Varying Levels Of Risk Research Articles

Multi-criteria analysis and mapping of the vulnerable flood risk areas of Jigawa State, Nigeria

This study evaluates flood risk factors and maps flood-prone areas in Jigawa State, Nigeria, using geospatial techniques. By integrating various flood-causing factors—such as rainfall distribution, elevation, slope, drainage network and density, land cover and use, flow accumulation, and soil type— through a multi-parametric approach, it employs the Analytical Hierarchy Process (AHP) to perform pairwise comparisons and assign priority weights to each factor. Spatial multi-criteria analysis (MCA) then ranks and identifies potential flood locations. Using ArcGIS's weighted overlay tool, these layers are combined to produce a final flood risk map, classifying areas into high, very high, moderate, low, and very low-risk categories. Elevation and slope were found to have the greatest impact on flooding, with normalized criterion weights of 30 and 22, respectively. The Consistency Ratio (CR) of 0.06 validated the strength of the judgment, and these AHP-derived factor weights were used to create the flood risk map showing areas with varying risk levels, highlighting eight local governments in the study area. —Auyo, Hadejia, Kirika-samma, Kafin-Hausa, Ringim, Miga, Jahun, and Dutse—compose areas of very high risk, while Guri, some portions of northern Kiri-kasamma, southern Jahun, northern Auyo, Birnin-Kudu, and northwestern Ringim local governments comprise high-risk areas. Areas of moderate risk comprise a small portion of northern Kirika-samma, larger portions of Gwaram, small portions of Jahun, Kafin-Hausa, Dutse and Birnin-kudu local governments respectively and areas of low to very low risks consist of many parts of Gwaram and some tiny portions of Birnin-kudu.

Evaluation Of Graha Nusantara’s Operational Risk Management Process Using ISO 31000:2018

Graha Nusantara is one of the business units of cooperative x which is engaged in building rental. Since the pandemic in 2020, Graha Nusantara has experienced a decrease in demand for building rentals which has led to a decrease in revenue. In addition, Graha Nusantara has also experienced problems related to its business operations such as canceling events by consumers for various reasons, building maintenance and management, employee management to those related to vendor partners. This causes Graha Nusantara to continue to lose money due to ineffective building operations which can actually be prevented by streamlining Graha Nusantara's operations. This study aims to evaluate operational risk management in the Graha Nusantara business unit, which is a building leasing business, with reference to risk management standards based on ISO 31000. The method used in this research is descriptive qualitative analysis with a case study approach, through interview and documentation data collection techniques. The results showed that risk management has not determined the scope, context and criteria process according to ISO 31000 and nineteen risks with various levels of risk were identified. Therefore, this study recommends handling each risk and it is expected that management establishes a risk management policy, especially operational risk

A deep learning method for beat-level risk analysis and interpretation of atrial fibrillation patients during sinus rhythm

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. The great majority of algorithms can only distinguish “AF rhythm in AF patients” from “sinus rhythm in healthy individuals” . However, AF patients do not always exhibit AF rhythm, most of the time they present with sinus rhythms, and there is also a potential risk of AF in sinus rhythms. How to detect AF from sinus rhythm is a challenge. To address this, this paper proposes a novel artificial intelligence (AI) algorithm to distinguish “sinus rhythm in AF patients” and “sinus rhythm in healthy individuals” in beat-level. We cut 1 s of sinus beats from single-lead Electrocardiogram (ECG) data and fed them to the Net1d model, a deep learning model that processes one-dimensional data, to obtain the risk probability of each beat. Besides, we have also introduced the beat-level risk interpreters, trend risk interpreters, addressing the interpretability issues of deep learning models and the difficulty in explaining AF risk trends. Additionally, the beat-level information fusion decision is presented to enhance model accuracy. The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314, with an average accuracy of 0.6606 and an F1 score of 0.6470. By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0.7591, while the average accuracy and F1 score improve to 0.6887 and 0.6749. Compared to previous segment-level algorithms, we utilized beats as input, reducing data dimensionality and making the model more lightweight, facilitating deployment on portable medical devices. Furthermore, we draw new and interesting findings through average beat analysis and subgroup analysis, considering varying risk levels. Our code is publicly available at https://github.com/leijsen/ECGBeat4AFSinus.

