Parallelized Research Articles

Stochastic computer experiments of the thermodynamic irreversibility of bulk nanobubbles in supersaturated and weak gas-liquid solutions.

Spontaneous gas-bubble nucleation in weak gas-liquid solutions has been a challenging topic in theory, experimentation, and computer simulations. In analogy with recent advances in crystallization and droplet formation studies, the diffusive-shielding stabilization and thermodynamic irreversibility of bulk nanobubble (bNB) mechanisms are revisited and deployed to characterize nucleation processes in a stochastic framework of computer experiments using the large-scale atomic/molecular massively parallel simulator code. Theoretical bases, assumptions, and limitations underlying the irreversibility hypothesis of bNBs, and their computational counterparts, are extensively described and illustrated. In essence, it is established that the irreversibility hypothesis can be numerically investigated by converging the system volume (due to the finiteness of interatomic forces) and the initial dissolved-gas concentration in the solution (due to the single-bNB limitation). Helium nucleation in liquid Pb17Li alloy is selected as a representative case study, where it exhibits typical characteristics of noble-gas/liquid-metal systems. The proposed framework lays down the bases on which the stability of gas-bNBs in weak and supersaturated gas-liquid solutions can be inferred and explained from a novel perspective. In essence, it stochastically marches toward a unique irreversible state along out-of-equilibrium nucleation/growth trajectories. Moreover, it does not attempt to characterize the interface or any interface-related properties, neither theoretically nor computationally. It was concluded that bNBs of a few tens of He-atoms are irreversible when dissolved-He concentrations in the weak gas-liquid solution are at least ∼50 and ∼105 molm-3 at 600 and 1000K (and ∼80 MPa), respectively, whereas classical molecular dynamics -estimated solubilities are at least two orders of magnitude smaller.

An adapted constraint‐programming formulation of the resource‐constrained project scheduling problem applied to the identical parallel machines group shop and mixed shop scheduling problems

AbstractIn this paper, we study the group shop and the mixed shop scheduling problems with single and identical parallel machines at each workstation with the makespan criterion. We adapted a constraint programming formulation previously presented for the classical resource‐constrained project scheduling problem. The effectiveness of our approach is evident in the fact that it achieved optimality in 107 out of 130 classical group shop scheduling problem instances and in 320 classical mixed shop scheduling problem instances. In the last set, we obtained 13 new optimal solutions.

Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm

Establishing a national comprehensive PNT (Positioning, Navigation, and Timing) system has become a consensus among major countries worldwide. As a crucial component in completing the entire PNT system, the 5G NR (new radio) time service signal plays a vital role. This paper proposes a 5G NR time service signal that uses a spread spectrum system, shares the 5G signal frequency band, but does not occupy the bandwidth of the 5G communication signal. This timing service signal has relatively low power, making it appear “submerged” within the power of the 5G communication signal. The spread spectrum code for this timing signal employs the chaotic orthogonal composite sequence proposed in this paper. Compared to traditional spread spectrum sequences, this sequence offers better security than m-sequences, improved autocorrelation than Walsh sequences, and an effective suppression of the short-period characteristics exhibited when the Skew Tent-Map chaotic sequence takes special values. This paper simulates the capture of the 5G NR time service signal in an environment with a signal-to-noise ratio of 10 dB using an FFT-based parallel code phase search algorithm, successfully capturing the 5G NR time service signal and verifying the feasibility of the proposed chaotic orthogonal composite sequence as a spread spectrum code.

Mathematical models and an effective exact algorithm for unrelated parallel machine scheduling with family setup times and machine cost

Mathematical models and an effective exact algorithm for unrelated parallel machine scheduling with family setup times and machine cost

Spontaneous Extradural Hematoma in a Sickle Cell Anemia Patient with Hyperinflammation and Thrombotic Microangiopathy Successfully Treated with Eculizumab: A Case Report and Review of the Literature.

The event of extradural hematoma in the absence of head trauma is a rare central nervous system complication of sickle cell disease. We report here a case of spontaneous extradural hematoma in a patient being treated for sickle cell vasoocclusive crisis complicated by hyperinflammation and thrombotic microangiopathy. The significance of inflammation as an integral component of the pathomechanism of vasoocclusive crisis in patients with sickle cell disease and the role of heme in activating the complement system's alternative pathway are highlighted in this case report. A teenage patient with sickle cell disease developed a spontaneous right parietal extradural hematoma while receiving treatment for sickle cell vasoocclusive crisis. The concurrent events of hyperinflammation, disseminated intravascular coagulation, hyperhemolysis syndrome, thrombotic microangiopathy, and refractory postoperative bleeding complicated this patient's clinical course after surgical evacuation of extradural hematoma. This patient was subsequently treated with eculizumab and improved in the days following. Treatment with the anti-C5 monoclonal antibody eculizumab, which targets and inhibits terminal complement system activation, reversed the deleterious cascade of events in this patient with sickle cell disease.

PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning

Temporal Knowledge Graphs (TKGs) enable effective modeling of knowledge dynamics and event evolution, facilitating deeper insights and analysis into temporal information. Recently, extrapolation of TKG reasoning has attracted great significance due to its remarkable ability to capture historical correlations and predict future events. Existing studies of extrapolation aim mainly at encoding the structural and temporal semantics based on snapshot sequences, which contain graph aggregators for the association within snapshots and recurrent units for the evolution. However, these methods are limited to modeling long-distance history, as they primarily focus on capturing temporal correlations over shorter periods. Besides, a few approaches rely on compiling historical repetitive statistics of TKGs for predicting future facts. But they often overlook explicit interactions in the graph structure among concurrent events. To address these issues, we propose a PotentiaL concurrEnt Aggregation and contraStive learnING (PLEASING) method for TKG extrapolation. PLEASING is a two-step reasoning framework that effectively leverages the historical and potential features of TKGs. It includes two encoders for historical and global events with an adaptive gated mechanism, acquiring predictions with appropriate weight of the two aspects. Specifically, PLEASING constructs two auxiliary graphs to capture temporal interaction among timestamps and correlations among potential concurrent events, respectively, enabling a holistic investigation of temporal characteristics and future potential possibilities in TKGs. Furthermore, PLEASING incorporates contrastive learning to strengthen its capacity to identify whether queries are related to history. Extensive experiments on seven benchmark datasets demonstrate the state-of-the-art performances of PLEASING and its comprehensive ability to model TKG semantics.

Heavy-load Nonapod: A novel flexible redundant parallel kinematic machine for multi-DoF forming process

The high-performance multi-DoF forming process (MDFP) necessitates a 6-DoF forming machine tool with high normal and lateral stiffness to bear large normal and lateral forming force of millions of Newton (MN). However, the payload of parallel kinematic machine (PKM) is generally limited to thousands of Newton (kN), which restricts its application in MDFP. Therefore, this paper aims to develop a novel heavy load PKM with high stiffness for MDFP. To maximise the normal stiffness, a 6-PSS PKM with zero base angle and horizontal driver is proposed. Further, the inner force transfer model of 6-PSS PKM is established, indicating that the normal stiffness will be maximised when the link force approaches to be vertical. Consequently, a design criterion for maximising normal stiffness, i.e., the root mean square error (RMSE) for horizontal projection of all links should be minimised, is established. To maximise the lateral stiffness, general force balance equations of 6-PSS PKM are derived, indicating that lateral force can cause unintended negative force of links, significantly reducing the lateral stiffness. Thus, a novel auxiliary 3-SPS configuration is employed to provide additional force system to mitigate this negative force via hydraulic links. Correspondingly, a design criterion for maximising lateral stiffness, i.e., all link force should remain positive, is proposed. By combining aforementioned design criterion and kinetostatic models, a near-singular 6-PSS PKM with maximising normal stiffness is achieved, and dimension parameters of 3-SPS PKM with maximising lateral stiffness are optimised. On this basis, a novel flexible redundant 6-PSS/3-SPS PKM with both high normal and lateral stiffness is proposed, and a novel heavy load Nonapod with payload of 8 MN and payload-mass ratio of 40 is developed, showing good stiffness performance. The plastic deformation mechanisms of multi-DoF formed aviation bevel gear are revealed, and experimentally formed aviation bevel gear in the new Nonapod achieves good accuracy, microstructure and mechanical performance. This work provides a new methodology for synthesis of heavy load PKM with high normal and lateral stiffness, and has significant application prospect in PKM under heavy load working condition.

The fight against disinformation and its consequences: measuring the impact of “Russia state-affiliated media” on Twitter

On February 28th, shortly after the Russian invasion of Ukraine on February 24th, Twitter announced the expansion of its labelling policy for “Russia state-affiliated media”, in order to address disinformation in favour of the Russian government.. While this ‘soft’ approach does not include the removal of content, it entails issues for freedom of expression and information. This article investigates the consequences of this labelling policy for the range and impact of accounts labelled “Russia state-affiliated media” during the Ukrainian war. Using an iterative detection method, a total of 90 accounts of both media outlets and individual journalists with this label were identified. The analysis of these accounts’ information and timeline, as well as the comparison of the impact of their tweets before and after February 28th with an ARIMA model, strongly suggests, that this policy, despite its limited scope, could have contributed to a reduction in the impact of the sampled tweets, among other concurrent events. These results provide empirical evidence to guide critical reflection on this content moderation policy.

