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Selecting a "useful" model among a set of competing candidate models is a common problem in many disciplines and particularly in engineering applications. For a modeller, the best model fulfills a certain purpose best (e.g., the ability to accurately predict new data, good interpolation, precise estimates of lower/upper percentiles, etc.), which is typically assessed by comparing model simulations to the observed data. Approximate Bayesian computation (ABC) which is a statistical learning technique is used in this work for reliability model selection and parameter calibration using small/moderate fatigue life data sets. This is always the case in material fatigue due to the high cost and low efficiency of fatigue tests which are bottleneck problem when designing components/structures with regard to fatigue. The ABC is a likelihood‐free based method which means that it needs only a generative model to simulate the data. The proposed method provides a formal rank of the competing reliability models by eliminating gradually the least likely models in a parsimonious manner. It is flexible since it offers the possibility to use different metrics to measure the discrepancy between simulated and observed data. The choice of the appropriate distance function depends essentially on the purpose of model selection. Through various examples, it has been demonstrated that the ABC method has a number of very attractive properties, which makes it especially useful in probabilistic risk assessment and reliability analysis. [ABSTRACT FROM AUTHOR]

Crowd motion simulation requires specification of a range of parameters, each reflecting certain aspects of agent behavior. But what parameters matter the most? Are they all equally important? The question is important given that available data and resources for parameter calibration are limited, and priorities often need to be made. Here, for the first time, a full-spectrum sensitivity analysis of crowd model parameters is reported. It is shown that estimates of simulated evacuation time are, by far, most dependent on the value of locomotion/operational parameters, especially those that determine discharge rate at bottlenecks. The next most critical set of parameters are those that influence change of direction choices. If a crowd simulation model fails to reproduce bottleneck flows accurately, efforts to refine other modeling layers will be in vain. Similarly, if the model fails to represent exit choice adaptation/changing accurately, efforts to refine the exit choice model will be fruitless. [ABSTRACT FROM AUTHOR]

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non‐expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high‐quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single‐organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments. [ABSTRACT FROM AUTHOR]

Graph domain adaptation (GDA) aims to address the challenge of limited label data in the target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for different-level (node-level, graph-level) adaptation tasks exhibit variations in domain feature extraction, and most of them solely rely on representation alignment to transfer label information from a labeled source domain to an unlabeled target domain. However, this approach can be influenced by irrelevant information and usually ignores the conditional shift of the downstream predictor. To effectively address this issue, we introduce a target-oriented unsupervised graph domain adaptive framework for graph adaptation called TO-UGDA. Particularly, domain-invariant feature representations are extracted using graph information bottleneck. The discrepancy between two domains is minimized using an adversarial alignment strategy to obtain a unified feature distribution. Additionally, the meta pseudo-label is introduced to enhance downstream adaptation and improve the model's generalizability. Through extensive experimentation on real-world graph datasets, it is proved that the proposed framework achieves excellent performance across various node-level and graph-level adaptation tasks. [ABSTRACT FROM AUTHOR]

We investigate model order reduction for discrete time-delay systems via orthogonal polynomial expansion in the frequency domain. The transfer function of systems is expanded in the framework of Laguerre function basis. We show that Laguerre coefficients of the states satisfy a linear system and reduced models generated by projection methods can preserve some Laguerre coefficients of the original systems. We prove that the subspace spanned by Laguerre coefficients is exactly a high order Krylov subspace, thereby leading to an efficient computation of projection matrices and a more accurate coefficient-matching property in the two-sided framework. Further the Laguerre expansion of systems is employed to enable an approximate but fast execution of balanced truncation for discrete time-delay systems. Specifically, Gramians are approximated based on the derived Laguerre coefficients, which circumvents the main bottleneck of balanced truncation methods. Finally, two numerical examples are simulated to demonstrate the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Innovation is one of the key competitiveness strategies for small, medium, and large companies. Production line innovations using Lean methods are currently one of the most important ways to change the shape of the production line in a positive direction. Emphasis must be placed on the resulting innovation and the form of the production line, which should be as efficient and functional as possible and meet the required parameters of productivity and quality assurance. The primary goal of production line innovations using Lean methods is the elimination of bottlenecks in the production process that do not add value and the balancing of individual assembly stations. The aim of the contribution was to design a semi-automatic line for the completion and testing of seat locks under the name D3P2. The result of the solution is the creation of a robotic workplace that will ensure a higher production capacity and the related packaging of finished products. On the assembly line, an analysis of line balancing was carried out using the Yamazumi graph to eliminate unnecessary assembly process activities, Cycle time and Tact time. An analysis of the measurements of individual production activities and places was carried out on the line, and the proposal for a new production line was subsequently shared. Its main goal was to meet all customer requirements through high flexibility. The result of the analysis and applied measures was a reduction in the cycle time of the new line, balancing of individual posts and economic benefits. [ABSTRACT FROM AUTHOR]

Autoencoders (AEs) are a type of unsupervised neural networks, which can be used to solve various tasks, e.g., dimensionality reduction, image compression, and image denoising. An AE has two goals: (i) compress the original input to a low-dimensional space at the bottleneck of the network topology using an encoder, (ii) reconstruct the input from the representation at the bottleneck using a decoder. Both encoder and decoder are optimized jointly by minimizing a distortion-based loss which implicitly forces the model to keep only the information in input data required to reconstruct them and to reduce redundancies. In this paper, we propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. To this end, we propose an additional loss term, based on the pairwise covariances of the network units, which complements the data reconstruction loss forcing the encoder to learn a more diverse and richer representation of the input. We tested our approach across different tasks, namely dimensionality reduction, image compression, and image denoising. Experimental results show that the proposed loss leads consistently to superior performance compared to using the standard AE loss. • We propose a scheme to avoid redundant features in the bottleneck representation of autoencoders. • We explicitly penalize the pair-wise correlations between the features and learn a diverse compressed embedding. • The proposed penalty acts as an unsupervised regularizer and can be integrated into any autoencoder model. [ABSTRACT FROM AUTHOR]

