Optimal Sequence Research Articles

Optimal weight impulse extraction: New impulse extraction methodology for incipient gearbox condition monitoring

Gear faults in a transmission system generally cause impulse components in vibration signals, which is a crucial symbol for gearbox fault diagnosis. However, their related signals are often interfered or even submerged by the noisy meshing components (NMC) of gearboxes in degradation, which introduces challenges for incipient fault detection and condition monitoring. Commonly employed deconvolution-based methods attempt to design a filter to extract impulse components. However, these methods fail to address the interference issue of the NMC on deconvolution process. To overcome this limitation, this paper proposes an optimal weight impulse extraction (OWIE) methodology to suppress the NMC and highlight impulse component in vibration signal. Different from deconvolution-based methods, the proposed method locates impulses adaptively driven by waveform itself. A non-impulse part is suppressed through point-to-point removal, while impulse components are highlighted by subtracting a weighted signal from a raw signal. An iterative procedure is utilized to solve the optimal weight sequence by maximizing the kurtosis of an impulse signal. The effectiveness of the proposed OWIE is validated through a simulation case study and a run-to-failure experiment of gearboxes. Results demonstrate that the OWIE is sensitive to incipient faults and is suitable for the health condition monitoring of gearboxes.

Ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning

RNA N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in controlling mRNA stability, processing, and translation. Consequently, accurate identification of ac4C sites across the genome is critical for understanding gene expression regulation mechanisms. In this study, we have developed ac4C-AFL, a bioinformatics tool that precisely identifies ac4C sites from primary RNA sequences. In ac4C-AFL, we identified the optimal sequence length for model building and implemented an adaptive feature representation strategy that is capable of extracting the most representative features from RNA. To identify the most relevant features, we proposed a novel ensemble feature importance scoring strategy to rank features effectively. We then used this information to conduct the sequential forward search, which individually determine the optimal feature set from the 16 sequence-derived feature descriptors. Utilizing these optimal feature descriptors, we constructed 176 baseline models using 11 popular classifiers. The most efficient baseline models were identified using the two-step feature selection approach, whose predicted scores were integrated and trained with the appropriate classifier to develop the final prediction model. Our rigorous cross-validations and independent tests demonstrate that ac4C-AFL surpasses contemporary tools in predicting ac4C sites. Moreover, we have developed a publicly accessible web server at https://balalab-skku.org/ac4C-AFL/.

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

AbstractMedical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.

Transient modeling of a solid oxide fuel cell using an efficient deep learning HY-CNN-NARX paradigm

Control and monitoring systems are crucial for ensuring optimal performance, efficiency, and longevity of Solid Oxide Fuel Cells (SOFC). Developing a model to accurately capture the transient behavior of the SOFC under dynamic operation is essential for these applications. As electrochemical, mass-transfer, and thermal equations are involved in SOFCs’ dynamic, physics-based approaches to capture all the complexity of SOFC systems are often too computationally expensive for real-time implementation. In this paper, an innovative multi-input neural network is developed to build an accurate model of SOFC that is computationally efficient. The proposed algorithm uses experimental data and is a modified nonlinear autoregressive exogenous (NARX) network. An optimal sequence of the most recent observations (output voltages) that characterize the SOFC dynamics is passed through stacked 1D convolutional layers (CNN) to extract latent spatial/temporal information. Then the output is concatenated with current and past exogenous inputs. The fusion of learned features, representing the internal dynamics and the effect of exogenous inputs, is then fed to a fully connected network for one-step ahead performance prediction. This hybrid structure, called HY-CNN-NARX, is capable of identifying the transient dynamics of the SOFCs by unrolling information from historical outputs and inputs within a feedforward framework. To evaluate the model performance, lab-scale tubular SOFCs are experimentally tested at 650–750 °C under various dynamic operations and initial conditions, and the transient and steady-state responses of the SOFC are collected. Once the model is validated using a wide operating range of data, the generalization and prediction performance of the proposed network is compared with a conventional NARX and a recurrent stacked Long short-term memory (LSTM) model. The comparative results demonstrate that the proposed HY-CNN-NARX model outperforms the NARX model on unseen data with an accuracy improvement of 59.2% for root mean square error and 53.7% for mean absolute percentage error. In addition, the prediction performance of the HY-CNN-NARX is comparable to its recurrent counterpart, the LSTM model, but with a 39.3% faster execution time.

