Risk Avoidance Research Articles

The Effect of Changes in VAT Rates and Company Risks on Tax Avoidance in Coal Mining Companies

One of the largest contributors to state income is mining companies, where in 2019 mining companies contributed tax revenues of IDR 33.43 trillion. Based on Law No. 11 of 2020 concerning Job Creation which amends the VAT Law Article 4A paragraph (2), Coal was previously not subject to VAT, and in 2022 the VAT rate will change to 11%, which was previously 10%, so from this explanation the aim of this research is to determine the relationship between changes in VAT rates on tax avoidance and the relationship between company risk and tax avoidance. The research object used is from the Indonesian Stock Exchange for Coal Mining Companies in Indonesia from the year corresponding to the start of VAT imposition and changes in rates, namely 2021-2023. Data were analyzed using multiple regression analysis, and to determine the sample using purposive sampling technique. From the results of the hypothesis test, it was found that there is no significant relationship between changes in VAT rates and tax avoidance, and there is a significant relationship between company risk and tax avoidance.

Social capital, health risk avoidance, and the enrollment of health insurance: The case of rural middle-aged and elderly in China

Low enrollment intention threatens the funding pools of rural insurance schemes in developing countries. The purpose of this study is to investigate how social capital enhances the enrollment of health insurance among rural middle-aged and elderly. We propose that social capital directly increases health insurance enrollment, while indirectly influences health insurance through health risk avoidance. We used data from the China Health and Retirement Longitudinal Study (wave 4) dating the year of 2018, instrumental variable estimation was introduced to deal with the endogeneity problem, and the mediation analysis was used to examine the mechanism of social capital on insurance enrollment. The results show that social capital is positively related to social health insurance enrollment, and the relationship between social capital and social health insurance enrollment is mediated by health risk avoidance.

Operational Risk Control Strategies and Employee Performance of Commercial Banks in Cameroon

This study investigates the extent to which operational risk control strategies influence employee performance of commercial banks in Cameroon. Primary data was obtained by administering close-ended questionnaires to respondents of 14 selected commercial banks in Cameroon. Data analysis was based on descriptive statistics and Ordinary Least Squares regression. Results showed that amongst the operational risk control strategies under consideration, risk retention and risk avoidance significantly influence the employee performance of commercial banks in Cameroon whereas risk transfer and risk reduction showed no significant influence on the employee performance. Therefore, commercial banks in Cameroon should place more emphasis on internal control, auditing programs, provision for operational loss as well as putting in place codes of conduct, enhancing technology and screening of employees in order to increase employee performance.

Risk avoidance and social anxiety in adolescence: Examination of event-related potentials and theta-dynamics on the Balloon Risk Avoidance Task

Adolescents are at relatively high-risk for developing anxiety, particularly social anxiety. A primary hallmark of social anxiety is the impulse to avoid situations that introduce risk. Here, we examined the neural and behavioral correlates of risk avoidance in adolescents (N=59) 11 to 19 years of age. The Balloon Risk Avoidance Task was used with concurrent electroencephalography to measure event-related potentials (frontal P2; late slow-wave; N2, feedback-related negativity, FRN; posterior P3) and oscillatory dynamics (midfrontal theta, 4–7 Hz) in response to unsuccessful and successful risk avoidance conditions. Social anxiety was measured using the Social Phobia and Anxiety Inventory for Children. Results indicated that, across the whole sample, youth exhibited smaller P3, larger FRN, and larger theta responses to unsuccessful risk avoidance. Youth reporting high (compared to low) levels of social anxiety exhibited larger P2, slow-wave, and FRN responses to unsuccessful, compared to successful, risk avoidance. Further, greater social anxiety was associated with reduced theta responses to successful avoidance. Youth with higher levels of social anxiety showed smaller theta responses to both conditions compared to those with low levels of social anxiety. Taken together, the ERP-component differences and weakened theta power in socially anxious youth following unsuccessful avoidance are informative neural correlates for socially anxious youth during risk avoidance.

