Information For Decision-making Research Articles

A prospective observational comparative study to compare the diagnostic efficacy of the Fenyo Lindberg scoring system and Yash scoring system in patients of acute appendicitis in tertiary care hospitals in central India

Background Acute appendicitis is a typical surgical emergency requiring prompt and accurate diagnosis for timely intervention. This prospective observational comparative study aims to assess the diagnostic efficacy of two scoring systems, the Yash scoring system and the Fenyo-Lindberg scoring system, compared to the gold standard of histopathological diagnosis. The study is conducted at A.V.B.R.H., a tertiary care hospital in Central India, focusing on providing valuable insights into the comparative performance of these scoring systems in the local patient population. Methods The study adopts a prospective observational comparative design, enrolling clinically diagnosed cases of acute appendicitis from the outpatient and inpatient departments of the Surgery department at A.V.B.R.H. Patients willing to undergo surgery are included. At the same time, those with complicating factors or contraindications are excluded. Data collection involves thorough clinical evaluations, scoring system assessments, and comprehensive investigations, with histopathology as the reference standard. Statistical analyses include descriptive statistics, comparative analyses, primary outcome assessments, and regression analyses, ensuring a robust evaluation of the diagnostic efficacy of the scoring systems. Expected Outcome Anticipated outcomes include a comprehensive understanding of the diagnostic efficacy of the Yash and Fenyo-Lindberg scoring systems in acute appendicitis. The study aims to identify which scoring system demonstrates superior accuracy, sensitivity, and specificity, providing valuable information for clinical decision-making. The results will contribute to optimizing diagnostic protocols for acute appendicitis, potentially influencing patient management and surgical interventions. This study’s findings are expected to be relevant to the local setting and broader contexts, enhancing the knowledge surrounding acute appendicitis diagnosis.

MAXIMIZING RETURNS: UNVEILING THE IMPACT OF LIQUIDITY, EFFICIENCY, AND DIVIDEND POLICY ON ASSETS

With an emphasis on Return on Assets (ROA), this study examines the interdependent dynamics of liquidity, efficiency, and dividend policy on business financial performance. With a quantitative research design, the study seeks to clarify how these components together affect organizational effectiveness. Regression analysis is used in the study to look at the associations between the dependent variable, ROA, and the important independent variables, current assets, quick ratio, liquidity, and dividend, using data from ITC Ltd. that was obtained from Moneycontrol over a five-year period (2019–2023). The findings show a strong relationship between liquidity, efficiency, dividend policy, and return on assets (ROA), with liquidity standing out as the main factor and efficiency and dividend policy following closely behind. The results highlight how crucial these variables are in determining financial performance and provide insightful information for organizational resource allocation and strategic decision-making. The study does, however, admit certain limitations, such as the possibility of wider generalizability and the dependence on data from a single organization. Further research is recommended to increase the sample size, carry out longitudinal studies, and investigate qualitative techniques in order to gain a deeper comprehension of and identify the causal factors behind the dynamics of financial success.

Machine Learning-based Extrapolation of Crop Cultivation Cost

It is important to comprehend the relation between operational expenses such as labour, seed, irrigation, insecticides, fertilizers and manure costs necessary for the cultivation of crops. A precise cost for the cultivation of crops can offer vital information for agricultural decision-making. The main goal of the study is to compare machine learning (ML) techniques to measure relationships among operational cost characteristics for predicting crop cultivation costs before the start of the growing season using the dataset made available by the Ministry of Agriculture and Farmer Welfare of the Government of India. This paper describes various ML regression techniques, compares various learning algorithms as well as determines the most efficient regression algorithms based on the data set, the number of samples and attributes. The data set used for predicting the cost with 1680 instances includes varying costs for 14 different crops for 12 years (2010-2011 to 2021-2022). Ten different ML algorithms are considered and the crop cultivation cost is predicted. The evaluation results show that Random Forest (RF), Decision Tree (DT), Extended gradient boosting (XR) and K-Neighbours (KN) regression provide better performance in terms of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) rate while training and testing time. This study also compares different ML techniques and showed significant differences using the statistical analysis of variance (ANOVA) test. The optimal hyperparameters for the ML models are found using the gridsearchCV and randomizedsearchCV functions, which improves the model's capacity for generalisation.

