搜索结果：Family practice management

Indian Railway workshops form a critical component of rolling stock maintenance infrastructure, employing more than 2.5 lakh personnel across 44 major workshops nationwide. However, safety management in many workshops still relies on fragmented manual processes, resulting in delayed approvals, incomplete documentation, and increased exposure to operational hazards. Field safety observations indicate that lacerations (28.7%) and abrasions (21%) remain among the most frequent workplace injuries, highlighting the need for structured digital safety workflows. This paper presents the Integrated Digital Management System for Railway Workshops, a modular multi-workflow digital platform developed to improve safety governance and workflow transparency. The proposed system integrates four primary modules: Machine and Plant Management, Permit-to-Work (PTW) Management, Contract Management, and Incident Management. The Permit-to-Work module digitizes hazardous work authorization in accordance with IS 17893:2022, while the Contract Management module supports workforce validation and regulatory oversight. The Incident Management module enables rapid reporting, investigation tracking, and correcti

Considerable research effort has been devoted to the study of Policy in the domain of Information Security Management (ISM). However, our review of ISM literature identified four key deficiencies that reduce the utility of the guidance to organisations implementing policy management practices. This paper provides a comprehensive overview of the management practices of information security policy and develops a practice-based model that addresses the four aforementioned deficiencies. The model provides comprehensive guidance to practitioners on the activities security managers must undertake for security policy development and allows practitioners to benchmark their current practice with the models suggested best practice. The model contributes to theory by mapping existing information security policy research in terms of the defined management practices.

Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orchestrating the execution of end-to-end processes. The rise of Generative and Agentic Artificial Intelligence (AI) is opening the way for another such wave. However, this wave is poised to be different because it shifts the focus from automation to autonomy and from design-driven management of business processes to data-driven management, leveraging process mining techniques. This position paper, based on a keynote talk at the 2025 Workshop on AI for BPM, outlines how process mining has laid the foundations on top of which agents can sense process states, reason about improvement opportunities, and act to maintain and optimize performance. The paper proposes an architectural vision for Agentic Business Process Management Systems (A-BPMS): a new class of platforms that integrate autonomy, reasoning, and learning into process management and execution. The paper contends that such systems must support a continuum of processes, spanning from

Traditional organizations are increasingly becoming software producing organizations. This software is enabling them to integrate business processes between different departments and with other organizations through Application Programming Interfaces (APIs). The main task of managing APIs is to ensure that the APIs are easy to use by third parties, such as providing helpful documentation, monitoring API performance, and even monetizing API usage. The knowledge on API management is scattered across academic literature. In this document, we describe a Systematic Literature Review (SLR) that has the goal of collecting API Management practices and capabilities related to API Management, as well as proposing a comprehensive definition of the topic. In the scope of this work, a practice is defined as any practice that has the express goal to improve, encourage and manage the usage of APIs. Capabilities are defined as the ability to achieve a certain goal related to API Management, through the execution of two or more interrelated practices. We follow a standard method for SLRs in software engineering. We managed to collect 24 unique definitions for the topic, 114 practices and 39 capabil

In academic literature portfolio risk management and hedging are often versed in the language of stochastic control and Hamilton--Jacobi--Bellman~(HJB) equations in continuous time. In practice the continuous-time framework of stochastic control may be undesirable for various business reasons. In this work we present a straightforward approach for thinking of cross-asset portfolio risk management and hedging, providing some implementation details, while rarely venturing outside the convex optimisation setting of (approximate) quadratic programming~(QP). We pay particular attention to the correspondence between the economic concepts and their mathematical representations; the abstractions enabling us to handle multiple asset classes and risk models at once; the dimensional analysis of the resulting equations; and the assumptions inherent in our derivations. We demonstrate how to solve the resulting QPs with CVXOPT.

