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It has been recently argued that the inclusive hadroproduction at the LHC of a Higgs boson in association with a jet can be sensitive to the high-energy dynamics. Moreover, the impact of the resummation at FCC energies is expected to be large also in the inclusive cross section for the main Higgs production channel in proton-proton collisions, namely the gluon fusion. As the energy increases, a pure $k_t$-factorization framework or a collinear-factorization approach supplemented by the high-energy resummation are adequate formalisms to describe these processes. In both cases, the fundamental missing ingredient for a next-to-leading logarithmic description is the Higgs boson impact factor. We present the full next-to-leading result for the forward Higgs impact factor, obtained in the infinite top-mass limit, discussing possible future extensions, such as the calculation of the impact factor in the central rapidity region., 4 pages. Contribution to the 2023 QCD session of the 57th Rencontres de Moriond

Objective: To study burnout of Russian physicians in the conditions of COVID-19 pandemic and how their work with coronavirus-infected patients influenced it. According to a three-factor model of burnout developed by Maslach and Jackson, this syndrome includes emotional exhaustion, depersonalization, and reduction of personal accomplishment. Design: A cross-sectional survey study. Setting: Large medical practice. Participants: Physicians of different specialties. Methods: Data collection was conducted from June 23 to July 12, 2020. We developed a Google form including a questionnaire and psychological inventories and placed it in a medical portal. Maslach Burnout Inventory — Human Services Survey for Medical Personnel was used to study burnout; the Hospital Anxiety and Depression Scale was used to determine anxiety and depression. Results: Of all the physicians who took part in the study (N = 599), 31.2 % worked with coronavirus-infected patients. Of the medical personnel who treated COVID-19 patients, 63.6% noted increased workload during the pandemic. Compared to other physicians, they more often had a high degree of emotional exhaustion (43.3 % vs 33.0 %, φ* = 2.404, P ≤ 0.01) and depersonalization (41.7 % vs 34, 0%, φ* = 1.803, P ≤ 0.05). An overwhelming majority of physicians, without any dependence on work with infected patients, had an absence of anxiety and depression. The identified interrelations between the symptoms of burnout, anxiety, depression; age and career stage in medical personnel were identical, except for weak correlations between age and emotional exhaustion (rs = -0.097, P ≤ 0.05), as well as career stage and personal accomplishment (rs = 0.102, P ≤ 0.05) in those physicians who worked with COVID-19 patients. The structure of burnout was identical in all physicians and did not depend on interaction with the infected patients. Conclusion: Public health authorities should reduce the workload on physicians involved in treating infected patients against the backdrop of the pandemic. Psychotherapeutic measures focused on preventing burnout should reduce its number among physicians interacting with patients infected with the coronavirus.

The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private information. This results in agents that forgo their individual goals in favour of social good, which can potentially be exploited by selfish defectors. We argue that cooperation also requires agents' identities and boundaries to be respected by making sure that the emergent behaviour is an equilibrium, i.e., a convention that no agent can deviate from and receive higher individual payoffs. Inspired by advances in mechanism design, we propose to solve the problem of cooperation, defined as finding socially beneficial equilibrium, by using mediators. A mediator is a benevolent entity that may act on behalf of agents, but only for the agents that agree to it. We show how a mediator can be trained alongside agents with policy gradient to maximize social welfare subject to constraints that encourage agents to cooperate through the mediator. Our experiments in matrix and iterative games highlight the potential power of applying mediators in MARL.

Contract design involves a principal who establishes contractual agreements about payments for outcomes that arise from the actions of an agent. In this paper, we initiate the study of deep learning for the automated design of optimal contracts. We formulate this as an offline learning problem, where a deep network is used to represent the principal's expected utility as a function of the design of a contract. We introduce a novel representation: the Discontinuous ReLU (DeLU) network, which models the principal's utility as a discontinuous piecewise affine function where each piece corresponds to the agent taking a particular action. DeLU networks implicitly learn closed-form expressions for the incentive compatibility constraints of the agent and the utility maximization objective of the principal, and support parallel inference on each piece through linear programming or interior-point methods that solve for optimal contracts. We provide empirical results that demonstrate success in approximating the principal's utility function with a small number of training samples and scaling to find approximately optimal contracts on problems with a large number of actions and outcomes.

