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International audience; Metal foam embedded in phase change materials (PCM) has been shown to significantly improve the storage of latent heat thermal energy. Nonetheless the presence of metal foam also reduces natural convection, energy storage and increases cost. To address this issue, we modelled the internal flow of heat transfer in a PCM, paraffin wax, filled with metal foam at the top or the bottom, with filling height ratio ξ of 0.25, 0.5 and 0.75. The liquidsolid phase transition was studied by numerical simulations. Results show that natural convection in the pure paraffin wax area is higher, and melting time is shorter, in the bottom-filled than in the top-filled configuration. These differences increase with filling height ratio. By contrast, in metal foam-paraffin composite region, melting time is longer in the bottom-filled configuration due to heat loss. Interestingly, we observed significant changes in the interface shape of liquid-solid PCM at the junction of the pure paraffin and the metal foam-paraffin composite region. The liquid fraction formulas for different metal foam filled configurations are established as the function of Fourier number, Rayleigh number and filling height ratio.

International audience; Robust dynamic Bayesian network (DBN) is a valid tool for disruption propagation estimation in the supply chain under data scarcity. However, one of assumptions in robust DBN is that the Markov transition matrix is fixed and fully known, which is unpractical. To make up this deficiency, a novel and general robust DBN is, for the first time, proposed in this work to assess the worst-case oriented supply chain disruption risk under ripple effect. The study focuses on a supply chain with multiple suppliers and one manufacturer over a time horizon, in which only probability intervals of related probabilities are known. The objective is to obtain the worst-case supply chain disruption risk, measured by the probability of the manufacturer in the fully disrupted state in the final time period. For the problem, a new and general nonconvex programming model is established. Then, a case study is conducted to compare our approach with the classic DBN and robust DBN in the literature.

International audience; Buses are expected to be punctual, but deviation from the schedule is a common occurrence. Transit signal priority (TSP) and conditional signal priority (CSP) are methods to help the bus perform the schedule better by giving traffic signal priority to the bus. Markov decision process (MDP) is suitable for modeling such sequential decision process. In this article, we point out the Markov property of the system based on the analysis of the bus driving process. We model the process of bus driving with CSP as a Markov decision process. Then, the Deep Q Network (DQN) algorithm is applied to solve this MDP model. To the best of our knowledge, this is the first time that the bus scheduling problem with CSP has been modeled as an MDP model and solved by a learning algorithm. Numerical experiments verify the applicability of the DQN algorithm to solve this MDP model.

International audience; Uncertain disruption risks negatively influence the performance of supply chains by reducing the facilities' capacity. Proactive and recovery strategies for building resilient supply chains have received increasing attention from academia. Therefore, in this work, we study a resilient supply chain designing problem considering the uncertain occurrence and extent of disruptions, and the stochastic demand during time periods. For the problem, a two-stage min-max relative regret robust model is developed. The first stage is to determine the optimal facility locations and investments in protection systems, and the second stage decides the recovery actions and the quantity of products transported between different facilities and customers. The objective is to minimise the worst-case total relative regret cost. Finally, a case study is conducted, and some insights are given through the results of sensitivity analysis.

International audience; The rapid growth of the internet of things has induced the integration of many microelectronic devices in physical objects, yet the generated heat decreases the performance and stability of microelectronic devices. Therefore, new materials such as gradient metal foams (GMFs) have been recently designed to improve heat transfer. In this paper, an experimental visualization setup was built to investigate the effect of the GMFs gradient layers number and the arrangement order on the pool boiling heat transfer performance. Results show that increasing the number of gradient layers enhances the heat transfer when the copper foam pore density is low. By contrast, at high pore density of 50 PPI, increasing layers hardly changes heat transfer. The bubbles dynamic behavior on the metal foams surface of with different gradient structures is also different. When bubbles detach upward, the temperature of the metal foam is lower, and the temperature gradient is higher. When bubbles detach sideward, the bubble escape is much shorter, and the bubble detachment frequency and size increase. Combined with the theoretical research, the metal foam gas-liquid flow heat transfer model were constructed. The advantages and disadvantages of GMFs with different structures and the applicable scenarios are analyzed.

International audience; Combined chemo/gene therapy of cancer through different action mechanisms has been emerging to enhance the therapeutic efficacy towards cancer, and still remains a challenging task due to the lack of highly effective and biocompatible nanocarriers. In this work, we report a new nanosystem based on amphiphilic phosphorus dendron (1-C12G1) micelles to co-deliver microRNA-21 inhibitor (miR-21i) and doxorubicin (DOX) for combination therapy of triple negative breast cancer. The amphiphilic phosphorus dendron bearing a long linear alkyl chain and ten protonated pyrrolidine surface groups was prepared and was demonstrated to form micelles in water solution and have a hydrodynamic size of 103.2 nm. The micelles are shown to be stable, enable encapsulation of an anticancer drug DOX with optimal loading content (80%) and encapsulation efficiency (98%), and can compress miR-21i to form polyplexes to render it with good stability against degradation. The co-delivery system of 1-C12G1@DOX/miR-21i polyplexes has a pH-dependent DOX release profile, and can be readily phagocytosed by cancer cells to inhibit them due to the different anticancer mechanisms, which was further validated after intravenous injection to treat an orthotopic triple-negative breast tumor model in vivo. With the proven biocompatibility under the studied doses, the developed amphiphilic phosphorus dendron micelles could be developed as an effective nanomedicine formulation for synergistic cancer therapy.

