in which the coefficients are classified in accordance with symmetry principles on the atom and lattice. Thereafter, the underlying physicochemical mechanisms of chemical pressure and chemical bonding are found responsible for the mechanical responses of bulk modulus and hardness of solids. At last, a physical prediction model was established for crystalline solids and demonstrated the feasibility of the predictive design of novel superhard materials. It is believed that by constructing suitable digital ensemble descriptors, this intelligent computation architecture and consequent physical prediction models on the basis of causal analytic relations are able to generalize by depicting crystalline solids with covalent and ionic bonds in other crystallographic structures.

Design and Analysis of Surface-Mounted Permanent-Magnet Field-Modulation Machine for Achieving High Power Factor

Along with the establishment of general air-gap field modulation theory, a new type of field-modulation machine has attracted increasing attention. However, the low power factor always plagued its popularization. This paper is to focus on the simplest structure surface-mounted permanent-magnet field-modulation (SMPMFM) machine, aiming at exploring the implementation of high power-factor design. The key is that the power factor calculation formula is derived parametrically, hence the originally vague causal relationship between the structural parameters and power factor become clear, guided by which the initial structure design for high power factor achievement is determined, meanwhile the power factor adjustment evaluation and the electromagnetic performance comparison with the help of finite element analysis (FEA) are conducted to verify the validity of the proposed power factor calculation formula. Finally, a feasible high-power-factor SMPMFM prototype is fabricated and the experimental testing is performed, which has a good agreement with the formular calculation and FEA results. Hence, the validity of the proposed analysis for achieving high power-factor design of field-modulation machines is verified.

Integrated Optimal Control for Electrolyte Temperature With Temporal Causal Network and Reinforcement Learning.

The electrowinning process is a critical operation in nonferrous hydrometallurgy and consumes large quantities of power consumption. Current efficiency is an important process index related to power consumption, and it is vital to operate the electrolyte temperature close to the optimum point to ensure high current efficiency. However, the optimal control of electrolyte temperature faces the following challenges. First, the temporal causal relationship between process variables and current efficiency makes it difficult to estimate the current efficiency accurately and set the optimal electrolyte temperature. Second, the substantial fluctuation of influencing variables of electrolyte temperature leads to difficulty in maintaining the electrolyte temperature close to the optimum point. Third, due to the complex mechanism, building a dynamic electrowinning process model is intractable. Hence, it is a problem of index optimal control in the multivariable fluctuation scenario without process modeling. To get around this issue, an integrated optimal control method based on temporal causal network and reinforcement learning (RL) is proposed. First, the working conditions are divided and the temporal causal network is used to estimate current efficiency accurately to solve the optimal electrolyte temperature under multiple working conditions. Then, an RL controller is established under each working condition, and the optimal electrolyte temperature is placed into the controller's reward function to assist in control strategy learning. An experiment case study of the zinc electrowinning process is provided to verify the effectiveness of the proposed method and to show that it can stabilize the electrolyte temperature within the optimal range without modeling.

Towards a more accurate assessment of the tourism-corruption nexus

ABSTRACT This note sheds light on methodological concerns regarding the assessment of the relationship between tourism and corruption. For the most part, researchers have utilized regression-based techniques to assess the impact of corruption on tourist inflows, without entertaining the possibility of feedback from tourist inflows to corruption. Alternatively, we present dynamic cointegrated error-correction modeling as a superior methodology for examining the potential bi-directionality of short- and long-run causal relations between corruption and tourist inflows.

초등교사의 경청과 공감이 교사-학생관계 교사효능감에 미치는 영향

Objectives This study aims to draw educational implications by assessing the influences of elementary school teachers’ ‘attentive listening’ and ‘empathy’ on ‘teacher efficacy for teacher-student relations’ mediated by ‘self-esteem.’ Methods To understand the correlation and causality between variables set in this study, exploratory factor analysis, reliability analysis, correlation analysis, simple regression analysis, multiple regression analysis, mediator variable regression analysis were conducted. Results First, attentive listening had a significant impact on teacher efficacy for teacher-student relations (H1 adopted: B=.563, p<.001). Second, empathy had a significant impact on teacher efficacy for teacher-student relations (H2 adopted: B=.452, p<.001). Third, attentive listening had a significant impact on self-esteem (H3 adopted: B=.454, p<.001). Fourth, empathy had a significant impact on self-esteem (H4 adopted: B=.424, p<.001). Fifth, self-esteem had a significant impact on teacher efficacy for teacher-student relations (H5 adopted: B=.590, p<.001). Sixth, attentive listening had a significant impact on teacher efficacy for teacher-student relations mediated by self-esteem (H6 adopted: β=.357, p<.001). Seventh, empathy had a significant impact on teacher efficacy for teacher-student relations mediated by self-esteem (H7 adopted: β=.294, p<.001). In addition, in the relations of attentive listening and empathy with teacher efficacy for teacher-student relations, self-esteem had a partial mediation effect. In addition, as for the influence of the independent variable on teacher efficacy for teacher-student relations, attentive listening had a relatively greater impact than empathy. Conclusions This study drew educational implications by revealing the causal relation and influence of several variables affecting ‘teacher efficacy for teacher-student relations.’ The results of this study suggest that attentive listening and empathy are needed for smooth teacher-student relation. Smoother teacher-student relations can be expected when attentive listening and empathy are mediated with positive self-esteem. Hopefully, this study would contribute to the promotion of elementary school teachers’ ‘teacher efficacy for teacher-student relations’ and help research related to attentive listening and empathy.