In this work, we present a new benchmarking suite with new real-life inspired skewed workloads to test the performance of concurrent index data structures. We started this project to prepare workloads specifically for self-adjusting data structures, i.e., they handle more frequent requests faster, and, thus, should perform better than their standard counterparts. We looked over the commonly used suites to test performance of concurrent indices trying to find an inspiration: Synchrobench, Setbench, YCSB, and TPC - and we found several issues with them. The major problem is that they are not flexible: it is difficult to introduce new workloads, it is difficult to set the duration of the experiments, and it is difficult to change the parameters. We decided to solve this issue by presenting a new suite based on Synchrobench. Finally, we highlight the problem of measuring performance of data structures. We show that the relative performance of data structures highly depends on the workload: it is not clear which data structure is best. For that, we take three state-of-the-art concurrent binary search trees and run them on the workloads from our benchmarking suite. As a result, we get six experiments with all possible relative performance of the chosen data structures.

Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain. In this article we discuss these limitations and ways to mitigate them. Next, we present an overview of currently available neuromorphic AI projects in which these limitations are overcome by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida, Mythic). Also, we present the principle of classifying neuromorphic AI systems by the brain features they use: connectionism, parallelism, asynchrony, impulse nature of information transfer, on-device-learning, local learning, sparsity, analog, and in-memory computing. In addition to reviewing new architectural approaches used by neuromorphic devices based on existing silicon microelectronics technologies, we also discuss the prospects for using a new memristor element base. Examples of recent advances in the use of memristors in neuromorphic applications are also given.

Ripple effect is a specific area of SC disruptions and a strong stressor to SC resilience. Research on the ripple effect analyses how one or more disruptive events propagate through the SC and impa...

The COVID-19 pandemic has created significant uncertainty in all areas of life, including supply chains (SCs). This paper presents a new risk-averse mixed-integer nonlinear problem mathematical model for the design and planning of a two-echelon resilient SC network. Disruption events, which can partially or completely reduce the available capacity, are included in the model. The model’s objective is to minimise the total costs by determining the optimal facility location and capacity, allocation flows and resilience actions for hedging against disruption risk. A solution procedure is tested through computational experiments, and managerial insights were formed based on a numerical example for several disruption configurations, with a specific case of long-term crises similar to the COVID-19 pandemic. The results showed that recovery activities are the most efficient actions to take for a short-term disruption event. Besides, proactive resilience investment in a protection system and flexibility enhancement allows the SC to handle the disruption period with a limited increase in network building costs and overcapacity.

When dealing with binary classification of data with only one labeled class data scientists employ two main approaches, namely One-Class (OC) classification and Positive Unlabeled (PU) learning. The former only learns from labeled positive data, whereas the latter also utilizes unlabeled data to improve the overall performance. Since PU learning utilizes more data, we might be prone to think that when unlabeled data is available, the go-to algorithms should always come from the PU group. However, we find that this is not always the case if unlabeled data is unreliable, i.e. contains limited or biased latent negative data. We perform an extensive experimental study of a wide list of state-of-the-art OC and PU algorithms in various scenarios as far as unlabeled data reliability is concerned. Furthermore, we propose PU modifications of state-of-the-art OC algorithms that are robust to unreliable unlabeled data, as well as a guideline to similarly modify other OC algorithms. Our main practical recommendation is to use state-of-the-art PU algorithms when unlabeled data is reliable and to use the proposed modifications of state-of-the-art OC algorithms otherwise. Additionally, we outline procedures to distinguish the cases of reliable and unreliable unlabeled data using statistical tests.