Novel integer L-shaped method for parallel machine scheduling problem under uncertain sequence-dependent setups

We study scheduling problems on unrelated parallel machines with uncertainty in job processing and sequence-dependent setup times. We first formulate this problem as a two-stage stochastic program using a scenario-based approach, in which the first-stage decisions involve job-machine assignments, and the second-stage deals with job sequencing. We then propose an innovative modified integer L-shaped method, uniquely designed to decompose the primary problem into independent subproblems at both the scenario and machine levels. We have augmented this method with a specialized integer optimality cut, which is further strengthened using a sequential lifting procedure. To broaden the applicability of our model and solution, we have adapted it to a distributionally robust optimization (DRO) framework under Wasserstein ambiguity. This process involves reformulating the DRO model into a mixed-integer linear program (MILP) using conic duality theory. Additionally, we integrate a distribution separation program to define the worst-case probability distribution during each iteration of the proposed integer L-shaped method, enhancing the computational speed for solving the DRO model. We conducted a series of numerical experiments to illustrate the effectiveness and scalability of the proposed solution methodology, which demonstrated highly promising results. Our innovative decomposition algorithms showed superior performance, achieving solution times that are significantly faster (up to two orders of magnitude) compared to the traditional integer L-shaped method for the stochastic scheduling model and the direct MILP reformulation of the DRO model. These results were obtained for problem instances solved to the exactness within the time limit. Moreover, the solution derived from the DRO model exhibited greater robustness in out-of-sample analysis with simulated data compared to the solution obtained from the stochastic model.

Variable neighbourhood search for parallel machine scheduling with a single loading server: a truck-scheduling perspective

ABSTRACT In this paper, we study a class of parallel machine scheduling problem with a single loading and multiple unloading servers. We show an application of this problem in a truck-scheduling context. We design a mixed-integer-programming formulation and use it to solve instances of various sizes with different characteristics. The obtained results reveal that a state-of-the-art commercial solver is only able to tackle instances of limited size. To solve larger instances, a general variable neighborhood search is proposed. Our comprehensive computational experiments demonstrate how the proposed metaheuristic can provide high-quality solutions in reasonable computing time (a few seconds). This type of performance is particularly relevant when considering operational problems that must be tackled on a daily basis. Finally, a sensitivity analysis is performed with respect to the number of trucks.

A note on scheduling identical parallel machines with preemptions and setup times

We correct, extend and improve recent algorithmic and computational complexity results for the identical parallel machine scheduling problem with preemptions and setup times preceding uninterrupted job parts. The objective is to minimise the makespan. Our results include a proof of strong NP-hardness for an arbitrary number of machines, and a pseudo-polynomial algorithm and an FPTAS for the two machines case.

Design complexity based flexible order dispatching for additive manufacturing production

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

Use of Infliximab in the Treatment of Immune Checkpoint Inhibitors-Related Myocarditis: A Systematic Review of Case Reports

Immune-mediated myocarditis is uncommon, progresses rapidly and has high mortality rate. Infliximab has been used in practice, particularly in steroid refractory cases, but evidence supporting the use is still lacking. This review aims to provide an overview of infliximab use in managing immune-related myocarditis. A systematic search was conducted using the PubMed and Cochrane Library databases as well as manual searching of bibliographies, from inception to 1 October 2021. Eligible studies were selected by the inclusion criteria. All the included studies were assessed for methodological quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Case Reports. There were 13 articles with total 14 reported cases of immune-related myocarditis treated with infliximab. Most patients developed myocarditis with high severity grade, concurrent immune-related adverse events (irAEs), particularly neuromuscular irAEs and multiple potentially fatal complications. Most patients received single dose of infliximab, whereas few received multiple doses. Four patients improved clinically or biochemically, but 10 patients did not improve or deteriorated clinically with the addition of infliximab. Half of the reported cases did not survive due to myocarditis and its complications. Conflicting results were observed when infliximab was used in patients with heart failure and higher dose did not appear to be beneficial in these patients. Concomitant irAEs, high severity grade and occurrence of potentially fatal complications indicate the need to escalate the treatment by using additional immunosuppressive agents such as infliximab. However, there is no standardisation on the infliximab treatment regimen, outcomes have been inconsistent and safety data is limited.

