Variable and Invariable,Explore the Value-Creation Techniques of Management Accounting by Benchmarking the Financial Management of Large Enterprises

With the development of information technology such as ChatGPT,artificial intelligence,5G,and so on,technological innovation brings about a qualitative change in the economic business.This change is embodied in breakthroughs in the field of science and technology and the iterative upgrading process of products.The traditional business model undoubtedly fails to adapt to the management needs of modern enterprises,while traditional financial practices and knowledge reserves cannot meet these needs.Faced with sudden technological changes and the emergence of financial sharing,financial robots,etc.,there is an urgent need for financial practitioners to transform from traditional financial accounting to value-creating management accounting.In the future,financial professionals with the ability to embrace uncertainty,adapt to environmental changes,and find breakthroughs in uncertain environments will be the most sought-after by enterprises.This paper aims to benchmark the excellent financial management models of large enterprises and conduct thematic research on financial management in large enterprises.It seeks to explore,in the era of big data management accounting,the road and art of creating value in enterprises.The objective is to provide references for colleagues in the financial field and to explore effective ways to generate value for enterprise management ideas,thus serving as a reference point in the financial transformation journey.

Research on the Optimization of Enterprise Financial Management under the Financial Sharing Model

With the advancement of globalization and information technology,the financial sharing mode has gradually emerged as a crucial means for enterprises to optimize their financial management.Particularly within the context of economic globalization,informatization,and digital transformation,enterprises find themselves navigating a rapidly evolving market environment by intensifying competition.To enhance efficiency and competitiveness,many enterprises have embraced the financial sharing model to streamline financial management processes,curtail costs,and bolster the execution of corporate strategies.This article aims to dissect the definition and essence of the financial sharing model and its significance in the realm of enterprise financial management.Drawing upon this analysis and aligning with the needs of enterprise financial management,the article proposes ideas for optimizing management practices,aspiring to foster reform and innovation in enterprise financial management while enhancing its level of financial management and ability to respond to financial risks.This contribution seeks to provide valuable insights for practitioners in the field.

Current state and influencing factors in airbag management among emergency department nurses:A multicenter study

BACKGROUND The emergency department(ED)plays a critical role in establishing artificial airways and implementing mechanical ventilation.Managing airbags in the ED presents a prime opportunity to mitigate the risk of ventilator-associated pneumonia.Nonetheless,existing research has largely overlooked the understanding,beliefs,and practical dimensions of airway airbag management among ED nurses,with a predominant focus on intensive care unit nurses.AIM To investigate the current status of ED nurses'knowledge,beliefs,and practical behaviors in airway airbag management and their influencing factors.METHODS A survey was conducted from July 10th to August 10th,2023,using convenience sampling on 520 ED nurses from 15 tertiary hospitals and 5 sary hospitals in Shanghai.Pathway analysis was utilized to analyze the influencing factors.RESULTS The scores for ED nurses'airway airbag management knowledge were 60.26±23.00,belief was 88.65±13.36,and behavior was 75.10±19.84.The main influencing factors of airbag management knowledge included participation in specialized nurse or mechanical ventilation training,department,and work experience in the department.Influencing factors of airbag management belief comprised knowledge,department,and participation in specialized nurse or mechanical ventilation training.Primary influencing factors of airbag management behavior included knowledge,belief,department,participation in specialized nurse or mechanical ventilation training,and professional title.The belief in airbag management among ED nurses acted as a partial mediator between knowledge and behavior,with a total effect value of 0.513,and an indirect effect of 0.085,constituting 16.6%of the total effect.CONCLUSION ED nurses exhibit a positive attitude toward airbag management with relatively standardized practices,yet there remains room for improvement in their knowledge levels.Nursing managers should implement interventions tailored to the characteristics of ED nurses'airbag management knowledge,beliefs,and practices to enhance their airbag management proficiency.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Management of lateral pelvic lymph nodes in rectal cancer:Is it time to reach an Agreement?

