How to avoid reputation damage in financial restatement? The role of investor relations management

In order to examine the effects of avoiding reputation damage by investor relations management under certain corporate governance structures and mechanisms, samples of 1120 listed companies are used to research the influence on financial restatements by corporate governance. Then the moderating effects of investor relations management on financial restatements are analyzed. The result is that the more dispersed the equity, the higher the probability of financial restatements will be (This includes the government-controlled companies). Also the higher the proportion of independent directors and the higher the level of investor relations management, the lower the probability of financial restatements will be. Furthermore, as a moderating variable, investor relations management can eliminate the negative effects of corporate governance, enhance the effect of independent directors and reduce the probability of financial restatement.

Impact of Zika virus non-structural protein mutations on hippocampal damage

The Zika virus(ZIKV),a member of the Flaviviridae family,attracted worldwide attention for its connection to severe neurological effects,notably microcephaly in newborns,first reported during the 2015 epidemic in Brazil.Yet,its impact goes beyond fetal and neonatal abnormalities,also affecting the central nervous system(CNS)in both children and adults,leading to enduring cognitive and behavioral impairments.

Imperative for long-term management and surveillance in Kawasaki disease

Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.

Enhancing perianal disease management with integrated physical and psychological approaches

This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.

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.

Novel damage constitutive models and new quantitative identification method for stress thresholds of rocks under uniaxial compression

Four key stress thresholds exist in the compression process of rocks,i.e.,crack closure stress(σ_(cc)),crack initiation stress(σ_(ci)),crack damage stress(σ_(cd))and compressive strength(σ_(c)).The quantitative identifications of the first three stress thresholds are of great significance for characterizing the microcrack growth and damage evolution of rocks under compression.In this paper,a new method based on damage constitutive model is proposed to quantitatively measure the stress thresholds of rocks.Firstly,two different damage constitutive models were constructed based on acoustic emission(AE)counts and Weibull distribution function considering the compaction stages of the rock and the bearing capacity of the damage element.Then,the accumulative AE counts method(ACLM),AE count rate method(CRM)and constitutive model method(CMM)were introduced to determine the stress thresholds of rocks.Finally,the stress thresholds of 9 different rocks were identified by ACLM,CRM,and CMM.The results show that the theoretical stress−strain curves obtained from the two damage constitutive models are in good agreement with that of the experimental data,and the differences between the two damage constitutive models mainly come from the evolutionary differences of the damage variables.The results of the stress thresholds identified by the CMM are in good agreement with those identified by the AE methods,i.e.,ACLM and CRM.Therefore,the proposed CMM can be used to determine the stress thresholds of rocks.

An equivalent target plate damage probability calculation mathematics model and damage evaluation method

Aiming at the requirement of damage testing and evaluation of equivalent target plate based on the explosion of intelligent ammunition, this paper proposes a novel method for damage testing and evaluation method of circumferential equivalent target plate. Leveraging the dispersion characteristics parameters of fragment, we establish a calculation model of the fragment power situation and the damage calculation model under the condition of fragment ultimate penetration equivalent target plate. The damage model of equivalent target plate involves the fragment dispersion density, the local perforation damage criterion, the tearing damage model, and the damage probability. We use the camera to obtain the image of the equivalent target plate with fragment perforation, and research the algorithm of fragment distribution position recognition and fragment perforation area calculation method on the equivalent target plate by image processing technology. Based on the obtained parameters of the breakdown position and perforation area of fragments on equivalent target plate, we apply to damage calculation model of equivalent target plate, and calculate the damage probability of each equivalent target plate, and use the combined probabilistic damage calculation method to obtain the damage evaluation results of the circumferential equivalent target plate in an intelligent ammunition explosion experiment. Through an experimental testing, we verify the feasibility and rationality of the proposed damage evaluation method by comparison, the calculation results can reflect the actual damage effect of the equivalent target plate.

Stress-corrosion coupled damage localization induced by secondary phases in bio-degradable Mg alloys:phase-field modeling

In this study,a phase-field scheme that rigorously obeys conservation laws and irreversible thermodynamics is developed for modeling stress-corrosion coupled damage(SCCD).The coupling constitutive relationships of the deformation,phase-field damage,mass transfer,and electrostatic field are derived from the entropy inequality.The SCCD localization induced by secondary phases in Mg is numerically simulated using the implicit iterative algorithm of the self-defined finite elements.The quantitative evaluation of the SCCD of a C-ring is in good agreement with the experimental results.To capture the damage localization,a micro-galvanic corrosion domain is defined,and the buffering effect on charge migration is explored.Three cases are investigated to reveal the effect of localization on corrosion acceleration and provide guidance for the design for resistance to SCCD at the crystal scale.