Primary T cell has been transformed into a cell‐based delivery platform that synthesizes complex biologics at the disease site with spatiotemporal resolution. This broadly applicable technology can circumvent toxicities due to systemic administration of biologics that necessitates the use of high doses and may diffuse to the healthy tissues. Its clinical translation, however, has been impeded by manufacturing bottlenecks. In this work, a range of process parameters were investigated for increasing the production yield of the primary T cells engineered for delivery function. Compared to the common spinoculation‐based method, the transduction yield was enhanced ~2.5‐fold by restricting the transduction reaction volume for maximizing the lentivector‐to‐T‐cell contact. Cell density and cytokines used in the expansion process were adjusted to achieve >100‐fold expansion of the T‐cell‐based delivery platform in 14 days, and the function of these cells was validated in vivo using intraperitoneally implanted tumor cells. The primary T‐cell‐based delivery platform has human applications because it can be scaled and administrated to express a broad range of therapeutic proteins (e.g., cytokines, interferons, enzymes, agonists, and antagonists) at the disease site, obviating the need for systemic delivery of large doses of these proteins. [ABSTRACT FROM AUTHOR]

Automatic biomedical relation extraction (bioRE) is an essential task in biomedical research in order to generate high-quality labelled data that can be used for the development of innovative predictive methods. However, building such fully labelled, high quality bioRE data sets of adequate size for the training of state-of-the-art relation extraction models is hindered by an annotation bottleneck due to limitations on time and expertise of researchers and curators. We show here how Active Learning (AL) plays an important role in resolving this issue and positively improve bioRE tasks, effectively overcoming the labelling limits inherent to a data set. Six different AL strategies are benchmarked on seven bioRE data sets, using PubMedBERT as the base model, evaluating their area under the learning curve (AULC) as well as intermediate results measurements. The results demonstrate that uncertainty-based strategies, such as Least-Confident or Margin Sampling, are statistically performing better in terms of F1-score, accuracy and precision, than other types of AL strategies. However, in terms of recall, a diversity-based strategy, called Core-set, outperforms all strategies. AL strategies are shown to reduce the annotation need (in order to reach a performance at par with training on all data), from 6% to 38%, depending on the data set; with Margin Sampling and Least-Confident Sampling strategies moreover obtaining the best AULCs compared to the Random Sampling baseline. We show through the experiments the importance of using AL methods to reduce the amount of labelling needed to construct high-quality data sets leading to optimal performance of deep learning models. The code and data sets to reproduce all the results presented in the article are available at https://github.com/oligogenic/Deep_active_learning_bioRE. [ABSTRACT FROM AUTHOR]

Dual-resource-constrained scheduling problems (DRCSP) have been hotly debated during the last two decades. DRCSPs focus on the causes and consequences of problems arising from improper simultaneous planning of human resources and machines in various industrial sectors, yielding decreasing productivity of industrial factories. This research focuses on time-oriented problems (including makespan, setup, transportation), cost-oriented problems (including execution cost, workforce salary, raw material, maintenance, and repair), workforce-oriented problems (salary, homogeneous workers, overload workers, aged workers, mobile workers) and performance and energy problems in DRCSPs. To this end, special attention is given to the root and reasons for emerging significant issues, such as the lack of machines, system imbalance, material transferring, bottleneck machines, voids, and human resource over-allocation that usually happens during the scheduling of dual-resource-constrained systems. For this purpose, an in-depth analysis has been conducted by reviewing 122 dominant research papers available in the literature from 2006 to 2022. The advantages, limitations, and significant drawbacks of scheduling DRC problems have been discussed for each studied case. This study's results clarified gaps to be addressed in studied areas of DRCSP, showing that various aspects of workforce, energy, and performance should receive more attention in future research. A list of directions for future research is provided at the end of the paper. [ABSTRACT FROM AUTHOR]

Recently, the Microservices whole link whole life cycle optimization framework has been a research hotspot in the academic and engineering fields, especially how to encapsulate the component security mechanism and conduct high concurrency testing based on the existing active framework. The whole link delay, load, and cost have always been the key optimization goals of Microservices splitting and deployment logic. Based on this, this paper proposes a multi-objective optimization Microservices framework that takes into account the security mechanism, Define the fitness function, define the upper and lower limits, and perform multi-dimensional constraints to filter for global and local optima. At the same time, reconstruct the circuit breaker current limiting mechanism, dynamically detect protocol parameters, and perceive the logical relationship of heartbeat status in real-time. Experimental results have shown that this innovative framework can solve scenarios such as high concurrency, high reliability, and high availability Performance bottleneck, business degradation, especially in financial and securities scenarios. [ABSTRACT FROM AUTHOR]

