Modular Tool Research Articles

Personality Exceptions

Adding character depth in narrative planners is a complex subject with many approaches. One common strategy used to add depth to characters is to imbue them with personality traits. One attribute that few personality models consider, however, is how character behavior should change when the character is exposed to reminders of powerful psychological influences. Through creating and refining a new digital construct known as Personality Exceptions, I will provide a tool that makes it much easier for personality systems to support such conditions. I intend to make a modular tool that researchers can attach to existing personality models to enhance the character depth of their characters and, thus, improve the overall quality of computational narratives.

Programmable Circular Multivalent Nanobody‐Targeting Chimeras (mNbTACs) for Multireceptor‐Mediated Protein Degradation and Targeted Drug Delivery

AbstractMultispecific therapeutics hold significant promise in drug delivery, protein degradation, and cell recruitment to address clinical issues of tumor heterogeneity, resistance, and immune evasion. However, their modular engineering remains challenging. We developed a targeted degradation platform, termed multivalent nanobody‐targeting chimeras (mNbTACs), by encoding diverse nanobody codons on a circular template using DNA printing technology. The homo‐ or hetero‐ mNbTACs specifically recognized membrane targets in a multivalent manner and simultaneously recruited scavenger receptors to favor clathrin‐/caveolae‐dependent endocytosis and lysosomal degradation of multiple proteins with high efficiency and selectivity. We demonstrated that a bispecific doxorubicin‐loaded mNbTAC, named Doxo‐mvNbsPPH, passively accumulated at tumor sites, specifically interacted with PD−L1 and HER2 targets, and was rapidly transported into lysosome, inducing potent immunogenic cell death and alleviating immune checkpoint evasion. The synergistic boosting of innate and adaptive immunity promoted the infiltration and proliferation of CD8+ T cells in tumor microenvironment (an 11‐fold increase) with high toxicity and low exhaustion, eventually enhancing antitumor efficacy. Our mNbTAC platform provides multispecific therapeutics with variable valences and programmed species, whereas it induces targeted protein degradation through multireceptor‐mediated endocytosis and lysosomal degradation without the need for lysosome‐targeting receptors, representing a general and modular tool to harness extracellular proteome for disease treatment.

Programmable Circular Multivalent Nanobody-Targeting Chimeras (mNbTACs) for Multireceptor-Mediated Protein Degradation and Targeted Drug Delivery.

Multispecific therapeutics hold significant promise in drug delivery, protein degradation, and cell recruitment to address clinical issues of tumor heterogeneity, resistance, and immune evasion. However, their modular engineering remains challenging. We developed a targeted degradation platform, termed multivalent nanobody-targeting chimeras (mNbTACs), by encoding diverse nanobody codons on a circular template using DNA printing technology. The homo- or hetero- mNbTACs specifically recognized membrane targets in a multivalent manner and simultaneously recruited scavenger receptors to favor clathrin-/caveolae-dependent endocytosis and lysosomal degradation of multiple proteins with high efficiency and selectivity. We demonstrated that a bispecific doxorubicin-loaded mNbTAC, named Doxo-mvNbsPPH, passively accumulated at tumor sites, specifically interacted with PD-L1 and HER2 targets, and was rapidly transported into lysosome, inducing potent immunogenic cell death and alleviating immune checkpoint evasion. The synergistic boosting of innate and adaptive immunity promoted the infiltration and proliferation of CD8+ T cells in tumor microenvironment (an 11-fold increase) with high toxicity and low exhaustion, eventually enhancing antitumor efficacy. Our mNbTAC platform provides multispecific therapeutics with variable valences and programmed species, whereas it induces targeted protein degradation through multireceptor-mediated endocytosis and lysosomal degradation without the need for lysosome-targeting receptors, representing a general and modular tool to harness extracellular proteome for disease treatment.

Cost modeling for the GWh-scale production of modern lithium-ion battery cells

Battery production cost models are critical for evaluating the cost competitiveness of different cell geometries, chemistries, and production processes. To address this need, we present a detailed bottom-up approach for calculating the full cost, marginal cost, and levelized cost of various battery production methods. Our approach ensures comparability across research fields and industries, reflecting capital and imputed interest costs. We showcase the model with case studies of a prismatic PHEV2 hardcase cell and a cylindrical 4680 cell in four different chemistries. Our publicly available browser-based modular tool incorporates up-to-date parameters derived from literature and expert interviews. This work enables researchers to quickly assess the production cost implications of new battery production processes and technologies, ultimately advancing the goal of reducing the cost of electrified mobility.