Impact Damage Testing Study of Shanxi-Beijing Natural Gas Pipeline Based on Decision Tree Rotary Tiller Operation

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.

YOLO-Based Damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.

A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders

Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.

Parameter calibration of the tensile-shear interactive damage constitutive model for sandstone failure

The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.

Conservative management of an early abdominal complication during a Tullio percutaneous endoscopic excision of renal pelvis urothelial carcinoma:A case report

BACKGROUND Upper tract urothelial carcinoma(UTUC)representing only a small fraction of all urothelial tumors.It predominantly affects the renal pelvis in men,often coexisting with bladder carcinoma.UTUC displays a more aggressive genetic profile compared to bladder neoplasms,with the majority of patients presenting with advanced disease.Histologically,about a quarter of UTUC cases exhibit high-grade subtypes,associated with a worse prognosis.Tobacco use and past exposure to aromatic amines are significant risk factors for UTUC.Imaging modalities such as Uro-computed tomography and magnetic resonance imaging play crucial roles in diagnosis.Ureterorenoscopy is vital for direct visualization and biopsy sampling,but its limited sampling capacity presents challenges in determining tumor depth and staging.Traditionally,nephroureterectomy with bladder cuff excision has been the gold-standard treatment for UTUC.Endoscopic conservative treatment has recently emerged as a viable option for selected patients,offering comparable oncological outcomes to radical surgery.Percutaneous access is also feasible for larger intrarenal tumors.CASE SUMMARY We present a case of an 84-year-old woman who underwent percutaneous endoscopic excision of a renal pelvis neoplasm using the Tullio laser.Despite successful tumor removal,the patient experienced a postoperative complication with abdominal fluid leakage.Conservative management effectively resolved the complication.Given the patient's age and refusal for radical surgery,the conservative approach proved to be a valid therapeutic choice.CONCLUSION Overall,UTUC remains a diagnostic and therapeutic challenge due to its rarity.However,advances in endoscopic and percutaneous techniques provide valuable alternatives for selected patients,warranting further exploration in this evolving 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.

Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

Design and Implementation of Library Seating Management System

The library seating management system is an important part of the library’s modern services. The system is based on the Spring and Spring Boot framework, and the system realizes a series of functions such as real-time query, reservation, and cancellation of seat resources, providing users with great convenience. With a simple operation, users can know the availability of seats in the library in real time and reserve them according to their needs. At the same time, the system also provides rich management functions, enabling administrators to easily configure and manage seat resources. The addition, deletion, modification and review of users, the generation of seats, the viewing of user usage records, and the addition or deletion of points for users’ usage can also be carried out. This not only improves the management efficiency, but also provides more scientific and accurate data support for the management of the library. The system not only optimizes the user experience, but also promotes the scientific management and efficient utilization of library resources, and provides strong support for the modern services of the library.

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.

Leadership and Top Management Influence on Total Quality Management Implementation in the Ghanaian Construction Industry

This study aims to determine the key and underlying Leadership and Top Management (LTM) factors that have a significant impact on sustaining the implementation of Total Quality Management (TQM) within the construction industry in Ghana. The research methodology employed in this study was a quantitative technique. Questionnaires were distributed to 641 participants within construction industry in Ghana. Questionnaires retrieved for the analysis were 536. Three steps approached were used for the data analysis. These include Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) analysis. After conducting the EFA and CFA, SEM was also used to analyze the construct validity. The SEM analysis helps to determine four key indicator variables for the leadership and top management construct. These include Leadership/Top Management approach to employees’ management, Leadership/Top Management understanding of TQM, Leadership/Top Management empowerment of employees to resolve quality issues, and Leadership/Top Management endorsement of TQM. All the four indicator variables were found to be good of fit and closely associated with the dependent variable. The study adds to the body of knowledge by using EFA, CFA and SEM techniques to establish key leadership and top management factors affecting TQM implementation in Ghana’s construction industry. The findings in general suggested that leadership and top Management factors identified have a direct positive impact on sustaining TQM implementation in the Ghanaian construction industry. Consequently, the leadership and top management factors identified in this study can help improve TQM in the Ghanaian construction industry.

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.