RegretNet is a recent breakthrough in the automated design of revenue-maximizing auctions. It combines the flexibility of deep learning with the regret-based approach to relax the Incentive Compatibility (IC) constraint (that participants prefer to bid truthfully) in order to approximate optimal auctions. We propose two independent improvements of RegretNet. The first is a neural architecture denoted as RegretFormer that is based on attention layers. The second is a loss function that requires explicit specification of an acceptable IC violation denoted as regret budget. We investigate both modifications in an extensive experimental study that includes settings with constant and inconstant number of items and participants, as well as novel validation procedures tailored to regret-based approaches. We find that RegretFormer consistently outperforms RegretNet in revenue (i.e. is optimal-er) and that our loss function both simplifies hyperparameter tuning and allows to unambiguously control the revenue-regret trade-off by selecting the regret budget., NeurIPS 2022

The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications. The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic. The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications. Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, i.e., adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability. Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise. Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream.

Scheduling in Industry 4.0 systems belongs to a class of problems that have mixed structural-temporal-logical constraints. In other words, a strong coupling is considered when product and process are created simultaneously. As a result of the proven NP-hardness of such problems, solution methods have extensively utilized different decomposition principles. The known decomposition methods in discrete optimization are founded on the difficulties in deriving analytical properties. The existing solutions in continuous optimization are based on the maximum principle and yield a dynamic process decomposition using the natural logic of time. By combining the advantages of continuous and discrete optimization, this chapter develops a decomposition method for shop floor scheduling in Industry 4.0 manufacturing systems. Technically, this study proposes to decompose dynamically the large-scale assignment matrix according to the precedence relations between the operations of the jobs and considers only the operations that satisfy these precedence relations at a given time point in small-dimensional, discrete optimization models. Continuous optimization is used to generate a schedule from the assignments found in the discrete optimization models at each time point by extremizing the Hamiltonian function at this time point subject to scheduling objective(s). In addition, the execution of the operations in time can be accurately modeled in continuous time as a continuous state variable; the machine availability and capacity disturbances at the machines are also considered. The method developed provides further insights into decomposition methods for scheduling and is supported by an analytical analysis and an algorithmic realization.

In this chapter, we present an introduction to scheduling in Industry 4.0 and cloud manufacturing systems. We elaborate on the peculiarities of scheduling and sequencing problems in the context of Industry 4.0 and smart manufacturing. We delineate recent research streams and summarize the structure and contribution of the book.

This chapter develops a model for dynamic integrated scheduling of information services and logistics flows in the omnichannel system. The proposed service-oriented description makes it possible to coordinate the information services and material process schedules simultaneously. It also becomes possible to determine the volume of information services needed for physical supply processes. In addition, impact of disruptions in information services on the schedule execution in the physical structure is analyzed. The results provide a base for information service scheduling according to actual physical process execution.

We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design an...

International audience; An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviors by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g., food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualization of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas.

This article proposes a sparse computation-based method for optimizing neural networks for reinforcement learning (RL) tasks. This method combines two ideas: neural network pruning and taking into account input data correlations; it makes it possible to update neuron states only when changes in them exceed a certain threshold. It significantly reduces the number of multiplications when running neural networks. We tested different RL tasks and achieved 20-150x reduction in the number of multiplications. There were no substantial performance losses; sometimes the performance even improved.

International audience; Uncovers fundamental principles and recent developments in network dynamics and control Introduces applications to supply chains, manufacturing and logistics systems Utilizes techniques from control engineering, operations research, industrial engineering and computer science

Despite the numerous breakthroughs achieved with Reinforcement Learning (RL), solving environments with sparse rewards remains a challenging task that requires sophisticated exploration. Learning from Demonstrations (LfD) remedies this issue by guiding the agent's exploration towards states experienced by an expert. Naturally, the benefits of this approach hinge on the quality of demonstrations, which are rarely optimal in realistic scenarios. Modern LfD algorithms require meticulous tuning of hyperparameters that control the influence of demonstrations and, as we show in the paper, struggle with learning from suboptimal demonstrations. To address these issues, we extend Self-Imitation Learning (SIL), a recent RL algorithm that exploits the agent's past good experience, to the LfD setup by initializing its replay buffer with demonstrations. We denote our algorithm as SIL from Demonstrations (SILfD). We empirically show that SILfD can learn from demonstrations that are noisy or far from optimal and can automatically adjust the influence of demonstrations throughout the training without additional hyperparameters or handcrafted schedules. We also find SILfD superior to the existing state-of-the-art LfD algorithms in sparse environments, especially when demonstrations are highly suboptimal.