International audience; Gradient metal foam is an advanced heat transfer material that decreases resistance to bubble escape and enhances the transfer of boiling heat. In this study, boiling heat transfer and bubble behavior were studied in an experimental set-up with copper foam bilayers configurated either in positive or inverse gradients, utilizing deionized water as working fluid. Positive gradient refers to arranging metal foam layers with high pore density at the bottom, above the heat source, and low pore density on the top. Results show that the heat transfer is higher for gradient metal foam surfaces, of 6.14×105 W/m2, versus a uniform metal foam surface, of 3.94×105 W/m2. For the positive gradient configuration, boiling heat transfer performance first increased with the pore density, then decreased when the pore density was higher than 60 pores per inch (PPI). By contrast, for the inverse gradient, the heat transfer performance was nearly constant with increasing pore density. At the low pore density, the inverse gradient performed better than the positive gradient during the whole boiling process. At high pore density, the positive gradient structure performed better in heat transfer at the early boiling stage. Three main types of bubble escape were observed: For the positive gradient bilayer, the bubbles moved up or down without lateral interference. In contrast, for inverse gradient, the bubbles mostly escape from the sides, which is easy to induce bubble merging. The inverse gradient surface generates larger bubbles, while the positive gradient surface produces a higher frequency of bubble detachment. Accordingly, two liquid replenishment models are proposed: for the positive gradient, external liquid replenishes from the side into the copper foam, while for inverse gradient, the liquid is mainly replenished from the top.

International audience; The outbreak of extraordinary disruptive events, e.g., the COVID-19 pandemic, has greatly impacted the orderly operation in global supply chains (SCs), and may lead to the SC breakdown. Regulatory actions, such as government interventions during the pandemic, can greatly mitigate the disruption propagation (i.e., the ripple effect) and improve SC viability. However, existing works that focus on the disruption propagation management have not considered the possibility of such interventions. Motivated by the fact, in this study, we investigate a new disruption propagation management problem in a multi-echelon SC with limited intervention budget. The aim is to minimize disruption risk measured by the disrupted probability of target participants in the SC. For the problem, a novel approach, combining the Causal Bayesian Network (CBN), the do-calculus and the mathematical programming, is developed. Specially, two mixed-integer non-linear programming models are constructed to determine appropriate interventions. To enhance the proposed mathematical models, two valid inequalities are proposed. Then, a problem-specific genetic algorithm (GA) is developed for handling large-scale problem instances. Numerical experiments on a case study and randomly generated instances are conducted to evaluate the efficiency of the proposed models, the valid inequalities and the GA. Based on experiment analysis, managerial insights are drawn.

International audience; As a kind of material with high thermal conductivity, metal foam embedded in phase change materials (PCM) can significantly improve the thermal energy storage performance of PCM, so as to better serve the latent heat thermal energy storage (LHTES). However, the presence of metal foam will suppress the natural convection of PCM, reduce its LHTES capacity and increase the total cost. In view of the above problems, this study designed LHTES units with metal foam filled at the top or the bottom with different height ratios. The melting heat transfer characteristic was studied by numerical simulations. Results show that the flow rate of liquid paraffin in the bottom filling configuration is one to two orders of magnitude higher than that in the top filling configuration, which plays a positive role in natural convection and is more conducive to heat transfer enhancement. However, the top filling configuration performs better in the heat storage capacity. The metal foam with a filling height ratio of 0.75 can increase the heat storage capacity by 7.47% while saving 25% material. On this basis, we proposed a new criterion, named the comprehensive strengthening goodness factor (CSGF), for evaluating the heat transfer performance of LHTES system. CSGF is different from the general criterion in that it can evaluate the heat storage capacity and the economic performance at the same time. Under the evaluation of CSGF, the optimum metal foam-paraffin configurations for different occasions were selected.

