Actual Results Research Articles

A model for visual evacuation of people considering the effects of gravity

This paper improves the visual change-based personnel evacuation model by considering the evacuees’ gravity. Specifically, first, the new model incorporates the gravity formula in the model’s mechanic part to consider the influence of gravity. Second, the new model involves rules for determining the visual range of personnel moving in the stairwell. Third, the proposed model investigates the influence of the angle and width of the stairwell, the number of people, and other factors during personnel evacuation under the influence of gravity. The model is developed in Python and is compared with actual results, revealing that the proposed model is more realistic considering the evacuation time compared to current models. Indeed, under a fixed number of people, when the stairwell angle is less than 34°, the evacuation time decreases as the angle increases, and when the stairwell angle exceeds 34°, the evacuation time is almost unchanged. Additionally, under a fixed number of evacuees, the evacuation time decreases as the width of the stairwell increases, and due to stairwell width space redundancy, the evacuation time tends to stabilize. The results of the new model research provide reference for the design of building safety evacuation, thereby improving the safety of buildings.

Effects of textile type on the flexural properties of prefabricated hollow-core slabs strengthened with PVA fibers-improved TRM

In this study, the utilization of textile-reinforced mortar (TRM) in the strengthening and rehabilitation field is investigated. To regulate crack width, polyvinyl alcohol (PVA) fibers were incorporated into the TRM matrix. Nine prefabricated hollow-core slabs strengthened with TRM, along with one reference slab, were subjected to static loading using a 4-point bending apparatus. The experimental variables consisted of the type of textile (glass, basalt, and carbon) and the number of textile layers (one, two, and three layers). The experimental findings demonstrated that the inclusion of PVA fibers improved the TRM by effectively controlling crack width and increasing energy absorption capacity. Among the textile types, carbon TRM-strengthened slabs exhibited the highest flexural capacity. This capacity increased by 74%, 119%, and 132% for slabs strengthened with one, two, and three layers of textile, respectively. Glass TRM-strengthened slabs demonstrated superior deformability, with an increase rate ranging from 202% to 331%. Finally, to predict the flexural capacity and deflection of the tested slabs, a theoretical analysis was conducted and validated by comparing the theoretical predictions to the actual test results for accuracy and reliability.

Eco-friendly road construction: Harnessing fly ash and waste marble powder for sustainable road construction using a fuzzy logic assessment

<p>More than seventy percent of mineral gets wasted during mining and polishing processing, and all this dumped waste threatens the aquifer. Utilizing this waste in construction works helps to reduce the environmental effects. This research aims to protect the environment by implementing mineral waste collected from neyveli lignite fly ash with waste marble waste powder in road-base construction. Accessing the different required combinations of mineral admixtures through laboratory tests poses a challenge. This study identify a suitable combination of fly ash with waste marble powder for assessing the compaction properties and compressive strength for road base construction through fuzzy logic. A fuzzy model was created by using MATLAB software. It consists of two input and four output parameters, fly ash and marble waste powder are the input parameters with four different combinations, and the output factors are dry unit weight, water content, unconfined compressive strength and CBR. The fuzzy model was evaluated with laboratory test results. The study concluded that only a minimum percentage of deviations between the predicted strength and actual results from the laboratory. In future research, this study helps to reduce the time and cost of laboratory tests for different combinations.</p>

Research on the Scaling Mechanism and Countermeasures of Tight Sandstone Gas Reservoirs Based on Machine Learning