In this editorial,we proceed to comment on the article by Chua et al,addressing the management of metastatic lateral pelvic lymph nodes(mLLN)in stage II/III rectal cancer patients below the peritoneal reflection.The treatment of this nodal area sparks significant controversy due to the strategic differences followed by Eastern and Western physicians,albeit with a higher degree of convergence in recent years.The dissection of lateral pelvic lymph nodes without neoadjuvant therapy is a standard practice in Eastern countries.In contrast,in the West,preference leans towards opting for neoadjuvant therapy with chemoradiotherapy or radiotherapy,that would cover the treatment of this area without the need to add the dissection of these nodes to the total mesorectal excision.In the presence of high-risk nodal characteristics for mLLN related to radiological imaging and lack of response to neoadjuvant therapy,the risk of lateral local recurrence increases,suggesting the appropriate selection of strategies to reduce the risk of recurrence in each patient profile.Despite the heterogeneous and retrospective nature of studies addressing this area,an international consensus is necessary to approach this clinical scenario uniformly.

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

Optimization Management of Industrial Organizations Based on Performance Indicators

This paper proposes an intelligent management system (IMS) to help managers in their delicate and tedious task of exploiting the plethora of data (indicators) contained in management dashboards. This system is based on intelligent agents, ontologies and data mining. It is implemented by PASSI (Process for Agent Societies Specification and Implementation) methods for agent design and implementation, the Methodology for Knowledge Modeling and Hot-Winters for data prediction. Intelligent agents not only track indicators but also store the knowledge of managers within the company. Ontologies are used to manage the representation and presentation aspects of knowledge. Data mining makes it possible to: make the most of all available data;model the industrial process of data selection, exploration and modeling;and transform behaviors into predictive indicators. An instance of the IMS named SYGISS, currently in operation within a large brewery organization, allows us to observe very interesting results: the extraction of indicators is done in less than 5 minutes whereas manual extraction used to take 14 days;the generation of dashboards is instantaneous whereas it used to take 12 hours;the interpretation of indicators is instantaneous whereas it used to take a day;forecasts are possible and are done in less than 5 minutes whereas they did not exist with the old management. These important contributions help to optimize the management of this organization.

Application of PDCA Cycle in Hand Hygiene Management of Psychiatric Medical Staff

Objective:To study the application effect of the plan-do-check-act(PDCA)cycle management in the hand hygiene management of psychiatric medical staff.Methods:One hundred and twenty medical staff from a psychiatric hospital from May 2023 to December 2023 were selected and divided into two groups.The control group(May 2023 to August 2023)applied the conventional management model,and the observation group(September 2023 to December 2023)applied the PDCA cycle management.The hand hygiene compliance,hand hygiene knowledge,and hygiene qualifications were compared,including the amount of hand sanitizer used.Results:The proportion of medical staff’s hand hygiene compliance and hand hygiene knowledge mastery scores in the observation group were higher than those in the control group(P<0.05);the hand hygiene passing rate in the observation group was higher than that of the control group(P<0.05);the daily amount of hand sanitizer per patient bed and the amount of hand sanitizer used was higher than that of the control group(P<0.05).Conclusion:The PDCA cycle management model for psychiatric medical staff promoted the improvement of hand hygiene compliance and increased their hand hygiene qualifications.It is suitable for further popularization and application in future clinical practice.

Digital Transformation of Enterprise Human Resource Management Enabled by Big Data

With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterprises,which is crucial to the competitiveness of enterprises.Enterprises need to attract,retain,and motivate excellent employees,thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market.To maintain advantages in the fierce market competition,enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness.At the same time,this paper analyzes the dilemma faced by enterprise human resource management,points out the new characteristics of enterprise human resource management enabled by big data,and puts forward feasible suggestions for enterprise digital transformation.

Exploration of Human Resource Management in Enterprise Business Management

Human resource management is an integral part of enterprise operation and management,contributing to the avoidance of financial risks,alleviation of performance problems,promotion of organizational development,enhancement of operational efficiency,and fostering sustainable enterprise development.Enterprise management should prioritize attention to human resource management by establishing an independent human resource management department and strategically planning human resource management tasks.This involves building an information management platform,developing a professional human resource management model to mitigate market risks,actively recruiting human resource management talents to form a competent team,and enhancing the human resource management and supervision system.These measures are essential for comprehensively improving the level of enterprise operation and management.