Purpose: The literature on Supply Chain Resilience faces a steep and significant interest in recent years owing to the pandemic and disruptions in global trade. As the literature amplifies due, this paper aims to provide transparency about the intersection of Supply Chain Resilience and transport networks. Existing literature reviews do not consider this aspect. Methodology: This paper conducts a Systematic Literature Review using the keywords "Supply Chain Resilience" combined with "Transport/Infrastructure networks" to identify the relationship between transport networks and Supply Chain Resilience. The method, as a result, identifies about 251 articles from 2004 to 2022, of which 36 relevant papers are included. Findings: Excluded overview papers address Supply Chain Resilience and transport independently. As a result of the full-text screening, a shift to quantitative methods can be observed. Network theory and mathematical programming models stick out. However, a list of specific research gaps for future research proposed in the literature remains. In particular, a dedicated transport network like highways is seldomly considered. Originality: This research improves the understanding of the relationship between Supply Chain Resilience and transport networks with the interrelation of transport and supply chain disruptions.

Today, "data science" is positioned as the best tool strong enough for revolutionizing supply chain and logistics (SC&L) companies. Different sources of data generate very large diverse data sets that demand separate specific data science technologies in line with big data analysis. The fast growing research on data science and big data analysis (DS&BDA) encouraged us to conduct a systematic comprehensive literature review on DS&BDA applications/techniques in the SC&L fields. Investigation on the recent relevant review studies around the topic illustrated several gaps in reviewing former studies and assisted us to develop a conceptual framework for our proposed reviewing process. Our keyword search found 13724 papers published from 2000 to 2019. With the help of a systematic research methodology, we focused on 227 prosperous publications from high-ranked journals and applied a detailed coding system according to our conceptual framework. We will provide some insights from the results section here. Finally, significant research gaps are introduced to be investigated as future studies by researchers in the domain of SC&L.

We present a new study on helicity amplitudes and cross sections for the exclusive production of $ρ$ mesons at the EIC in high-energy factorization. In this framework the analytic expression of amplitudes takes the form of a convolution between an off-shell impact factor, depicting the ($γ^* \to ρ$) transition, and a nonperturbative density, known as Unintegrated Gluon Distribution (UGD) that encodes information about the proton structure at low $x$ and evolves according to the BFKL equation. We come out with an evidence that observables sensitive to the polarizations of the incoming virtual photon and of the emitted meson allow us to discriminate among different UGD models and to gather quantitative information on the proton content at high energies., 6 pages, 1 figure. Presented by F.G. Celiberto at DIS2022: XXIX International Workshop on Deep-Inelastic Scattering and Related Subjects, Santiago de Compostela, Spain, May 2-6 2022

We calculate cross sections for the exclusive diffractive leptoproduction of $\rho$-mesons, $\gamma^*~p~\to~\rho~p$, within the framework of high-energy factorization. Cross sections for longitudinally and transversally polarized mesons are shown. We employ a wide variety of unintegrated gluon distributions available in the literature and compare to HERA data. The resulting cross sections strongly depend on the choice of unintegrated gluon distribution. We also present predictions for the proton target in the kinematics of the Brookhaven EIC., Comment: 21 pages, 8 figures. arXiv admin note: text overlap with arXiv:1808.02395; version published in Eur. Phys. J. C

We present here some arguments to support our suggestion that data on the helicity structure for the hard exclusive electroproduction of $$\rho $$ ρ mesons at HERA (and in possible future high-energy electron-proton colliders) provide useful information to constrain the $$\kappa $$ κ -shape of the unintegrated gluon distribution in the proton.