A Comprehensive Thermodynamic Analysis of Gas Turbine Combined Cycles With Pressure Gain Combustion Based on Humphrey Cycle

Abstract The paper describes a comprehensive thermodynamic analysis of the gas turbine combined cycle (CC) equipped with pressure gain combustion (PGC) based on the Humphrey cycle. PGC is represented by a steady-state zero-dimensional constant volume combustion (CVC) model with practical loss models for a realistic interpretation. Simulations were performed in WTEMP (Web-based Thermo-Economic Modular Program) software, a modular cycle analysis tool developed at the University of Genova. Results were analyzed at the optimistic and realistic PGC loss scenarios. For the PGC open cycle, with optimistic combustor losses, efficiency was higher than Joule at all operating conditions. However, with realistic combustor losses, a higher TIT was found to alleviate some of the losses in the combustor and at a pressure ratio of 25, a 1700°C TIT cycle showed a benefit of 1.7 p.p., while 1300°C TIT showed no advantage. Moreover, with the reduction in first turbine stage efficiency from pulsating outflow, only the high TIT cycle showed benefit at pressure ratios less than 15. Looking into combined cycles with realistic combustor losses, PGC was found to be advantageous in terms of specific work but not efficiency at actual operating conditions. However, with a further reduction of combustor-related losses, the PGC CC could outperform the Joule equivalent in terms of efficiency. The sensitivity analysis with losses showed that the mitigation of constant pressure combustion loss is more important than the inlet pressure loss for PGC in CC application.

Symmetry and Historicity in the Game of Life: Modifying Rules to Simulate Evolutionary Dynamics

The Game of Life (GoL) is a paradigmatic computer simulation that exhibits the emergence of complex properties of the whole from relatively simple sets of heuristic rules operating at lower organizational levels. Therefore, it is widely understood as a valuable tool for investigating global properties of evolutionary processes. Cognizant of refined concepts that emerged in recent debates on the central role of historical contingency (historicity) in evolution, we modify the original GoL rules by introducing an updating feedback loop and a probability factor that reflect the degree of historicity in pattern evolution as both stochastic path dependence and sensitivity to initial conditions. We examine this trait in simulations of the emergence and breaking of bilateral and radial symmetries commonly observed in the evolution of life, most prominently as evolving body plans. We show that the implementation of historicity parameters leads to a more realistic sequential and gradual alternating emergence and the breaking of new symmetries than the original set of rules. Apart from its more realistic representations of evolutionary processes, the new approach allows for easier exploration of the parameter space, demonstrates the sequential and gradual emergence and breaking of symmetries, and provides a publicly available and modular simulation tool. We discuss the exploratory potential of the modified GoL platform we designed as an extended thought experiment to study the parameter space defining the historicity of biological processes.

Scanpro is a tool for robust proportion analysis of single-cell resolution data

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.

Моделирование конструкций сборного абразивного инструмента

Introduction. Grinding is one of the most common types of finishing. It allows the production of surfaces with the required quality parameters and is one of the most available and productive methods for machining high-strength and difficult-to-machine materials. Grinding wheels represent the most prevalent application of grinding technology in mechanical engineering. The use of this abrasive tool helps to increase processing productivity by ensuring the removal of a significant layer of material. In addition, grinding wheels have a longer service life and are widely used in the implementation of hybrid technologies based on the combination of mechanical (abrasive), electrical, chemical, and thermal effects in various combinations. A variety of tool body shapes and types of abrasives allow the use of wheels in a wide variety of production areas. One of the ways to analyze and design a new tool is numerical simulation. In this research, graphic modeling was selected as the most appropriate method for representing the future design of the tool. This approach allows for a more straightforward conceptualization process compared to other modeling techniques. The purpose of the work is to simulate a modular abrasive tool in order to analyze and synthesize structures to increase the efficiency of tool support for the manufacture of products made of high-strength and difficult-to-process materials using traditional or hybrid processing technologies. Research methodology. Theoretical studies are carried out using the basic principles of system analysis, geometric theory of surface formation, cutting tool design, graph theory, mathematical and computer simulation. To solve the problem, we have studied the available designs of modular grinding wheels. There has also been the analysis of the types of abrasive parts, methods of fastening of the abrasive cutting part on the wheel’s body, the materials used for the manufacture of the body, the characteristics of the body of the wheel, and fastening schemes. Results and discussions. A simulation technique based on graphic modelling theory has been developed. A comprehensive investigation of the existing design of the grinding wheel has enabled the identification of the key structural elements that define its design. The data obtained has been used to create a generalized graphic simulation of a modular abrasive tool. This simulation integrates all the components and displays a conditional constructive relationship between them. The developed design methodology was tested on an example of two designs of modular grinding wheels. The theoretical studies established that the design efficiency of modular abrasive tools can be increased by 2–4 times by using the developed simulation technique.

