Welded joints in rail steel structures are typically assessed for fatigue using two different stress range approaches: nominal stress range and hot-spot stress range when using SN methods. The nominal stress range is a traditionally simplified method that provides a conservative estimation but lacks accuracy in considering stress concentrations. On the other hand, the hot-spot stress range method is a more advanced and refined approach that offers a more precise evaluation of stress concentration, making it suitable for complex geometries. The BS7608-2015 British standard, Guide to Fatigue Design and Assessment of Steel Products, incorporated the hot-spot method for evaluating weld joints, especially when using numerical methods such as Finite Element Analysis (FEA). The weld classes are now categorically defined for both nominal and hot-spot approaches in new introductions, whereas earlier, it was based on the nominal stress approach only. Choosing the appropriate stress method depends on various factors, including the weld joint geometry, stress orientations, loading conditions, the desired level of accuracy, and primarily the available SN curve data for predicting fatigue damage. The work presented in this paper explores the hot-spot stress approach for determining stress in weld fatigue assessment for Rail Track Maintenance Equipment. The identified welds were assessed for variation in hot-spot stress on different mesh types, weld modeling techniques, and their effect on the fatigue damage factor using IIW and BS7608 guidelines. The joints under study were F2 and D class with nominal and hot-spot stress assessment, respectively, as per BS7608. These are more common weld joints in structural evaluations of rail equipment. The hot-spot approach for stress variation was studied on smaller models first. Subsequently, the approach was applied to assess the weld fatigue in rail equipment, and the results were compared with those obtained using the nominal stress approach.

The present work is focused on the simulation of Casson (non-Newtonian) nanofluid flow over an inclined stretching sheet. The study considers the influence of an imposed magnetic field, heat source/sink, thermal radiation and chemical reaction under the multi slip effects. The study includes the application of wall suction/injection and Navier's first-order slip to analyse the velocity, temperature, and concentration at the wall. The governing equations have been transformed into nonlinear ordinary differential equations (ODEs) with similarity transformations. By employing the homotopy analysis method (HAM), we have successfully derived the numerical solution for the nonlinear ordinary differential equations (ODEs) and their corresponding boundary conditions. The impact of various parameters on the velocity, temperature, and concentration field has also been demonstrated. Multiple slip flow is utilised in various practical domains like micro-electro-mechanical systems, nano-electro-mechanical systems, micro-organism flow, and rarefied gas flow, among others.

The amount of information produced about any item or user has reached a very staggering level. Not only the volume of data, the velocity of data has reached an unprecedented magnitude. For any information retrieval or information processing system to work efficiently, it should be able to process massive amounts of data in real-time. Modern systems face a lot of challenges in managing data with high volume and velocity, especially when these systems are required to generate accurate predictions in a timely fashion. The most efficient way to ensure that modern information retrieval systems can adapt to the current volume and velocity of data is to implement them over a parallel and distributed environment. In this paper, we put forward a method for enhancing the scalability and performance of recommender systems in big data environments. By using the Euclidean distance to calculate the cosine similarity we introduce a technique which is efficient in parallelizing the algorithm for distributed environments. Thereby improving the computational efficiency and scalability of the recommender system. This enables such systems to manage large datasets with high accuracy and speed. With the help of parallel processing, our method can assist modern information retrieval systems keep up with the pace of ever-growing demands of data velocity and volume, ensuring real-time performance and robust scalability.

This work continues the assessment of the application of carbon nanotubes (CNTs) mixed with zirconia (ZrO₂). The study examined the compressive, bending, and bond strengths of samples containing and lacking carbon nanotubes. Zirconia carbon nanotubes (ZrO₂) in the concentrations of 0.00 %, 0.01 %, 0.02 %, 0.03 %, 0.04 %, and 0.05 % were the subjects of six mixtures whose resistance was measured. The results were analyzed using the finite element method with the ANSYS 15.0 program. ANSYS 15.0 software is used to analyze compressive and bending loads as well as the conventional zirconia model. Showcase the advantages of moderately utilizing carbon nanotubes. Zirconia's mechanical properties can be improved more effectively by mineral/chemical mixtures or fibers without the issues related to carbon nanotube dispersion. Provide evidence of the advantages of moderately utilizing carbon nanotubes. Without the issues related to carbon nanotube dispersion or the health hazards of handling Nanomaterials, zirconia's mechanical properties can be improved more effectively by mineral/chemical mixtures or fibers. The maximum and ideal load for the load was found to be 163.5 MPa, which was approved in all tests after the six models were finished with their designs in the ANSYS program. This was based on the von mises stress value and the maximum shear stress value less than the yield strength of the basic material used. After making numerous attempts, this load was selected by increasing the load by a specific percentage until it reached the ideal load, at which point the original model was able to support the load without experiencing any problems. The results of the ANSYS program were compared and examined, and they showed that the models' resistance to deformations, displacements, stresses, and various strains greatly increased when carbon nanotubes were added. By adding more carbon nanotubes, those models will be more resilient to the strains and deformations caused by compressive loads. The deformation rate decreased by 60%, which was a very noticeable decrease, especially in the sixth model where the carbon percentage was 5%.