Coastal Vulnerability Index (CVI) Assessment: Evaluating Risks Associated with Human-Made Activities along the Limassol Coastline, Cyprus

Coastal risk assessment is crucial for coastal management and decision making, especially in areas already experiencing the negative impacts of climate change. This study aims to investigate the coastal vulnerability due to climate change and human activities in an area west of the Limassol district’s coastline, in Cyprus, on which there have been limited studies. Furthermore, an analysis is conducted utilising the Coastal Vulnerability Index (CVI) by exploiting eight key parameters: land cover, coastal slope, shoreline erosion rates, tidal range, significant wave height, coastal elevation, sea-level rise, and coastal geomorphology. These parameters were assessed utilising remote sensing (RS) data and Geographical Information Systems (GISs) along a 36.1 km stretch of coastline. The results exhibited varying risk levels of coastal vulnerability, mainly highlighting a coastal area where the Kouris River estuary is highly vulnerable. The study underscores the need for targeted coastal management strategies to address the risks associated with coastal erosion. Additionally, the CVI developed in this study can be exploited as a tool for decision makers, empowering them to prioritise areas for intervention and bolster the resilience of coastal areas in the face of environmental changes.

A comprehensive analysis and risk evaluation of microplastics contamination in Australian commercial plant growth substrates: Unveiling the invisible threat

In Australia, quality standards for composts and potting mixes are defined by AS4454-2012 and AS3743-2012. These standards outline key parameters, including physicochemical properties, nutrient content, and plant toxicity. However, they do not address emerging pollutants like microplastics (< 1mm). This study investigates the prevalence and characteristics of MPs in commercial plant growth substrates (PGS), including nineteen potting mixes and five composts, revealing a significant occurrence of MPs, with concentrations ranging from 233 to 7367 particles Kg-1 and an average of 1869 ± 109 particles Kg-1. MPs categorized by shape, size, and color, with fragments (491 ± 34 particles Kg-1), white colour (3700 ± 917 particles Kg-1), and size 500µm being predominant. The polymer composition was diverse, with polyethylene being the most prevalent, followed by polypropylene and others. Polyterpene, Polyalkene, Pentaerythritol, and Propylene glycol were identified in PGS for the first time. The structural equation model showed that physicochemical properties like pH, EC, TOC, and heavy metals influence MPs abundance and characteristics. The Polymer Risk Index and Pollution Load Index indicated varying risk levels among the samples. These findings highlight the need to address MPs contamination in PGS to ensure ecosystem safety and human health. Environmental ImplicationsThe study reveals significant microplastic contamination in Australian commercial PGS, with compost exhibiting higher levels than potting mixes. Application of these contaminated substrates can lead to microplastic release, which can further alter soil properties, disrupt microbial communities, and leach environmental toxins. Current regulations primarily target larger plastic items, neglecting the issue of microplastics (<1mm). The findings underscore the necessity for updated standards and quality controls for commercial PGS. Enhancing public awareness and improving waste sorting are crucial steps to mitigate contamination. Effective source separation is essential for producing high-quality compost and potting mixes. Collaborative efforts among policymakers, industries, and communities are vital for advancing waste management practices and environmental protection.

Treatment of Superficial Vein Thrombosis: Recent Advances, Unmet Needs and Future Directions.

Once considered relatively benign, superficial vein thrombosis (SVT) of the lower limbs is linked to deep vein thrombosis (DVT) or pulmonary embolism (PE) in up to one fourth of cases. Treatment goals include alleviating local symptoms and preventing SVT from recurring or extending into DVT or PE. Fondaparinux 2.5 mg once daily for 45 days is the treatment of choice for most patients with SVT. Potential alternatives include intermediate-dose low-molecular-weight heparin or the direct oral factor Xa inhibitor rivaroxaban, however, these require further evidence. Despite these treatment options, significant gaps remain, including the role of systemic or topical anti-inflammatory agents alone or combined with anticoagulants, and the optimal duration of anticoagulation for patients at varying risk levels. Additionally, the efficacy and safety of factor Xa inhibitors other than rivaroxaban, management of upper extremity SVT, and optimal treatment for SVT near the sapheno-femoral or sapheno-popliteal junctions are not well understood. This narrative review aims to summarize current evidence on anticoagulant treatment for SVT, highlight key unmet needs in current approaches, and discuss how ongoing studies may address these gaps.