Assessment of Municipal Solid Waste Management in the Farmgate Area of Dhaka North City Corporation

This investigation is focused on conducting a thorough analysis of Municipal Solid Waste Management (MSWM). MSWM encompasses a range of interdisciplinary measures that govern the various stages involved in managing unwanted or non-utilizable solid materials, commonly known as rubbish, trash, junk, refuse, and garbage. These stages include generation, storage, collection, recycling, transportation, handling, disposal, and monitoring. The waste materials mentioned in this context exhibit a wide range of items, such as organic waste from food and vegetables, paper, plastic, polyethylene, iron, tin cans, deceased animals, byproducts from demolition activities, manure, and various other discarded materials. This study aims to provide insights into the possibilities of enhancing solid waste management in the Farmgate area of Dhaka North City Corporation (DNCC). To accomplish this objective, the research examines the conventional waste management methods employed in this area. It conducts extensive field surveys, collecting valuable data through interviews with local residents and key individuals involved in waste management, such as waste collectors, dealers, intermediate dealers, recyclers, and shopkeepers. The results indicate that significant amounts of distinct waste categories are produced daily. These include food and vegetable waste, which amount to 52.1 tons/day;polythene and plastic, which total 4.5 tons/day;metal and tin-can waste, which amounts to 1.4 tons/day;and paper waste, which totals 5.9 tons/day. This study highlights the significance of promoting environmental consciousness to effectively shape the attitudes of urban residents toward waste disposal and management. It emphasizes the need for collaboration between authorities and researchers to improve the current waste management system.

Development and Application of Integrated Indicators for Assessing Healthcare Waste Management Systems in Kenyan Hospitals

Healthcare waste management (HCWM) is an important aspect of healthcare delivery globally because of its hazardous and infectious components that have potential for adverse health and environmental impacts. The paper introduces a set of indicators for assessing HCWM systems in hospitals. These indicators are: HCWM policies and standard operating procedures, management and oversight, logistics and budget support, training and occupational health and safety, and treatment, disposal and waste treatment equipment housing. By plotting a mark on a continuum which is defined as good and poor on the extremes and is connected with all other marks in a spoke arrangement, it’s possible to describe a baseline for HCWM in any specific hospital. This baseline can be used to improve awareness of the actors and policy-makers, compare the same hospital at a different point in time, to compare observations by different evaluators and to track improvements. Results suggest that in Kenya, the application of such indicators is useful for evaluating which priorities should be addressed to improve outcomes in HCWM systems. Systematic sampling technique was used to identify and collect data by use of observational checklist, interviews, visual verification and review of documents and a HCWM assessment tool. The objective is to suggest an integrated management tool as a method to identify prevailing problems with a HCWM system. The method can be replicated in other contexts worldwide, with a focus on the developing world. The integrated indicators focus on management of HCW and not its potential impact on human health and environment, an area recognized to be critical for future research.

On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

Etiology and management of urethral calculi:A systematic review of contemporary series

Objective:To conduct a systematic literature review on urethral calculi in a contemporary cohort describing etiology,investigation,and management patterns.Methods:A systematic search of MEDLINE and Cochrane Central Register of Controlled Trials(CENTRAL)databases was performed.Articles,including case reports and case series on urethral calculi published between January 2000 and December 2019,were included.Full-text manuscripts were reviewed for clinical parameters including symptomatology,etiology,medical history,investigations,treatment,and outcomes.Data were collated and analyzed with univariate methods.Results:Seventy-four publications met inclusion criteria,reporting on 95 cases.Voiding symptoms(41.1%),pain(40.0%),and acute urinary retention(32.6%)were common presenting features.Urethral calculi were most often initially investigated using plain X-ray(63.2%),with almost all radio-opaque(98.3%).Urethral calculi were frequently associated with coexistent bladder or upper urinary tract calculi(16.8%)and underlying urethral pathology(53.7%)including diverticulum(33.7%)or stricture(13.7%).Urethral calculi were most commonly managed with external urethrolithotomy(31.6%),retrograde manipulation(22.1%),and endoscopic in situ lithotripsy(17.9%).Conclusion:This unique systematic review of urethral calculi provided a summary of clinical features and treatment trends with a suggested treatment algorithm.Management in contemporary urological practice should be according to calculus size,shape,anatomical location,and presence of urethral pathology.

Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

Patient satisfaction and follow-up adherence to glaucoma case management clinic in China

AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.

A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.