The influence of psychological factors on investment decision-making: Psychological features of economic relations formation

The article aimed to analyse the influence of psychological factors on the investment decision-making process, which shapes the economic behaviour of investors in Ukraine. The research involved surveys and experimental tasks conducted among investors, with the data analysed using statistical methods to identify the impact of psychological factors on investment decisions. Fear and greed have been found to be most significant emotions affecting investment decisions, whereas fear leads to excessive caution and risk avoidance. In contrast, greed drives investors to take on excessive risks. Overconfidence in one’s knowledge and skills results in an overestimation of opportunities and an increased risk of losses, while confirmation bias leads investors to seek information that supports their previous decisions, ignoring conflicting data. Expert opinions and group behaviour significantly influence investment decisions, particularly under conditions of uncertainty, often resulting in herd behaviour and the formation of economic bubbles. The experimental tasks demonstrated that investors frequently make irrational decisions under the influence of emotions and cognitive biases, even when they can access complete information about risks and opportunities. The survey results also indicated that investors with higher levels of financial literacy are less susceptible to the influence of emotions and cognitive biases, highlighting the importance of financial education in improving the quality of investment decisions. These findings illustrate how psychological factors can distort the investment decision-making process and underscore the necessity of considering these factors when developing investment strategies

Analysis of progressive collapse disaster and its anchoring effectiveness in jointed rock tunnel

AbstractThe complexity and variability of the structural distribution and combination characteristics of jointed rock masses make the response mechanism of tunnel rock collapse different, and there is a lack of systematic research on the existing perimeter rock instability mode and bolt support scheme. Based on numerical simulations of the block system structure of a nodular rock mass, the existing theory of bolt support is compared and analyzed to explore the scope of their respective applications. Combined with the spatial and temporal transport law of block instability, a new batch instability model of jointed rock tunnels is proposed, which reveals the progressive collapse catastrophe evolution mechanism of a collapsed interlocking block system after the instability of the key blocks and elucidates the coupling mechanism between the bolts and the block system structure as well as their anchoring effectiveness. Finally, for the actual tunnel project, the instability batches of the surrounding rock are identified, and the corresponding optimized design of the bolt support is presented, which has achieved good support effects. The research results can provide important theoretical guidance and practical engineering value for risk avoidance, disaster identification and targeted prevention and control of dangerous rock fall and chain collapse instability disasters in jointed rock body tunnels.

The Effects of Daily-Living Risks on Breast Cancer-Related Lymphedema.

Conventional advice to reduce the risk of breast cancer-related lymphedema (BCLE) suggests avoidance of daily-living risks, and limited research has investigated these risks. This study aimed to examine the occurrence, patterns, and effects of daily-living risks on BCLE. A cross-sectional design was used to collect data from 567 patients at a metropolitan cancer center in the United States. The Lymphedema Risk-Reduction Behavior Checklist was used to assess the occurrence of 11 daily-living risks. Descriptive, regression, and factor analyses were performed. Significant odds of BCLE were associated with infection (odds ratio [OR] 2.58, 95% confidence interval [CI] 1.95-3.42), cuts/scratches (OR 2.65, 95% CI 1.97-3.56), sunburn (OR 1.89, 95% CI 1.39-3.56), oil splash or steam burns (OR 2.08, 95% CI 1.53-3.83), and insect bites (OR 1.59, 95% CI 1.18-2.13). The daily-living risks were clustered into factors related to skin trauma and carrying objects. Skin trauma risk was significantly associated with BCLE (B=0.539, z=3.926, OR 1.714, 95% CI 1.312-2.250; p<0.001). Having three, four, or five skin trauma risks significantly increased the odds of BCLE to 4.31, 5.14, and 6.94 times, respectively. The risk of carrying objects had no significant or incremental effects on BCLE. Complete avoidance of daily-living risks is challenging given 52.73% of patients incurred more than five daily-living risks. Our study findings underscore the importance of 'what to do' strategies to minimize infection and skin trauma.

Fast automatic docking control for electric tractors

In this study, we investigate the automatic docking of an electric tractor with a semi-trailer in narrow and long-distance scenarios. We propose an automatic docking method based on a multi-sensor fusion high-precision positioning algorithm and a multi-strategy docking control algorithm. To solve the heading-angle error of the involved camera due to mechanical errors, a compensation value in the form of angle deviation is introduced. To alleviate the increase in the camera detection error with longitudinal distance, a piecewise coordinate compensation function is proposed. Repositioning is performed using light detection and ranging (Lidar) to improve the pose-perception accuracy to the millimeter level. To avoid the interference of point clouds on the relocation accuracy of Lidar, a search-and-location method that combines both small and large neighborhoods is proposed. To achieve the desired docking success rate, a multi-strategy-adjustment control algorithm is used to enable comprehensive adjustment, including approximate control of the maximum angle based on lateral deviation and fine proportional-integral-differential (PID) control based on lateral deviation and longitudinal distance, security check cycle and premature warning mechanism, and risk avoidance control. Finally, the reliability and stability of the design scheme are verified by conducting multiple experiments involving actual vehicles.

Striatal GABA levels correlate with risk sensitivity in monetary loss.