The Sulige gas field is a typical “three lows” (low permeability, low pressure, and low abundance) tight sandstone gas reservoir, with formation pressures often characterized by abnormally high or low pressures. The complex geological features of the reservoir further deviate from conventional understanding, impacting the effective implementation of wellbore blockage removal measures. Therefore, it is imperative to establish the wellbore blockage mechanism, prediction model, and effective prevention measures for the target area. In this study, based on field data, we first experimentally analyzed the water quality and types of blockage in the target area. Subsequently, utilizing a BP neural network model, we established a model for predicting the risk of wellbore blockage and analyzing mitigation measures in the target reservoir. The model’s prediction results, consistent with on-site actual results, demonstrate its reliability and accuracy. Experimental results show that the water quality in the target area is mainly a CaCl2 type, and the predominant scales produced are CaCO3 and BaSO4. Model calculations reveal that temperature, pressure, and ion concentration all influence scaling, with BaSO4 more influenced by pressure and CaCO3 more influenced by temperature. Under the combined effect of temperature, pressure, and ion concentration, different types of scales exhibit distinct trends in scaling quantity. Combining scaling quantity calculations with wellbore contraction ratios, it was found that when the temperature, pressure, and ion concentration are within a certain range, the wellbore contraction rate can be controlled below 4%. At this point, the wellbore scaling risk is minimal, and preventive measures against wellbore scaling can be achieved by adjusting production systems, considering practical production conditions. This study investigates the mechanism of scaling in wellbores of tight sandstone gas reservoirs and proposes a cost-effective scaling prevention measure. This approach can guide the prediction of scaling risks and the implementation of scaling prevention measures for gas wells in tight sandstone reservoirs.

Who would be the winner? A prognostic nomogram for predicting the benefit of postoperative radiotherapy ± chemotherapy in patients with locally advanced gastric cancer: TROD-02-01 study

We aimed to develop a basic, easily applicable nomogram to improve the survival prediction of the patients with stage II/III gastric cancer (GC) and to select the best candidate for postoperative radiotherapy (RT). In this multicentric trial, we retrospectively evaluated the data of 1597 patients with stage II/III GC after curative gastrectomy followed by postoperative RT±chemotherapy (CT). Patients were divided into a training set (n=1307) and an external validation set (n=290). Nomograms were created based on independent predictors identified by Cox regression analysis in the training set. The consistency index (C-index) and the calibration curve were used to evaluate the discriminative ability and accuracy of the nomogram. A nomogram was created based on the predictive model and the identified prognostic factors to predict 5-year cancer-specific survival (CSS) and progression-free survival (PFS). The multivariate Cox model recognized lymph node (LN) involvement status, lymphatic dissection (LD) width, and metastatic LN ratio as covariates associated with CSS. Depth of invasion, LN involvement status, LD width, metastatic LN ratio, and lymphovascular invasion were the factors associated with PFS. Calibration of the nomogram predicted both CSS and PFS corresponding closely with the actual results. In our validation set, discrimination was good (C-index, 0.76), and the predicted survival was within a 10% margin of ideal nomogram. In our relatively large cohort, we created and validated both CSS and PFS nomograms that could be useful for underdeveloped or developing countries rather than Korea and Japan, where the D2 gastrectomy is routinely performed. This could serve as a true map for oncologists who must make decisions without an experienced surgeon and a multidisciplinary tumor board.

Prediction of bridge structure deformation and strain based on dynamic testing and intelligent algorithms

The field load test is a direct and effective method for evaluating the performance of bridge structures. However, existing bridge field static load tests are costly and inefficient; moreover, they obstruct traffic and cause unavoidable damage to the bridge structure. As an alternative the the static load test, a random model update method based on bridge dynamic load tests and the Bayesian inference is proposed in this paper. The bridge static load test results were predicted with a high accuracy. To speed up the Bayesian method to infer the posterior probability density of the updated parameters, the Gaussian process was used in place of the finite element model, and the Bayesian inference used the Markov chain Monte Carlo method based on the delayed rejection adaptive Metropolis algorithm. First, the parameters to be modified for the bridge structure analysis model were determined based on the global sensitivity analysis method. Second, a uniform design sampling method was used to establish the Gaussian process optimization model to update the random model of the bridge structure. Finally, a reinforced concrete truss arch bridge was used to verify the correctness of the static load results of the bridge predicted by the random model update method based on dynamic load testing and Bayesian inference. The research results reveal that the prediction results of the bridge static load test based on the dynamic load test and Bayesian inference method agree with the actual test results.