The need for faster and deeper transitions toward more sustainable development pathways is now widely recognized. How to meet that need has been at the center of a growing body of academic research and real-world policy implementation. This paper presents our perspective on some of the most powerful insights that have emerged from this ongoing work. In particular, we highlight insights on how sustainability transitions can be usefully conceptualized, how they come about and evolve, and how they can be shaped and guided through deliberate policy interventions. Throughout the paper, we also highlight some of the many how questions that remain unresolved and on which progress would be especially helpful for the pursuit of sustainable development. Our approach to these "how" questions on sustainability transitions draws on two strands of solution-driven research and policy advice: one emerging from studies of how human societies interact with nature and the other emerging from studies of how those societies interact with their technologies. Consumption-production systems have been a focus of extensive work in both strands. To help build bridges between them, we recently brought together a cross-section of relevant scholars for a PNAS Special Feature on "Sustainability transitions in consumption-production systems." Their contributions are summarized in a companion paper we have written to introduce the Special Feature [F. W. Geels, F. Kern, W. C. Clark, Proc. Natl. Acad. Sci. U.S.A. (2023)]. We draw on that work in the Perspective we present here as well as our reading of the relevant literatures. [ABSTRACT FROM AUTHOR]

The integration of structure from motion (SFM) and unmanned aerial vehicle (UAV) technologies has allowed for the generation of very high-resolution three-dimensional (3D) point cloud data (up to millimeters) to detect and monitor surface changes. However, a bottleneck still exists in accurately and rapidly registering the point clouds at different times. The existing point cloud registration algorithms, such as the Iterative Closest Point (ICP) and the Fast Global Registration (FGR) method, were mainly developed for the registration of small and static point cloud data, and do not perform well when dealing with large point cloud data with potential changes over time. In particular, registering large data is computationally expensive, and the inclusion of changing objects reduces the accuracy of the registration. In this paper, we develop an AI-based workflow to ensure high-quality registration of the point clouds generated using UAV-collected photos. We first detect stable objects from the ortho-photo produced by the same set of UAV-collected photos to segment the point clouds of these objects. Registration is then performed only on the partial data with these stable objects. The application of this workflow using the UAV data collected from three erosion plots at the East Tennessee Research and Education Center indicates that our workflow outperforms the existing algorithms in both computational speed and accuracy. This AI-based workflow significantly improves computational efficiency and avoids the impact of changing objects for the registration of large point cloud data. [ABSTRACT FROM AUTHOR]

DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment. A new way to align DIA type proteomics data is described and shown that retention time alignment increases data completeness and decreases quantitative variance in multicenter synthetic datasets and also real-world patient datasets. [ABSTRACT FROM AUTHOR]

Background: Chromatographic peakpicking continues to represent a significant bottleneck in automated LC–MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality. Results: Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram, and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70–80% down to 1–5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone. Conclusions: We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation. [ABSTRACT FROM AUTHOR]

Nuclear power tube plates are made from the high-strength, low-carbon alloy steel SA-5083, which has high values of toughness and plasticity, though it is forged with poor consistency and entails serious work hardening. It requires a large number of deep holes with a high machining accuracy and high surface quality to be processed. However, the quality of the processed holes is often not up to the standard of the Boring and Trepanning Association (BTA) for the deep-hole drilling of tube plates; this has led to deep-hole processing becoming a bottleneck in the manufacture of steam generators for the main equipment of nuclear power islands. The variation laws of the diameter, roundness, perpendicularity, roughness, microhardness, and residual stress in relation to the feed, speed, and drilling depth are explored in the macro- and micro-dimensions; also explored is the wear morphology of BTA drills. The internal influence mechanisms between them are revealed in order to provide a scientific basis for the control of surface quality and machining accuracy as well as the optimization of process parameters. Our research results indicate that the guide block wear is mainly concentrated at the top 1–2 mm and that the drilling depth and feed have a great influence on the machining diameter. The hole wall roughness is between 0.3 and 0.6 μm, the maximum microhardness is about 2.15 times the hardness of the matrix material, and the residual stress is compressive stress. With increases in the feed and drilling depth, the hole diameter and the roughness increase. With an increase in the speed, the roughness decreases and the compressive stress of the BTA deep-hole drilling wall increases. [ABSTRACT FROM AUTHOR]

The current research attempts to determine the degree to which three primary elements (Human, Structural, and relational capital) and intellectual capital are related to profitability of SMEs technological inventions in China's Jiangsu province of as part of the country's intellectualization process in the post-industrial era. The research utilizes data from 1,450 SME listings on the Shenzhen stock market between 2012 and 2020. The VAICTM model was used in this inquiry. Using PLS-SEM, the study evaluated four significant hypotheses. The research indicates that human and structural capital have a substantial meaningful correlation regarding SMEs success. The research also discovered a favorable association between human and structural capital and technological innovation in Jiangsu province throughout the post-industrial period. However, a strong negative correlation occurred between relational capital and the financial performance of small and medium-sized enterprises (SMEs). The research uncovered a substantial positive relationship between the technological innovation of SMEs and their financial success. The link between intellectual capital (HC, SC, and RC) and company performance was shown to be mediated and moderated by technological innovation. According to the findings, technical innovation considerably moderates the link between Human Capital and firm performance. The research indicated that Technical Innovation (TI) moderates the association between Structural Capital (RC) and firm performance. In contrast, Technical Innovation (TI) does not alter the association between Relational Capital (RC) and firm success in a substantial way. [ABSTRACT FROM AUTHOR]

