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Deviation of the first toe to the outside, interconnected with the deviation of the first metatarsal bone to the inside, occurs in 46% of patients of the older age group and is called valgus deformity of the first toe. The negative impact of this pathology on the quality of life of patients is the reason for seeking medical help, the gold standard of which is surgical correction, and the basic surgical technique is osteotomy (sawing the bone and fixing its fragments with implants) of the first metatarsal. At the same time, an ideal osteotomy should provide initial stability in the early postoperative period. However, a large number of ways to perform osteotomy, as well as the advantages and disadvantages of each of the surgical techniques, do not allow to consider one of them as the most successful. In this regard, the aim of the work was to develop and validate a biomechanical model of osteotomy of the first metatarsal bone to analyze its stability and reliability depending on the type of osteotomy, the degree of displacement of bone fragments, as well as the number of fastening screws. In this study, biomechanical modeling of the most commonly used variants of osteotomy of the first metatarsal bone of the foot in the surgical treatment of its valgus deformity was carried out. For this purpose, 10 models of osteotomies of a separate first metatarsal bone were created, which were then subjected to static loading to analyze their stress-strain state and assess their success. Successful (stable and reliable) treatment options were identified, as well as unsuccessful ones. Two of the ten options considered were unsuccessful – scarf type osteotomies with displacement of bone fragments by 2/3 of its diameter and fixed with a single screw. It was revealed that osteotomies of the chevron type showed higher stability in comparison with scarf osteotomies. As a result, we note that in this study, numerical experiments were conducted for the first time to compare the stability and strength of the most commonly used variants of osteotomies based on a single bone model. A biomechanical model of scarf osteotomy of the first metatarsal bone has been developed and validated on the basis of mechanical experiments.

Preoperative planning for the treatment of the consequences of diseases and injuries of the spino-pelvic complex surgical treatment is a mandatory procedure and should ensure the selection of implants, modes and techniques for their installation, as well as the reconstruction of the optimal biomechanics of the operated segment. For each individual patient, the surgeon chooses a treatment variant based on his qualitative and quantitative individual parameters. Therefore, the task of developing some measurable characteristics (criteria) seems to be urgent, with the help of which it would be possible to choose a successful variant for each specific patient. In surgery of the spino-pelvic complex pathologies, it is customary to use questionnaires of the patient’s quality of life to assess the long-term treatment results. During preoperative planning, surgeons also calculate geometric parameters to assess the degree of deformity and select the necessary correction. At the same time, a geometrically and anatomically correctly planned operation may not be successful in terms of assessing the strength of implanted structures and the “bone-implant” system as a whole. This paper presents the results of the development and testing of criteria for assessing the success of surgical reconstructive treatment of the consequences of the spino-pelvic complex diseases and injuries. Three groups of criteria have been identified: geometric, biomechanical, and clinical. Geometric and biomechanical criteria make it possible to obtain short-term postoperative prognosis. The use of clinical success criteria makes it possible to formulate long-term postoperative prognosis. The criteria for assessing success do not offer the surgeon any new treatment variant, but only provide a tool for quantitative comparison of the treatment variants that are considered by him and the choice of successful ones among them. The criteria for evaluating the success of treatment are implemented in the Smart Plan Ortho preoperative planning system developed at Saratov University, which provides a full cycle of preoperative planning in accordance with the planning–modeling–forecast methodology.

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic's impacts have been devastating for the global economy, and particularly for manufacturing companies. Many firms were unprepared for a crisis of this magnitude and adapted their operations in an ad-hoc manner. Some of these adaptations were facilitated by digital technologies (DTs). This article examines extant literature using a systematic review methodology in order to extend our understanding of what DTs have been used in manufacturing companies during the pandemic, how DTs have helped manufacturing companies cope with the pandemic, and how these innovations can be used in the post-pandemic world. The major outcome of our study is a novel classification of DT applications in the pandemic setting in the manufacturing industry. In addition, we propose an agenda for future research. [ABSTRACT FROM AUTHOR]

Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models. [ABSTRACT FROM AUTHOR]

We have measured the absolute doubly differential angular sputtering yield for 20 keV Kr+ impacting a polycrystalline Cu slab at an incidence angle of θi = 45° relative to the surface normal. Sputtered Cu atoms were captured using collectors mounted on a half dome above the sample, and the sputtering distribution was measured as a function of the sputtering polar, θs, and azimuthal, ϕs, angles. Absolute results of the sputtering yield were determined from the mass gain of each collector, the ion dose, and the solid angle subtended, after irradiation to a total fluence of ∼1 × 1018 ions/cm2. Our approach overcomes shortcomings of commonly used methods that only provide relative yields as a function of θs in the incidence plane (defined by the ion velocity and the surface normal). Our experimental results display an azimuthal variation that increases with increasing θs and is clearly discrepant with simulations using binary collision theory. We attribute the observed azimuthal anisotropy to ion-induced formation of micro- and nano-scale surface features that suppress the sputtering yield through shadowing and redeposition effects, neither of which are accounted for in the simulations. Our experimental results demonstrate the importance of doubly differential angular sputtering studies to probe ion sputtering processes at a fundamental level and to explore the effect of ion-beam-generated surface roughness. [ABSTRACT FROM AUTHOR]

Disruptions can adversely affect profitability, service level, and even the viability of supply networks (SNs). Natural disasters, the COVID-19 pandemic, and geopolitical tensions heightened disruption risks for SNs. We propose a methodological framework to guide the design of resilient SNs, with a focus on efficient resilience. In fact, despite an increasing economic and social interest in SN resilience and a large variety of qualitative resilience frameworks, there is a lack of model-based frameworks to guide decision makers in the complex task of designing resilient SNs. Differently to the existing literature, that mostly focuses on industry- or disruption-specific approaches, the framework of this paper is general, independent from sectors and causes. It consists of three steps to guide practitioners through model-based stress testing of their SNs: it integrates and organises methods from the literature, from the selection of data to be collected to the validation of the resilient and efficient SN. We illustrate the application of descriptive, predictive, and prescriptive analytics at each phase using real-life case studies data supplemented by literature references. Finally, we present an industrial case study with the step-by-step application of the framework highlighting its strengths and objectives. [ABSTRACT FROM AUTHOR]