Determining the impact of the CRM system on the financial indicators of the enterprise using econometric methods

CRM systems help to improve customer relationship management, provide an opportunity for more accurate sales forecasting, optimization of marketing campaigns and increase in overall customer satisfaction. The purpose of the paper is to determine the impact of the CRM system on the financial indicatots of the enterprise. The study is based on the methods of econometric modeling, system analysis, financial analysis, works of domestic and foreign scientists, materials from periodicals, and data from the financial statements of the enterprise. A correlation analysis was conducted between the selected independent indicators to determine the degree of their relationship. Based on this, it was determined which factors have the highest correlation with each other, which made it possible to focus on the most important aspects of financial management. A regression multivariate model of the relationship between such factors as inventory, accounts receivable, sales costs, other operating expenses, and the resultant indicator - net working capital (NWC) - is built. The significance of the model as a whole and the significance of the regression coefficients are determined. It is predicted that the net working capital of the enterprise in 2023 will be less than in 2022, which is due to factors such as a high level of accounts receivable, sales costs and other operating expenses. After the integration of the CRM system, the company implemented a number of measures aimed at improving the management of these factors. In particular, the company introduced automation of receivables management processes, optimized sales and other operating expenses. As a result of these measures, the company's NWC increased at the end of 2023. This is confirmed by both the econometric model and the actual results.

Modeling and prediction of tennis matches at Grand Slam tournaments

In this manuscript, different approaches for modeling and prediction of tennis matches in Grand Slam tournaments are proposed. The data used here contain information on 5,013 matches in men’s Grand Slam tournaments from the years 2011–2022. All regarded approaches are based on regression models, modeling the probability of the first-named player winning. Several potential covariates are considered including the players’ age, the ATP ranking and points, odds, elo rating as well as two additional age variables, which take into account that the optimal age of a tennis player is between 28 and 32 years. We compare the different regression model approaches with respect to three performance measures, namely classification rate, predictive Bernoulli likelihood, and Brier score in a 43-fold cross-validation-type approach for the matches of the years 2011 to 2021. The top five optimal models with highest average ranks are then selected. In order to predict and compare the results of the tournaments in 2022 with the actual results, a comparison over a continuously updating data set via a “rolling window” strategy is used. Also, again the previously mentioned performance measures are calculated. Additionally, we examine whether the assumption of non-linear effects or additional court- and player-specific abilities is reasonable.

Research on Key Technologies for Container Ship Loading Test Based on 3D Laser Scanning

_ Traditional accuracy check methods for cargo hold in container ships rely solely on manual and visual operations, which are time-consuming and resource-intensive. Addressing the challenge of extracting and analyzing key data, such as cell guides and container pedestals, from large-scale point clouds obtained through three-dimensional (3D) laser scanning in container ship trial runs, this paper proposes an algorithmic framework based on 3D laser scanning. Building upon this framework, an improved coordinate-axis filtering RANSAC algorithm is employed to optimize the extraction of cell guide planes. Additionally, an algorithmic process based on Bhattacharyya distance is utilized to automatically extract container pedestal point clouds. Furthermore, a combination of the genetic algorithm and the ICP algorithm is proposed to achieve the fitting of the container pedestal edge contour through point cloud registration. Experimental results demonstrate the consistency between the extracted cell guide and container pedestal data and the actual results, indicating the high practical value of the proposed methodology. Keywords container ship loading test; cargo hold accuracy check; 3D point cloud processing

Calculation model for bearing capacity of steel-CFRP composite pipeline under internal pressure

The Carbon Fiber Reinforced Polymer (CFRP)-wrapped steel pipeline system is emerging as an alternative for the repair and long-distance transportation of oil and gas. This system is recognized as a composite material. Under internal pressure, the pipe predominantly undergoes circumferential stretching. This study derives the tensile constitutive model of the steel-CFRP composite material using uniaxial tensile tests and the rule of mixtures. Subsequently, utilizing this newly calibrated constitutive model, a computational model to assess the internal pressure capacity of the steel-carbon fiber composite pipe was established. Finally, a comparison between the theoretical outcomes of the computational model and the actual internal pressure test results revealed a high degree of correlation. This holds substantial significance for the design and practical implementation of this novel composite pipeline system.