Comparison of vestibular aqueduct visualization on computed tomography and magnetic resonance imaging in patients with Ménière’s disease

BackgroundThe vestibular aqueduct (VA) serves an essential role in homeostasis of the inner ear and pathogenesis of Ménière’s disease (MD). The bony VA can be clearly depicted by high-resolution computed tomography (HRCT), whereas the optimal sequences and parameters for magnetic resonance imaging (MRI) are not yet established. We investigated VA characteristics and potential factors influencing MRI-VA visibility in unilateral MD patients.MethodsOne hundred patients with unilateral MD underwent MRI with three-dimensional sampling perfection with application optimized contrasts using different flip angle evolutions (3D-SPACE) sequence and HRCT evaluation. The imaging variables included MRI-VA and CT-VA visibility, CT-VA morphology and CT-peri-VA pneumatization.ResultsThe most frequent type of MRI-VA and CT-VA visualization was invisible VA and continuous VA, respectively. The MRI-VA visibility was significantly lower than CT-VA visibility. MRI-VA visibility had a weak positive correlation with ipsilateral CT-VA visualization. For the affected side, the MRI-VA visualization was negatively correlated with the incidence of obliterated-shaped CT-VA and positively with that of tubular-shaped CT-VA. MRI-VA visualization was not affected by CT-peri-VA pneumatization.ConclusionIn patients with MD, the VA visualization on 3D-SPACE MRI is poorer than that observed on CT and may be affected by its osseous configuration. These findings may provide a basis for further characterization of VA demonstrated by MRI and its clinical significance.

Resilience-oriented repair crew and network reconfiguration coordinated operational scheduling for post-event restoration

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC–NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid’s resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33-bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level.

Wave excitation force prediction for arrays of wave energy converters in directional waves

The implementation of advanced control strategies is an effective means of maximizing the energy production of wave energy converters (WECs), and most of them require future wave excitation forces to determine the optimal control sequence. The WECs deployed in arrays can enable a significant reduction in the cost of wave energy and improve prediction performance by incorporating excitation forces from the array. In this paper, a critical comparison of the excitation prediction methods, including the time series, machine learning, and neural network, for a two-body heaving point absorber WEC is presented. Considering the symmetry of the WEC and arrays, the directional spectrum is utilized to determine the reference measurement of wave excitation. The prediction methods are applied to isolated WEC and different array layouts. Moreover, additional sensitivity analyses are performed to evaluate the performance. The results show that the autoregressive with extra input (ARX) model has the best performance in short-term wave excitation force prediction. In most cases, the ARX model is weakly influenced by the sampling period and can adapt well to changes in the directional distribution and sea states of irregular waves, and the ARX model requires the least amount of computational time in the simulations.

Two-phase optimization of binary sequences with low peak sidelobe level value

The search for binary sequences with low peak sidelobe level value represents a formidable computational problem. To locate better sequences for this problem, we designed a stochastic algorithm that uses two fitness functions. In these fitness functions, the value of the autocorrelation function has a different impact on the final fitness value. It is defined with the value of the exponent over the autocorrelation function values. Each function is used in the corresponding optimization phase, and the optimization process switches between these two phases until the stopping condition is satisfied. The proposed algorithm was implemented using the compute unified device architecture and therefore allowed us to exploit the computational power of graphics processing units. This algorithm was tested on sequences with lengths L=2m−1, for 14≤m≤20. From the obtained results it is evident that the two-phase optimization improved the efficiency of the algorithm significantly, the solver speed is increased significantly by using graphics processing units, new-best known solutions were achieved, and the achieved peak sidelobe level values were significantly less than L.

Response to lorlatinib rechallenge in a case of ALK-rearranged metastatic NSCLC with a resistance mutation to second generation TKIs.