International audience; Supply chain (SC) has been continuously affected by the epidemic and political situation, which has greatly impacted the SC disruption risk management. This situation evokes attention to control the SC inventory management under ripple effect. In addition, the need for supplier selection and disruption risk management can affect the SC inventory management over the planning time horizon. However, most existing works focus on inventory management, supplier selection, and disruption risk assessment separately. This work investigate an integrated inventory management, supplier selection, and disruption risk assessment under ripple effect. For the problem, a novel mixed-integer optimal control blended with dynamic Bayesian network (DBN) is formulated. The mixed-integer optimal control, proposed by Sager (2005), can portray the dynamic process consisting of continuous and discrete control variables. The DBN is applied to establish probabilistic relationships among SC participants, and to evaluate the disruption risk of the manufacturer. Two performance indexes are proposed to minimize the total cost and the disrupted probability for the manufacturer simultaneously. Then, a solution method is established to solve the problem, and an illustrative example is presented to demonstrate the proposed method.

International audience; Due to the impact of the global COVID-19, numerous industries have suffered from the disruption propagating along the supply chain, i.e. the ripple effect. To reduce adverse impact of the ripple effect, supply chain (SC) risk management under it is becoming an increasingly hot topic in both practice and research. In our former research, a robust dynamic bayesian network (DBN) approach has been developed for disruption risk assessment, whereas the solution methods adopted before (commercial solvers and simulated annealling algorithm) are not efficient enough, especially for large-size instances. For this reason, a new reinforcement learning variable neighborhood search (QVNS) is developed for solving the robust DBN optimization model, where the Q-learning algorithm is implemented to select the most efficient neighborhood structure in different stages of the search process. We conduct computational experiments on randomly generated instances, which indicates that Q-learning algorithm can improve significantly the performance of the VNS on large-size instances of the robust DBN optimization problem.

International audience; Post-disaster relief scheduling problem in sustainable humanitarian supply chains (SHSCs) has received increasing attentions from academia. Most existing works focus on the uncertain relief supplies. However, since the destruction caused by disaster can not be estimated accurately and timely, relief demands also can be uncertain. Therefore, in this paper, we study a post-disaster relief scheduling problem in SHSCs considering uncertain relief supplies and demands simultaneously. The objective is to minimize the expected total unsatisfied demand rate, adverse environment impact and economic cost on the upper level decision, and to maximize the expected total survivors' perceived satisfaction on the lower level decision. For the problem, a new stochastic bi-level optimization model is first established. And a hybrid solution approach including a sample average approximation method, a prime-dual algorithm, a linearization technique and a global criteria method is further devised. Finally, a case study is conducted.

International audience; Lot-sizing and pricing are two important manufacturing decisions that impact together the profit of a company. Existing works address the joint lot-sizing and pricing problem without backlogging, although it is a usual strategy that permits to satisfy customer demand with delay. In this work, we study a new multi-product joint lot-sizing and pricing problem with backlogging and limited production capacity. The objective is to maximize the total company profit over a finite planning horizon. For the problem, a mixed integer nonlinear programming (MINLP) formulation is given. Then, several optimality properties are provided and a tighter MINLP model is established based on these properties. According to the NP-hard nature and non-linearity of the model, a model based heuristic that focuses on efficiently solving small-sized instances is proposed and a genetic algorithm (GA) with new progressive repair strategy is developed to address large-sized instances. Managerial insights are drawn based an illustrative example. Numerical experiments are conducted on 64 benchmark based instances and 105 randomly generated instances with up to 10 products and 12 periods, which validates the MINLP formulation and shows the efficiency of the proposed solution methods.

International audience; Existing works usually focus on the single-product disassembly line balancing problem (DLBP). In practice, end-of-life (EOL) products to be disassembled may be heterogeneous, and the actual processing time of each task may vary with its assigned worker. This work studies a stochastic multi-product DLBP with workforce assignment, to minimise the system cost. Due to historical data scarcity, we assume that only partial distributional information of uncertain task processing times is known. Exceeding the preset cycle time may lead to a disassembly performance reduction, thus we control the cycle time violation via conditional Value-at-Risk (CVaR) constraints, i.e. in a risk-averse fashion. For the problem, we first propose a novel formulation with distributionally robust CVaR constraints. Then some valid inequalities are proposed, leading to an improved model. Two solution approaches, i.e. an exact cutting-plane method and an approximation method, are further proposed and compared, via numerical experiments. Some managerial insights are also drawn.