The present study will attempt to investigate the energy dissipation in a stair-shaped stilling basin, developed as an improved model of the USBR Type III basin. For this purpose, an initial step of planning and modeling of the flume was undertaken, followed by experimental setup and data collection on the water level, critical depth, velocity, and discharge. In each of these two models, experiments were conducted for 10 variations in discharge. The energy dissipation ratio for the stair-shaped model reached 81.59%, as opposed to 78.99% for the USBR Type III. That means that the efficiency in the stair-shaped model is 2.6% higher. The velocity varied between 19.17 and 29.80 m/s for the USBR Type III model and between 17.42 and 28.14 m/s for the stair-shaped model. The maximum water level in USBR Type III was 'this', while in the stair-shaped model, it is +22.95, thus showing better energy dissipation. The stair-shaped model, also closely lies with the hydraulic jump state according to Elevatorski's formula and shows a value of 7% skewness. Further recommendations on topographic and geological conditions are warranted for the application of a stair-shaped basin.

This study aims to identify the most suitable deep learning model for early detection of dental caries in a new database of dental diseases. The study compares the performance of residual and dense networks using standard performance metrics. Dental caries is categorized into four classes based on dental practitioner recommendations. A novel database consisting of 1064 intraoral digital RGB images from 194 patients was collected in collaboration with Bharati Vidyapeeth’s Dental College, Pune. These images were cropped to obtain a total of 987 single-tooth images, which were divided into 888 training, 45 testing, and 54 validation images. In Phase I experimentation, ResNet50V2, ResNet101V2, ResNet152, DenseNet169, and DenseNet201 were utilized. Phase II focused on ResNet50V2, DenseNet169, and DenseNet201, while Phase III concentrated on DenseNet169 and DenseNet201. For Phase I experimentation, the overall accuracy of dental caries classification ranged from 0.55 to 0.84, with DenseNet exhibiting superior performance. In Phase II, the overall accuracy varied from 0.72 to 0.78, with DenseNet achieving the highest accuracy of 0.78. Similarly, in Phase III, DenseNet201 surpassed other models with an overall accuracy of 0.93. The DenseNet201 algorithm shows promise for detecting and classifying dental caries in digital RGB images. This finding is significant for the future development of automated mobile applications based on dental photographs, which could assist dental practitioners during examinations. Additionally, it could enhance patient understanding of dental caries severity, thereby promoting dental health awareness.

Sound classification is considered as one of the most important areas of classification domain, but the least developed compared to speech and voice recognition. In this study, we focus on the works that deal with sound classification by making a comparative study based on feature extraction and classification methods as well as the targeted sound corpus. Next, we present an overview of sound classification systems that utilize deep learning techniques, aiming to compare them with traditional learning methods. Based on our previous studies and conclusions, and considering that the challenge in choosing classification methods lies in balancing accuracy and computational cost, we conducted experiments using SVMs (support vector machines) with different kernels and MFCCs (Mel frequency Cepstral coefficients). Tests are carried out for the classification of some indoor abnormal sounds, then the number of classes is increased to cover a wider variety of sounds in order to observe and study the system's behavior. Finally, the results obtained in this work are promising and motivate us to explore deeper tests which are mentioned in the discussion and conclusion section.

Loop unrolling is a well-known code-transforming method that can enhance program efficiency during runtime. The fundamental advantage of unrolling a loop is that it frequently reduces the execution time of the unrolled loop when compared to the original loop. Choosing a large unroll factor might initially save execution time by reducing loop overhead and improving parallelism, but excessive unrolling can result in increased cache misses, register pressure, and memory inefficiencies, eventually slowing down the program. Therefore, identifying the optimal unroll factor is of essential importance. This paper introduces three ensemble-learning techniques—XGBoost, Random Forest (RF), and Bagging—for predicting the efficient unroll factor for specific programs. A dataset comprises various programs derived from many benchmarks, which are Polybench, Shootout, and other programs. More than 220 examples, drawn from 20 benchmark programs with different loop iterations, used to train three ensemble-learning methods. The unroll factor with the biggest reduction in program execution time is chosen to be added to the dataset, and ultimately it will be a candidate for the unseen programs. Our empirical results reveal that the XGBoost and RF methods outperform the Bagging algorithm, with a final accuracy of 99.56% in detecting the optimal unroll factor.

