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In this paper, we study an integrated production and outbound distribution scheduling model with one manufacturer and one customer. The manufacturer has to process a set of jobs on a single machine and deliver them in batches to the customer. Each job has a release date and a delivery deadline. The objective of the problem is to issue a feasible integrated production and distribution schedule minimizing the transportation cost subject to the production release dates and delivery deadline constraints. We consider three problems with different ways how a job can be produced and delivered: non-splittable production and delivery (NSP–NSD) problem, splittable production and non-splittable delivery problem and splittable production and delivery problem. We provide polynomial-time algorithms that solve special cases of the problem. One of these algorithms allows us to compute a lower bound for the NP-hard problem NSP–NSD, which we use in a branch-and-bound (B&B) algorithm to solve problem NSP–NSD. The computational results show that the B&B algorithm outperforms a MILP formulation of the problem implemented on a commercial solver.

We consider the two-machine flow-shop serial-batching scheduling problem where the machines have a limited capacity in terms of the number of jobs. Two criteria are considered here. The first criterion is the number of batches to be minimized. This criterion reflects situations where processing of any batch induces a fixed cost, which leads to a total cost proportional to the number of batches. The second criterion is the makespan. This model is relevant in different production contexts, especially when considering joint production and inbound delivery scheduling. We study the complexity of the problem and propose two polynomial-time approximation algorithms with a guaranteed performance. The effectiveness of these algorithms is evaluated using numerical experiments. Exact polynomial-time algorithms are also provided for some particular cases.

We study the problem of minimizing the number of late jobs on a single machine where job processing times are known precisely and due dates are uncertain. The uncertainty is captured through a set of scenarios. In this environment, an appropriate criterion to select a schedule is to find one with the best worst-case performance, which minimizes the maximum number of late jobs over all scenarios. For a variable number of scenarios and two distinct due dates over all scenarios, the problem is proved NP-hard in the strong sense and non-approximable in pseudo-polynomial time with approximation ratio less than 2. It is polynomially solvable if the number s of scenarios and the number v of distinct due dates over all scenarios are given constants. An O ( n log  n ) time s -approximation algorithm is suggested for the general case, where n is the number of jobs, and a polynomial 3-approximation algorithm is suggested for the case of unit-time jobs and a constant number of scenarios. Furthermore, an O ( n s + v −2 /( v −1) v −2 ) time dynamic programming algorithm is presented for the case of unit-time jobs. The problem with unit-time jobs and the number of late jobs not exceeding a given constant value is solvable in polynomial time by an enumeration algorithm. The obtained results are related to a min-max assignment problem, an exact assignment problem and a multi-agent scheduling problem.

Problems of scheduling n jobs on a single machine to maximize regular objective functions are studied. Precedence constraints may be given on the set of jobs and the jobs may have different release times. Schedules of interest are only those for which the jobs cannot be shifted to start earlier without changing job sequence or violating release times or precedence constraints. Solutions to the maximization problems provide an information about how poorly such schedules can perform. The most general problem of maximizing maximum cost is shown to be reducible to n similar problems of scheduling n-1 jobs available at the same time. It is solved in O(mn+n[squared]) time, where m is the number of arcs in the precedence graph. When all release times are equal to zero, the problem of maximizing the total weighted completion time or the weighted number of late jobs is equivalent to its minimization counterpart with precedence constraints reversed with respect to the original ones. If there are no precedence constraints, the problem of maximizing arbitrary regular function reduces to n similar problems of scheduling n-1 jobs available at the same time. [PUBLICATION ABSTRACT]

Par sa théorie sur le don/contre-don, Marcel MAUSS nous propose une interprétation anthropologique et ethnologique des mécanismes gérant les différents rapports qui sous-tendent les sociétés archaïques. Dans ce compte rendu, nous ne sommes pas limités à résumer sa réflexion, mais nous avons tenté également d'en dévoiler l'apport et l'impact sur l'Histoire de l'anthropologie. De même, nous avons essayé de mettre en exergue l'actualité de sa réflexion qui s'avère fructueuse dans l'étude des rapports économiques et sociaux de notre monde contemporain.

Self-supervised pre-training has proven highly effective for many computer vision tasks, particularly when labelled data are scarce. In the context of Earth Observation (EO), foundation models and various other Vision Transformer (ViT)-based approaches have been successfully applied for transfer learning to downstream tasks. However, it remains unclear under which conditions pre-trained models offer significant advantages over training from scratch. In this study, we investigate the effectiveness of pre-training ViT-based Masked Autoencoders (MAE) for downstream EO tasks, focusing on reconstruction, segmentation, and classification. We consider two large ViT-based MAE pre-trained models: a foundation model (Prithvi) and SatMAE. We evaluate Prithvi on reconstruction and segmentation-based downstream tasks, and for SatMAE we assess its performance on a classification downstream task. Our findings suggest that pre-training is particularly beneficial when the fine-tuning task closely resembles the pre-training task, e.g. reconstruction. In contrast, for tasks such as segmentation or classification, training from scratch with specific hyperparameter adjustments proved to be equally or more effective.