Edge localized modes (ELMs) suppression by resonant magnetic perturbations (RMPs) was studied with the non-linear magneto-hydro-dynamic (MHD) code JOREK for the ITER H-mode scenarios at 15 MA, 12.5 MA, 10 MA/5.3 T. The main aim of this work was to demonstrate that ELMs can be suppressed by RMPs while the divertor 3D footprints of heat and particle fluxes remain within divertor material limits. The unstable peeling–ballooning modes responsible for ELMs without RMPs were modelled first for each scenario using numerically accessible parameters for ITER. Then the stabilization of ELMs by RMPs was modelled with the same parameters. RMP spectra, optimized by the linear MHD MARS-F code, with main toroidal harmonics N = 2, N = 3, N = 4 have been used as boundary conditions of the computational domain of JOREK, including realistic RMP coils, main plasma, scrape off layer (SOL) divertor and realistic first wall. The model includes all relevant plasma flows: toroidal rotation, two fluid diamagnetic effects and neoclassical poloidal friction. With RMPs, the main toroidal harmonic and the non-linearly coupled harmonics remain dominant at the plasma edge, producing saturated modes and a continuous MHD turbulent transport thereby avoiding ELM crashes in all scenarios considered here. The threshold for ELM suppression was found at a maximum RMP coils current of 45 kAt–60 kAt compared to the coils maximum capability of 90 kAt. In the high beta poloidal steady-state 10 MA/5.3 T scenario, a rotating QH-mode without ELMs was observed even without RMPs. In this scenario with RMPs N = 3, N = 4 at 20 kAt maximum current in RMP coils, similar QH-mode behaviour was observed however with dominant edge harmonic corresponding to the main toroidal number of RMPs. The present MHD modelling was limited in time by few tens of ms after RMPs were switched on until the magnetic energy of the modes saturates. As a consequence the thermal energy was still evolving on this time scale, far from the ITER confinement time scale and hence only the form of 3D footprints on the divertor targets can be indicated within this set-up. Also note, that the divertor physics was missing in this model, so realistic values of fluxes are out of reach in this modelling. However the stationary 3D divertor and particle fluxes could be simply extrapolated from these results to the stationary situation considering that a large power fraction should be radiated in the core and SOL and only about 50 MW power is going to the divertor, which is an arbitrary, but reasonable number used here. The 3D footprints with RMPs show the characteristic splitting with the main RMP toroidal symmetry. The maximum radial extension of the footprints typically was ∼20 cm in inner divertor and ∼40 cm in outer divertor with stationary heat fluxes decreasing further out from the initial strike point from ∼5 MW m−2 to ∼1 MW m−2 assuming a total power in the divertor and walls is 50 MW. The heat fluxes remain within the divertor target and baffle areas, however with rather small margin in the outer divertor which could be an issue for the first wall especially in transient regimes when part of the plasma thermal energy is released due to switching on the RMP coils. This fact should be considered when RMPs are applied with a more favorable application before or soon after the L–H transition, although optimization is required to avoid increasing the L–H power threshold with RMPs.

International audience; Growing global trade brings an increasing challenge to intelligent maritime transportation, which is an important branch of the intelligent transportation system. Developing efficient technologies to improve the performance of intelligent maritime transportation is especially important. Most existing works for integrated berth allocation and quay crane assignment assume that all the equipment is available over the time horizon, however, there exists frequently time-consuming quay crane maintenance activities in the maritime port. It is recognized that maintenance activities can impact the loading/unloading activities. In this paper, we study a new bi-objective optimization model of integrated berth allocation and quay crane assignment with preventive quay crane maintenance activities. The two objectives are minimizing the total turnaround time of vessels and the total penalty cost of quay crane maintenance earliness and tardiness. For the considered problem, an appropriate integer linear programming model is formulated, and an ϵ-constraint-based two-phase iterative heuristic is designed based on the characteristics of our problem. Computational results on a case study and randomly generated instances show the efficiency of the proposed algorithm.

International audience; Aptamers are RNA or DNA oligonucleotides interacting to form unique 3D target conformations with high affinity and specificity, and are emerging as a powerful class of ligands for therapeutic applications. In addition, dendrimers are well-defined nano-sized symmetric polymeric molecules. In this review, we provide an analysis of the use of dendrimers modified with aptamers as nonviral vectors to specifically target tumor cells. Various anticancer agents have been encapsulated with dendrimers complexing with aptamers, including epirubicin, camptothecin, Bcl-xL short hairpin (sh)RNA, and 5-fluorouracil rhodamine-labeled dextran. Other types of polymeric nanoparticle (NP)–aptamer bioconjugates have also been developed and loaded with Pt(IV) derivatives, to target specific tumor cells.

International audience; The collection-disassembly problem plays an important role in a reverse supply chain. It coordinates the collection and disassembly activities for end-of-life (EOL) products. Most existing works consider the deterministic problems. However, in practice, demands of reusable components in EOL products may be uncertain. Besides, it is usually difficult to exactly obtain probability distributions of uncertain demands, due to inadequate historical data. This paper studies a collection-disassembly problem under partial known distributional information of component demands, in which equipments of the disassembly site, corresponding to different disassembly capacities, have to be selected. The objectives are to minimise the system cost and to maximise the customer service level. For the problem, a novel distributionally robust bi-objective formulation is proposed. Based on the Monte Carlo simulation and an ambiguity set, a sample average approximation (SAA) model and an approximated mixed integer programming (MIP) model are constructed, respectively. Then the two approximated formulations are solved, via the ε-constraint framework, and compared in numerical experiments.