The main bottleneck during the manufacture of complex geometry thin-walled GFRC structures is the time taken to make the timber or CNC machined moulds for each panel. Complex geometries are comprised of many unique panel forms and the extensive time and high costs of their manufacture often prevents their architectural intent from being fully realised. A novel mould-making process is proposed that uses a state-of-the-art flexible table with computer-controlled actuators to create free-formed geometry, fast-curing, dual-density, polyurethane moulds. This mould-making process was successfully tested by using sprayed GFRC to manufacture 9 different double curved intermediate moulds for a 10 m high GFRC self-supporting, thin-walled hyperbolic shell, with 12 mm thick panels at the base of the structure. The completed structure showcased the effectiveness of the novel mould-making process by reducing the production time from an estimated 100 days to 10 days. The primary outcome was the development and application of a new manufacturing method capable of casting complex geometry thin-walled GFRC panels with good surface quality that was suited to more rapid, cost-effective and automated large-scale production. [ABSTRACT FROM AUTHOR]

The reaction path Hamiltonian (RPH) can be used to calculate chemical reaction rate constants, going beyond transition-state theory in taking account of recrossing by providing an approximation to the dynamic transmission coefficient. However, the RPH necessitates the calculation of the Hessian matrix at a number of points along the minimum energy path; the associated computational cost stands as a bottleneck in RPH calculations, especially if one is interested in using high-accuracy electronic structure methods. In this work, four different Hessian update schemes (symmetric rank-1, Powell-symmetric Broyden, Bofill, and TS-BFGS updates) are assessed to see whether or not they reliably reproduce calculated transmission coefficients for three different chemical reactions. Based on the reactions investigated, the symmetric rank-1 Hessian update was the least appropriate for RPH construction, giving different transmission coefficients from the standard analytical Hessian approach, as well as inconsistent frequencies and coupling properties. The Bofill scheme, the Powell-symmetric Broyden scheme, and the TS-BFGS scheme were the most reliable Hessian update methods, with transmission coefficients that were in good agreement with those calculated by the standard RPH calculations. The relative accuracy of the different Hessian update schemes is further rationalized by investigating the approximated Coriolis and curvature coupling terms along the reaction-path, providing insight into when these schemes would be expected to work well. Furthermore, the associated computational cost associated with the RPH calculations was substantially reduced by the tested update schemes. Together, these results provide useful rules-of-thumb for using Hessian update schemes in RPH simulations. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 has revolutionized the industrial sector, enabling the development of compact, precise, and interconnected assets. This transformation has not only generated vast amounts of data but also facilitated the migration of learning and optimization processes to edge devices. Consequently, modern industries can effectively leverage this paradigm through distributed learning to define product quality and implement predictive maintenance (PM) strategies. While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating a global AI model on a parameter server (PS) through aggregation of locally trained models from edge devices. We propose an innovative approach: analog aggregation over-the-air of updates transmitted concurrently over wireless channels. This leverages the waveform-superposition property in multi-access channels, significantly reducing communication latency compared to conventional methods. However, it is vulnerable to performance degradation due to channel properties like noise and fading. In this study, we introduce a method to mitigate the impact of channel noise in FL over-the-air communication and computation (FLOACC). We integrate a novel tracking-based stochastic approximation scheme into a standard federated stochastic variance reduced gradient (FSVRG). This effectively averages out channel noise's influence, ensuring robust FLOACC performance without increasing transmission power gain. Numerical results confirm our approach's superior communication efficiency and scalability in various FL scenarios, especially when dealing with noisy channels. Simulation experiments also highlight significant enhancements in prediction accuracy and loss function reduction for analog aggregation in over-the-air FL scenarios. [ABSTRACT FROM AUTHOR]

The article presents a summary of what the author wrote 30 years ago about data integrity. He discusses the aim of the first column which was to consider data integrity within an automated chromatography laboratory, sample labels and other disasters, and analytical instrument qualification. Also mentioned are blank forms and master templates and laboratory information management systems (LIMS) projects and data integrity.

Nowadays, series production systems are broadly used in various branches of industry. In these kinds of production systems, any disruption in production flow can significantly increase production cost and time; therefore, a proper maintenance strategy considerably reduces the company's costs and enhance organizational performance. In this research, a novel bottleneck-centered opportunistic maintenance policy for a series production line is proposed. First of all, the bottleneck is detected by the Lowest Production Rate method. Then, preventive maintenance time interval, reliability threshold, and number of maintenance cycles are determined by the particle swarm optimization algorithm. Finally, a novel opportunistic maintenance strategy is proposed based on analyzing the cost savings of preventive maintenance advancement and postponement. To demonstrate the model's effectiveness, the proposed bottleneck-based opportunistic maintenance model is compared with two other maintenance models, and the results confirmed that the proposed strategy could efficiently eliminate unnecessary production breaks and achieve cost reduction. [ABSTRACT FROM AUTHOR]

Three-dimensional (3-D) point cloud data are increasingly being used to describe a wide range of physical objects in detail, corresponding to customized and flexible shape designs. The advent of a new generation of optical sensors has simplified and reduced the costs of acquiring 3-D data in near-real-time. However, the variation of the acquired point clouds, and methods to describe them, create bottlenecks in manufacturing practices such as Reverse Engineering (RE) and metrology in additive manufacturing. We address this issue by developing an automated variance modeling algorithm that utilizes a physical object's local geometric descriptors and Bayesian Extreme Learning Machines (BELMs). Our proposed ensemble and residual BELM-variants are trained by a scanning history that is composed of multiple scans of other, distinct objects. The specific scanning history is selected by a new empirical Kullback–Leibler divergence we developed to identify objects that are geometrically similar to an object of interest. A case study of our algorithm on additively manufactured products demonstrates its capability to model the variance of point cloud data for arbitrary freeform shapes based on a scanning history involving simpler, and distinct, shapes. Our algorithm has utility for measuring the process capability of 3-D scanning for RE processes. [ABSTRACT FROM AUTHOR]

