Vehicle Accident Analysis and Reconstruction Methods

In this third edition of Vehicle Accident Analysis & Reconstruction Methods, Raymond M. Brach and R. Matthew Brach have expanded and updated their essential work for professionals in the field of accident reconstruction. Most accidents can be reconstructed effectively using of calculations and investigative and experimental data: the authors present the latest scientific, engineering, and mathematical reconstruction methods, providing a firm scientific foundation for practitioners. Accidents that cannot be reconstructed using the methods in this book are rare. In recent decades, the field of crash reconstruction has been transformed through the use of technology. The advent of event data records (EDRs) on vehicles signaled the era of modern crash reconstruction, which utilizes the same physical evidence that was previously available as well as electronic data that are measured/captured before, during, and after the collision. There is increased demand for more professional and accurate reconstruction as more crash data is available from vehicle sensors. The third edition of this essential work includes a new chapter on the use of EDRs as well as examples using EDR data in accident reconstruction. Early chapters feature foundational material that is necessary for the understanding of vehicle collisions and vehicle motion; later chapters present applications of the methods and include example reconstructions. As a result, Vehicle Accident Analysis & Reconstruction Methods remains the definitive resource in accident reconstruction.

On some aspects of R&D monitoring of R&D and technological works of military and dual-use character: between security and science

The article analyzes the role and importance of monitoring research, development and technological works of military and dual-use character performed by organizations subordinate to the Ministry of Education and Science of Russia. The problems related to the process of collecting and processing information are considered. The ways of increasing the effectiveness of monitoring, aimed primarily at ensuring the security of the state and the development of its scientific, engineering and technological potential, are proposed.

Machine Learning and Urban Drainage Systems: State-of-the-Art Review

In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies.

Defining a Path Toward the Use of Fast-Scan Cyclic Voltammetry in Human Studies

Fast Scan Cyclic Voltammetry (FSCV) has been used for decades as a neurochemical tool for in vivo detection of phasic changes in electroactive neurotransmitters in animal models. Recently, multiple research groups have initiated human neurochemical studies using FSCV or demonstrated interest in bringing FSCV into clinical use. However, there remain technical challenges that limit clinical implementation of FSCV by creating barriers to appropriate scientific rigor and patient safety. In order to progress with clinical FSCV, these limitations must be first addressed through (1) appropriate pre-clinical studies to ensure accurate measurement of neurotransmitters and (2) the application of a risk management framework to assess patient safety. The intent of this work is to bring awareness of the current issues associated with FSCV to the scientific, engineering, and clinical communities and encourage them to seek solutions or alternatives that ensure data accuracy, rigor and reproducibility, and patient safety.

Estimates of Classes of Generalized Special Functions and Their Application in the Fractional k , s -Calculus Theory

In this article, we aim to develop new k , s -fractional integral and differential operators containing S -functions as kernels in a form of generalized k -Mittag-Leffer functions. We also set up various properties of such operators. Furthermore, we consider a variety of implications of the major outcomes that will be very useful in the implementation of scientific, engineering, and technical problems.

Solution of Parabolic Partial Differential Equations by Non-Polynomial Cubic Spline Technique

Parabolic partial differential equation having a great impact on our scientific, engineering and technology. Enormous research have been conducted for the solution of parabolic PDEs. . In this research work, we introduced a novel technique for the numerical solution of fourth order PDEs. The novel technique is based upon the polynomial cubic cutting method (PCSM) was used with Adomian breakdown technique (ADM).The constraint for the alternative variables was decomposed by Edomian decomposition for the successive approximation. A numerical test problem of parabolic PDEs solved by purposed technique

The relation between parietal GABA concentration and numerical skills

Several scientific, engineering, and medical advancements are based on breakthroughs made by people who excel in mathematics. Our current understanding of the underlying brain networks stems primarily from anatomical and functional investigations, but our knowledge of how neurotransmitters subserve numerical skills, the building block of mathematics, is scarce. Using 1H magnetic resonance spectroscopy (N = 54, 3T, semi-LASER sequence, TE = 32 ms, TR = 3.5 s), the study examined the relation between numerical skills and the brain’s major inhibitory (GABA) and excitatory (glutamate) neurotransmitters. A negative association was found between the performance in a number sequences task and the resting concentration of GABA within the left intraparietal sulcus (IPS), a key region supporting numeracy. The relation between GABA in the IPS and number sequences was specific to (1) parietal but not frontal regions and to (2) GABA but not glutamate. It was additionally found that the resting functional connectivity of the left IPS and the left superior frontal gyrus was positively associated with number sequences performance. However, resting GABA concentration within the IPS explained number sequences performance above and beyond the resting frontoparietal connectivity measure. Our findings further motivate the study of inhibition mechanisms in the human brain and significantly contribute to our current understanding of numerical cognition's biological bases.

Analytical study of software development process model variants

Software Engineering is a branch of Computer Science that evolved as a result of urgent need to deal with decades of software crisis, characterized by low theoretical knowledge and practice of the construction of error-free and efficient software. The introduction of well-organized scientific, engineering and management strategies in the process of software development no doubt led to major breakthroughs, and solutions to software failures. One of the obvious game-changer in this regard is the evolution of Software Development Life Cycle, also known as Software Process Model for driving the different phases of software construction. A sound understanding of the process model is therefore inevitable, not just for software developers, but also to users and researchers. Such a theoretical cum practical understanding will enhance decisions on which process model is best for a particular job or perspective. This invariably, contributes immensely to the probability of success or failure of the project in question. Thus, the necessity for this research. This work presents an unambiguous expository of selected software development model variants. A total of four process model variants were studied, in a theoretical, visual and analytical manner. The variants were analyzed using strength versus weakness (SVW) tabular scenario. This work was concluded by presenting guides towards choice of these models. This research is expected to be a useful reference to software practitioners and researchers.

A Nonlinear System Identification Method Based on Adaptive Neural Network

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.