Neural Systems with Numerically Matched Input-Output Statistic: Isotonic Bivariate Statistical Modeling

Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are “holes” in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure

Leveraging Advanced Analytics for Backorder Prediction and Optimization of Business Operations in the Supply Chain

Businesses can unlock valuable insights by leveraging advanced analytics techniques to optimize supply chain processes and address backorders. Backorders occur when a customer order cannot be fulfilled immediately due to lack of available supply. Root causes of backorders can range from supply chain complications and manufacturing miscalculations to logistical challenges. While a surge in demand might initially seem beneficial, backorders come with inherent costs, leading to supply chain disruptions, dissatisfied customers, and lost sales. This research aimed to assess the efficacy of predictive analytics in detecting early backorder signs and to understand how parameter tuning influences the performance of these predictive models. The foundation of this study was laid through an exhaustive literature review. In-depth Exploratory Data Analytics/ EDA was utilized to investigate datasets, followed by rigorous preprocessing steps, including data cleaning, feature engineering, scaling, and resampling. Machine learning models were subsequently trained, tuned, and assessed using appropriate evaluation metrics. Findings from this research underscored the value of predictive analytics in early backorder identification. They also offered a comparative analysis of machine learning algorithms and highlighted the significance of parameter tuning. Additionally, they established the necessity of multi-metric evaluations for imbalanced datasets. Thus, the study has provided a fundamental framework that can serve as a basis for future research endeavors

Imaging light transport at the femtosecond scale

Paper, milk, clouds and white paint share a common property: they are opaque disordered media through which light scatters randomly rather than propagating in a straight path. For very thick and turbid media, indeed, light eventually propagates in a ‘diffusive’ way, i.e. similarly to how tea infuses through hot water. Frequently though, a material is neither perfectly opaque nor transparent and the simple diffusion model does not hold. In this work, we developed a novel optical-gating setup that allowed us to observe light transport in scattering media with sub-ps time resolution. An array of unexplored aspects of light propagation emerged from this spatio-temporal description, unveiling transport regimes that were previously inaccessibile due to the extreme time scales involved and the lack of analytical models

Imaging light transport at the femtosecond scale

Paper, milk, clouds and white paint share a common property: they are opaque disordered media through which light scatters randomly rather than propagating in a straight path. For very thick and turbid media, indeed, light eventually propagates in a ‘diffusive’ way, i.e. similarly to how tea infuses through hot water. Frequently though, a material is neither perfectly opaque nor transparent and the simple diffusion model does not hold. In this work, we developed a novel optical-gating setup that allowed us to observe light transport in scattering media with sub-ps time resolution. An array of unexplored aspects of light propagation emerged from this spatio-temporal description, unveiling transport regimes that were previously inaccessibile due to the extreme time scales involved and the lack of analytical models

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Multiscale Mechanical, Structural, And Compositional Response Of Tendon To Static And Dynamic Loading During Healing

The extracellular matrix (ECM) is a major component of the biomechanical environment with which tendon cells (tenocytes) interact. Alterations to matrix structural and mechanical properties due to mechanical loading may promote normal tendon homeostasis or create pathological conditions. For example, fatigue loading of tendon elevates collagen fiber waviness (crimp), which correlates linearly with tissue laxity. The tendon ECM may also be altered following tendon injury. Aberrant tissue phenotypes caused by tendon ruptures are exemplified not only at transcript and protein levels, but also can extend to include disorganized collagen structure, inferior mechanical properties, and reduced in vivo limb function in animals. This dissertation explores the interface between dynamic loading and tendon healing across multiple length scales using living tendon explants. This work begins to define the implications of macroscale mechanical loading on collagen structure and tenocyte response in uninjured and healing tendon, and provides a foundation for the development of new strategies to improve tendon healing. Ultimately, this work helps our understanding of tendon’s multiscale response to loading, provides a framework for the micromechanical environment that tenocytes interact in response to dynamic loading and healing, and lays important groundwork for benchmarks for tendon tissue engineering. The multiscale response to mechanical loading, which is a hallmark of clinical rehabilitation protocols, is necessary to determine the ramifications of various macroscale loading protocols. Additionally, these results provide benchmarks for the environments in which tendon cells may experience following cell delivery therapies. Several exciting future avenues of research are possible that would highly impact basic science research of tendon function and lead to potentially translatable approaches that could improve tendon injury onset and healing response. In conclusion, this dissertation provides a strong foundation on which future experimental and computational studies can build to fully elucidate the multiscale mechanisms that govern strain transfer in tendon