Gaining an Understanding of DevOps from its Enablers to Its Impact on Performance

Despite these efforts' merit, academics have had trouble keeping up with the radical shifts in how software is created and supplied to clients. DevOps has revolutionized the way IT businesses are intended to operate as the result of years of hard effort and improvement to software delivery methods, techniques, and philosophies. Despite its widespread use and the positive effects, it has had on IT businesses' bottom lines, few people outside of the industry really understand what it is, how it operates, or if it can genuinely lead to better IT performance. This study provides a methodology that bridges the gap between these macro-level elements and the actual results of IT departments by focusing on the enabling components of DevOps, such as technological and management skills, and IT culture. Moreover, this study suggests the values of a perfect DevOps organization, which have a profound impact on IT Outcomes when they are in harmony with the firm's Delivery Approach. Information technology (IT) experts with prior DevOps experience were used to compile the survey results. In all, 176 American respondents provided their information. This alignment, in turn, has a substantial impact on IT Outcomes. This study adds not only to the growing body of literature on DevOps and software delivery, which is essential to the success of any IT firm, but also to the development of important underlying theory in these areas.

Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the collaborative Kriging spatial interpolation method based on Kriging spatial interpolation is adopted to process the rainfall data into multi-index fused surface data. The rainfall data after spatial interpolation are used as the input sample data of the support vector machine early warning model, and the optimal parameters of the support vector machine are calculated by the sea squirt algorithm, and then the debris flow early warning results are output. After experimental analysis, the model can obtain rainfall surface data. After calculation by the model, the accuracy of the early warning probability of debris flow is improved, and the early warning result is consistent with the actual result of debris flow.

An Improved VO Method for Collision Avoidance of Ships in Open Sea

In order to effectively deal with collisions in various encounter situations in open water environments, a ship collision avoidance model was established, and multiple constraints were introduced into the velocity obstacle method, a method to determine the ship domain by calculating the safe distance of approach was proposed. At the same time, the ship collision avoidance model based on the ship domain is analyzed, and the relative velocity set of the collision cone is obtained by solving the common tangent line within the ellipse. The timing of starting collision avoidance is determined by calculating the ship collision risk, and a method for ending collision avoidance is proposed. Finally, by comparing the simulation experiments of the improved algorithm with those of the traditional algorithm and the actual ship experiment results of manual ship maneuvering, it is shown that the method can effectively avoid collisions based on safe encounter distances that comply with navigation experience in different encounter situations. At the same time, it has better performance in collision avoidance behavior. It has certain feasibility and practical applicability.

Integrated multivariate digital representation model for rock microstructure

Unravelling the intricate relationships between mineralogy, defects, and other microstructural features during the rock fracturing process is challenging due to the inherent invisibility of rocks. In this study, we propose a novel integrated multivariate digital representation model that discretizes the rock into differential elements and applies the Matter-Element Theory to represent the microstructure. This model successfully captures the complexity and diversity of rock microstructural properties, enabling transparency of the internal rock structure. CT scanning, mechanical loading, and rock-mineral identification experiments were performed on rock samples to validate the model. Microstructural data, including pores, fractures, and minerals at different stages of failure, were obtained and integrated into the model. The results show that the integrated digital model is an accurate representation of the distribution states of internal microstructural features within rocks. The influence of microstructural features on rock fracture was investigated by extracting microstructural data corresponding to specific fracture evolution by comparing the model output with actual rock fracture results. This research provides a theoretical basis for the extraction of universal laws and characteristics, facilitating the comprehensive investigation of diverse microstructural features. In addition, it will enable the dynamic assessment of rock fracture and the early warning of rock hazards in engineering applications.

Research on the data-driven inter-well fracture channeling identification method for shale gas reservoirs