Charting new waters with CRAMMTS: A survey-driven cybersecurity risk analysis method for maritime stakeholders

This article presents a novel survey-based cybersecurity risk assessment model, CRAMMTS (Cyber Risk Analysis Method for Maritime Transportation Systems), specifically designed for the maritime sector, addressing a critical gap in the literature. Our study contributes significantly in three ways: firstly, through a comprehensive critical literature review of 31 maritime guidelines and 95 scholarly articles, identifying the need for a new cybersecurity risk assessment method; secondly, by developing CRAMMTS, an adaptation of the ISRAM risk analysis method, incorporating the International Maritime Organization's criteria and enabling participation from maritime professionals, especially policymakers and leaders. The third contribution is a case study, the practical application of CRAMMTS in surveying 80 maritime professionals, assessing their perception of cybersecurity risks, and identifying varying risk levels, with the highest associated with cyber threat actors. This approach proved effective in assessing risks at both tactical and strategic levels and providing a clear, quantitative risk metric for decision-making. Our research underscores the maritime sector's need for a holistic, easily implementable cybersecurity risk analysis method that engages leaders and adapts to various Maritime Transportation System scopes, thereby enhancing cybersecurity risk assessment in this crucial domain.

Risk-constrained strategic resilience enhancement of power distribution and natural gas systems through G2P and P2G planning and distributed load Restoration against flood

Floods, categorized as high-impact low-probability (HILP) events, pose significant risks to power distribution systems (PDS) and natural gas systems (NGS). Ensuring mutual support during emergencies through energy sharing, facilitated by coupling devices, highlights the necessity for optimal integration. However, managing these systems separately by different operators calls for coordinated yet decentralized operation. Hence, this paper introduces a risk-constrained bi-level model to integrate PDS and NGS. The upper level deals with siting and sizing power to gas (P2G) and gas to power (G2P) units. Meanwhile, the lower level employs decentralized load restoration using the alternating direction method of multipliers (ADMM) algorithms, with each system individually minimizing its own risk. To account for the unpredictable nature of HILP events surpassing a predefined risk threshold (α), this study defines Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) for each system as resilience criteria. Validation of the model is demonstrated using an IEEE 33-bus PDS and a 20-node NGS, and applied to a real-world large-scale PDS in Khuzestan province, Iran. Across various scenarios, higher system risks prompt an increase in the number and size of planned coupling devices, particularly to address the most severe 5% of scenarios. This optimization leads to enhanced energy sharing, resulting in a resilience improvement of nearly 35% in specific cases, along with a notable 25% reduction in both active and reactive power loss. Additionally, the sensitivity analysis explores the impact of varying risk levels and thresholds on outcomes, demonstrating how investors can utilize the proposed model across diverse risk perspectives.

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention

BackgroundAccurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level.MethodsIn this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules.ResultsTo evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI.ConclusionsAERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.

Development and validation of a prediction model for falls among older people using community-based data.

The aim of this study was to develop a calculation tool to predict the risk of falls 1year in the future and to find the cutoff value for detecting a high risk based on a database of individuals who participated in a community-based group exercise. We retrospectively reviewed a total of 7726 physical test and Kihon Checklist data from 2381 participants who participated in community-based physical exercise groups. We performed multilevel logistic regression analysis to estimate the odds ratio of falls for each risk factor and used the variance inflation factor to assess collinearity. We determined a cutoff value that effectively distinguishes individuals who are likely to fall within a year based on both sensitivity and specificity. The final model included variables such as age, sex, weight, balance, standing up from a chair without any aid, history of a fall in the previous year, choking, cognitive status, subjective health, and long-term participation. The sensitivity, specificity, and best cutoff value of our tool were 68.4%, 53.8%, and 22%, respectively. Using our tool, an individual's risk of falls over the course of a year could be predicted with acceptable sensitivity and specificity. We recommend a cutoff value of 22% for use in identifying high-risk populations. The tool may benefit healthcare workers in screening community-dwelling older adults with various levels of risk for falls and support physicians in planning preventative and follow-up care.