Modern diffraction experiments (e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies (i.e. the parameter turn‐on‐off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi‐histogram datasets such as from multi‐bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram‐specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction (MAUD), the MAUD Interface Language Kit (MILK) is developed along with an updated text batch interface for MAUD. The open‐source software MILK is computer‐platform independent and is packaged as a Python library that interfaces with MAUD. Using MILK, model selection (e.g. various texture or peak‐broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high‐level Python interface. A high‐level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD. Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye–Scherrer, a tool for visualizing and inspecting the results of multi‐parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer. [ABSTRACT FROM AUTHOR]

We study how to learn semantic segmentation of 3D point clouds from small training sets. The problem arises because annotating 3D point clouds is a lot more time-consuming and error-prone than annotating 2D images. On the one hand this means that one cannot afford to create a large enough training dataset for each new project. On the other hand it also means that there is not nearly as much public data available as there is for images, which one could use to pretrain a generic feature extractor that could then, with only little dedicated training data, be adapted ("fine-tuned") to the task at hand. To address this bottleneck we explore the possibility to transfer knowledge from the 2D image domain to 3D point clouds. That strategy is of particular interest for mobile mapping systems that capture both point clouds and images, in a fully calibrated setting that makes it easy to connect the two domains. We find that, as expected, naively segmenting in image space and mapping the resulting labels onto the point cloud is not sufficient, as visual ambiguities, residual calibration errors, etc. affect the result. Instead, we propose a system that learns to merge image evidence from a varying number viewpoint, and 3D geometry information, into a common representation that encodes point-wise 3D semantics. To validate our approach we make use of a new mobile mapping dataset with 88M annotated 3D points and 2205 oriented multi-view images. In a series of experiments, we show how much label noise is caused by simplistic label transfer, and how well existing semantic segmentation architectures can correct it. Finally, we demonstrate that adding our learned 2D-to-3D multi-view label transfer significantly improves the performance of different segmentation backbones. [ABSTRACT FROM AUTHOR]

Biopharmaceuticals are complex therapeutic protein molecules produced in living cells and have been a major driving force for drug development in the pharmaceutical sector in recent years. Monoclonal antibodies (mAbs) are biological macromolecules used for treating life-threatening and rare illnesses. mAbs with post-translation alterations can be observed during the assessment of charge variants. Controlling the charge variant profile of therapeutic protein is a regulatory requirement to confirm that the macromolecule complies with the quality parameters to ensure patient safety. Unfortunately, manufacturing these biopharmaceuticals is very expensive. However, the emergence of biosimilars has reduced developmental cost across the biopharmaceutical industry. The advent of biosimilars has constrained the development of more efficient downstream bioprocesses that are mainly considered the bottleneck of the manufacturing process. This review focuses on the existing methods for charge variants separation and process optimization and indicates new approaches for future developments. It also provides a comprehensive summary for the biological community about the impact of charge variants. [ABSTRACT FROM AUTHOR]

In manufacturing systems, simulation modeling plays an important role in creating some changes instead of working on real systems. Manipulation in a real system is more costly than manipulation in a simulated model. In this research, we tried to use a simulation approach to recognize and minimize bottlenecks of a production line, which will decrease costs and improve productivity. To achieve our objectives, we chose a case and analyzed its production line. By using our case study strategy, we tried to collect our data and adapt a conceptual model of production processes drawing on an operation process chart (OPC). After that, we created a simulation model of the production processes by using the popular Arena simulation software 13.5. By running the Arena model, some bottlenecks were found. Ultimately, we proposed some solutions to obviate bottlenecks and reduce the total costs of production. [ABSTRACT FROM AUTHOR]

To reduce the weight and volume, and extend the life of the proton-exchange membrane fuel cell (PEMFC) which is considered as a kind of near zero emission green energy, titanium ultra-thin sheet is selected to fabricate the core components — bipolar plates for its smaller density and high corrosion resistance. In order to break through the bottlenecks in the manufacture of micro-channels with big ratio of depth and width, a kind of three-stage stamping was investigated for its characters such as high productivity and low cost. Finite element (FE) simulation and optimization of three-stage stamping was carried out considering the reduction of thickness by analyzing the effect of the radius of punch and female die corner, punch displacement, and ratio of rib and channel width, etc. Then, the mold was designed, and experiments were performed using a servo drive machine. The arc design of punch end in the first stage can decrease the reduction of thickness from 47 to 22%. The best ratio between rib and channel width is 0.4–0.6, and radius of final channel corner is 0.15 mm. Experimental results of three-stage stamping show that the max reduction of thickness is about 24%, which is similar with that obtained from FE simulation. Microstructure with uniform small grain size and high surface quality are both helpful for improving the properties of bipolar plates required by PEMFC. This means that the developed three-stage stamping process is very suitable for massive manufacturing the bipolar plates with titanium ultra-thin sheet. [ABSTRACT FROM AUTHOR]