The issue of inter-well fracture channeling in shale reservoirs is becoming increasingly prominent, significantly impacting the production of nearby wells. Therefore, it is crucial to accurately determine the location of fracture channeling in order to effectively design anti-channeling measures and optimize reservoir fracturing. In this paper, a data-driven fracture propagation model and fracture channeling identification method are established. In the model, the fracture morphology is fitted by the bottom-hole flowing pressure constraint. The bottom-hole flowing pressure (pwp) calculated by the construction pump pressure and the fluid wellbore flow is mainly considered as the real solution. The bottom-hole flowing pressure (pwf) calculated by the construction displacement and the fracture morphology is used as the constraint variable, and the fracture parameters are changed using the SPSA optimization algorithm to realize the dynamic fitting of the fracture morphology. In order to accurately describe the position of fracture channeling, the seepage radius of the fracture boundary is introduced to calculate the volume of fracture reconstruction. The volume coefficient of repeated reconstruction is used as the quantitative evaluation index of fracture channeling. This approach enables an accurate depiction of the position of fracture channeling. Finally, the model method is applied to the actual fracture channeling well. The study shows that the fracture length of the well inversion is greater than the well spacing, and there is a possibility of inter-well fracture channeling. The volume coefficient of repeated reconstruction is 8%, similar to the critical fracture channeling index. There are nine fracturing sections with fracture channeling, and the maximum fracture channeling coefficient is 14.2%. This paper successfully explains the reason for cross-well fracture channeling, and its conclusion aligns with the actual monitoring results. The proposed method in this paper effectively identifies the location of fracture channeling and offers guidance for optimizing channeling prevention in subsequent designs.

Method to Forecast the Presidential Election Results Based on Simulation and Machine Learning

The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation in forecasting tasks is promising because the former presents good results but requires a good balance between data quantity and quality, and the latter supplies said requirement; nonetheless, each technique has its limitations, parameters, processes, and application contexts, which should be treated as a whole to improve the results. This study proposes a systematic method to build a model to forecast the PERs with high precision, based on the factors that influence the voter’s preferences and the use of ML and Simulation techniques. The method consists of four phases, uses contextual and synthetic data, and follows a procedure that guarantees high precision in predicting the PER. The method was applied to real cases in Brazil, Uruguay, and Peru, resulting in a predictive model with 100% agreement with the actual first-round results for all cases.

A special target detection called You Only Looks Once

This paper proposes a unique target detection called You Only Look Once (YOLO for short) and a recognition method. Unlike the previous idea of many classifiers with object detection functions, the object detection box is set as a spatially separated bounding box and the regression problem of related class probabilities is realized. The neural network model can directly scan the entire image during testing, predicting bounding boxes and class probabilities from the complete picture. At the same time, because the whole detection channel relies on a single neural network, it is more straightforward and concise when upgrading and updating. The unified architecture used in this paper is fast, with smaller versions of the YOLO model processing a staggering 155 frames per second. At the same time, it has also achieved excellent results in mAP. Compared with other detection systems, YOLOv3 has optimized many past problems, including positioning errors. At the same time, the probability of predicting false detections in the absence of false detections is small. Finally, like the YOLO base model, YOLOv3 may produce significant errors when processing abstract works of art and images with a large number of small objects. However, its actual results are still better than detection methods such as Region -Convolutional Neural Networks (R-CNN).

Mechanism for leaching of fluoride ions from carbon dross generated in high-temperature and low-lithium aluminum electrolytic systems

Carbon dross, a hazardous solid waste generated during aluminum electrolysis, contains large amounts of soluble fluoride ions for the main components of the electrolyte (such as Na3AlF6 and NaF). Response surface methodology (RSM) was used to investigate the mechanism for fluoride ion leaching from carbon dross via water leaching, acid leaching and alkali leaching, and the kinetic and thermodynamic principles of the leaching process were revealed. The RSM predicted the optimum conditions of water leaching, alkali leaching and acid leaching, and the conditions are as follows: temperature, 50 °C; shaking speed, 213 r·min-1; particle size, 0.075 mm; shaking speed, 194 r·min-1; liquid-solid ratio, 12.6 mg·L-1; sodium hydroxide concentration, 1.53 mol·L-1; liquid-solid ratio, 25.0 mg·L-1; sulfuric acid concentration, 2.00 mol·L-1; and temperature, 60 °C，and actual results which were almost consistent with the predicted results were gained. The fluoride ions in the alkaline and acid leaching solutions were mainly the dissociation products of fluorides such as Na3AlF6, Na5Al3F14 and CaF2, as indicated by thermodynamics calculations. In particular, the fluoride compounds dissolved in alkali solution were Na3AlF6, Na5Al3F14, AlF3, ZrF4, K3AlF6, while the acid solution could dissolve only Na3AlF6 and CaF2. The leaching kinetics experiments showed that the leaching rate fit the unreacted shrinking core model [1–2/3α-(1-α)2/3 =kt] and that the leaching process was controlled by internal diffusion. This study provides theoretical guidance for the removal of soluble fluoride ions from carbon dross and will also assist in the separation of electrolytes from carbon dross. Environmental ImplicationCarbon dross, a hazardous waste generated during the aluminum electrolysis production process, contains a large amount of soluble fluoride. Improper storage will lead the fluoride ions pollution in soil, surface water or groundwater under the direct contact between carbon dross and rainfall, snow or surface runoff. The influence of wind will cause carbon dross dust to pollute further areas. With the human body long-term contact with fluoride ion contaminated soil or water, human health will be seriously harmed.

