Statistical Analysis Of Historical Data Research Articles

Development of an improved Bayesian network method for maritime accident safety assessment based on multiscale scenario analysis theory

The strait is infrastructure for connecting ports, ensuring the national economy and human life. Assessing maritime safety is crucial for strait operation and maintenance. Current methods over-rely on statistical analysis of historical data, which lacks scientific algorithms and cannot accurately assess safety timeliness. Therefore, this study proposes an improved Bayesian network (BN) model based on the multiscale scenario analysis theory to process to the maritime safety assessment process. Firstly, the maritime safety assessment model is constructed based on the knowledge element theory (KET) and scenario analysis theory (SAT). Secondly, a heuristic learning algorithm is used to construct the BN model of maritime accidents to define scenario elements and key parameters. Thirdly, a scenario simulation is conducted with the Bohai Strait as a case to assessment its emergency capability in catastrophic accidents. This paper successfully developed a maritime accident safety assessment framework using an improved BN algorithm within a scenario analysis perspective, significantly enhancing the accuracy and reliability of safety assessments. This methodology used in this study can be used a tool suitable for the safety assessment of major infrastructure accidents at the national level and the results obtained can serve as a reference for dynamically revising emergency plans.

Research on the Employment Dilemmas and Countermeasures for College Students in the Post-Pandemic Era

Under the impact of the COVID-19 pandemic, the global economy has undergone profound changes, presenting unprecedented challenges to the job market for college graduates. This paper focuses on students in the field of economics and management, employing methods such as surveys, forums, and in-depth interviews to analyze the employment dilemmas and their causes in the post-pandemic era for college students. The study finds that students face a series of issues in aspects like employment information access, work skills, job-seeking conditions, professional qualities, social cognition, and career development. To address these challenges, the paper proposes six countermeasures and work plans, including improving employment services, strengthening school-enterprise cooperation, using data for guidance, conducting quality development, enhancing cognitive internship management, and statistical analysis of historical data. The aim is to enhance students’ employability and professional qualities, better adapting them to the job market in the post-pandemic era.

In Memoriam: Mats Morell

In Memoriam: Mats Morell

A Survey on Deep Learning for Building Load Forecasting

Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focuses on two directions: the required load for a specific area, e.g., a city, and the required load for a building. Building power forecasting is challenging due to the frequent fluctuation of the required electricity and the complexity and alterability of each building’s energy behavior. This paper focuses on the application of Deep Learning (DL) methods to accurately predict building (residential, commercial, or multiple) power consumption, by utilizing the available historical big data. Research findings are compared to state-of-the-art statistical models and AI methods of the literature to comparatively evaluate their efficiency and justify their future application. The aim of this work is to review up-to-date proposed DL approaches, to highlight the current research status, and to point out emerging challenges and future potential directions. Research revealed a higher interest in residential building load forecasting covering 47.5% of the related literature from 2016 up to date, focusing on short-term forecasting horizon in 55% of the referenced papers. The latter was attributed to the lack of available public datasets for experimentation in different building types, since it was found that in the 48.2% of the related literature, the same historical data regarding residential buildings load consumption was used.

Retrospective quality by design r(QbD) for lactose production using historical process data and design of experiments

Quality by Design (QbD) is a popular formal approach for designing, upscaling and optimizing industrial production facilities towards guaranteed quality. To avoid the many costly experiments required for QbD, historical production data may be exploited for optimization instead in what is known as a retrospective QbD (rQbD) study. Current rQbD literature does limitedly discuss data-driven identification of Critical Process Parameters (CPPs) to optimize limited process knowledge availability, and does not cover situations where technical operation limits have not yet been fully explored and/or where parallel equipment (lines) are used. This work presents a new rQbD strategy that addresses these challenges by balancing knowledge that can be obtained from statistical analysis of historical data, together with process experts with a carefully designed set of plant-scale experiments within current operational limits. This novel strategy is demonstrated on a long-running industrial lactose production facility. By digitally and experimentally exploring historical operation variability, we found new operational regimes for this production that may lead to up to 7% product quality improvement, reduced energy consumption and increased process understanding. Although optimizing a specific process by necessity requires a process-specific approach, the way in which we systematically optimize the current process with Hybrid AI (combining available knowledge with new insights from historical data) shows that approaches that are currently used in prospective process upscaling may be modified to be invaluable for optimization of full-scale processes with a long operational history.