Pressure plays essential roles in chemistry by altering structures and controlling chemical reactions. The extreme-pressure polarizable continuum model (XP-PCM) is an emerging method with an efficient quantum mechanical description of small- and medium-sized molecules at high pressure (on the order of GPa). However, its application to large molecular systems was previously hampered by a CPU computation bottleneck: the Pauli repulsion potential unique to XP-PCM requires the evaluation of a large number of electric field integrals, resulting in significant computational overhead compared to the gas-phase or standard-pressure polarizable continuum model calculations. Here, we exploit advances in graphical processing units (GPUs) to accelerate the XP-PCM-integral evaluations. This enables high-pressure quantum chemistry simulation of proteins that used to be computationally intractable. We benchmarked the performance using 18 small proteins in aqueous solutions. Using a single GPU, our method evaluates the XP-PCM free energy of a protein with over 500 atoms and 4000 basis functions within half an hour. The time taken by the XP-PCM-integral evaluation is typically 1% of the time taken for a gas-phase density functional theory (DFT) on the same system. The overall XP-PCM calculations require less computational effort than that for their gas-phase counterpart due to the improved convergence of self-consistent field iterations. Therefore, the description of the high-pressure effects with our GPU-accelerated XP-PCM is feasible for any molecule tractable for gas-phase DFT calculation. We have also validated the accuracy of our method on small molecules whose properties under high pressure are known from experiments or previous theoretical studies. [ABSTRACT FROM AUTHOR]

Effective heat dissipation is the bottleneck problem for the development and commercialization of GaN-based high-power electronic and photonic devices. To address this challenge and explore the underlying mechanism of phonon transportation across the GaN/AlN heterointerface, in this work, we formed three types of GaN/AlN heterostructures with distinctively different interfacial morphologies by annealing recrystallization approach. It is found that the interfacial thermal conductance (ITC) of GaN/AlN heterostructures can be remarkably improved by tailoring the interfacial crystal structure and phase morphology. Besides the commonly amorphous phase and ideal ordered wurtzite phase, we further found that AlN may present an additional stable rock salt phase at the interfacial region, and its significant effect on interfacial thermal transport has been observed. Using molecular dynamics simulation, we systematically investigated the effects of different GaN/AlN heterojunctions on the ITCs. Our results suggest that heat dissipation at the GaN/AlN interface is dominated by phonons scattered diffusely by the amorphous region at interfaces and the ITC can be significantly enhanced by recrystallizing the amorphous AlN to rock salt one. Furthermore, through phonon vibrational spectrum, we revealed that phonon modes dominate the energy transport across the interfaces of wurtzite AlN/GaN, amorphous AlN/GaN, and rock salt AlN/GaN are significantly different. Finally, we found the ITC increased with the temperature due to the enhanced inelastic phonon scattering and the presence of additional excited phonon modes at higher temperatures. The findings elucidated here provide a clearer insight into the effect of interfacial microstructures on the interfacial thermal resistance of GaN–substrate interface, which also provide a viable heat management strategy for the high-power GaN-based devices. [ABSTRACT FROM AUTHOR]

The ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics simulations is heavily dependent on the knowledge of a low-dimensional manifold (parameterized by a reaction coordinate or RC) that typically distinguishes between relevant metastable states, and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work we propose a deep learning based state predictive information bottleneck approach to learn the RC from high-dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is connected to the committor in chemical physics and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time delay or how far into the future the algorithm should make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations. [ABSTRACT FROM AUTHOR]

With the growing prosperity of optical systems, the demand for high-precision optical components is becoming increasingly urgent. The mid-spatial frequency error generated on the optical components surface during the manufacturing process has become a bottleneck that restricts the improvement of the manufacturing accuracy of optical components. In this review, the processing techniques and polishing principles for optical components manufacturing are first briefly described and the source of mid-spatial frequency error is analyzed in detail. Then, several widely adopted suppression techniques in optical components manufacturing for mid-spatial frequency error are introduced in detail, such as the optimization methods for polishing path and polishing tool structure, and ion beam layer removal method. Moreover, their basic concepts, polishing mechanisms, development history, and characteristics are presented. Finally, the development of suppression techniques of mid-spatial frequency error for optical polishing is summarized and prospected. This paper analyses the problems of mid-spatial frequency error in optical components manufacturing, which may be worth investigating in the future. [ABSTRACT FROM AUTHOR]

Currently, we live in a culture of being overly busy, but this does not translate into efficiency, speed of implementation of the actions taken. Enterprises are constantly looking for methods and tools to make them more efficient. The most popular method of production management is Lean Manufacturing, less known is Theory of Constraints. This work is a continuation of the research on the comparison of these methods with apply a computer simulation, which the analyzed production process in the selected enterprise, after 24 hours and week. An attempt was made to simplify the comparison of the methods based on the obtained simulation in terms of costs. In analyzed case, more advantageous solution is to use the DBR method. To produce various orders that do not require 100% production on the bottleneck position, the use of Kanban is a frequent practice as it provides greater flexibility in order execution. [ABSTRACT FROM AUTHOR]

Urban road networks have promoted high-quality travel for residents by increasing connectivity and intelligence. But road congestion has not been effectively alleviated, causing a loss of time and energy. At present, the recovery of urban road networks mainly considers removing the failed edges. Considering the recovery cost and time, it is important to take active maintenance behavior to restore these networks. One of the key problems is dispatching traffic workers reasonably to achieve timely maintenance. In this paper, a flow-distribution-based process and execution (FD-PE) model is established for solving congestion. The maintenance centers (MC) study the reasons for and spread of congestion by edge flow. Based on the genetic algorithm (GA), two models of maintenance for urban road networks are developed, which include a single MC-centered dispatching plan and the co-scheduling of MCs. Both models aim at minimizing recovery time and allocating maintenance resources. The road network in Zhengzhou is borrowed as a case to explain the feasibility of the proposed models. The results show that on the premise of dividing network regions, it is reasonable to take a single MC to recover congestion. Compared with a single MC, the co-scheduling of MCs may save more time. [ABSTRACT FROM AUTHOR]