Cooperative play (co-play) is often positioned as a family-beneficial practice that can strengthen parent-child bonds and support parental mediation in games. Yet co-play in user-generated virtual worlds (UGVWs) can be disrupted by real-time harms that parents cannot easily prevent. Roblox, a platform with millions of user-generated virtual worlds and a large child player base, illustrates this challenge. Prior work on harmful UGVW design highlights risks beyond content problems, including manipulative monetization prompts, unmoderated social interactions, emergent in-world behaviors, and narrative designs that may normalize harmful ideologies. Current governance and moderation approaches, largely adapted from social media, focus on static artifacts and often fail to capture interactive and emergent harms in virtual worlds. This workshop paper asks: how might UGVWs and their platforms be designed to minimize harms that specifically impair family co-play experiences?

Generative AI technologies, particularly Large Language Models (LLMs), have transformed information management systems but introduced substantial biases that can compromise their effectiveness in informing business decision-making. This challenge presents information management scholars with a unique opportunity to advance the field by identifying and addressing these biases across extensive applications of LLMs. Building on the discussion on bias sources and current methods for detecting and mitigating bias, this paper seeks to identify gaps and opportunities for future research. By incorporating ethical considerations, policy implications, and sociotechnical perspectives, we focus on developing a framework that covers major stakeholders of Generative AI systems, proposing key research questions, and inspiring discussion. Our goal is to provide actionable pathways for researchers to address bias in LLM applications, thereby advancing research in information management that ultimately informs business practices. Our forward-looking framework and research agenda advocate interdisciplinary approaches, innovative methods, dynamic perspectives, and rigorous evaluation to ensure fairnes

Parameter monitoring and control systems are crucial in the industry as they enable automation processes that improve productivity and resource optimization. These improvements also help to manage environmental factors and the complex interactions between multiple inputs and outputs required for production management. This paper proposes an automation system for broiler management based on a simulation scenario that involves sensor networks and embedded systems. The aim is to create a transmission network for monitoring and controlling broiler temperature and feeding using the Internet of Things (IoT), complemented by a dashboard and a cloud-based service database to track improvements in broiler management. We look forward this work will serve as a guide for stakeholders and entrepreneurs in the animal production industry, fostering sustainable development through simple and cost-effective automation solutions. The goal is for them to scale and integrate these recommendations into their existing operations, leading to more efficient decision-making at the management level.

In the modern world, the development of Artificial Intelligence (AI) has contributed to improvements in various areas, including automation, computer vision, fraud detection, and more. AI can be leveraged to enhance the efficiency of Autonomous Smart Traffic Management (ASTM) systems and reduce traffic congestion rates. This paper presents an Autonomous Smart Traffic Management (STM) system that uses AI to improve traffic flow rates. The system employs the YOLO V5 Convolutional Neural Network to detect vehicles in traffic management images. Additionally, it predicts the number of vehicles for the next 12 hours using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). The Smart Traffic Management Cycle Length Analysis manages the traffic cycle length based on these vehicle predictions, aided by AI. From the results of the RNN-LSTM model for predicting vehicle numbers over the next 12 hours, we observe that the model predicts traffic with a Mean Squared Error (MSE) of 4.521 vehicles and a Root Mean Squared Error (RMSE) of 2.232 vehicles. After simulating the STM system in the CARLA simulation environment, we found that the Traffic Management Congestion Flow Rate with A

Vehicular Ad Hoc Networks (VANETs) support the information dissemination among vehicles, Roadside Units (RSUs), and a Trust Authority (TA). A trust model evaluates an entity or data or both to determine truthfulness. A security model confirms authentication, integrity, availability, non repudiation issues. With these aspects in mind, many models have been proposed in literature. Furthermore, many information dissemination approaches are proposed. However, the lack of a model that can manage traffic incidents completely inspires this work. This paper details how and when a message needs to be generated and relayed so that the incidents can be reported and managed in a timely manner. This paper addresses this challenge by providing a traffic incident management model to manage several traffic incidents efficiently. Additionally, we simulate this model using the VEINS simulator with vehicles, RSUs, and a TA. From the experiments, we measure the average number of transmissions required for reporting a single traffic incident while varying the vehicle density and relaying considerations. We consider two types of relaying. In one series of experiments, messages from regular vehicles and