International audience; Conventional small molecular chemical drugs always have challenging limitations in cancer therapy due to their high systemic toxicity and low therapeutic efficacy. Nanotechnology has been applied in drug delivery, bringing new promising potential to realize effective cancer treatment. In this context, we develop here a new nanomicellar drug delivery platform generated by amphiphilic phosphorus dendrons (1-C17G3.HCl), which could form micelles for effective encapsulation of a hydrophobic anticancer drug doxorubicin (DOX) with a high drug loading content (42.4%) and encapsulation efficiency (96.7%). Owing to the unique dendritic rigid structure and surface hydrophilic groups, large steady void space of micelles can be created for drug encapsulation. The created DOX-loaded micelles with a mean diameter of 26.3 nm have good colloidal stability. Strikingly, we show that the drug-free micelles possess good intrinsic anticancer activity and act collectively with DOX to take down breast cancer cells in vitro and the xenografted tumor model in vivo through upregulation of Bax, PTEN, and p53 proteins for enhanced cell apoptosis. Meanwhile, the resulting 1-C17G3.HCl@DOX micelles significantly abolish the toxicity relevant to the free drug. The findings of this study demonstrate a unique nanomicelle-based drug delivery system created with the self-assembling amphiphilic phosphorus dendrons that may be adapted for chemotherapy of different cancer types.

International audience; The disassembly is a fundamental basis in converting End-of-Life (EOL) products into useful components. Related research becomes popular recently due to the increasing awareness of environmental protection and energy conservation. Yet, there are many opening questions needed to be investigated, especially the efficient coordination of different-level decisions under uncertainty is a big challenge. In this paper, a novel integrated stochastic disassembly line balancing and planning problem is studied to minimise the system cost, where component yield ratios and demands are assumed to be uncertain. In this work, machine specificities are considered for task processing, such as price, ability, and capacity. For the problem, a two-stage non-linear stochastic programming model is first constructed. Then, it is further transformed into a linear formulation. Based on problem property analysis, a valid inequality is proposed to reduce the search space of optimal solutions. Finally, a sample average approximation (SAA) and an L-shaped algorithm are adopted to solve the problem. Numerical experiments on randomly generated instances demonstrate that the valid inequality can save around 11% of average computation time, and the L-shaped algorithm can save around 64% of average computation time compared with the SAA algorithm without a big sacrifice of the solution quality.

International audience; Development of novel nanomedicines to inhibit pro-inflammatory cytokine expression and reactive oxygen species (ROS) generation for anti-inflammatory therapy of acute lung injury (ALI) remains challenging. Here, we present a new nanomedicine platform based on tyramine-bearing two dimethylphosphonate sodium salt (TBP)-modified amphiphilic phosphorus dendron (C11G3) nanomicelles encapsulated with antioxidant drug curcumin (Cur).

International audience; Assembly line worker assignment and balancing problem (ALWABP) is an important research topic originated from sheltered work centres for disabled, in which workforce is assumed to be heterogeneous due to their disabilities. Since the employment of disabled workers may sustain higher absenteeism rates due to their health, especially under COVID-19, employing temporary workers to fill labour shortage is a crucial issue. In addition, in practice, the movement of workers between stations on assemble lines can increase the flexibility of worker assignment. In this study, we investigate a new risk-averse ALWABP with uncertain disabled worker availability, limited temporary workers and moving workers. The objective is to minimise the risk-averse weighted sum of the cycle time and the number of employed temporary workers. For the problem, a risk-averse two-stage stochastic programming model is formulated. The first stage assigns specific tasks (called fixed tasks) to stations, while the second stage assigns workers and remaining tasks (called flexible tasks) to stations. A genetic algorithm combining K-means clustering approach and variable neighbourhood search (GAKV) is designed. Experiment results show the superiority of the GAKV in terms of solution quality and computation time compared with sample average approximation (SAA). In addition, managerial insights are drawn.

International audience; Tubular structure tracking is an important task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their powerful ability in tracing tubular structures, by which a tubular structure can be naturally treated as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulty, for instances the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek favourable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal path-based tubular structure tracing algorithms.

International audience; In the last two decades, maintenance as an essential method to prevent machine breakdown has achieved undoubted importance in process industries and manufacturing systems. With ever increasing high-quality products demand, the number of tool change or machine maintenance become quite remarkable. Besides, industry 4.0 supports manufacturing enterprises to explore more efficient and intelligent scheduling modes. Machine periodic maintenance has a significant impact on the scheduling of many manufacturing companies, it therefore has become one of the factors they must consider. Despite this, theoretical analysis for the machine scheduling problem with periodic maintenance has not received considerable critical attention in previous studies. Motivated by scheduling practice, this work revisits a single machine scheduling problem with periodic maintenance to minimize the number of tardy jobs. First, a polynomial-time solvable case is identified. Then we propose a dynamic programming algorithm to solve the general case. At last, the non-approximability is proven.