Non-identical parallel machines batch processing problem to minimize the makespan: Models and algorithms

This paper studies a parallel heterogeneous machine batching and scheduling problem in which weighted jobs are first batched, and the batches are then assigned and sequenced on machines of varying capacities. The duration of a batch is the longest time needed to process a job, and the objective is that of minimizing the makespan, or the sum of the batches durations on the machine finishing last. The authors develop polynomial-size mathematical formulations and a variable neighborhood search metaheuristic. Extensive computational results suggest that a flow-based formulation outperforms a compact formulation, despite its larger number of variables. The metaheuristic is capable of producing high-quality solutions within a limited computing time.

Synchronisation of operations and maintenance in distributed unrelated parallel machine systems: a hierarchical optimisation framework

ABSTRACT Synchronisation of operations and maintenance means that production and maintenance departments work together effectively to achieve integrated optimisation of production scheduling and maintenance activities. This paper addresses such an integrated optimisation problem towards distributed unrelated parallel machine systems, where deteriorating processing time and machine failures are considered. For the trade-off between production and maintenance to obtain a minimal makespan in the worst-case scenario, a hierarchical optimisation framework with reinforcement learning-based encoder and knowledge-based intelligent optimiser (HOFRK) is developed. Experimental results show that HOFRK can be treated as an excellent optimiser for enterprise information systems to solve the proposed problem.

Parallel virus machines

Parallel virus machines

A mathematical model and solution algorithms for optimising system-level configurations of reconfigurable single part flow lines

This study addresses a system-level configuration selection problem for reconfigurable single part flow lines (R-SPFLs) with parallel identical flexible machines at each stage. The problem is to determine the number of stages, the number of machines of a certain type at each stage and the assignment of operations to each stage in order to satisfy the demand of a part type. Precedence relations among the operations and space limitations are also considered. For the objective of minimising the sum of machine purchase and operation processing costs, a nonlinear integer programming model is developed that gives the optimal solutions. Then, due to the problem's complexity, a basic variable neighbourhood search algorithm is proposed that constructs an initial solution using a greedy-type heuristic and improves it using a shaking and a local search improvement methods. Moreover, the basic algorithm is extended to a general one that uses a variable neighbourhood descent method. Computational results show that the two algorithms outperform the previous one significantly in both solution quality and computation times, and the general algorithm improves the basic one significantly. Finally, the applicability of the variable neighbourhood search algorithms is shown by reporting a brief case study.

The Parallel Compact Object CALculator: An Efficient General Relativistic Initial Data Solver for Compact Objects

Every numerical general relativistic investigation starts from the solution of the initial value equations at a given time. Astrophysically relevant initial values for different systems lead to distinct sets of equations that obey specific assumptions tied to the particular problem. Therefore, a robust and efficient solver for a variety of strongly gravitating sources is needed. In this work, we present the OpenMP version of the Compact Object CALculator (COCAL) on shared memory processors. We performed extensive profiling of the core COCAL modules in order to identify bottlenecks in efficiency, which we addressed. Using modest resources, the new parallel code achieves speedups of approximately one order of magnitude relative to the original serial COCAL code, which is crucial for parameter studies of computationally expensive systems such as magnetized neutron stars, as well as its further development towards more realistic scenarios. As a novel example of our new code, we compute a binary quark system where each companion has a dimensionless spin of 0.43 aligned with the orbital angular momentum.

Fair scheduling on parallel batch machines with non-identical capacities

The fairness of jobs from agents is considered in an advanced manufacturing factory and the fair scheduling of jobs on parallel batch machines is studied for two competing agents. Each agent and the system aim to minimize the makespan of their own jobs and the weighted sum makespans of the two agents, respectively. A modified full-batch longest processing time (MFBLPT) algorithm is proposed to minimize the makespan, and a Kalai–Smorodinsky fairness (KSF) algorithm is proposed to obtain fair schedules. These algorithms are analysed based on worst-case ratios, and the results show that these ratios are related to the workloads and importance of agents. Computational experiments are conducted to compare the performance of algorithm KSF with that of machine-centric and agent-centric algorithms. The results show that the pursuance of fairness leads to interest losses of a system, and that a proper preference of an agent leads to good performance of algorithm KSF.

Combinatorial optimization methods for yarn dyeing planning

AbstractManaging yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.