Tác động của Thông tư 200 đến mô hình xếp hạng tín dụng sử dụng phương pháp trí tuệ nhân tạo

The study investigates the impact of Circular 200 on the credit rating model for small, medium, and large enterprises in Vietnam during the period 2008-2018, using Artificial Neural Network (ANN) methodology. The research compares credit rating models before and after the implementation of Circular 200 between 2008-2014 and 2015-2018, using data from 39,162 businesses in Vietnam obtained from the Orbis database. Results indicate that NITA, ROE, liquidity, and current payment ratio are important independent variables with significant differences in credit rating models before and after Circular 200. The research method proves useful for investors in analyzing investment risks for informed investment decisions. Vietnamese financial institutions can apply the model to identify specific credit rating issues for borrowers, enabling them to formulate appropriate credit policies and set different interest rates for varying risk levels. Future research directions include (1) enhancing data and variables, (2) improving data analysis methods, and (3) enhancing performance metrics.

Total combined risk of false conformity decisions in pharmaceutical equivalence of acetaminophen oral solution

Pharmaceutical equivalence studies are fundamental to ensure the interchangeability, quality, efficacy, and safety of generic and similar medications. This work addresses the crucial aspect of estimating the total combined risk of false conformity decisions in such studies, focusing on acetaminophen oral solutions. Traditionally, these studies rely on pairwise comparisons, but this work introduces a multiple comparison evaluation (for 3 or more medicines). Based on measurement uncertainty evaluations and Monte Carlo simulations, the study reveals varying risk levels among different acetaminophen products. Products (A and E), with pH test results near or exceeding specification limits, exhibit higher risks. Furthermore, the impact of correlations between measured parameters within a single product on total combined risk values was considered. The assessment of risk considering multiple comparisons was affected by the number of medications considered (simultaneous comparisons between 3, 4, or 5 medicines). This study highlights the critical role of rigorous analytical assessment and underscores the need to consider the measurement uncertainty data and correlations between measured parameters in decision-making in pharmaceutical quality assurance.

Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: A national cohort study.

Cognitive impairment is a highly heterogeneous disorder that necessitates further investigation into the distinct characteristics of populations at varying risk levels of cognitive impairment. Using a large-scale registry cohort of elderly individuals, we applied a data-driven approach to identify novel clusters based on diverse sociodemographic features. A prospective cohort of 6398 elderly people from the Chinese Longitudinal Healthy Longevity Survey, followed between 2008-14, was used to develop and validate the model. Participants were aged ≥60 years, community-dwelling, and the Chinese version of the Mini-Mental State Examination (MMSE) score ≥18 were included. Sixty-nine sociodemographic features were included in the analysis. The total population was divided into two-thirds for the derivation cohort (n = 4265) and one-third for the validation cohort (n = 2133). In the derivation cohort, an unsupervised Gaussian mixture model was applied to categorise participants into distinct clusters. A classifier was developed based on the most important 10 factors and was applied to categorise participants into their corresponding clusters in a validation cohort. The difference in the three-year risk of cognitive impairment was compared across the clusters. We identified four clusters with distinct features in the derivation cohort. Cluster 1 was associated with the worst life independence, longest sleep duration, and the oldest age. Cluster 2 demonstrated the highest loneliness, characterised by non-marital status and living alone. Cluster 3 was characterised by the lowest sense of loneliness and the highest proportions in marital status and family co-residence. Cluster 4 demonstrated heightened engagement in exercise and leisure activity, along with independent decision-making, hygiene, and a diverse diet. In comparison to Cluster 4, Cluster 1 exhibited the highest three-year cognitive impairment risk (adjusted odds ratio (aOR) = 3.31; 95% confidence interval (CI) = 1.81-6.05), followed by Cluster 2 and Cluster 3 after adjustment for baseline MMSE, residence, sex, age, years of education, drinking, smoking, hypertension, diabetes, heart disease and stroke or cardiovascular diseases. A data-driven approach can be instrumental in identifying individuals at high risk of cognitive impairment among cognitively normal elderly populations. Based on various sociodemographic features, these clusters can suggest individualised intervention plans.