International audience; During the past two decades, tremendous progress has been made in the dendrimer-based delivery of therapeutic molecules including, for instance, small molecules, macromolecules, and genes. This review deals with recent successes in the development of promising biocompatible phosphorus dendrimers, a specific type of dendrimer, to deliver genes to treat cancers.

International audience; Stochastic scheduling has received much attention from both industry and academia. Existing works usually focus on random job processing times. However, the uncertainty existing in job release times may largely impact the performance as well. This work investigates a stochastic parallel machine scheduling problem, where job release times and processing times are uncertain. The problem consists of a two-stage decision-making process: (i) assigning jobs to machines on the first stage before the realisation of uncertain parameters (job release times and processing times) and (ii) scheduling jobs on the second stage given the job-to-machine assignment and the realisation of uncertain parameters. The objective is to minimise the total cost, including the setup cost on machines (induced by job-to-machine assignment) and the expected penalty cost of jobs' earliness and tardiness. A two-stage stochastic program is proposed, and the sample average approximation (SAA) method is applied. A scenario-reduction-based decomposition approach is further developed to improve the computational efficiency. Numerical results show that the scenario-reduction-based decomposition approach performs better than the SAA, in terms of solution quality and computation time.

International audience; Inaccuracy of buses is a common situation. A common practice is that a certain amount of slack is usually added to the schedule of bus operation, so that the bus can execute the schedule in most cases. On the other hand, slack means that buses sometimes have to wait for a while at the station or slow down while driving. Since the bus cannot accelerate the driving process by itself, this method cannot make the bus fully implement the schedule. Researchers invented the Transit signal priority (TSP) and conditional signal priority (CSP), the purpose of which is to give the bus signal priority to speed up when it is delayed to a certain extent. Some previous work has studied the driving process of buses with CSP. However, there is still room for further improvement in the mathematical description of the bus driving process based on CSP. In this article, we analyze the driving state of the bus under different CSP states, that is, positive and negative. Then a series of representative and operational assumptions are given. These assumptions can be used as the basis for future research on such topics. With the assumptions, we give a mathematical model of the bus driving process using CSP. According to some performance indicators of the bus driving process obtained in the modeling process, an optimization goal is established to comprehensively improve the driving effect of the bus. Mathematical analysis and numerical solution verify the applicability of the model.

International audience; The novel human coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has caused a pandemic. There are currently several marketed vaccines and many in clinical trials targeting SARS-CoV-2. Another strategy is to repurpose approved drugs to decrease the burden of the COVID-19 (official name for the coronavirus disease) pandemic. as the FDA (U.S. Food and Drug Administration) approved antiviral drugs and anti-inflammatory drugs to arrest the cytokine storm, inducing the production of pro-inflammatory cytokines. Another view to solve these unprecedented challenges is to analyze the diverse nanotechnological approaches which are able to improve the COVID-19 pandemic. In this original minireview, as promising candidates we analyze the opportunity to develop biocompatible dendrimers as drugs themselves or as nanocarriers against COVID-19 disease. From the standpoint of COVID-19, we suggest developing dendrimers as shields against COVID-19 infection based on their capacity to be incorporated in several environments outside the patients and as important means to stop transmission of SARS-CoV-2.

International audience; Research to develop active dendrimers by themselves or as nanocarriers represents a promising approach to discover new biologically active entities that can be used to tackle unmet medical needs including difficult diseases. These developments are possible due to the exceptional physicochemical properties of dendrimers, including their biocompatibility, as well as their therapeutic activity as nanocarriers and drugs themselves. Despite a large number of academic studies, very few dendrimers have crossed the ‘valley of death’ between. Only a few number of pharmaceutical companies have succeeded in this way. In fact, only Starpharma (Australia) and Orpheris, Inc. (USA), an Ashvattha Therapeutics subsidiary, can fill all the clinic requirements to have in the market dendrimers based drugs/nancocarriers. After evaluating the main physicochemical properties related to the respective biological activity of dendrimers classified as first-in-class or best-in-class in nanomedicine, this original review analyzes the advantages and disavantages of these two strategies as well the concerns to step in clinical phases. Various solutions are proposed to advance the use of dendrimers in human health.

International audience; Based on phenotypic screening, the major advantages of phosphorus dendrimers and dendrons as drugs allowed the discovery of new therapeutic applications, for instance, as anti-cancer and anti-tuberculosis agents. These biological activities depend on the nature of the chemical groups (neutral or cationic) on their surface as well as their generation. As lessons to learn, in the oncology domain, the increase in the generation of metallo-dendrimers is in the same direction as the anti-proliferative activities, in contrast to the development of polycationic dendrimers, where the most potent anti-tuberculosis phosphorus dendrimer was observed to have the lowest generation (G0). The examples presented in this original analysis of phosphorus dendrimers and dendrons provide support for the lessons learned and for the development of new nanoparticles in nanomedicine.