Unifying considerations and evidence of macrophage activation mosaicism through human CSF1R and M1/M2 genes

Addressing the mononuclear phagocyte system (MPS) and macrophage M1/M2 activation is important in diagnosing hematological disorders and inflammatory pathologies and designing therapeutic tools. CSF1R is a reliable marker to identify all circulating MPS cells and tissue macrophages in humans using a single surface protein. CSF1R permits the quantification and isolation of monocyte and dendritic cell (DC) subsets in conjunction with CD14, CD16, and CD1c and is stable across the lifespan and sexes in the absence of overt pathology. Beyond cell detection, measuring M1/M2 activation in humans poses challenges due to response heterogeneity, transient signaling, and multiple regulation steps for transcripts and proteins. MPS cells respond in a conserved manner to M1/M2 pathways such as interleukin-4 (IL-4), steroids, interferon-γ (IFNγ), and lipopolysaccharide (LPS), for which we propose an ad hoc modular gene expression tool. Signature analysis highlights macrophage activation mosaicism in experimental samples, an emerging concept that points to mixed macrophage activation states in pathology.

MEIAT-CMAQ: A modular emission inventory allocation tool for Community Multiscale Air Quality Model

The Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model (MEIAT-CMAQ) refines emission inventories by providing detailed spatial (horizontal and vertical), temporal, and species allocations, enhancing the accuracy of CMAQ performance. Its efficient algorithm and modular design offer flexibility for managing both gridded and tabulated inventories, widely used in various sectors. In addition, the shapefiles with specific shapes supported by MEIAT-CMAQ can address the allocation challenges in transportation emissions. The evaluation of MEIAT-CMAQ, using model-ready inventories before (BASE scenario), and after allocation without (EXPR scenario) or with (EXPR-V scenario) vertical allocation, demonstrates significant improvements in the mean bias (MB) of gaseous pollutants (O₃, NO₂, CO). In both the EXPR and EXPR-V scenarios, the MB for O3 exhibits notable enhancements, with respective improvements of 5.7% and 26.9%. For NO₂, corresponding MB improvements are even more pronounced, reaching 27.6% and 61.7% in the EXPR and EXPR-V scenarios, respectively. Likewise, enhancements are observed in the MBs of CO, demonstrating increases of 8.4% and 45.2% in the EXPR and EXPR-V scenarios, respectively. Moreover, with regard to spatial accuracy, the incorporation of the MEIAT-CMAQ model yields significant improvements. Specifically, in the EXPR scenarios, spatial accuracy for O3 and NO2 demonstrates respective enhancements of 13.5% and 9.5%. Furthermore, the inclusion of vertical allocation leads to additional enhancements in CO, NO₂, and PM2.5, resulting in improvements of 17.6%, 16.6%, and 23.2%, respectively. MEIAT-CMAQ provides an efficient method for transforming coarse-resolution emission inventories into high-resolution files directly useable in the model, offering enhanced flexibility for users to select any period for generating model-ready emission files. This capability provides substantial technical support for automating processes within business departments and significantly improves the performance of high-resolution modeling and forecasting.

A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets

Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite’s effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios.

Stereoselective Synthesis of Polysubstituted Conjugated Dienes Enabled by Photo‐Driven Sequential Sigmatropic Rearrangement

AbstractWe here reported a highly stereoselective method for the synthesis of polysubstituted conjugated dienes from α‐aryl α‐diazo alkynyl ketones and pyrazole‐substituted unsymmetric aminals under mild conditions, which was promoted by photo‐irridation and involved with 1,6‐dipolar intermediate and quadruple sigmatropic rearrangements, was successfully developed. In this transformation, the cleavage of four bonds and the recombination of five bonds were implemented in one operational step. This protocol provided a modular tool for constructing dienes from amines, pyrazoles and α‐alkynyl‐α‐diazoketones in one‐pot manner. The results of mechanistic investigation indicated that the plausible reaction path underwent the 1,6‐sigmatropic rearrangement instead of the 1,5‐sigmatropic rearrangement.