Several anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors (TKIs) have been developed for the treatment of echinoderm microtubule-associated protein-like 4 (EML4)-ALK-rearranged non-small cell lung cancer (NSCLC), with the newer generation agents brigatinib, alectinib and lorlatinib showing prolonged responses. With the increasing number of target therapies available, the optimal sequence is yet to be defined, as resistance profiles may evolve over time and in response to sequential ALK inhibitors. Therefore, ALK-targeted strategies may be personalized based upon the presence of specific ALK resistance mutations. Here, we report on the case of a patient who has been treated with a sequence of three ALK TKIs after receiving diagnosis of ALK-rearranged metastatic NSCLC in 2015 and gained further benefit upon lorlatinib rechallenge after the acquisition of the G1202R resistance mutation to second generation TKIs. In this case, the first ALK resistance mutation detected after progression on first line TKI, the I1171N, is a common resistance mutation after alectinib and confers sensitivity to brigatinib, that the patient received afterwards with a long-term disease stability. The second ALK resistance mutation detected after a chemotherapy interval, the G1202R, is the most common resistance mutation after second generation ALK TKIs and has been associated with sensitivity to third generation TKIs, such as lorlatinib. This case of a patient with EML4-ALK-rearranged NSCLC shows that sequential treatment with next-generation ALK TKIs, including rechallenge, can induce profound remissions, even in heavily pretreated patients, and that ALK-targeted strategies may be personalized by considering the presence of distinct ALK resistance mutations.

Quasi-Stochastic Departure Metering Model Considering Non-Deterministic Taxi Time, Standard Instrument Departure Track Time, and a New Delay–Conflict Relationship

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal flight departure schedule and sequence at airports. The proposed formulation addresses two key gaps in the existing departure metering strategies: (i) the variability and effects of the standard instrument departure (SID) procedure and (ii) the prospect of en route conflicts because of departure delays. The variability and effects of the SID procedure are captured through a non-deterministic parameter defined as the SID track time. The probability of en route conflicts because of departure delay is estimated based on a new delay–conflict relationship. Data used for implementation and validation of the model was collected from operations on Runway 7 of Chennai International Airport, India. Overall results indicate that the proposed departure metering model can act as a pre-emptive departure delay strategy framework that takes care of the SID procedure and en route conflict possibility with no harm to the runway throughput. The implications of this work hint toward better management of the airport operations and the performance of the overall air traffic management system.

Model Predictive Control for Trajectory Planning Considering Constraints on Vertical Load Variation

To address the issue of centrifugal force affecting the vertical load during the stability and trajectory planning of autonomous vehicles during high-speed cornering and obstacle avoidance, a model predictive control of trajectory planning and tracking is proposed that considers the roll factor using only a two-degrees-of-freedom vehicle dynamics model. Firstly, a trajectory planning controller is designed. As a predictive model, a dual-track two-degrees-of-freedom vehicle dynamics model is established. This model describes the relationship between tire lateral forces and vertical loads using a quadratic nonlinear tire model. To reflect the actual dynamic state of the vehicle, the controller incorporates a nonlinear constraint that considers vertical load variations. The nonlinear optimization problem is transformed into a simplified quadratic programming problem by using the Jacobian matrix method to linearize the constraints. By fitting a fourth-degree polynomial curve to the discrete points calculated by the replanning algorithm, an optimal collision-free trajectory is obtained. Secondly, an MPC trajectory tracking controller is designed to control the vehicle in real time along the optimal trajectory from the planning, incorporating control quantity constraints, control increment constraints, and lateral angle constraints to maintain the vehicle’s motion state. We transform the trajectory tracking control problem into a quadratic programming problem, solving for the optimal control sequence for the autonomous vehicle to track the trajectory, achieving an optimized solution and rolling time domain control. Finally, the effectiveness of the vehicle’s obstacle avoidance planning and tracking under high-speed double-lane-change maneuver conditions is validated using the Simulink simulation platform. The results indicate that the designed planning and tracking controllers effectively improve the obstacle avoidance planning and tracking control for high-speed autonomous vehicles.

Sequence planning for on-orbit robotic assembly based on symbiotic organisms search with diversification strategy

The demands for efficiency and stability in space missions have necessitated the development of sequence planning schemes for on-orbit robotic assembly of space structures. A sequence planning method is proposed in this work based on robotic kinematic analysis and Symbiotic Organisms Search (SOS) algorithm with a diversification strategy. With feasibility as the constraint and assembly cost as the objective function, the assembly sequence planning is regarded as an optimization problem. The assembly relationship constraint is depicted as the dependency between assembly steps represented by a directed acyclic graph. An abstract modeling method based on robotic kinematics is proposed to construct geometric feasibility constraint, accommodating the interference stemming from the multi-degree-of-freedom motion of the robotic system. A diversification strategy is exploited to address the issue of local optima arising from solving the optimal sequence based on SOS. A cost prediction method using previously calculated populations as samples is proposed to reduce the reliance on robotic dynamic analysis for cost calculation. Finally, simulations are carried out to validate the practicability of the proposed method.