International audience; End-of-life product recycling is a hot research topic in recent years, which can reduce the waste and protect the environment. To disassemble products, the disassembly line balancing is a principal problem that selects tasks and assigns them to a number of workstations under stochastic task processing times. In existing works, stochastic task processing times are usually estimated by probability distributions or fuzzy numbers. However, in real-life applications, only their partial information is accessible. This paper studies a bi-objective stochastic disassembly line balancing problem to minimise the line design cost and the cycle time, with only the knowledge of the mean, standard deviation and upper bound of stochastic task processing times. For the problem, a bi-objective chance-constrained model is developed, which is further approximated into a bi-objective distribution-free one. Based on the problem analysis, two versions of the ϵ-constraint method are proposed to solve the transformed model. Finally, a fuzzy-logic technique is adapted to propose a preferable solution for decision makers according to their preferences. A case study is presented to illustrate the validity of the proposed models and algorithms. Experimental results on 277 benchmark-based and randomly generated instances show the efficiency of the proposed methods.

Direct quantification of terrestrial biosphere responses to global change is crucial for projections of future climate change in Earth system models. Here, we synthesized ecosystem carbon-cycling data from 1,119 experiments performed over the past four decades concerning changes in temperature, precipitation, CO2 and nitrogen across major terrestrial vegetation types of the world. Most experiments manipulated single rather than multiple global change drivers in temperate ecosystems of the USA, Europe and China. The magnitudes of warming and elevated CO2 treatments were consistent with the ranges of future projections, whereas those of precipitation changes and nitrogen inputs often exceeded the projected ranges. Increases in global change drivers consistently accelerated, but decreased precipitation slowed down carbon-cycle processes. Nonlinear (including synergistic and antagonistic) effects among global change drivers were rare. Belowground carbon allocation responded negatively to increased precipitation and nitrogen addition and positively to decreased precipitation and elevated CO2. The sensitivities of carbon variables to multiple global change drivers depended on the background climate and ecosystem condition, suggesting that Earth system models should be evaluated using site-specific conditions for best uses of this large dataset. Together, this synthesis underscores an urgent need to explore the interactions among multiple global change drivers in under-represented regions such as semi-arid ecosystems, forests in the tropics and subtropics, and Arctic tundra when forecasting future terrestrial carbon-climate feedback. National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31430015, 31830012]; US NSFNational Science Foundation (NSF) [DEB-0955771]; ClimMani COST actionEuropean Cooperation in Science and Technology (COST) [ES1308] We thank J. Wang (Hebei University), S. Yang (Institute of Botany, Chinese Academy of Sciences), L. Zhou (East China Normal University), C. Qiao (Xinyang Normal University) and H. Li (Henan University) for their help in meta-analyses and interaction analyses, and H. Li, Y. Liu (Institute of Tibetan Plateau Research, Chinese Academy of Sciences) and Y. He (Peking University) for their help in plotting figures. This work was financially supported by the National Natural Science Foundation of China (grant nos. 31430015 and 31830012). This study emerged from the INTERFACE Workshop in Beijing, China (https://www.bio.purdue.edu/INTERFACE/) supported by the US NSF DEB-0955771. We also acknowledge support from the ClimMani COST action (ES1308). Public domain – authored by a U.S. government employee

International audience; In nanomedicine, the widespread concern of nanoparticles in general, and dendrimers, in particular, is the analysis of key in-vivo physicochemical parameters to ensure the preclinical and clinical development of ‘safe’ bioactive nanomaterials. It is clear that for biomedical applications, biocompatible dendrimers, used as nanocarriers or active per se, should be devoid of toxicity and immunogenicity, and have adequate PK/PD behaviors (adequate exposure) in order to diffuse in different tissues. Functionalization of dendrimers has a dramatic effect on in-vivo physicochemical parameters. In this review, we highlighted key in-vivo physicochemical properties, based on data from biochemical, cellular and animal models, to provide biocompatible dendrimers. Up-to-date, only scarce studies have been described on this topic.

International audience; The versatile functionalization of thiazoles and benzothiazoles at different locations in their backbone offers the possibility to design many substituted heterocyclic derivatives bearing active groups for applications in coordination chemistry, catalysis, and medicinal chemistry. The grafting of various phosphorus groups (phosphines, phosphine oxides, phosphonates, phosphinic acids, etc.) via different strategies leads to many phosphorus thiazoles and benzothiazoles acting per se as catalysts for a number of reactions for complexation with metals such as Ir, Au, Pt, Pd, and Cu, for which the catalytic properties were investigated.