The article is an overview of the virtual meeting of researchers who are developing critical theory of society in different forms and in different countries. Restrictions on social activities in the period of COVID-19 pandemics have impacted (among other social structures and interactions) academic exchanges and scientific communications. Overcoming the routine of digital substitutes for traditional academic community life, the participants of the new scientific event contributed intensively to dialectical discourse of oppression and emancipation. The global roundtable was organized around 9 main presentations made by leading critical theorists. Preserving basic ideas from the Frankfurt school and other lines of Marxism and neo-Marxism, critical theorists are working to open new sources for development of rational and at the same time humanist and contextualized critique of new forms of alienation (including digital one) and new forms of oppression (including neo-colonialist one). Participants of the global roundtable have shared idea of permanent reflection aimed at continuation of the critical theorizing and at the emancipation of human beings., Статья представляет обзор виртуальной встречи исследователей, развивающих критическую теорию общества в различных формах в разных странах. Ограничения, налагаемые на социальную деятельность в период распространения вируса COVID-19, оказали воздействие на все социальные структуры и взаимодействия, включая академические обмены и научную коммуникацию. Преодолевая рутину цифровых заменителей традиционной жизни академического сообщества, участники нового научного мероприятия существенно развили диалектический дискурс подавления и эмансипации. Глобальный круглый стол был организован вокруг девяти основных докладов ведущих теоретиков. Сохраняя базовые идеи Франкфуртской школы и других марксистских и неомарксистских течений, теоретики работают над открытием новых источников для развития рациональной и в то же время гуманистической и контекстуализированной критики новых форм отчуждения (включая цифровое) и новых форм подавления (включая неоколониализм). Участники глобального круглого стола разделяют идею перманентной рефлексии, нацеленной на продолжение критической рефлексии и на эмансипацию людей от новых форм порабощения.

The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur, for example the breakdown of a vehicle. While solving the VRSP in various settings has been an active area in operations research (OR) for decades, the ever-growing complexity of modern railway networks makes dynamic real-time scheduling of traffic virtually impossible. Recently, multi-agent reinforcement learning (MARL) has successfully tackled challenging tasks where many agents need to be coordinated, such as multiplayer video games. However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner. Submissions had to bring as many trains (agents) to their target stations in as little time as possible. While the best submissions were in the OR category, participants found many promising MARL approaches. Using both centralized and decentralized learning based approaches, top submissions used graph representations of the environment to construct tree-based observations. Further, different coordination mechanisms were implemented, such as communication and prioritization between agents. This paper presents the competition setup, four outstanding solutions to the competition, and a cross-comparison between them., Comment: 28 pages, 8 figures

We calculate cross sections for the exclusive diffractive leptoproduction of $��$-mesons, $��^*~p~\to~��~p$, within the framework of high-energy factorization. Cross sections for longitudinally and transversally polarized mesons are shown. We employ a wide variety of unintegrated gluon distributions available in the literature and compare to HERA data. The resulting cross sections strongly depend on the choice of unintegrated gluon distribution. We also present predictions for the proton target in the kinematics of the Brookhaven EIC., 21 pages, 8 figures. arXiv admin note: text overlap with arXiv:1808.02395; version published in Eur. Phys. J. C

We present a study on inclusive emissions of a double $��_c$ or of a $��_c$ plus a light-flavored jet system as probe channels in the semi-hard regime of QCD. Our formalism relies on the so-called hybrid high-energy/collinear factorization, where the standard collinear description is supplemented by the $t$-channel resummation �� la BFKL of energy logarithms up to the next-to-leading accuracy. We make use of the JETHAD modular interface, suited to the analysis of different semi-hard reactions, employing the novel KKSS19 parameterization for the description of parton fragmentation into $��_c$ baryons. We provide predictions for rapidity distributions and azimuthal correlations, that can be studied at current and forthcoming LHC configurations, hunting for possible stabilizing effects of the high-energy series., 28 pages, 9 figures; updated references, version published in Eur. Phys. J. C

International audience

International audience; In this paper, we introduce cloud supply chain which is a business model based on cloud-enabled networking of some third-party physical and digital assets to design and manage a supply chain network. Cloud supply chain integrates concepts and technology of Industry 4.0 and digital platforms emerging in the “supply chain-as-a-service” paradigm. This paper conceptualizes the cloud supply chain as a new and distinct research area. Through analysis of practical cases, we deduce some generalized characteristics of the cloud supply chain. In the generalised model, we formalize supply chain multi-structural dynamics and dynamic service composition. Our results show that the main generalized characteristics of the cloud supply chain are related to (i) multi-structural dynamics; (ii) platforms, digital supply chains, ecosystems, and visibility, (iii) dynamic service composition with dynamically changing buyer/supplier roles, (iv) resilience and viability, and (v) intertwined supply networks and circular economy. We close by discussing future research directions including novel context of Industry 5.0.