Viral vectors represent a bottleneck in the manufacturing of cellular therapies. Electroporation has emerged as an approach for non-viral transfection of primary cells, but standard cuvette-based approaches suffer from low throughput, difficult optimization, and incompatibility with large-scale cell manufacturing. Here, we present a novel electroporation platform capable of rapid and reproducible electroporation that can efficiently transfect small volumes of cells for research and process optimization and scale to volumes required for applications in cellular therapy. We demonstrate delivery of plasmid DNA and mRNA to primary human T cells with high efficiency and viability, such as > 95% transfection efficiency for mRNA delivery with < 2% loss of cell viability compared to control cells. We present methods for scaling delivery that achieve an experimental throughput of 256 million cells/min. Finally, we demonstrate a therapeutically relevant modification of primary T cells using CRISPR/Cas9 to knockdown T cell receptor (TCR) expression. This study displays the capabilities of our system to address unmet needs for efficient, non-viral engineering of T cells for cell manufacturing. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Since the early 2010s, ABSA has achieved extraordinarily high accuracy with various deep neuralmodels. However, existing ABSA models with strong in-house performances may fail to generalize to some challenging cases where the contexts are variable, i.e., low robustness to real-world environments. In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training. First, we strengthen the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network. In the corpus perspective, we propose to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness. Last, we based on the rich pseudo data perform adversarial training to enhance the resistance to the context perturbation and meanwhile employ contrastive learning to reinforce the representations of instances with contrastive sentiments. Extensive robustness evaluations are conducted. The results demonstrate that our enhanced syntax-aware model achieves better robustness performances than all the state-of-the-art baselines. By additionally incorporating our synthetic corpus, the robust testing results are pushed with around 10% accuracy, which are then further improved by installing the advanced training strategies. In-depth analyses are presented for revealing the factors influencing the ABSA robustness. [ABSTRACT FROM AUTHOR]

We developed a general enhancement strategy to greatly expand the number of SERS labels. We selected 9, 14 and 15 SERS labels and obtained four kinds of complex SERS spectra by using the single ligand mixing strategy. Through our expansion of 26 single-ligand SERS labels, the SERS spectrum library can be greatly expanded. [Display omitted] The surface enhanced Raman scattering (SERS) reporters are rather limited, and the Raman peaks still overlap in varying degrees, making SERS multiplex coding a critical bottleneck in the exploration of SERS nanotechnology. Herein, we design a general strategy to expand the SERS probe scope to 26 probes of six types, which can be further expanded within a limited range, with stable performance and structure. By constructing (Au-aggregate)@Ag@silica and (Au-aggregate)@silica nanocomposites, we develop optimal enhancement strategies for each Raman molecules. Mixed signal-ligand SERS probes improve the complexity of Raman spectra and expand the coding capacity. By integrating the strategies, SERS inks are produced and applied in anti-counterfeiting. With these improvements, this work breaks the constrains of probe selection, bringing SERS one step closer to the sensor or anti-counterfeiting application. [ABSTRACT FROM AUTHOR]

This document is a news and product update from the Journal of Medical Engineering & Technology. It includes information about new products in the medical field. The first product mentioned is the Extech 480846 RF EMF Strength Meter, which monitors high frequency radiation. The second product is Primal VR software, which offers a virtual reality environment for anatomy teaching. The third product is the UniPulse 400, a defibrillator tester. The fourth product is the DxI 9000 Immunoassay Analyzer, which provides efficient testing in labs. The final product is the electroacupuncture machines from Pantheon Research. The document provides contact information for further inquiries about each product. [Extracted from the article]

Advancements in organoid culture have led to various in vitro mini-organs that mimic native tissues in many ways. Yet, the bottleneck remains to generate complex organoids with body axis patterning, as well as keeping the orientation of organoids during post-experiment analysis processes. Here, we present a workflow for culturing organoids with morphogen gradient using a CUBE culture device, followed by sectioning samples with the CUBE to retain information on gradient direction. We show that hiPSC spheroids cultured with two separated differentiation media on opposing ends of the CUBE resulted in localized expressions of the respective differentiation markers, in contrast to homogeneous distribution of markers in controls. We also describe the processes for cryo and paraffin sectioning of spheroids in CUBE to retain gradient orientation information. This workflow from gradient culture to sectioning with CUBE can provide researchers with a convenient tool to generate increasingly complex organoids and study their developmental processes in vitro. Using a CUBE culture device, the workflow for the culture of organoids permits retention of gradient direction and differentiation marker expression to follow complex developmental progress in vitro. [ABSTRACT FROM AUTHOR]

Severe casting defects exist on the cavity surface of aero-engine integral casting casing, leading to substandard cleanliness and surface integrity, which restricts the service performance of the casing. The finishing process for the complex cavity of casting casing is one of the technical bottlenecks in aero-engine manufacturing. Vibratory finishing has excellent potential for closed cavities because of the process media's fluidized nature. In this work, taking the particle separator main casing of turboshaft aero-engine as a practice, based on the principle of vibratory finishing, a new idea of pre-loading the media and changing the spatial posture of the casing was proposed, and processing experiments were carried out. The vibration acceleration of the casing and the dynamic stress generated on the surface were measured, and the unfinished and finished surfaces were analyzed. The dynamic stress on the casing surface is far less than the fatigue strength of aluminum alloy (AlSi7Mg), which proves the feasibility of vibratory finishing for the casting casing. Meanwhile, the collision and scratching of the steel shot caused the surface roughness of the cavity to drop by 2 ~ 4 grades, and most of the carbon elements were removed. The finished surfaces had better surface morphology and cleanliness. Denser oxide film and lower surface roughness significantly improved the service performance of the casing, in which the corrosion resistance and mechanical properties were improved considerably. This process can provide a new approach for cavity finishing of other types of casings. [ABSTRACT FROM AUTHOR]