New insights into black carbon light absorption enhancement: A comprehensive analysis of two differential behaviors.

High uncertainty in optical properties of black carbon (BC) involving heterogeneous chemistry has recently attracted increasing attention in the field of atmospheric climatology. To fill the gap in BC optical knowledge so as to estimate more accurate climate effects and serve the response to global warming, it is beneficial to conduct site-level studies on BC light absorption enhancement (Eabs) characteristics. Real-time surface gas and particulate pollutant observations during the summer and winter over Wuhan were utilized for the analysis of Eabs simulated by minimum R square (MRS), considering two distinct atmospheric conditions (2015 and 2017). In general, differences in aerosol emissions led to Eabs differential behaviors. The summer average of Eabs (1.92 ± 0.55) in 2015 was higher than the winter average (1.27 ± 0.42), while the average (1.11 ± 0.20) in 2017 summer was lower than that (1.67 ± 0.69) in winter. Eabs and RBC (representing the mass ratio of non-refractory constituents to elemental carbon) constraints suggest that Eabs increased with the increase in RBC under the ambient condition enriched by secondary inorganic aerosol (SIA), with a maximum growth rate of 70.6% in 2015 summer. However, Eabs demonstrated a negative trend against RBC in 2017 winter due to the more complicated mixing state. The result arose from the opposite impact of hygroscopic SIA and absorbing OC/irregular distributed coatings on amplifying the light absorbency of BC. Furthermore, sensitivity analysis revealed a robust positive correlation (R > 0.9) between aerosol chemical compositions (including sulfate, nitrate, ammonium and secondary organic carbon), which could be significantly perturbed by only a small fraction of absorbing materials or restructuring BC through gaps filling. The above findings not only deepen the understanding of BC, but also provide useful information for the scientific decision-making in government to mitigate particulate pollution and obtain more precise BC radiative forcing.

The Water Resources Rebound Effect Threatening the Achievement of Sustainable Development Goal 6 (SDG 6)

Coaction of climate change and human activities exerts a significant impact on the fate of water resources, provoking the rebound effect in water resources and threatening the achievement of SDG (sustainable development goal) 6. However, the mechanisms of interactions between this effect and SDG 6 and how to decrease this effect towards the achievement of SDG 6 are less known. In this paper, a water resources rebound effect (WRRE) model is integrated into a river basin sustainable development decision support system to simulate and project the interactions between the WRRE and SDG 6 under multiple combination scenarios. The results show that multiple drivers, including precipitation, technological advances, and ecological water flow, coaffect the WRRE, not the silo technology factor. The transfer fate of the saved water is a major root cause leading to the WRRE and harming the achievement of SDG 6, and the WRRE is positively correlated to SDG 6 overall, which, nevertheless, can sometimes be reversed by technological advances. Our outcomes indicate that decoupling SDG 6 from the WRRE can promote the achievement of SDG 6 and decrease the rebound effect, relying on holistic integration that couples climatic, socioeconomic, and behavioral interaction between SDG 6 and the WRRE and translation of scientific research into available decision-making information for strict scientific-research-based policy actions.

Cardiac Surgery 2023 Reviewed.