This study investigates the impact of Hofstede's cultural dimensions on abnormal core earnings management in multiple national cultural contexts. We employ an Ordinary Least Squares (OLS) regression model with abnormal core earnings as the dependent variable. The independent variables analyzed include Hofstede's dimensions: Power Distance Index (PDI), Individualism (IDV), Masculinity (MAS), and Uncertainty Avoidance Index (UAI). Our findings reveal that individualism is positively associated with abnormal core earnings, suggesting that cultures characterized by high individualism may encourage practices that inflate earnings due to the prominence of personal achievement and rewards. In contrast, masculinity negatively correlates with abnormal core earnings, indicating that the risk-taking attributes associated with masculine cultures may deter earnings management. Interestingly, uncertainty avoidance is positively linked to abnormal core earnings, supporting the notion that managers tend to engage more in earnings management to minimize fluctuations in financial reports in cultures with high uncertainty avoidance. The relationship between power distance and abnormal core earnings i

Regarding the concepts of urban management, digital transformation, and smart cities, various issues are presented. Currently, we like to attend to location allocation problems that can be a new part of digital transformation in urban management (such as locating and placing facilities, locating and arranging centers such as aid and rescue centers, or even postal hubs, telecommunications, electronic equipment, and data centers, and routing in transportation optimization). These issues, which are seemingly simple but in practice complex, are important in urban environments, and the issue of accurate location allocation based on existing criteria directly impacts cost management, profit, efficiency, and citizen satisfaction. In recent years, researchers have used or presented various models and methods for location allocation problems, some of which will be mentioned in this article. Given the nature of these problems, which are optimization problems, this article will also examine existing research from an optimization perspective in summary. Finally, a brief conclusion will be made of the existing methods and their weaknesses, and suggestions will be made for continuing the path an

In the management of environment the Environmental Management Accounting (EMA) is essential for corporate or companies because corporate sectors are the main parties of environmental humiliation as they are existed in the environment and for protecting environment a branch of accounting is emerged which is called environmental management accounting. The objective of the study is to develop a compliance framework for EMA and appraise the ER practices in selected industries in Bangladesh. In conducting the study, 50 environmental sensitive industries were selected from DSE. A compliance checklist was developed on 75 aspects of EMA and ER under 13 groups. In developing the compliance index binary method is used i.e. 1= if ER practices; 0= if not practices. Further the level of EMR/ER practices have been evaluated in terms of selected independent variables of the company viz. total assets, total sales, return on equity and size of board. The study found that the environmental management accounting in the manufacturing companies is in poor level. The maximum compliance is 67% and the lowest is 20%. The TA, TS BS and SP have been considered to find out the explanatory variables. In most

With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime mo

Implementing resource efficient energy management systems in facilities and buildings becomes increasingly important in the transformation to a sustainable society. However, selecting a suitable configuration based on multiple, typically conflicting objectives, such as cost, robustness with respect to uncertainty of grid operation, or renewable energy utilization, is a difficult multi-criteria decision making problem. The recently developed concept identification technique can facilitate a decision maker by sorting configuration options into semantically meaningful groups (concepts). In this process, the partitioning of the objectives and design parameters into different sets (called description spaces) is a very important step. In this study we focus on utilizing the concept identification technique for finding relevant and viable energy management configurations from a very large data set of Pareto-optimal solutions. The data set consists of 20000 realistic Pareto-optimal building energy management configurations generated by a many-objective evolutionary optimization of a high quality Digital Twin energy management simulator. We analyze how the choice of description spaces, i.e.