Yakma Alın Kaynağıyla Birleştirilmiş S355jr Yapı Çeliklerinde ITAB’ın Ansys ile Analizi

In this study, we focus on predicting the HAZ, which is a region whose mechanical properties are not clearly known, by modeling factors such as temperature input and heat turbulence, which may have different properties in the welding region. This is important for the safety of welded structures, because if the mechanical and thermal properties of these structures, which are used in many areas, can be determined in advance, they can be used more safely. In the study, flash butt welding method was preferred and holes were opened at regular intervals in the samples that were brought to appropriate dimensions for the welding process, and temperature values were measured and recorded using thermocouples and data loggers during the welding process. Additionally, the maximum temperature reached in the welding area was measured using a thermal camera during welding. Microstructural changes occurring in the joining regions of the welded test pieces were examined using light microscopy and scanning electron microscopy (SEM). Micro hardness measurements were made to determine mechanical properties. Using the ANSYS program, three-dimensional models were created using the finite element method under the same experimental conditions and compared with the experimental results. As a result of this comparison, it was seen that the findings were compatible with the actual results. For this reason, it is emphasized that the use of simulation programs is important to predict the properties of welded structures and possible problems.

Assessment on Strength and Stiffness Properties of Aged Structural Timber

Despite the growing popularity of wood and wood-based products in the construction industry, there has been insufficient focus on assessing the condition, preservation, and potential reuse of existing timber. While numerous standards evaluate the quality of freshly sawn timber, there is currently no standardized system for assessing the strength properties of aged and reused timber. The lack of these guidelines is also one of the reasons the results obtained in numerous research are often fluctuating, and we cannot draw clear conclusions. The matter is further complicated by the lack of data on old in-situ wood and its exploitation, which would help to evaluate its condition. Consequently, there is a real practical need to assess the condition of old timber to avoid unnecessary demolition and the loss of valuable and structurally sound building material. What sets this study apart from others is that, in addition to destructive testing, the 4-point non-destructive (ND) bending tests were conducted on all four faces of test specimens. This provided an opportunity to assess the wood visually and then find connections to associate external characteristics with real properties. This methodology aimed to determine whether it is feasible to visually assess the most practical way to use wooden elements in construction. If this question arises, which face of the beam would be better suited for the tension side and which for the compression side? The old timber used in testing originated from an old library building located on Vaksali Street, Tartu, Estonia and is estimated to be about 120 years old. This paper investigates and compares the collected data with a Nordic standard for grading fresh-sawn timber and two established Italian standards for visually assessing aged timber. This comparison contributes to developing a standardized framework for future visual assessments. ND and destructive four-point bending tests were performed to validate and find appropriate visual characteristics to determine the strength and stiffness of the timber elements. The primary goals of this study were first to compare the results obtained from existing ND methods with actual results and secondly to provide guidelines for better visual grading of wood in the future, based on Nordic Standard INSTA 142 (2010) and Italian standards UNI 11119 (2004) and UNI 11035 (2010) Contrary to previous research conclusions, the visual assessment results yielded unexpected outcomes. The results show that the grading standards significantly underestimated the real strength of the wood, and even more, none of the visual assessments overestimated the real strength of the specimens. Therefore, based on prior research and the findings derived from this study, there is evident a substantial potential for extensive development and optimization within this field.