We reviewed the cardiac surgical literature for 2023. PubMed displayed almost 34,000 hits for the search term "cardiac surgery AND 2023." We used a PRISMA approach for a results-oriented summary. Key manuscripts addressed the mid- and long-term effects of invasive treatment options in patient populations with coronary artery disease (CAD), comparing interventional therapy (percutaneous coronary intervention [PCI]) with surgery (coronary artery bypass graft [CABG]). The literature in 2023 again confirmed the excellent long-term outcomes of CABG compared with PCI in patients with left main stenosis, specifically in anatomically complex chronic CAD, but even in elderly patients, generating further support for an infarct-preventative effect as a prognostic mechanism of CABG. For aortic stenosis, a previous trend of an early advantage for transcatheter (transcatheter aortic valve implantation [TAVI]) and a later advantage for surgical (surgical aortic valve replacement) treatment was also re-confirmed by many studies. Only the Evolut Low Risk trial maintained an early advantage of TAVI over 4 years. In the mitral and tricuspid field, the number of interventional publications increased tremendously. A pattern emerges that clinical benefits are associated with repair quality, making residual regurgitation not irrelevant. While surgery is more invasive, it currently generates the highest repair rates and longest durability. For terminal heart failure treatment, donor pool expansion for transplantation and reducing adverse events in assist device therapy were issues in 2023. Finally, the aortic diameter related to adverse events and technical aspects of surgery dominated in aortic surgery. This article summarizes publications perceived as important by us. It cannot be complete nor free of individual interpretation, but provides up-to-date information for patient-specific decision-making.

МЕТОД ДВОКАСКАДНОЇ ІМПЛАНТАЦІЇ ПРИХОВАНОЇ ІНФОРМАЦІЇ НА ОСНОВІ СТЕГАНОКОМПРЕСІЙНИХ ПЕРЕТВОРЕНЬ

In the article, the need for further development of the state is directly related to: solving issues of increasing defense capability and information security; development of information and intelligent systems. The necessity of ensuring the required level of completeness of information, compliance with the requirements of its relevance, achievement and maintenance of the appropriate level of integrity, accessibility and confidentiality is substantiated. The article emphasizes that for this purpose, complex systems of coding and information protection are being built. It is shown that recently, in order to further increase the level of security of information resources, methods of hidden embedding of information have been used. In this field of scientific and applied research, the direction of timely delivery of integral video information in a secure mode is outlined. For protection, the following can be used: meta-messages, which are formed on the basis of intelligent analysis of video frames; separate video segments of aerial photographs containing the most important information for decision-making. This article examines a class of methods of steganographic transformations, which are associated with embedding messages in digital containers, which are formed by a stream of video segments (VS). However, existing steganographic systems are based mainly on the use of the amount of psychovisual (PSV) redundancy available for reduction. Therefore, an increase in steganographic capacity leads to a loss of integrity and efficiency of delivery of video-container information. Steganocompression coding in the stegano-polyadic basis was created on the basis of taking into account the amount of redundancy permissible for reduction. In this case, in the process of compression of the components of the VS, the direct embedding of hidden information is organized. Concealment of information is carried out by technological stages, depending on the compression transformation of the transitional syntax of the quilted sequence. The key stage is the technology of two-stage implementation of the elements of the concealed message added to the stegano sequences to the syntax of the steganocompression representation of the BC components.

If not me then we: Goal tradeoffs in decision-making for the self, ingroup, and outgroup

Navigating the social world requires individuals to balance multiple goals, including the drives to improve one's own outcomes, aid ingroup members, and help or hurt outgroup members. While self-interest and intergroup bias are both well-established motivational phenomena, less is known about how these goals may interact. Here we examine the nature of goal tradeoffs in intergroup decision-making using a novel task in which participants simultaneously make monetary decisions for themselves, an arbitrary ingroup, and the corresponding outgroup. Across four behavioural studies and one eye-tracking study (total N = 704), we find that goals in intergroup contexts are pursued sequentially rather than concurrently, with non-linear upweighting of group-related goals when self-related goals cannot be pursued. Further, we find evidence for stronger self-ingroup than self-outgroup tradeoffs, which manifest in both altered attention to information and altered use of the attended information in decision-making. The results shed light on the cognitive structuring of interrelated goals in intergroup decision-making, furthering our understanding of when and how both intergroup biases and prosocial behaviour may emerge.

Cost-effectiveness of restorative treatments for permanent molars with severe molar incisor hypomineralization: perspectives for the Brazilian public health system.