A Socioecological Perspective to Understand Perceived Risks and Benefits of Oral Pre-exposure Prophylaxis Towards the Promotion of Combination HIV Prevention Strategies

ABSTRACT HIV incidence rates remain disproportionately high among adolescent girls and young women in Sub-Saharan Africa, where women are 5–7 times more likely to become HIV-infected than their male peers. Oral pre-exposure prophylaxis (PrEP), a first-generation licenced biomedical innovation, can benefit populations at substantial risk of HIV acquisition, with options for independent product use for women in vulnerable situations. However, adoption of new product innovations bears several complexities with limited product knowledge, product confidence and low perceptions of risk. This study utilizes a socioecological perspective to explore perceptions of HIV risk and perceived barriers or benefits of oral PrEP with a sample of adolescent girls and young women and young men in rural KwaZulu-Natal. A theory-driven data-informed analysis utilizing the Health Belief Model and participatory research was used to engage with both men and women from HIV high-burden districts. The study identified various levels of risk using the socioecological perspective and found a specific association among women of PrEP as assurance of lower risk of HIV infection, whereas men perceived PrEP as insurance for risky behaviour. Revisiting perceptions of risk in terms of risk profiles should be ongoing and this study highlights that strategic health communication is needed to increase understanding of HIV risk in order to promote PrEP as part of HIV combination prevention for those willing to adopt new health innovations.

Machine learning-based survival prediction nomogram for postoperative parotid mucoepidermoid carcinoma

Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I–IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model’s clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.

Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

Abstract Background Malaria remains a significant public health concern in Ghana, with varying risk levels across different geographical areas. Malaria affects millions of people each year and imposes a substantial burden on the health care system and population. Accurate risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease hotspots. This study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented, and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria control. Methods The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical Service. We studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal models. The malaria risk for each region was mapped to visually identify regions with malaria hotspots. Clustering and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models, respectively. Parameter estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R software. Results The spatial model indicated an increased risk of malaria in the North East, Bono East, Ahafo, Central, Upper West, Brong Ahafo, Ashanti, and Eastern regions. The spatiotemporal model results highlighted an elevated malaria risk in the North East, Upper West, Upper East, Savannah, Bono East, Central, Bono, and Ahafo regions. Both spatial and spatiotemporal models identified the North East, Upper West, Bono East, Central, and Ahafo Regions as hotspots for malaria risk. Substantial variations in risk were evident across regions (H = 104.9, P &lt; 0.001). Although climatic and economic factors influenced malaria infection, statistical significance was not established. Conclusions Malaria risk was clustered and varied among regions in Ghana. There are many regions in Ghana that are hotspots for malaria risk, and climate and economic factors have no significant influence on malaria risk. This study could provide information on malaria transmission patterns in Ghana, and contribute to enhance the effectiveness of malaria control strategies.

Implementing a vegetation-based risk index to support management actions in Mediterranean coastal dunes