Decision-making under risk is a common challenge. It is known that risk-taking behavior varies between contexts of reward and punishment, yet the mechanisms underlying this asymmetry in risk sensitivity remain unclear. This study used a monetary task to investigate neurochemical mechanisms and brain dynamics underpinning risk sensitivity. Twenty-eight participants engaged in a task requiring selection of visual stimuli to maximize monetary gains and minimize monetary losses. We modeled participant trial-and-error processes using reinforcement learning. Participants with higher subjective utility parameters showed risk preference in the gain domain (r = -0.59) and risk avoidance in the loss domain (r = -0.77). Magnetic resonance spectroscopy (MRS) revealed that risk avoidance in the loss domain was associated with γ-aminobutyric acid (GABA) levels in the ventral striatum (r = -0.42), but not in the insula (r = -0.15). Using functional magnetic resonance imaging (fMRI), we tested whether risk-sensitive brain dynamics contribute to participant risky choices. Energy landscape analyses demonstrated that higher switching rates between brain states, including the striatum and insula, were correlated with risk avoidance in the loss domain (r = -0.59), a relationship not observed in the gain domain (r = -0.02). These findings from MRS and fMRI suggest that distinct mechanisms are involved in gain/loss decision making, mediated by subcortical neurometabolite levels and brain dynamic transitions.

Effect of decision-making principle on P2G–CCS–CHP complementary energy system based on IGDT considering energy uncertainty

As one of the most promising large-scale de-fossilization technologies currently available, Power-to-Gas (P2G) is expected to promote utilization of renewable sources and mitigate the effects of greenhouse gas. Through the coupling of P2G, Combined Heat and Power (CHP) as well as Carbon Capture and Storage (CCS), the P2G–CCS–CHP microgrid energy system could achieve hierarchical energy transfer and green-powered interconversion of gas, electricity and heat flows. However, the conversion of multiple energy shapes between different facilities is deeply intertwined with renewable energy sources, greatly increasing risk of unbalance operation and impacting the overall robustness and economic viability of system. To address these challenges, this study proposes a decision-making framework based on Information Gap Decision Theory (IGDT) method for a P2G–CCS–CHP system. The real-time balancing of heat & power loads and supply, the mechanism of gas-electricity-heat interaction, as well as recycling pattern of synthetic fuel within the system are carefully addressed to achieve optimal operation. Different scheduling schemes are obtained by setting up two separating decision principles, namely risk-seeking (RS) and risk-avoiding (RA), to solve these uncertainties and ensure robust decision making. Meanwhile, adaptability of decision-making methodology is analyzed based on several criteria including share of recycled green-powered fuel, ecological friendliness, carbon trading benefit, etc. Results show that adding of P2G module enables a 12.6% reduction in carbon emissions and generates 1252.2 kg of green powered gas, highlighting the eco-friendly nature of the multi energy system (MES). About 21.5% of the renewable energy flows participate the interconversion of electric-heat-gas and about 17.7% of total carbon flow is circulated as synthetic fuel within the system. In addition, conservative tendency is witnessed in the case of risk-seeking decision mode, leading to an approximate 13.22% increase in the operation costs, compared with risk-avoiding mode. The proposed IGDT decision strategy has shown significant compatibility in dealing with the impact of multiple uncertainties.

Modeling and Analysis of Microscopic Risk Avoidance Behavior of Homogeneous Driver Groups Under Risk Scenarios

Modeling and Analysis of Microscopic Risk Avoidance Behavior of Homogeneous Driver Groups Under Risk Scenarios

The use of anthropogenic areas helps explain male brown bear movement rates and distance travelled during the mating season

AbstractDuring the reproductive period, mating strategies are a significant driver of adaptations in animal behaviour. For instance, for polygamous species, greater movement rates during the mating season may be advantageous due to the increased probability of encountering several potential mates. The brown bear Ursus arctos is a solitary carnivore that lives at low densities, with a polygamous mating system and an extended mating season of nearly 3 months. Here, we hypothesized that male brown bears may show changes in movement patterns and space‐use behaviour during their mating season. Using long‐term (2002–2013) telemetry data from the Finnish Karelia male population (n = 24 individuals; n = 10 688 GPS locations), we first analysed daily movement metrics, that is, speed, net and total distance with respect to the period (mating vs. post‐mating) and several environmental predictors. Then, we conducted a step‐selection analysis for each of these periods. Throughout the year, male bears selected forested/shrub habitats and increased movement rates near main roads. During the mating season, reproductive needs seem to trigger roaming behaviour in adult males to maximize encounter rates with potential receptive females. However, all movement metrics increased within areas of high human activity, suggesting a bear response to a higher risk perception while using those areas. During the post‐mating period, overlapping with the bear hyperphagia and the hunting season, males selected anthropogenic areas farther from main roads and trails, suggesting a trade‐off between foraging opportunities and risk avoidance.