To undertake a cost-effectiveness analysis of restorative treatments for a first permanent molar with severe molar incisor hypomineralization from the perspective of the Brazilian public system. Two models were constructed: a one-year decision tree and a ten-year Markov model, each based on a hypothetical cohort of one thousand individuals through Monte Carlo simulation. Eight restorative strategies were evaluated: high viscosity glass ionomer cement (HVGIC);encapsulated GIC; etch and rinse adhesive + composite; self-etch adhesive + composite;preformed stainless steel crown; HVGIC + etch and rinse adhesive + composite; HVGIC + self-etch adhesive + composite, and encapsulated GIC + etch and rinse adhesive + composite. Effectiveness data were sourced from the literature. Micro-costing was applied using 2022 USD market averages with a 5% variation. Incremental cost-effectiveness ratio (ICER), net monetary benefit (%NMB), and the budgetary impact were obtained. Cost-effective treatments included HVGIC (%NMB = 0%/0%), encapsulated GIC (%NMB = 19.4%/ 19.7%), and encapsulated GIC + etch and rinse adhesive + composite (%NMB = 23.4%/ 24.5%) at 1year and 10years, respectively. The benefit gain of encapsulated GIC + etch and rinse adhesive + composite in relation to encapsulated GIC was small when compared to the cost increase at 1year (gain of 3.28% and increase of USD 24.26) and 10years (gain of 4% and increase of USD 15.54). Within the horizon and perspective analyzed, the most cost-effective treatment was encapsulated GIC restoration. This study can provide information for decision-making.

Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma.

To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC). The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit. 166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908. This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.

Traffic state estimation from vehicle trajectories with anisotropic Gaussian processes

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations.

Objectivization study of acupuncture Deqi and brain modulation mechanisms: a review.

Deqi is an important prerequisite for acupuncture to achieve optimal efficacy. Chinese medicine has long been concerned with the relationship between Deqi and the clinical efficacy of acupuncture. However, the underlying mechanisms of Deqi are complex and there is a lack of systematic summaries of objective quantitative studies of Deqi. Acupuncture Deqi can achieve the purpose of treating diseases by regulating the interaction of local and neighboring acupoints, brain centers, and target organs. At local and neighboring acupoints, Deqi can change their tissue structure, temperature, blood perfusion, energy metabolism, and electrophysiological indicators. At the central brain level, Deqi can activate the brain regions of the thalamus, parahippocampal gyrus, postcentral gyrus, insular, middle temporal gyrus, cingulate gyrus, etc. It also has extensive effects on the limbic-paralimbic-neocortical-network and default mode network. The brain mechanisms of Deqi vary depending on the acupuncture techniques and points chosen. In addition, Deqi 's mechanism of action involves correcting abnormalities in target organs. The mechanisms of acupuncture Deqi are multi-targeted and multi-layered. The biological mechanisms of Deqi are closely related to brain centers. This study will help to explore the mechanism of Deqi from a local-central-target-organ perspective and provide information for future clinical decision-making.

Behavioral factors influencing investment decision-making in the Nigerian real estate stock market

This paper examines the behavioral factors that influence investment decision-making in the Nigerian property stock market, with a survey of registered stockbrokers with the Nigeria Exchange Group (NGX), to provide information for optimal investment decision-making. The study adopted a direct survey method. Relevant information was obtained from registered Nigerian stockbrokers via a closed-end questionnaire, and their responses were analyzed using principal component factor (PCF), and ordinary least square (OLS) regression analyses. The study confirms the influence of behavioral factors on investment decisions in the real estate stock market. Buying decisions dominate during bull markets, but investors prefer selling decisions when the market is down. Six behavioral principal factors were extracted. Investor responsiveness factor explains the dynamics of investment decisions in the optimistic property stock market, while herd effect factor predicts investors' investment behavior in the pessimistic market. The empirical evidence of emotional trading underscores the importance of behavioral factors in achieving significant property stock market performance and optimal investment decision making in the Nigeria real estate stock market.