Coastal dunes play a crucial role in mitigating sea-related impacts and safeguarding coastlines. However, increasing human influence and natural factors such as sea-level rise underscore the need for effective coastal risk assessment methodologies. This study introduces a comprehensive coastal risk index covering 24 km of the Italian coastline within the protected area of San Rossore Park (Tuscany, Italy). The study area, distinguished by its notable coastal dune ecosystems, holds naturalistic, cultural, and economic importance. Nevertheless, diverse uses, zoning, and human impact variables pose challenges.By incorporating geological, socioeconomic, cultural, and ecological parameters, the index integrates a range of data sources and field observations. This research focused on developing and applying a vegetation-based risk index (VRI) within a geographic information system (GIS) framework, recognizing the ecological importance of dune vegetation in mitigating coastal erosion. Analysisrevealed varying risk levels within the study area. Half of the San Rossore Park coastline exhibited low risk values, 37.5% had moderate risk values, and 12.5% had high risk values. The publicly accessible northernmost section displays excellent preservation of dune habitats but faces heightened risk due to anthropogenic impacts. Conversely, the central-southern portion, inaccessible to the public, registers high-risk levels linked to variables associated with coastal erosion.Furthermore, the results highlight areas with heightened cultural and ecological vulnerabilities aligned with elevated risk levels. The index facilitates clear and intuitive cartographic representations of coastal risk, identifying variable categories that substantially influence on risk determination. Tailored strategies, including mitigating human pressure in the northern sector and implementing erosion management in the central-southern region, are recommended.In summary, this study not only provides a practical tool for assessing and managing coastal areas and directing attention to specific threats but also supports stakeholders in informed decision-making. The VRI enhances global sustainable coastal conservation, deepening our understanding of coastal risks and providing valuable insights for effective management strategies.

Performance Feedback and Innovative Search Strategies: An Integrative Perspective of Motivation and Capacity for Risk Taking

AbstractInnovative search is a critical component of strategic decision making, and its different dimensions convey various levels of risk. Prior research on the behavioural theory of the firm has focused on motivation logic or capacity logic to predict the effects of performance feedback on risk taking but has paid limited attention to their joint effects. To construct a comprehensive framework in the behavioural theory of the firm, we consider both motivation and capacity logic to analyse the impact of performance feedback on risk taking. We argue that underperformance feedback positively affects innovative search scope and that overperformance feedback entails a U‐shaped relationship with innovative search scope, while innovative search depth shows the opposite trend. Using a unique dataset consisting of 9,730 firm‐year observations of 2,102 Chinese listed firms from 2008 to 2018, we conduct fixed‐effect regression analyses to test our hypotheses and find support for most theoretical predictions. We also identify the boundary roles of environmental dynamism. This study contributes to the literature on the behavioural theory of the firm by predicting innovative search strategies using a consistent and integrative framework.

Deep Learning Model Based Behavioural Recognition Technology for Electricity Operators and Its Safety Guardianship Analysis

Abstract This study leverages the Openpose system to capture skeletal key points of electric power operators, simplifying network complexity by sharing convolutional layers during the ReLU activation phase. We introduce a graph convolutional network (GCN) to model these skeletal sequences, creating a spatio-temporal deep learning approach for behavior recognition. Tested on a relevant dataset, our Openpose-GCN network demonstrates stability with a training loss of 0.11 after 700 iterations, achieves over 90% accuracy in recognizing operator actions and behaviors, and maintains a recognition error below 0.003 for operations with varying risk levels. These findings underscore the potential of our approach to enhance electric power operation safety through real-time risk warning and control.

Impact of cardiac rehabilitation on pre- and post-operative transcatheter aortic valve replacement prognoses.

Transcatheter aortic valve replacement (TAVR) is a relatively new treatment method for aortic stenosis (AS) and has been demonstrated to be suitable for patients with varying risk levels. Indeed, among high-risk patients, TAVR outcomes are comparable to, or even better, than that of the traditional surgical aortic valve replacement (SAVR) method. TAVR outcomes, with respect to post-surgical functional capacity and quality of life, have also been found to be improved, especially when combined with cardiac rehabilitation (CR). CR is a multidisciplinary system, which integrates cardiology with other medical disciplines, such as sports, nutritional, mind-body, and behavioral medicine. It entails the development of appropriate medication, exercise, and diet prescriptions, along with providing psychological support, ensuring the cessation of smoking, and developing risk factor management strategies for cardiovascular disease patients. However, even with CR being able to improve TAVR outcomes and reduce post-surgical mortality rates, it still has largely been underutilized in clinical settings. This article reviews the usage of CR during both pre-and postoperative periods for valvular diseases, and the factors involved in influencing subsequent patient prognoses, thereby providing a direction for subsequent research and clinical applications.