An Analysis Of Factors Affecting The Number Of Safety Incidents In Civil Aviation

Aviation is the youngest of the transport industries, yet despite its short history, it is considered one of the most important spheres of transport, both in terms of passenger and cargo transportation. Civil aviation is used by an increasing number of people, and the number of aircraft used by airlines around the world continues to grow. An inherent element that is a particularly important aspect of this mode of transportation is security. In civil aviation, there are numerous dangers associated with events occurring before the flight, during the flight, as well as those associated with the landing process. The events need to be controlled and their causes actively sought and ultimately prevented. The Polish Civil Aviation Authority, as part of the creation of the National Civil Aviation Safety Program, developed the National Safety Plan 2020-2023. The document covers threats identified in the Systemic, European, and National Areas. They are characterized and classified based on the materiality (significance) of the event. The aim of this article is to characterize and analyze selected factors (e.g. collisions with birds, helicopter events) that affect the number of safety incidents in civil aviation. The background of the study was the analysis and synthesis of the literature on the subject, while the main research method was the statistical analysis of historical data on aviation incidents. The data provided in Poland's National Security Plan 2020-2023 were used to distinguish the factors associated with the threats present and synthetically evaluate their impact. The analyses made it possible to identify areas of particular safety risks and form the basis for further detailed research.

Design and Development of Welding Data Processing System

Through database technology, welding data management technology and automatic analysis storage technology, a management system for welding process monitoring is designed and developed in C# environment, which realizes the integrated automatic management of real-time analysis, processing, storage and uploading of welding data; at the same time, it can make statistical analysis of historical data and generate reports. In addition, the system also has scalability, which provides technical support for the construction of “intelligent pipeline and smart pipe network” in the future.

Indirect Fault Diagnosis of Fixed Marine Observing Buoy Based on Bayesian Network

Abstract Fault diagnosis of a fixed marine observing buoy (FMOB) is important for ensuring the reliability of an ocean observing system. Aiming at the difficulty that most FMOB instruments lack direct operational status information in fault diagnosis, an indirect fault diagnosis method based on a Bayesian network (BN) for FMOBs is presented, through which the ocean observing series collected by the FMOB are effectively utilized and analyzed for fault diagnosis of FMOB equipment. The ocean observing series not only reflects the characteristics of the marine environment but also includes the operating status of FMOBs. Based on the analysis of an ocean observing series, the different fault symptoms of FMOBs are confirmed. According to the BN theory, the indirect fault diagnosis model is developed. The model structure is defined based on a comprehensive analysis of the causal relationships among faults and their symptoms. The model parameters are estimated according to experts' knowledge and statistical analysis of historical data. Three typical fault cases of FMOBs are selected for verifying the method. The results show that the BN-based indirect fault diagnosis method is reliable and effective for fault diagnosis of an FMOB even when the fault diagnosis evidence is incomplete and uncertain. Furthermore, this approach can work in a real-time way and help technicians to take effective measures to avoid big losses.

Development of a hybrid framework for inventory leanness in Technical Services Organizations.

Inherent uncertainties in demand and supply make it problematic for supply chains to accomplish optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. To cope with erratic demands, organizations have to maintain excessive inventory levels, sometimes taking up to one-third of an organization’s annual budget. The two most pressing concerns to handle in inventory management are: how much to order and when to order. Therefore, an organization ought to make the correct and timely decisions based on precise demand information to avoid excessive inventory accumulation resulting in enhanced competitive advantage. Owing to the significance of inventory control and analysis, this paper reports on developing and successfully implementing a hybrid framework for optimum level inventory forecasting in Technical Services Organizations. The proposed framework is based on a case study of one of Pakistan’s leading Technical Services Organization. The paper presents a statistical analysis of historical data and a comprehensive fault trend analysis. Both these analyses set a solid foundation for the formulation of a comparative analysis matrix based upon price and quantity based analysis of inventory. Finally, a decision criterion (Forecasting Model) is proposed using three primary forecasting techniques with minimum error calculations. The study’s finding shows a forecast error of 142.5 million rupees in the last five years, resulting in the accumulation of more than 25 thousand excessive inventory stock. Application of price and quantity based analysis identifies that 65% of the annual budget is significantly dependent upon only 9% (in terms of quantity) of "High Price and Small Quantity" Items (HS). These HS items are forecasted through three different forecasting methods, i.e., Weighted Moving Average, Exponential Smoothing, and Trend Projection, with Minimum Absolute Deviation to significantly reduce the forecasting error while predicting the future required quantity. The research work aims to contribute to the inventory management literature in three ways. First, a new comparative analysis matrix concept for identifying the most critical items is introduced. Second, a Multi-Criteria Forecasting Model is developed to capture a wide range of operations. Third, the paper suggests how these forecasting criteria can be integrated into a single interactive DSS to maintain optimum inventory level stock. Even though the DSS framework is based on data from a single organization, the application is expected to manage inventory stock in a wide range of manufacturing and services industries. This study’s proposed hybrid framework is the first of its kind that encapsulates all four dimensions of inventory classification criteria, forming a multi-criteria hybrid model within a DSS framework.