The WALLET Study: Financial Decision Making and Key Financial Behaviors Associated with Excess Spending

ABSTRACT Objectives The Wealth Accumulation and Losses in Later life Early Cognitive Transitions (WALLET) study data was used to examine correlates with excess spending in older adults who do and do not have early memory loss. Methods The WALLET study collected detailed financial information from participants’ primary checking account statements (n = 150). Information on participant sociodemographic, health, and disability status, memory functioning, financial decision-making, and financial literacy was also collected. Participants either had no memory problems or early memory loss. Bivariate and multiple regression analyses were conducted. Results The early memory loss group had significantly higher excess spending than those with no memory loss. Financial decision-making and higher-risk financial behaviors were also linked to higher excess spending. Early memory loss was no longer statistically significant after accounting for financial stressors and resources. Conclusions The multidimensional nature of financial capacity assessment has long been known. The WALLET study data is unique, however, in that it demonstrates the links between excess spending with decision-making, early memory loss, and a set of specific financial behaviors. Clinical Implications Real-world assessments of financial management and financial decision-making yield important information about how older adults are managing money and making key financial decisions. Checking account reviews can be used to determine excess spending.

Evolution Model and Driving Mechanism of Urban Logistics Land: Evidence from the Yangtze River Delta

Logistics land is the spatial carrier for the development of logistics enterprises. Its evolution mode and driving mechanism determine the level of high-quality development of the logistics industry, and serve as an important basis for urban planning and territorial spatial planning. This study introduced a Boston consulting group (BCG) matrix and geographically weighted regression (GWR) spatial econometric models to carry out empirical research on the Yangtze River Delta (YRD), in an effort to provide scientific information for evidence-based decision-making by governments and enterprises. The scale and ratio of logistics land (LLS and LLR) in the YRD showed significant spatial heterogeneity and autocorrelation, cities with large logistics land use converging from clusters to belts from 2000 to 2020, and agglomerations with high logistics land ratio (LLR) migrating from inland to coastal areas. Diversified models of logistics land evolution also emerged, such as high scale–high speed cities, low scale–low speed cities, high scale–low speed cities, and low scale–high speed cities. In addition, the driving mechanism of LLS and LLR was very complex, with a great difference in the intensity, nature and spatial effects of the influence of different factors. The inspiration from empirical case studies is urgent to revise the planning norms and clarify the LLS and LLR control standards for logistics land use. Meanwhile, the synergistic development target of the logistics industry in the new era is changing from the manufacturing industry to the commerce and trade industry; the establishment of planning zoning and the designing of differentiated management policies significantly improve the planning applicability.

Health care decision-maker perceptions and trends in the utilization of preapproval information for formulary decision-making.

Preapproval information exchange (PIE) has increased between biopharma companies and health care decision-makers (HCDMs) over the last several years. However, there still exists a gap in what HCDMs need and what biopharma companies are providing. To assess trends in the utilization of preapproval information by HCDMs and identify resources that may best support organizations in evaluating product information for formulary coverage prior to US Food and Drug Administration approval. A double-blinded, web-based survey was fielded to a research panel of HCDMs from FormularyDecisions from May 16, 2022, to May 23, 2022. A total of 30 advisors were invited to take the survey and 17 responded to the survey, with representation largely from health plans (41%), pharmacy benefit managers (24%), and integrated delivery networks (12%) across commercial, Medicare, and Medicaid lines of business. Of the respondents, 47% noted that the availability of preapproval information has shortened the time to make a formulary decision. Almost all respondents (90%) ranked the availability of clinical and economic information in a timely manner to evaluate budget impact as a top benefit for PIE. Respondents noted that Academy of Managed Care Pharmacy (AMCP) preapproval dossiers (88%), AMCP PIE webinars (76%), preapproval presentations/videos (65%), and posters and abstracts of clinical trials results (59%) were the main resources used to facilitate PIE. The availability of preapproval information for HCDMs (particularly content related to anticipated place in therapy and product pricing) has an impact on shortening formulary decision-making timelines.