Optimisation and evaluation of viral genomic sequencing of SARS-CoV-2 rapid diagnostic tests: a laboratory and cohort-based study

Sequencing of SARS-CoV-2 from rapid diagnostic tests (RDTs) can bolster viral genomic surveillance efforts; however, approaches to maximise and standardise pathogen genome recovery from RDTs remain underdeveloped. We aimed to systematically optimise the elution of genetic material from RDT components and to evaluate the efficacy of RDT sequencing for outbreak investigation. In this laboratory and cohort-based study we seeded RDTs with inactivated SARS-CoV-2 to optimise the elution of genomic material from RDT lateral flow strips. We measured the effect of changes in buffer type, time in buffer, and rotation on PCR cycle threshold (Ct) value. We recruited individuals older than 18 years residing in the greater Boston area, MA, USA, from July 18 to Nov 5, 2022, via email advertising to students and staff at Harvard University, MA, USA,and via broad social media advertising. All individuals recruited were within 5days of a positive diagnostic test for SARS-CoV-2; no other relevant exclusion criteria were applied. Each individual completed two RDTs and one PCR swab. On Dec 29, 2022, we also collected RDTs from a convenience sample of individuals who were positive for SARS-CoV-2 and associated with an outbreak at a senior housing facility in MA, USA. We extracted all returned PCR swabs and RDT components (ie, swab, strip, or buffer); samples with a Ct of less than 40 were subject to amplicon sequencing. We compared the efficacy of elution and sequencing across RDT brands and components and used RDT-derived sequences to infer transmission links within the outbreak at the senior housing facility. We conducted metagenomic sequencing of negative RDTs from symptomatic individuals living in the senior housing facility. Neither elution duration of greater than 10 min nor rotation during elution impacted viral titres. Elution in Buffer AVL (Ct=31·4) and Tris-EDTA Buffer (Ct=30·8) were equivalent (p=0·34); AVL outperformed elution in lysis buffer and 50% lysis buffer (Ct=40·0, p=0·0029 for both) as well as Universal Viral Transport Medium (Ct=36·7, p=0·079). Performance of RDT strips was poorer than that of matched PCR swabs (mean Ct difference 10·2 [SD 4·3], p<0·0001); however, RDT swabs performed similarly to PCR swabs (mean Ct difference 4·1 [5·2], p=0·055). No RDT brand significantly outperformed another. Across sample types, viral load predicted the viral genome assembly length. We assembled greater than 80% complete genomes from 12 of 17 RDT-derived swabs, three of 18 strips, and four of 11 residual buffers. We generated outbreak-associated SARS-CoV-2 genomes using both amplicon and metagenomic sequencing and identified multiple introductions of the virus that resulted in downstream transmission. RDT-derived swabs are a reasonable alternative to PCR swabs for viral genomic surveillance and outbreakinvestigation. RDT-derived lateral flow strips yield accurate, but significantly fewer, viral reads than matched PCR swabs. Metagenomic sequencing of negative RDTs can identify viruses that might underlie patient symptoms. The National Science Foundation, the Hertz Foundation, the National Institute of General Medical Sciences, Harvard Medical School, the Howard Hughes Medical Institute, the US Centers for Disease Control and Prevention, the Broad Institute and the National Institute of Allergy and Infectious Diseases.