A hybrid mathematical programming model and statistical approach for bidding price decision in construction projects

Bidding price decision is a key issue for the contractors and construction companies. The success/failure of the contractors in competitive biddings is directly dependent on their bidding strategy. This paper aims to develop a hybrid statistical and mathematical modeling approach for determining the optimum bidding price in construction projects. By statistical analysis of historical data, some uncertain parameters like the number of competitors and the cost of the project are estimated. Then, a scenario-based mathematical model for bidding price decision is proposed. In order to present a model in more accordance with the real-world situations, factors like risk, minimum acceptable rate of return (MARR) and opportunistic behavior are taken into account. In order to achieve an insensitive solution to the change in the realization of the input data from the scenarios, a robust mathematical model is used. The performance of the model is evaluated through some numerical problems. Furthermore, sensitivity analysis of the key parameters and robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model's effectiveness in real-world situations, a case study is analyzed by the proposed approach. Numerical results show that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model in a real-world situation.

Comparative Analysis of the Performance between Smart ßETA Strategies with Active and Passive Managed ETF; To What Extent They Outperform Overall? An Empirical Study of the Performance Based on a Statistical Analysis of Historical Data between Multi-Asset Smart ßeta, Active and Passive ETF

The Exchange Traded Fund (ETF) industry has gained much attractivity among investors in the last decade but remains still a shady market that has some technicities. This paperwork aims to address the origin of this concept and its recent tendencies by describing the main features and theoretical framework that surround it. To address the main research question, we will proceed by establishing a comparative study between Smart ßeta ETF with actively managed ETF instruments and passive ETF instruments to capture if there is an outperformance made by the Smart ßeta strategies. The primary approach for this research is to compute the overall daily returns over a 1-year timeframe1. Several ETF instruments will complete our study group with fifty-one different ETF funds. The overall risk-adjusted returns are computed according to the three main ratios: Sharp, Sortino and Jensen ratio. The first part will serve as a literature review of the critical theoretical concepts to enable the reader to achieve a good base understanding of the topic. The second part will be based on an empirical study to present our findings on this subject. In the end, we will offer a general conclusion that rewinds all the significant contributions on this topic with some recommendations. After studying the performance of fifty-one ETFs, we concluded, as in previous studies, that Smart Beta funds do not offer a risk-adjusted performance superior to active and passive strategies. NB: The data could be altered by the global COVID-19 pandemic and may be biased in term of representativity of the overall state of the market., inducing higher volatility than could impact the neutrality of this analysis.

Research of Economic Operation and Control Strategy for PV-Storage-Charging Integrated Power Station

This paper proposes an economic operation mode and control strategy for an PV-storage-charging integrated power station. By optimizing the capacity configuration and analyzing the mechanism relationship of its various operating modes, this paper establishes the system model including PV system power, energy storage SOC and charging spot power, and gives its economic operation constraints. Based on statistical analysis of historical data, the strategy optimizes the operation mode dynamically to control the grid power flow and balance the system power supply and load. Simulation results verify the economic and stable operation of this strategy.

A methodology for reliability assessment and prognosis of bearing axial cracking in wind turbine gearboxes

This article describes an interdisciplinary methodology to calculate the probability of failure for bearing axial cracking, the dominant failure mode in the intermediate and high-speed stages of many wind turbine gearboxes. This approach is mainly a physics-domain method with needed inputs from the data domain. The gearbox and bearing design along with operations data and component failure records from a wind power plant provide the input to physics-based models and define axial cracking damage metrics. The physics-domain models predict the bearing loads and sliding velocities, which are the essential elements for quantifying the accumulated frictional energy. Both accumulated frictional energy and electrical energy generation are proposed as damage metrics for bearing axial cracking. A first-order reliability method is then used to compare the proposed damage metrics to failure threshold functions and calculate the probability of failure of each individual bearing. Although the probability of failure for the failed turbines is not separated from the population, a feature engineering analysis shows the potential of frictional energy as a damage metric when combined with roller loads, bearing sliding speed, lubricant type, and terrain features. Through statistical analysis of historical data, the proposed methodology enables reliability assessment of axial cracking in individual wind turbine bearings and connects the reliability forecast with turbine design and operations.