ROMPER: The RAND/USC OPTIC Method for Policy Expert Ratings

We developed an expert panel approach for identifying expert views on the effectiveness and implementability of population-level policy interventions. ROMPER—the RAND/USC OPTIC Method for Policy Expert Ratings—involves an online, three-round, modified-Delphi process:•Experts rate and comment on policies according to domains of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence-to-Decision framework.•To identify consensus on policy effectiveness and implementability, expert ratings are analyzed using the Inter-Percentile Range Adjusted for Symmetry (IPRAS) technique from the RAND/UCLA Appropriateness Method and visualized using a forest plot. To explain consensus, expert comments are analyzed using reflexive thematic analysis and reported following the Standards for Reporting Qualitative Research.•To provide actionable information for decisionmakers, each policy is summarized in a “Policy Profile” adapted from GRADEPro Evidence-to-Decision tables.We validated ROMPER in two studies that successfully recruited the targeted sample size, retained experts through all three rounds, and examined consensus on which policies are (not) effective and implementable. ROMPER protocols, materials, data, and code are openly available on the Open Science Framework with Creative Commons licensing for replication and reuse. ROMPER provides a validated, replicable, open access approach for eliciting expert views on both policy effectiveness and implementability—and for summarizing (lack of) consensus specifically for policymakers.

Cooperative Decision-Making Approach for Multiobjective Finite Control Set Model Predictive Control Without Weighting Parameters

Finite control set model predictive control (FCS-MPC) has gained increasing popularity as an emerging control strategy for electrical drive systems. However, it is still a challenging task to optimize weighting parameters, as multiple objectives are involved in the customized cost function. A cooperative decision-making approach for FCS-MPC is proposed in this article, to solve the optimization problems with manifold control objectives. By splitting the cost function, the optimization problem underlying multi-objective FCS-MPC is separated into a series of decomposed optimization problems. By doing so, the dimension of the decomposed problem is reduced to one. To collect the information for decision-making, an efficient sorting algorithm is applied for each control objective. The theory behind the cooperative decision-making approach is comprehensively analyzed, to validate both the effectiveness and efficiency of the proposed scheme. More specifically, the highlight is the adaptive mechanism on the number of desired candidates, to obtain a decent performance for torque and flux. The candidate which minimizes the switching frequency is selected as the optimal. The proposed scheme is experimentally verified and compared with the existing FCS-MPC without weighting parameters.

Big Data Analytics: Future of Database

Abstract: Big data analytics is a revolutionary method for processing massive and complicated datasets, analysing them, and drawing important conclusions from them. Big data's introduction has caused a paradigm change in data management and analysis, giving businesses previously unheard-of opportunity to gain insightful information and make informed decisions. This study explores how big data analytics affects how well organization’s function, concentrating on the methods and tools applied across a range of sectors. We undertook an empirical analysis of case studies from businesses in the retail, healthcare, and financial sectors after conducting a thorough assessment of the literature. According to our research, using big data analytics leads to better decision-making, increased operational effectiveness, and a stronger competitive advantage. Data quality, analytics tools, and corporate culture are the main success determinants. However, issues with data privacy and the necessity for qualified data experts make the effective application of big data analytics difficult. This study adds to our understanding of big data analytics' revolutionary potential and offers useful information for practitioners and decision-makers who want to use data to their advantage.

Temporal Characterization of Fire Accidents in China

Based on the fire accident statistics, this study uses the seasonal index method to comprehensively analyze the temporal pattern of fire occurrence in China from 2010 to 2015. The study scientifically reflects that the number of fire occurrences has an extremely close relationship with time, and summarizes the temporal pattern and characteristics of fire occurrences. This study concludes that the high incidence of large fire accidents in China occurs in the time period of 3:00 a.m. to 5:00 a.m. The probability distribution model of the average total number of accidents in each time period was established and the Kolmogorov test was carried out, which shows that the time characteristics of fire accidents in China can be understood with the probability distribution model as a whole. The results of the analysis of the time characteristics of the fire accidents can provide decision-making information for combating fire accidents.