The kinematic relationships of ball release speed in elite female cricket pace bowlers

The ability of a pace bowler to deliver the cricket ball at faster speeds is linked with improved match performance as they require less deliveries to take a wicket and concede less runs per wicket taken. The aim of this study was to explore the relationship between ball release speed and selected pace bowling kinematics in 19 elite female pace bowlers. Participants bowled 18 match-intensity deliveries indoors from a full run-up. Peak ball release speed and three-dimensional pace bowling kinematic data were extracted from six deliveries that met specific selection criteria. Linear and non-linear relationships were assessed between ball release speed and selected pace bowling kinematics. Ball release speed was associated linearly with pelvis orientation at back foot contact ( r2 = 0.34, p = 0.048) and non-linearly with thoraco-pelvic lateral flexion at front foot flat ( r2 = 0.69, p &lt; 0.001). Compared to their slower counterparts, faster elite female pace bowlers were more side-on at back foot contact and more upright in the frontal plane at front foot flat. A side-on pelvis orientation at back foot landing is likely to permit greater whole-body angular momentum in the transverse plane of motion. A more upright trunk orientation at front foot flat may be indicative of delayed onset of trunk flexion and representative of optimal segmental sequencing to maximise ball release speed. This knowledge is likely to be useful in the coaching and development of female pace bowlers for the purposes of enhancing ball release speed and therefore match performance.

The Real-Life Impact of Primary Tumor Resection of Synchronous Metastatic Colorectal Cancer-From a Clinical Oncologic Point of View.

The complex medical care of synchronous metastatic colorectal (smCRC) patients requires prudent multidisciplinary planning and treatments due to various challenges caused by the primary tumor and its metastases. The role of primary tumor resection (PTR) is currently uncertain; strong arguments exist for and against it. We aimed to define its effect and find its best place in our therapeutic methodology. We performed retrospective data analysis to investigate the clinical course of 449 smCRC patients, considering treatment modalities and the location of the primary tumor and comparing the clinical results of the patients with or without PTR between 1 January 2013 and 31 December 2018 at the Institute of Oncotherapy of the University of Pécs. A total of 63.5% of the 449 smCRC patients had PTR. Comparing their data to those whose primary tumor remained intact (IPT), we observed significant differences in median progression-free survival with first-line chemotherapy (mPFS1) (301 vs. 259 days; p < 0.0001; 1 y PFS 39.2% vs. 26.6%; OR 0.56 (95% CI 0.36-0.87)) and median overall survival (mOS) (760 vs. 495 days; p < 0.0001; 2 y OS 52.4 vs. 26.9%; OR 0.33 (95% CI 0.33-0.53)), respectively. However, in the PTR group, the average ECOG performance status was significantly better (0.98 vs. 1.1; p = 0.0456), and the use of molecularly targeted agents (MTA) (45.3 vs. 28.7%; p = 0.0005) and rate of metastasis ablation (MA) (21.8 vs. 1.2%; p < 0.0001) were also higher, which might explain the difference partially. Excluding the patients receiving MTA and MA from the comparison, the effect of PTR remained evident, as the mOS differences in the reduced PTR subgroup compared to the reduced IPT subgroup were still strongly significant (675 vs. 459 days; p = 0.0009; 2 y OS 45.9 vs. 24.1%; OR 0.37 (95% CI 0.18-0.79). Further subgroup analysis revealed that the site of the primary tumor also had a major impact on the outcome considering only the IPT patients; shorter mOS was observed in the extrapelvic IPT subgroup in contrast with the intrapelvic IPT group (422 vs. 584 days; p = 0.0026; 2 y OS 18.2 vs. 35.9%; OR 0.39 (95% CI 0.18-0.89)). Finally, as a remarkable finding, it should be emphasized that there were no differences in OS between the smCRC PTR subgroup and metachronous mCRC patients (mOS 760 vs. 710 days, p = 0.7504, 2 y OS OR 0.85 (95% CI 0.58-1.26)). The role of PTR in smCRC is still not professionally justified. Our survey found that most patients had benefited from PTR. Nevertheless, further prospective trials are needed to clarify the optimal treatment sequence of smCRC patients and understand this cancer disease's inherent biology.