When welding high-strength steel and ultra-thin plates, traditional resistance spot welding has technical problems such as a narrow process window and difficulty in ensuring both welding strength and forming quality, making it one of the bottlenecks in the manufacture of high-end structural parts in the fields of vehicles, aviation, and aerospace. The core reason is that the traditional spot welding thermal cycle mode is single, the energy is highly concentrated, and the controllability is poor. Therefore, there is an inconsistency between its energy distribution and nugget quality. In view of the above difficulties, a new idea of resistance spot welding of special-shaped electrodes based on energy distribution design is proposed. By inlaying insulating ceramics and other materials on the electrode end face, a "composite annular electrode" is prepared to realize the regulation of energy distribution in the welding process. In this paper, experimental and numerical simulation are carried out to reveal the detailed influence of the diameter of the ceramic core on the joint forming and thermal distribution. The results show that the welding effect of composite annular electrodes is better than that of traditional electrodes under different thickness plates. The maximum strength of the joint can reach 2600 N. The maximum temperature during welding shall not exceed 1930 °C. And the optimal ceramic core diameter is optimized for ultra-thin plates and high-strength steel plates commonly used in automobiles. [ABSTRACT FROM AUTHOR]

Recruitment is a critical component of population dynamics and variability in recruitment underpins large fluctuations in population abundances of commercially valuable marine fishes. Marine pelagic fishes such as Atlantic herring (Clupea harengus) experience relatively high variability in recruitment that is driven by a wide range of biotic and abiotic factors. The relative importance and interaction of each factor for determining recruitment is poorly understood, and consequently, recruitment estimates are one of the largest uncertainties in fisheries management and predictions of future population sizes. Poor recruitment of Atlantic herring has been identified as a major issue and bottleneck for the species; therefore, factors influencing successful recruitment are of great interest to fisheries managers. Here we review studies that have examined the factors influencing survival at the egg stage, early larval stage, late larval stage, and juvenile stage to develop a more comprehensive understanding of the recruitment of Atlantic herring and provide recommendations to guide future research. We identified nine biotic and eight abiotic factors that have been found to substantially impact the recruitment of Atlantic herring, with temperature, prey availability, and predation being the most commonly investigated factors. We conclude it is not one factor that primarily determines recruitment, but rather a collection of many factors that vary temporally and spatially that drive the large variation observed in Atlantic herring recruitment year over year. A holistic approach is required to better understand recruitment and improve fisheries management decisions regarding Atlantic herring. [ABSTRACT FROM AUTHOR]

In new-tier cities with rapid urbanization, the reorganization of urban spatial functions and the development of road networks have brought novel challenges to traffic congestion control. Urban land use patterns have a significant correlation with urban traffic congestion. However, whether and how land use patterns of cities close to the roads affect road congestion is less to be discussed. This article investigated the relationship between land use patterns close to the urban trunk road network and traffic congestion in new tier cities Xi'an, China. We adopted the DBSCAN algorithm to cluster POIs and use the mixed POI clusters to label the socio-economic functions of roads. We found the spatial heterogeneity of POIs on the trunk road network and identified the impacts of the scales and types of POI on road congestion based on the empirical analysis. Compared to the POIs as origin and destination of the trips, the POIs as stopover points of the trips cause significantly more road congestion. The POIs with bidirectional flows at entrances/exits are more likely to cause road congestion than the POIs with unidirectional flows. Moreover, the POIs with flexible traffic flows increase road congestion, while the POIs with predicted traffic flows have no statistically significant correlation with road congestion. The results help urban planners to plan the scale, type, and location of POIs close to roads and to optimize the socio-economic functions of roads and alleviate road congestion. [ABSTRACT FROM AUTHOR]

On-ramps are considered to be one of the common traffic bottlenecks. In order to improve the operation efficiency of on-ramps, scholars worldwide have proposed various vehicle merging strategies. In this study, we designed different rules to express three collaborative strategies and studied their impact on on-ramp systems. Cellular automata models were used to simulate the systems under different situations, and the average speed and traffic flow rate of both the main roads and ramps were analyzed. The results show that (1) all the three merging strategies give excessive "priority" to the merging vehicle, leading to a severe reduction in the traffic performance of the main road; (2) nevertheless, these strategies have different effects on the entire system with a one-lane or two-lane main road. Due to the lane-changing behavior, the system with a two-lane main road has more advantages than that featured with a one-lane road, making the former system performing better than the latter under the same strategies; (3) the vehicles on the ramp and main road affect each other, and as the vehicle entering probabilities become large, the traffic flow rate on the main road decreases whereas that on the ramp increases. However, the effect is not unlimited, the flow rate on both roads finally reaches a stable level (forming a "platform"); and (4) large values of the merging safety distance parameter decrease the flow rate of the entire system. All the previous results provide a deep understanding of the impact of the three merging strategies on traffic flow, contributing to the design of on-ramp systems that have better operation efficiency and low levels of congestion. [ABSTRACT FROM AUTHOR]