9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019; Berlin; Germany; 28 August 2019 through 30 August 2019. Publicado en IFAC-PapersOnLine 52(13), p. 2488-2493 This paper aims to define the different considerations and results obtained in the implementation in an Intelligent Maintenance System of a laboratory designed based on basic concepts of Industry 4.0. The Intelligent Maintenance System uses asset monitoring techniques that allow, on-line digital modelling and automatic decision making. The three fundamental premises used for the development of the management system are the structuring of information, value identification and risk management.

Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion—the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply–demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.

International audience; This book has resulted from the activities of IFAC TC 5.2 “Manufacturing Modelling forManagement and Control”. The book offers an introduction and advanced techniques ofscheduling applications to cloud manufacturing and Industry 4.0 systems for larger audience.This book uncovers fundamental principles and recent developments in the theory andapplication of scheduling methodology to cloud manufacturing and Industry 4.0. The purposeof this book is to present recent developments in scheduling in cloud manufacturing andIndustry 4.0 and to systemize these developments in new taxonomies and methodologicalprinciples to shape this new research domain. This book addresses the needs of bothresearchers and practitioners to uncover the challenges and opportunities of schedulingtechniques’ applications to cloud manufacturing and Industry 4.0. For the first time, itcomprehensively conceptualizes scheduling in cloud manufacturing and Industry 4.0 systemsas a new research domain. The chapters of the book are written by the leading internationalexperts and utilize methods of operations research, industrial engineering and computerscience.

We propose the study of the inclusive production of two $��$ hyperons or a single $��$-particle in association with a jet, featuring high transverse momenta and large separation in rapidity, as a probe channel of the resummation of energy logarithms in the QCD perturbative series. We give predictions, shaped on kinematic ranges typical of CMS and of the backward CASTOR detector, for cross section and azimuthal-correlation moments between the two emitted objects, showing how considering the tag of $��$ baryons eases the comparison between theoretical results and experimental data in the phenomenological context of semi-hard reactions., 23 pages, 6 figures; updated references; slight changes in Eq. (14), updated Section 2.3, added 2 plots (Figs. 6 and 8), version published in Phys. Rev. D

Актуальность исследования обусловлена необходимостью изучения структуры каолиновых глин в связи с их широким промышленным применением. Физические и физико-химические свойства глин изменяются при различных техногенных воздействиях на них, что обусловлено преобразованием кристаллической структуры. Цель: изучение влияния давления на структуру каолинита. Объект: каолиновая глина Нижне-Увельского месторождения Челябинской области. Метод: инфракрасная спектроскопия. Инфракрасные спектры регистрировались на ИК Фурье-спектрометре в интервале 400…3800 см{-1}. Обработка ИК-спектров проводилась с помощью программного обеспечения Fspec. Результаты экспериментальных исследований показали, что обогащенная каолиновая глина Нижне-Увельского месторождения Челябинской области, не подверженная воздействию давлением, характеризуется частично упорядоченной (дефектной) структурой. При воздействии давлений от 10 до 650 МПа глина характеризуется частично упорядоченной структурой, при давлениях от 700 до 800 МПа - слабо и неупорядоченной структурой. Обработка каолина давлением повышает дефектность структуры на уровне пакета. Связи между Al-OH, Si-O-Al, внутренними и внутриповерхностными гидроксилами разрушаются, что приводит к вытеснению Al из октаэдрического и Si из тетраэдрического листов и формированию "дырочных" энергетических центров в кристаллической решетке каолинита. При этом влияние давления до 150 МПа на формирование дефектов в каолините выше, чем при давлениях от 150 до 800 МПа. Кроме того, при обработке каолина давлением происходит образование дефектов структуры на межпакетном уровне за счет скольжения, вращения и прокатки структурных пакетов между собой. The research is relevant due to the need to study the structure of kaolin clays in relation to their wide industrial application. Physical and physico-chemical properties of clays change under various technogenic impacts on them. The change is caused by crystalline structure transformation. The main aim of the research is to study the effect of pressure on the structure of kaolinite. The object of the research is the kaolin clay of the Nizhne-Uvelskoe deposit of the Chelyabinsk region. The method of the research is the infrared spectroscopy. Infrared spectra were recorded on an IR Fourier-spectrometer in the range 400...3800 cm{-1}. IR spectrum was processed using Fspec software. The results of experimental studies showed that the enriched kaolin clay of the Nizhne-Uvelskoe deposit of the Chelyabinsk region, which was not exposed to pressure, is characterized by a partially ordered (defective) structure. Under the influence of pressures from 10 to 650 MPa, the clay is characterized by a partially ordered structure, at pressures from 700 to 800 MPa - a poorly ordered and disordered structure. Pressure treatment of kaolin increases the defectiveness of the structure at the packet level. The bonds between Al-OH, Si-O-Al, inner and inner-surface hydroxyls are destroyed, which leads to the displacement of Al from the octahedral and Si from tetrahedral sheets and the formation of «hole» energy centers in the kaolinite crystal lattice. Moreover, the effect of pressure up to 150 MPa on formation of defects in kaolinite is higher than at pressures from 150 to 800 MPa. In addition, during the processing of kaolin by pressure, structural defects are formed at the inter packet level due to sliding, rotation and rolling of structural packets between themselves.