Unveiling the physico‐mechanical properties of kenaf yarn fiber‐reinforced acrylonitrile butadiene styrene composites: Effect of quasi‐isotropic lay‐up sequences

AbstractThe rapid advancement of polymer composites, particularly those reinforced with hybrid kenaf, has positioned them as viable structural materials across diverse industries such as automotive, defense, aerospace, marine, construction, and naval. Despite their growing popularity, recent advances in kenaf yarn‐reinforced thermoplastic acrylonitrile butadiene styrene (ABS) composites for structural applications need more comprehensive coverage in the literature. This article addresses this gap by critically examining the influence of quasi‐isotropic stacking orientation on water absorption, flexural strength, and impact properties in kenaf yarn fiber‐reinforced ABS composites. Prepared using hand layup and compression molding techniques, the composites featured varying stacking orientations, including 0°/0°/0°/0°, 0°/90°/0°/90°, −45°/90°/0°/45°, 90°/−45°/0°/45°, 90°/0°/45°/−45°, and 0°/45°/−45°/90° for kenaf yarn reinforced ABS. The findings reveal that the 0°/45°/−45°/90° configuration offers the best overall mechanical properties and water penetration resistance among quasi‐isotropic stacking sequences, exhibiting low water uptake and higher tensile and flexural strengths of 35.3 and 85.4 MPa, respectively, compared to other sequences. This suggests that incorporating ±45° plies contributes to a balanced distribution of mechanical strength and modulus across different directions, making the 0°/45°/−45°/90° configuration ideal for high‐strength structural applications in kenaf yarn/ABS composites.Highlights Fiber stacking orientation is the key factor affecting the mechanical performance of kenaf‐based composites. Using the hand layup technique, six stacking orientations (0°/0°/0°/0°, 0°/90°/0°/90°, −45°/90°/0°/45°, 90°/−45°/0°/45°, 90°/0°/45°/−45°, and 0°/45°/−45°/90°) were employed to create kenaf fiber‐reinforced ABS composites. A quasi‐isotropic sequence of 0°/45°/−45°/90° allows the least water penetration due to the strategic placement of ±45° plies in the middle layers of composites. 0°/45°/−45°/90° configuration offers the best flexural and tensile properties due to better force distribution within the ±45° plies in the middle layers. Under surface morphological analysis, the optimal fiber stacking sequence contributes to fewer voids and fiber pull‐outs.

Finite termination of the optimal solution sequence in parametric optimization

This paper investigates the finite termination of the optimal solution sequence in parametric optimization as a subproblem of the equilibrium problems. By introducing an augmented mapping on the solution set of the equilibrium problems, we establish the concept of augmented weak sharpness for this set relative to the sequence of optimal solutions in parametric optimization. Augmented weak sharpness is an extension of the concepts of weak sharpness and strongly non-degeneracy. Under the condition that the solution set of the equilibrium problems is augmented weak sharpness, sufficient conditions for the finite termination of the sequence of optimal solutions in parametric optimization are provided. For some special cases of the equilibrium problems (Mathematical Programming (MP), Variational Inequality Problems (VIP), etc.), these results extend the corresponding results under weak sharpness or strongly non-degeneracy conditions in the existing literature. Additionally, they also provide weaker sufficient conditions for the finite termination of many optimization algorithms.

ASOptimizer: Optimizing antisense oligonucleotides through deep learning for IDO1 gene regulation

Recent studies have highlighted the effectiveness of using antisense oligonucleotides (ASOs) for cellular RNA regulation, including targets that are considered undruggable; however, manually designing optimal ASO sequences can be labor intensive and time consuming, which potentially limits their broader application. To address this challenge, we introduce a platform, the ASOptimizer, a deep-learning-based framework that efficiently designs ASOs at a low cost. This platform not only selects the most efficient mRNA target sites but also optimizes the chemical modifications for enhanced performance. Indoleamine 2,3-dioxygenase 1 (IDO1) promotes cancer survival by depleting tryptophan and producing kynurenine, leading to immunosuppression through the aryl-hydrocarbon receptor (Ahr) pathway within the tumor microenvironment. We used ASOptimizer to identify ASOs that target IDO1 mRNA as potential cancer therapeutics. Our methodology consists of two stages: sequence engineering and chemical engineering. During the sequence-engineering stage, we optimized and predicted ASO sequences that could target IDO1 mRNA efficiently. In the chemical-engineering stage, we further refined these ASOs to enhance their inhibitory activity while reducing their potential cytotoxicity. In conclusion, our research demonstrates the potential of ASOptimizer for identifying ASOs with improved efficacy and safety.