Nowadays, digital evidence plays a vital role in criminal investigations and arraignments. Digital criminal Investigators can also use this as an opportunity if the vast amount of data is a current trial. Assess constructive and constructive data and advice from the defendant proof behind the crime in terms of issues. Identifying criminal or criminal activity is a big deal because it connects certain data sets. It set an innovative law framework to quickly and accurately solve problems within the law's boundary. In this regard, the machine learning approach Naive Bayes classification for digital criminology data sets is to identify criminals. The Naive Baye classification process is used for digital criminology data application. To approximate square estimate for data sets of digital criminology subgroups. Also, support the Hadoop Big Data System Understanding Map with Reduce programming with the Naive Bayes classifier. The experiment result was a huge accumulated failure in the data quality. Based on these data, the estimation parameter of the statistical model is reached. The least-square estimate estimates the parameters that deal with the statistical model in the experimental result.

The WBM’s viscosity, gel strength and ability to filter and control the filtration rate are central to the stabilization of the well bore as well as the transportation of the cuttings to the surface. WBM is comprehensively inexpensive and eco-friendly; it does not hinder the biodegradation process as compared to other chemicals that may be used in the drilling involving rig activities. It cans thermally change, regulate formation pressure, and support cuttings. WBM is also can be used in all types of formations and is not complicated in terms of its handling as well as disposing as compared to other drilling fluids. However, WBM has some limitations as it is influenced by shale hydration, formation water salinity and thermally less stable at high temperature formations. Hindered by formation solids, fluid loss to the formation and formation damage are other issues that must be dealt with efficiently during the drilling process using WBM. WBM is used in most practices of drilling especially in offshore drilling areas, environment sensitive areas, and areas that have certain restrictions on the types of fluids to be used in drilling. Effectiveness and flexibility in relation to various platforms and various rigs make it a prime candidate for the most orthodox as well as the most innovative operations. Some of the regular water based muds that are often in use are Spud mud, Low solid polymer mud KCl, PHPA polymer mud KCl, Glycol polymer mud, Salt Saturated mud and Drill – in mud.

This study aims to investigate the mechanical and morphological properties of hybrid composites fabricated from a Date Palm Mesh Fiber (DPMF) and glass wool reinforced with unsaturated polyester. The development of eco-friendly and efficient thermal insulation materials is crucial for reducing energy consumption and addressing environmental concerns. The hybrid composites were manufactured using the Bulk Molding Compound technique, and various factors such as fiber composition weight percentage, particle size, and quantities of DPMF and glass wool fibers were evaluated. Tensile, impact, and flexural bending tests were conducted to assess the mechanical properties of the composites. Design-Expert 12 software and analysis of variance ANOVA were employed to analyze the effects of fiber ratio, matrix ratio, and fiber size on the mechanical properties. The experimental results showed that the fiber content, DPMF content, and DPMF particle size in the matrix significantly influenced the mechanical properties of the hybrid composites. Increasing the fiber content and DPMF particle sizes improved the interfacial bonding between DPMF and the polymer matrix, enhancing the matrix's tensile strength and flexural strength of the composites. However, high amounts of DPMF resulted in poor energy absorption abilities of the composites under impact load. The fractography analysis using FESEM confirmed the mechanical test results by revealing a rough fracture surface in the composites reinforced with DPMF, indicating stronger bonding between the fibers and the unsaturated polyester matrix. This study highlights the potential of hybrid composites as eco-friendly and efficient thermal insulation materials and provides insights into the influence of various parameters on their mechanical properties.

Establishing model parameters is fast becoming more complex especially with generalized linear mixed models (GLMMs); which comprises of generalized linear models and classical linear mixed models. Evaluating generalized linear mixed models (GLMMs) parameters with maximum likelihood techniques involves some levels of complexity, to proffer solutions to this challenge, techniques involving approximation of integrals were considered in this paper. Some approximation methods for parameter estimation were considered to establish the most effective and adaptive model using a good number of model performance metrics/criteria. Penalized quasi-likelihood, adaptive gauss-Hermite quadrature, and Laplace approximation estimation techniques were considered to fit the real clinical data set with binary outcomes. Real-life data analysis showed some better fitness and superiority of an adaptive gauss-Hermit quadrature technique over some other existing estimation techniques using a set of model performance metrics. Data users at various levels of analysis may now consider adaptive gauss-Hermite quadrature technique over other estimation techniques in fitting GLMMs with binary responses. Coefficients of the model with good performance metrics were also considered in establishing effects of clinical follow-up on medical diagnoses of individual patients.