"9th IFAC Conference on Manufacturing Modelling, Management and Control" post-conference Proceedings are published in IFAC-PapersOnLine series (Elsevier) and indexed by Scopus, the Web of Science (WoS), and Google Scholar. All full papers presented are in Open Access in IFAC PapersOnLine Series, Volume 52, Issue 13:The Proceedings feature 478 full papers (2838 pages in total) written by more than 1500 authors from 69 countries and selected out of more than 850 submissions with the help of more than 750 reviewers.

International audience

The article studies the issues of rock culture as a specific open discourse system being a part of the Russian national culture. The article aims to identify the most promising ways for comprehensive analysis of this phenomenon in the context of modern humanitarian knowledge. The terms “cultural studies”, “discourse”, “synthetic text”, and “rock text” are specified. The article addresses a rock text and rock poetry at the present stage of humanitarian knowledge development; components determining the nature of the rock text compared to other poetic texts; a method that can describe the rock text as a complex synthetic phenomenon. The research purpose is to study the Russian rock culture in terms of modern humanitarian knowledge. The following methods were used: comparative, integrated, statistical, and linguocultural analysis. The article concludes that the rock work is closely associated with poetry, melody, musical rhythm, vocal, audience factor, and stage behavior. It can be considered as a synthetic phenomenon. The rock text cannot be interpreted and studied within narrow disciplines. Its nature and configuration cannot be the subject of individual scientific disciplines. Such large-scale, developing, holistic cultural phenomenon can be studied only within cultural disciplines with their specific methods. In particular, linguoculturological and culturological methods can be combined to study this phenomenon.

Bid leakage is a corrupt scheme in a first-price sealed-bid auction in which the procurer leaks the opponents' bids to a favoured participant. The rational behaviour of such participant is to bid close to the deadline in order to receive all bids, which allows him to ensure his win at the best price possible. While such behaviour does leave detectable traces in the data, the absence of bid leakage labels makes supervised classification impossible. Instead, we reduce the problem of the bid leakage detection to a positive-unlabeled classification. The key idea is to regard the losing participants as fair and the winners as possibly corrupted. This allows us to estimate the prior probability of bid leakage in the sample, as well as the posterior probability of bid leakage for each specific auction. We extract and analyze the data on 600,000 Russian procurement auctions between 2014 and 2018. We find that around 9% of the auctions are exposed to bid leakage, which results in an overall 1.5% price increase. The predicted probability of bid leakage is higher for auctions with a higher reserve price, with too low or too high number of participants, and if the winner has met the auctioneer in earlier auctions., This is long version of short paper 'Identifying Bid Leakage in Procurement Auctions: Machine Learning Approach', published at EC'19: https://dl.acm.org/doi/abs/10.1145/3328526.3329642