Patients’ perceptions matter: Risk communication and psychosocial factors in orthodontics

Effective risk communication is essential for achieving patient-centered oral health care, but the limited understanding of patients' subjective perceptions of orthodontic-related risks hinders this process. This study aimed to investigate adults' awareness, concerns, and risk-avoidance behaviors about long-term orthodontic risks, exploring their relationship with psychosocial factors. We included 498 adult patients (mean age, 27.3 ± 6.8 years; women, 75.5%) during their initial visits to the orthodontic department at a hospital in Chengdu, China. Participants' understanding of orthodontic risks was gauged before and after exposure to the Oral Health Education Comics (OHEC), a specifically designed digital tool. Concurrently, we used logistic regression models to investigate the associations between patients' depression, anxiety, self-esteem, perfectionism, and dentofacial esthetics with risk perceptions. Approximately 79.5% of participants initially reported low awareness of orthodontic risks, with most knowledge from online sources. Notably, the percentage of participants with high awareness increased to 64.8% after OHEC. The negative facial soft-tissue change was most concerning for participants: 53.4% showed high concerns, and 28.1% showed high avoidance. Furthermore, linear regression indicated positive associations between depression (β= 0.42 [95% confidence interval {CI}, 0.07-0.77]) and anxiety (β= 0.76 [95% CI, 0.35-1.18]) with orthodontic risk concerns, whereas risk avoidance was positively associated with depression (β= 0.62 [95% CI, 0.27-0.97]), anxiety (β= 1.09 [95% CI, 0.68-1.50]), and perfectionism (β= 0.24 [95% CI, 0.02-0.46]). Findings emphasize the imperative of streamlined risk communication in orthodontics. By incorporating comprehensible tools such as OHEC and integrating psychosocial evaluations, more refined patient-practitioner communication and psychosomatic-based dental care can be achieved.

Road–Rail Intermodal Travel Mode Choice Behavior Considering Attitude Factors

Road–rail intermodal transportation (RRIT) leverages the advantages of multiple transport modes and is crucial for addressing the current issue of imbalanced development in the transportation sector. However, passengers’ behavior in choosing RRIT remains unclear, and it is necessary to optimize travel service quality through analyzing RRIT choice behavior based on user perceptions. This study designed a stated preference experiment that included both direct and multi-modal travel options. A hybrid choice model considering attitude variables was constructed, and four latent attitude variables—convenience, economy, comfort, and riskiness—were extracted to analyze their impact on intercity travel mode choice behavior under conditions of ticket booking uncertainty. The results revealed that the ticket booking success rate is a critical factor in travelers’ decision-making. Passengers tend to choose travel options with higher ticket booking success rates, even if it entails a slight increase in the ticket prices for the high-speed rail to high-speed rail transfer option. The attitude variables significantly influence intercity travel mode choice behavior, with travelers generally exhibiting a preference for risk avoidance in their travel options. Moreover, there are differences among various groups of travelers in their preferences and demands for the convenience, economy, and comfort aspects of travel options. These research findings can enhance our understanding of the key factors influencing the selection of RRIT services, thereby supporting RRIT designers and planners in improving service quality and facilitating the future growth of RRIT.

PATIENT SAFETY GOALS, AFRICA: DEVELOPMENT OF A CONTEXT FITTING TOOL FOR PATIENT SAFETY AND CARE QUALITY ASSESSMENT IN AFRICA USING THE DECIDE(HUMAN FACTORS) MODEL.

Patient safety practice refers to processes or structures which when applied, reduce the probability of adverse events resulting from exposure to the healthcare system across a range of diseases and procedures. It aims at making health care safer for both clients and staff (Kohn, Corrigan, &amp; Donaldson, 2000). It is “A framework of organized activities that createcultures, processes, procedures, behaviours, technologies and environments in health care that consistently and sustainably lower risks, reduce the occurrence of avoidable harm, make errors less likely and reduce the impact of harm when it does occur.” (WHO, 2021). Quality of care is “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge” (IOM, 1990 and ahrq.gov., 2016). Healthcare quality is a level or value provided by any healthcare resource, as determined by some measurement (Maxwell, 1984).As with quality in other fields, it is an assessment of whether something is good enough and whether it is suitable for its purpose. Other definitions of healthcare quality include: “the degree of conformity with accepted principles and practices (standards), the degree of satisfying the patient’s needs, and the degree of attainment of acceptable outcomes, while making appropriate use of resources.” The Joint Commission defines care quality as “the optimal achievement of therapeutic benefit and avoidance of risk and minimization of harm”. Care quality is also referred to as freedom from deficiencies and therefore less costly because deficiencies which often lead to customer dissatisfaction require additional costs to manage them. Quality care conforms to relevant requirements or standards - the needs and expectations of clients or patients.

Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.

Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS. One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis. Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001). Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment. For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure. The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.

Does Surviving Pain Lower Your Risk Avoidance? [Conference Presentation Abstract

Does Surviving Pain Lower Your Risk Avoidance? [Conference Presentation Abstract

Research on green supply chain finance risk identification based on two-stage deep learning

As a resonance product between financial services and the upgrading of the green industry, green supply chain finance has garnered extensive attention in the process of ecological civilization construction. Effectively promoting the green transformation of small and medium-sized enterprises and achieving the "dual carbon" goals necessitate the avoidance of corporate green risks. However, the complex interdependence and information asymmetry among green supply chain finance enterprises result in data characteristics such as multi-source small samples and high-dimensional imbalance. To address these issues, this paper proposes a risk assessment model based on two-stage deep learning. In the first stage, we employ Generative Adversarial Network (GAN) to generate minority class default samples, and utilize Stacked Auto-Encoder (SAE) to extract data features with closed-form parameter calculation capability. In the second stage, the obtained features are input into a Deep Neural Network (DNN), and parameter learning and model optimization are conducted through joint training. Finally, to model low-order feature interactions, we integrate the Support Vector Machine (SVM) algorithm. The paper is grounded in the green innovation production of enterprises, collecting financial data of 176 upstream and downstream enterprises and corresponding core enterprise green indicators from 2013 to 2022. Experimental results demonstrate that GAN oversampling technique not only enhances the model's AUC metric but also significantly improves the F1 score. Compared with traditional deep learning methods, the proposed two-stage deep integration model effectively reduces training loss and exhibits superiority in identifying green supply chain finance risks.

Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula

Integrating renewable wind power (WP) into the grid exacerbates variability and challenges reliability. Establishing an effective forecasting system is crucial for risk avoidance, but achieving effective, interpretable, and accurate predictions remains an obstacle due to the high volatility and uncertainty of WP. This study proposes an innovative and trustworthy wind power forecasting system combined with point and interval predictions. Specifically, the adaptive multi-scale convolutional receptive field (AMSCF) combined with gated recurrent unit (GRU) for WP point prediction is proposed to capture the spatiotemporal dependencies of WP generation. This AMSCF method uses different size receptive fields, fuses them using channel dimension concatenation, operates pooling layer and adaptive fusion through field attention, which extracts hierarchical features for WP forecasting. Then conditional mixture Copula (Con-mCopula) function integrated with multiobjective optimizer is established for WP interval prediction, which reduces the WP interval prediction accuracy error as some information neglected. The wind power cluster dataset from Germany was selected for experiments. Based on the results, for both datasets, the designed prediction system can achieve better point and interval prediction performance resulting in a reduction of mean absolute error (MAE) by approximately 40 % compared to other existing components in the market. This study deconstructs the “Black Box” in deep network for energy forecasting through interpretability analysis, which accelerates the development of AI technology in WP forecasting system and provides decision-makers with trustworthy WP forecasting assistance.

Elephant pathway use in a human‐dominated landscape

Habitat loss and fragmentation are two of the biggest threats facing wildlife today. Understanding the role of wildlife pathways in connecting resource areas is key for maintaining landscape connectivity, reducing the impacts of habitat loss and helping address human–wildlife conflict. In this study, we used sign surveys and camera trapping to understand the fine scale movement of elephants moving between a protected area and agricultural zone in the Masai Mara, Kenya. We used generalised linear models to determine factors driving high frequency of pathway use by elephants. Our results showed strong seasonal trends in pathway use, with peaks coinciding with the dry season. However, no correlations between rainfall and pathway use were found. Temporal patterns of pathway use indicate that elephants use risk avoidance strategies by moving between the two areas at times of low human disturbance. Spatial analysis revealed that the most frequently used pathways were closer to farms, saltlicks and forest and those that had a higher percentage of forest cover. Our models also showed a positive relationship between pathway use and the number of elephant crop raiding incidents, highlighting that pathways can play a role in human–elephant conflict. As habitat loss continues, pathways may become more important for linking resources. However, they are also likely to facilitate movement into farmland. The results from this study provide an opportunity for planned management activities to ensure connectivity and to mitigated conflict.