Accounting for traffic disturbance in road infrastructure management: Optimal maintenance and rehabilitation planning for the society

Maintaining a high level of network performance at minimal cost is a central objective for road infrastructure management. Going beyond agency costs, decisions must consider the economic and environmental impacts of maintenance and repairs strategies due to traffic delays on commuters and providers of goods and services. A dynamic programming model is presented to determine optimal management policies for current and proposed paradigms by selecting the best maintenance and rehabilitation activities at each time step over the time horizon given the concomitant and predicted states of the bridges within the network as well as predicted future origin-destination flows. The empirical analysis is demonstrated for a small network of three viaducts in an urban environment in Montréal, Canada. The incremental user costs due to interventions on the structures of the network are obtained through traffic simulations based on trip-survey data to evaluate travel delays for all possible combinations of concomitant partial or complete road closures. The results show that optimal infrastructure management strategies in space and time differ substantially depending on whether only agency costs are used, or if policies also reflect socio-economic goals (including sustainability). The main findings indicate that by including user costs in decision processes, significant reductions in user costs are achieved with minimal impact on operator costs, thus easily satisfying a cost/benefit criterion. The findings provide new evidence for policymakers that the optimal sequencing of interventions in time and space on a network can significantly reduce societal costs, sustainability objectives and environmental impacts.

Tucatinib Combination Treatment After Trastuzumab-Deruxtecan in Patients With ERBB2-Positive Metastatic Breast Cancer

Little is known regarding the outcomes associated with tucatinib combined with trastuzumab and capecitabine (TTC) after trastuzumab-deruxtecan exposure among patients with ERBB2 (previously HER2)-positive metastatic breast cancer (MBC). To investigate outcomes following TTC treatment in patients with ERBB2-positive MBC who had previously received trastuzumab-deruxtecan. This cohort study included all patients with MBC who were treated in 12 French comprehensive cancer centers between August 1, 2020, and December 31, 2022. Tucatinib combined with trastuzumab and capecitabine administered at the recommended dose. Clinical end points included progression-free survival (PFS), time to next treatment (TTNT), overall survival (OS), and overall response rate (ORR). A total of 101 patients with MBC were included (median age, 56 [range, 31-85] years). The median number of prior treatment lines for metastatic disease at TTC treatment initiation was 4 (range, 2-15), including 82 patients (81.2%) with previous trastuzumab and/or pertuzumab and 94 (93.1%) with previous ado-trastuzumab-emtansine) exposure. The median duration of trastuzumab-deruxtecan treatment was 8.9 (range, 1.4-25.8) months, and 82 patients (81.2%) had disease progression during trastuzumab-deruxtecan treatment, whereas 18 (17.8%) had stopped trastuzumab-deruxtecan for toxic effects and 1 (1.0%) for other reasons. Tucatinib combined with trastuzumab and capecitabine was provided as a third- or fourth-line treatment in 37 patients (36.6%) and was the immediate treatment after trastuzumab-deruxtecan in 86 (85.1%). With a median follow-up of 11.6 (95% CI, 10.5-13.4) months, 76 of 101 patients (75.2%) stopped TTC treatment due to disease progression. The median PFS was 4.7 (95% CI, 3.9-5.6) months; median TTNT, 5.2 (95% CI, 4.5-7.0) months; and median OS, 13.4 (95% CI, 11.1 to not reached [NR]) months. Patients who received TTC immediately after trastuzumab-deruxtecan had a median PFS of 5.0 (95% CI, 4.2-6.0) months; median TTNT of 5.5 (95% CI, 4.8-7.2) months, and median OS of 13.4 (95% CI, 11.9-NR) months. Those who received TTC due to trastuzumab-deruxtecan toxicity-related discontinuation had a median PFS of 7.3 (95% CI, 3.0-NR) months. Best ORR was 29 of 89 patients (32.6%). Sixteen patients with active brain metastasis had a median PFS of 4.7 (95% CI, 3.0-7.3) months, median TTNT of 5.6 (95% CI, 4.4 to NR), and median OS of 12.4 (95% CI, 8.3-NR) months. In this study, TTC therapy was associated with clinically meaningful outcomes in patients with ERBB2-positive MBC after previous trastuzumab-deruxtecan treatment, including those with brain metastases. Prospective data on optimal drug sequencing in this rapidly changing therapeutic landscape are needed.