International audience

"9th IFAC Conference on Manufacturing Modelling, Management and Control" post-conference Proceedings are published in IFAC-PapersOnLine series (Elsevier) and indexed by Scopus, the Web of Science (WoS), and Google Scholar. All full papers presented are in Open Access in IFAC PapersOnLine Series, Volume 52, Issue 13:The Proceedings feature 478 full papers (2838 pages in total) written by more than 1500 authors from 69 countries and selected out of more than 850 submissions with the help of more than 750 reviewers.

The gluon content of the proton, in the high-energy regime, is embodied by the unintegrated gluon distribution (UGD), which describes the gluon emission probability, with a given longitudinal momentum fraction and transverse momentum. The UGD, formulated within the $��$-factorization approach, has universal validity and several models for it have been proposed so far. We will show that the polarized $��$-meson leptoproduction at HERA is a not trivial testfield for discriminating among existing models of UGD., 6 pages, 4 figures, presented by A.D. Bolognino at "Diffraction & Low-x 2018", Reggio Calabria, Italy, August 26 - September 1, 2018; to be published in the conference proceedings by Acta Phys. Polon. B

We present here some arguments to support our suggestion that data on the helicity structure for the hard exclusive electroproduction of $\rho$ mesons at HERA (and in possible future high-energy electron-proton colliders) provide useful information to constrain the $\kappa$-shape of the unintegrated gluon distribution in the proton., Comment: 20 pages, 4 figures; version accepted for publication on Eur. Phys. J. C

Earlier work carried out the University of Bristol has shown the possibility to accurately predict consolidation-driven defect formation in composite parts using a ply-by-ply approach. However, using this method, a typical model for a lab-scale specimen can easily reach the hundreds of thousands of degrees of freedom, which makes it unsuitable for the modelling of full-scale components and limits its applicability for the modelling of real industrial cases. The present contribution, proposes to overcome this difficulty using kinematically enhanced constitutive modelling. The paper first discusses the basis of kinematically enhanced modelling for layered structures made of soft anisotropic material. The ability of the proposed framework to significantly reduce the computational cost of the simulation while correctly capturing consolidation and defect formation processesis then established. Finally, conclusions are drawn on the prospect for modelling larger components.

International audience

The gluon content of the proton is embodied by the unintegrated gluon distribution (UGD), which has universal validity. In literature many models of UGD have been proposed so far. The polarized $��$-meson leptoproduction at HERA offers a nowadays unexplored testfield to discriminate among existing models of UGD, via the comparison with theoretical predictions formulated in the $��$-factorization approach., 6 pages, 3 figures, Talk given at the XIX Frascati Spring School "Bruno Touschek" in nuclear, subnuclear and astroparticle physics, Frascati, 7-11 May 2018

Uppaal SMC is a state-of-the-art tool for modelling and statistical analysis of hybrid systems, allowing the user to directly model the expected battery consumption in battery-operated devices. The tool employs a numerical approach for solving differential equations describing the continuous evolution of a hybrid system, however, the addition of a battery model significantly slows down the simulation and decreases the precision of the analysis. Moreover, Uppaal SMC is not optimized for obtaining simulations with durations of realistic battery lifetimes. We propose an analytical approach to address the performance and precision issues of battery modelling, and a trace extrapolation technique for extending the prediction horizon of Uppaal SMC. Our approach shows a performance gain of up to 80% on two industrial wireless sensor protocol models, while improving the precision with up to 55%. As a proof of concept, we develop a tool prototype where we apply our extrapolation technique for predicting battery lifetimes and show that the expected battery lifetime for several months of device operation can be computed within a reasonable computation time.

We present here some arguments to support our suggestion that data on the helicity structure for the hard exclusive electroproduction of $��$ mesons at HERA (and in possible future high-energy electron-proton colliders) provide useful information to constrain the $��$-shape of the unintegrated gluon distribution in the proton., 20 pages, 4 figures; version accepted for publication on Eur. Phys. J. C