Automated management requires decomposing high-level user requests, such as intents, to an abstraction that the system can understand and execute. This is challenging because even a simple intent requires performing a number of ordered steps. And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand. To tackle these challenges and support automated intent decomposition and execution, we explore the few-shot capability of Large Language Models (LLMs). We propose a pipeline that progressively decomposes intents by generating the required actions using a policy-based abstraction. This allows us to automate the policy execution by creating a closed control loop for the intent deployment. To do so, we generate and map the policies to APIs and form application management loops that perform the necessary monitoring, analysis, planning and execution. We evaluate our proposal with a use-case to fulfill and assure an application service chain of virtual network functions. Using our approach, we can generalize and generate the necessary steps to realize intents, thereby enabling intent automation

We present a reinforcement-learning (RL) framework for dynamic hedging of equity index option exposures under realistic transaction costs and position limits. We hedge a normalized option-implied equity exposure (one unit of underlying delta, offset via SPY) by trading the underlying index ETF, using the option surface and macro variables only as state information and not as a direct pricing engine. Building on the "deep hedging" paradigm of Buehler et al. (2019), we design a leak-free environment, a cost-aware reward function, and a lightweight stochastic actor-critic agent trained on daily end-of-day panel data constructed from SPX/SPY implied volatility term structure, skew, realized volatility, and macro rate context. On a fixed train/validation/test split, the learned policy improves risk-adjusted performance versus no-hedge, momentum, and volatility-targeting baselines (higher point-estimate Sharpe); only the GAE policy's test-sample Sharpe is statistically distinguishable from zero, although confidence intervals overlap with a long-SPY benchmark so we stop short of claiming formal dominance. Turnover remains controlled and the policy is robust to doubled transaction costs. T

Climate-related phenomena are increasingly affecting regions worldwide, manifesting as floods, water scarcity, and heat waves, significantly impairing companies' assets and productivity. It is essential for asset managers to quantify the exposure of their portfolios to such risk. To this aim, we develop a framework based on the Vasicek model for credit risk that introduces downward jumps due to climate phenomena in a company asset's dynamics. These negative shocks are designed to mirror the negative effect of extreme climate events. The model calibration relies on companies' asset intensity and geographical exposure. We apply the new multivariate firm value model with jumps to assess the impact of climate-related extreme events on expected and unexpected portfolio losses. Our findings indicate that expected losses increase over time, with pronounced differences in exposure observed across sectoral indices. From an environmental policy perspective, these results suggest the need for additional capital buffers to offset losses arising from physical climate risks, particularly in sectors with high asset intensity.

The purpose of the research presented in this article is to develop a dynamic system for forecasting and minimizing the risks of an industrial company based on their quantitative assessment. The article considers the conceptual apparatus of the essential content of risk management of an industrial enterprise, reviews the theoretical aspects of risk management systems and the most significant risk management methods from a practical point of view. The methodological apparatus of qualitative and quantitative analysis and risk assessment has been expanded on the basis of some conditionality of risk classification features identified and a systematic approach to the classification of risks of an industrial enterprise has been proposed, taking into account the dynamics of their impact on the object, the stages of building a dynamic risk management system are given. The article substantiates the need to supplement the dynamic risk management system of industrial enterprises with methods of qualitative and quantitative risk assessment in order to form effective risk management strategies

Flood risk managers seek to optimise Blue-Green Infrastructure (BGI) designs to maximise return on investment. Current systems often use optimisation algorithms and detailed flood models to maximise benefit-cost ratios for single rainstorm return periods. However, these schemes may lack robustness in mitigating flood risks across different storm magnitudes. For example, a BGI scheme optimised for a 100-year return period may differ from one optimised for a 10-year return period. This study introduces a novel methodology incorporating five return periods (T = 10, 20, 30, 50, and 100 years) into a multi-objective BGI optimisation framework. The framework combines a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a fully distributed hydrodynamic model to optimise the spatial placement and combined size of BGI features. For the first time, direct damage cost (DDC) and expected annual damage (EAD), calculated for various building types, are used as risk objective functions, transforming a many-objective problem into a multi-objective one. Performance metrics such as Median Risk Difference (MedRD), Maximum Risk Difference (MaxRD), and Area Under Pareto Front (AUPF) reveal that