We consider the the two-machine ow-shop serial-batching scheduling problem where the batches have limited size. Two criteria are considered here. The rst criterion is to minimize the number of batches. This criterion re ects situations where processing of any batch induces a xed cost, which leads to a total cost proportional to the number of batches. The second criterion is the makespan. We study the complexity of the problem and propose polynomial-time algorithms for some particular cases and an approximation algorithm with a guaranteed performance for the general case.

IntroductionDomestic violence is a major public health problem. The situation is alarming in Arab countries: the prevalence of domestic violence is 39.3% in Saudi Arabia, 55% in Morocco and 62.2% in Egypt.In Tunisia, a national survey carried out by the national family planning office in 2010, published in July 2011, drew attention for the first time to the frequency of this phenomenon in Tunisia and the recurrent nature of this form of violence. Unfortunately, few studies have focused on domestic violence during pregnancy and its impact on the mental health of expectant mothers.ObjectivesTo study the prevalence of domestic violence during pregnancy among Tunisian women consulting in the context of medical expertise and its association with anxiety.MethodsOur study was descriptive and analytical cross-sectional, carried out with women examined in the context of medical expertise following domestic violence at ‘Hedi Chaker hospital’,Sfax , from May 2021 until January 2022.An anonymous survey was asked to these ladies, it included a section for collecting socio-demographicdata.The HADS questionnaire was used to screen for anxiety.Results122 responses was collected. The average age of victims was 35.66 ± 9.94 years.All the women in our population study were married, and each one was a victim of at least one form of violence. The majority (86.1%) had children. Most of them had secondary (44.3%) or university (31.1%) level education.More than half of the women (63.9%) had no occupation.Sixty-five women (53.3%) were assaulted during pregnancy, 43% of whom suffered from complications of varying severity.Different consequences on pregnancy were reported with decreasing prevalence: 16.9% hospitalization in a gynecological ward, 13.8% abortion, 6.2% fetal death in utero and premature delivery in 4.6% of cases.According to the HADS, seventy-six of women surveyed (62.3%) had anxiety symptoms.Anxiety was significantly associated with exposure to violence during pregnancy (p=0.03).ConclusionsOur results showed a significant incidence of domestic violence during pregnancy and a significant association with anxiety.Different actions must be taken towards these anxious women such as: Identify a “referent” in maternity wards to screen for domestic violence and directing women to structures and shelters that can help and, above all, protect them.Disclosure of InterestNone Declared

In the last decade, the development of a new generation of membranes based on low-cost materials has been widely studied. These membranes demonstrate significantly higher performance than the conventional ceramic membranes currently used in membrane separation technology. This work is focused on the development of a low-cost flat UF ceramic membrane composed completely of sepiolite using a uniaxial pressing method with dimensions of 5.5 cm of diameter and 3 mm of thickness. The sintering temperatures used were from 650 to 800 °C. Several properties, such as morphology, porosity, permeability, mechanical strength, and chemical resistance, are investigated. The results show that the mean pore diameter is increased from 40 to 150 nm when the sintering temperature increases from 650 °C to 800 °C. At these temperatures, excellent mechanical strength of 18 MPa to 22 MPa and high chemical resistance were achieved. SEM results revealed a crack-free structure with a uniformly smooth surface. Permeability tests were conducted using dead-end filtration. The sepiolite membrane demonstrated an improvement in its water permeability from 18 to 41 L·m−2·h−1·bar−1 when the sintering temperature increased from 650 °C to 750 °C. The efficiency of the sepiolite membranes sintered at 650 °C and 700 °C were evaluated with the application of the removal of paracetamol (PCT) and indigo blue (IB) dye separately from two synthetic aqueous solutions representing the pharmaceutical and textile sectors. Excellent removal efficiency of almost 100% for both contaminants was observed at ambient temperature and a pressure of 3 bars. Membrane regeneration was achieved through simple rinsing with deionized water. According to this finding, the UF sepiolite membrane demonstrated reversible fouling, which is consistent with the fouling coefficient “FRR” value higher than 90%.

Swarm electrification-driven communities face significant challenges, including implementing advanced distributed control in areas with limited ICT access and establishing trust among villagers hesitant to grant access to their assets. This paper proposes a distributed DC microgrid architecture for P2P energy exchange in these communities, ensuring stability and an effective exchange operation. By implementing a Blockchain marketplace specifically designed to suit the rural context, the proposed architecture ensures tracing of exchange transactions to fairly settle participants. Validation experiments demonstrate its efficacy in achieving peak shaving. It provides 11% of the requester’s total demand from the community even while maintaining the constraint of reducing discharge–charge cycles to one per day, thereby preserving battery life. Additionally, the solution reduces prosumer production losses by 16% of the total PV production.