International audience; Maritime transportation is an important branch of intelligent transportation system (ITS). It is widely recognized that modern ITS technologies and advanced management methods, such as automated yard crane (YC) planning and scheduling, can significantly improve container terminal performance, and also impact the global performance of maritime transportation. In this paper, we investigate two YC scheduling with storage and retrieval tasks in a container block. The main contributions of this paper are: 1) container reshuffling operations and inter-crane interference constraint are both considered and 2) the dynamic processing times for retrieval containers are taken into consideration. These typical YC operation characteristics complicate the YC scheduling, and cause late delivery and economic loss. In this study, we focus on minimizing the maximum tardiness of container task and establishing an integer linear programming model. Regarding the NP-hardness nature of the problem, we develop a heuristic named dividing, sequencing, and comparing (DSC) and a genetic algorithm (GA) based on the characteristics of the problem. The computational results show the efficient performance of the developed algorithms, compared with the exact solutions via Cplex software for small size instances. The efficiency and effectiveness of DSC outperform those of GA for practical size instances.

International audience; Nanomedicine represents a very significant contribution in current cancer treatment; in addition to surgical intervention, radiation and chemotherapeutic agents that unfortunately also kill healthy cells, inducing highly deleterious and often life-threatening side effects in the patient. Of the numerous nanoparticles used against cancer, gold nanoparticles had been developed for therapeutic applications. Inter alia, a large variety of dendrimers, i.e. soft artificial macromolecules, have turned up as non-viral functional nanocarriers for entrapping drugs, imaging agents, and targeting molecules. This review will provide insights into the design, synthesis, functionalization, and development in biomedicine of engineered functionalized hybrid dendrimer-tangled gold nanoparticles in the domain of cancer theranostic. Several aspects are highlighted and discussed such as 1) dendrimer-entrapped gold(0) hybrid nanoparticles for the targeted imaging and treatment of cancer cells, 2) dendrimer encapsulating gold(0) nanoparticles (Au DENPs) for the delivery of genes, 3) Au DENPs for drug delivery applications, 4) dendrimer encapsulating gold radioactive nanoparticles for radiotherapy, and 5) dendrimer/dendron-complexed gold(III) nanoparticles as technologies to take down cancer cells.

International audience; Among the different types of dendrimers, those that incorporate phosphorus in various positions in their structures present fascinating properties and have wide applications in pharmaceutical fields such as drug delivery (including gene transfection), diagnosis and imaging. Therapeutic agents can be encapsulated within the dendritic architecture, or chemically attached or physically adsorbed onto the dendrimer surface. Alternatively, phosphorus dendrimers can be developed as drugs themselves, for instance to treat cancer, inflammations, infections and neurodegenerative diseases, although there are fewer examples of this second approach. Here, we review the most relevant in vivo biological activities of phosphorus dendrimers (whether as active drugs per se or as nanocarriers) across various therapeutic domains, including anti-inflammation, transmissible spongiform encephalopathies, ocular hypertension, infection, gene therapy, ultrasound-targeted microbubble destruction and two-photon in vivo imaging. We discuss the success of phosphorus dendrimers in nanomedicine and look ahead to future innovations in the field.

International audience; The uses of biochar as soil fertilizer can offset global warming and reduce dependence on limited mineral resources in the future circular economy, yet biochar may contain contaminants that can ultimately enter the food chain. In particular, persistent free radicals are emerging contaminants previously detected in biochar but underlying mechanisms of radical formation are not yet established. Here we studied radical generation during hydrothermal carbonization of waste sludge at 160-220 ºC for 0.5-2 h with solid weight ratios of 10%w-40%w using electron paramagnetic resonance and Fourier transform infrared spectrometry. Results reveal that radical concentration increases with temperature, reaction time, and weight ratio in sludge biochars, reaching a content of 47.2 × 10 15 spins/g for 220 ºC, 2 h heating, and 40%w solid ratio. Moreover, low temperature of about 160 ºC favors the production of oxygen-centered radicals, whereas higher temperature of 220 ºC produces carbon-centered radicals. Our findings imply that biochar ecotoxicity should be assessed prior applications to prevent adverse health effects.

International audience; Polymeric micelles are nanoassemblies that are formed by spontaneous arrangement of amphiphilic block copolymers in aqueous solutions at critical micelle concentration (CMC). They represent an effective system for drug delivery of, for instance, poorly water-soluble anticancer drugs. Then, the development of polyion complexes (PICs) were emphasized. The morphology of these complexes depends on the topology of the polyelectrolytes used and the way they are assembled. For instance, ionic-hydrophilic block copolymers have been used for the preparation of PIC micelles. The main limitation in the use of PIC micelles is their potential instability during the self-assembly/disassembly processes, influenced by several parameters, such as polyelectrolyte concentration, deionization associated with pH, ionic strength due to salt medium effects, mixing ratio, and PIC particle cross-linking. To overcome these issues, the preparation of stable PIC micelles by increasing the rigidity of their dendritic architecture by the introduction of dendrimers and controlling their number within micelle scaffold was highlighted. In this original concise Review, we will describe the preparation, molecular characteristics, and pharmacological profile of these stable nanoassemblies.