Methodology for Optimal Solution Search during the Freight Train Schedule Creation

Punctuality and the energy efficiency are the most significant criteria of immediate activity of every transport system. The problem of improving these characteristics is unfolded in this paper. The task of optimization the punctuality and energy efficiency is solved by adjusting departure headways and running times. Methodology of schedule parameters search is based on the statistical analysis of historical data. The study reveals a number of regularities that are characterizing the freight train flow at the main railway line. First of all, we note the important dependences between the punctuality characteristics and the chain position of the train.

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

Modelling ship collision risk based on the statistical analysis of historical data: A case study in Hong Kong waters

Collision, as a common type of ship accidents, leads to serious property loss and personal injury. In this paper, a new framework of quantitative risk assessment is proposed by quantifying the probability and the corresponding consequence based on the historical accident data. Firstly, the consequences of ship collisions are quantified and classified using an equivalent consequence method. Secondly, a decision tree model is established to analyse the impact of ship attributes on the collision consequences. The main ship attributes contributing to collision are determined, based on which, a BP neural network model is developed to estimate the probabilities of the different consequences. Thirdly, the collision risk is predicted by integrating the collision probabilities with the corresponding consequences. Fourthly, a case study in Hong Kong waters is investigated and the results are compared with the available references to validate the proposed framework. The new model can be used to assess present risks to plan preventive measures for the potential collision accidents.

The fertilization effect of global dimming on crop yields is not attributed to an improved light interception.

Global dimming, a decadal decrease in incident global radiation, is often accompanied with an increase in the diffuse radiation fraction, and, therefore, the impact of global dimming on crop production is hard to predict. A popular approach to quantify this impact is the statistical analysis of historical climate and crop data, or use of dynamic crop simulation modelling approach. Here, we show that statistical analysis of historical data did not provide plausible values for the effect of diffuse radiation versus direct radiation on rice or wheat yield. In contrast, our field experimental study of 3years demonstrated a fertilization effect of increased diffuse radiation fraction, which partly offset yield losses caused by decreased global radiation, in both crops. The fertilization effect was not attributed to any improved canopy light interception but mainly to the increased radiation use efficiency (RUE). The increased RUE was explained not only by the saturating shape of photosynthetic light response curves but also by plant acclimation to dimming that gradually increased leaf nitrogen concentration. Crop harvest index slightly decreased under dimming, thereby discounting the fertilization effect on crop yields. These results challenge existing modelling paradigms, which assume that the fertilization effect on crop yields is mainly attributed to an improved light interception. Further studies on the physiological mechanism of plant acclimation are required to better quantify the global dimming impact on agroecosystem productivity under future climate change.

Effect of Tornado Outbreaks on Morbidity and Mortality in Texas

Introduction:In the United States, tornadoes are the third leading cause of fatalities from natural disasters1. To aid prevention and mitigation of tornado-related morbidity and mortality, improvement in standardizing tornado specific threat analysis terminology was assessed. The largest number of tornado-related fatalities has occurred in the state of Texas for over a hundred years. The occurrence of tornadic clusters or “outbreaks” has not been formally standardized. The concept of “tornado outbreaks” is better defined and its role in fatality mitigation is addressed in this Institutional Review Board (IRB) approved study.Aim:To understand the role of “tornado outbreaks” related clusters in Texas in relationship to morbidity and mortality.Methods:This IRB approved (IRB2017- 0507) research study utilized GIS tools and statistical analysis of historical data to examine the relationship between tornado severity (based on the Fujita Scale), the number of tornadoes, and the trends in morbidity and mortality. This study was funded in part from The National Science Foundation grant (NSF Grant #1560106) in support of the CyberHealthGIS Research Experience for Undergraduates (REU).Results:A statistically significant difference was demonstrated between the severity of a tornado and related morbidity and mortality during “tornado outbreaks” in Texas during a defined 30-year period.Discussion:Understanding the role and discerning the impacts of “tornado outbreaks” as related to tornado severity has critical implications to disaster preparedness. Applications of this conclusion may improve shelter planning/preparation, timely warning, and educating the at-risk public. Subsequently, examining the likelihood and improved descriptions of “tornado outbreaks” may aid in reducing the number of tornado-related injuries and fatalities nationally.

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

A martingale-based temporal analysis of pre-earthquake anomalies at Jiuzhaigou, China, in the period of 2009-2018

Many studies on the relationship between outgoing longwave radiation (OLR) data observed by National Oceanic and Atmospheric Administration Satellites (NOAA) with strong earthquakes are based on a certain zone or a couple of earthquakes. But it’s hard to know if the algorithm works by looking at just one earthquake. In this paper, after algorithm analysis of OLR signals based on martingale theory during the 10 years from 2009 to 2018, a time series analysis of re-anomaly screening for the 10 years’ data is proposed. The experimental results show that this method can be more effective in the statistical analysis of historical data and further improve the reliability of prediction.