Stereoselective Synthesis of Polysubstituted Conjugated Dienes Enabled by Photo-Driven Sequential Sigmatropic Rearrangement.

We here reported a highly stereoselective method for the synthesis of polysubstituted conjugated dienes from α-aryl α-diazo alkynyl ketones and pyrazole-substituted unsymmetric aminals under mild conditions, which was promoted by photo-irridation and involved with 1,6-dipolar intermediate and quadruple sigmatropic rearrangements, was successfully developed. In this transformation, the cleavage of four bonds and the recombination of five bonds were implemented in one operational step. This protocol provided a modular tool for constructing dienes from amines, pyrazoles and α-alkynyl-α-diazoketones in one-pot manner. The results of mechanistic investigation indicated that the plausible reaction path underwent the 1,6-sigmatropic rearrangement instead of the 1,5-sigmatropic rearrangement.

SpikeExplorer: Hardware-Oriented Design Space Exploration for Spiking Neural Networks on FPGA

One of today’s main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. SpikExplorer enables hardware-centric multiobjective optimization, supporting target factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters leveraging Bayesian optimization, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits their needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180 mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs.

IModel: an interactive 3D crystal structure visualization program

This work presents present iModel, an interactive 3D crystal structure visualization program capable of reading crystal structure datafiles in various formats and rendering corresponding 3D models. Users can interact with the models by translating, rotating and zooming in on them. The software visualizes crystal structures, including atoms, bonds, unit cells and polyhedral shapes using spheres, lines and polyhedra, with customizable colors and sizes for individual atoms and bonds. The software features perspective and orthogonal projections, multiple chemical bond search modes, and a user-friendly toolbar for view control, and exports models as images or saves progress in project files. Developed on the LabVIEW platform with Python extensions, the iModel program leverages a queue message handler and producer–consumer pattern for a modular, reusable and powerful crystal structure visualization tool.

On-Demand Driven Dissipation for Cavity Reset and Cooling

We present a superconducting circuit device that provides active, on-demand, tunable dissipation on a target mode of the electromagnetic field. Our device is based on a tunable “dissipator” that can be made lossy when tuned into resonance with a broadband filter mode. When driven parametrically, this dissipator induces loss on any mode coupled to it with energy detuning equal to the drive frequency. We demonstrate the use of this device to reset a superconducting qubit’s readout cavity after a measurement, removing photons with a characteristic rate greater than 50μs−1. We also demonstrate that the dissipation can be driven constantly to simultaneously damp and cool the cavity, effectively eliminating thermal photon fluctuations as a relevant decoherence channel. Our results demonstrate the utility of our device as a modular tool for environmental engineering and entropy removal in circuit quantum electrodynamics. Published by the American Physical Society 2024

Phased nanopore assembly with Shasta and modular graph phasing with GFAse.

Reference-free genome phasing is vital for understanding allele inheritance and the impact of single-molecule DNA variation on phenotypes. To achieve thorough phasing across homozygous or repetitive regions of the genome, long-read sequencing technologies are often used to perform phased de novo assembly. As a step toward reducing the cost and complexity of this type of analysis, we describe new methods for accurately phasing Oxford Nanopore Technologies (ONT) sequence data with the Shasta genome assembler and a modular tool for extending phasing to the chromosome scale called GFAse. We test using new variants of ONT PromethION sequencing, including those using proximity ligation, and show that newer, higher accuracy ONT reads substantially improve assembly quality.

Accelerated semantic segmentation of additively manufactured metal matrix composites: Generating datasets, evaluating convolutional and transformer models, and developing the MicroSegQ+ Tool

Data-driven applications are penetrating every aspect of Additive Manufacturing (AM) to enable efficient resolution of key existing challenges. One such application is semantic segmentation which automatically quantifies post-process structure data from graphic or 3D computer representations. In industrial settings, these models can expedite the development of new materials or processes. However, Additively Manufactured (AMed) materials offer unique challenges, which require new models to be developed and trained. These challenges include out-of-balance classes (e.g., defects), massive labeling efforts, and significant data preparation costs. Recent applications of semantic segmentation in AM have shown the potential of ensemble-based approaches at the expense of increased parameters and computational burden while ignoring the minority classes. In this work, we propose a reproducible method and develop an associated tool to rapidly segment and subsequently quantify industrial AMed metallographic images. First, two new datasets representing AMed materials are generated from extensive experimentation. Subsequently, state-of-the-art models from convolutional and self-attention categories are evaluated in their ability to segment the datasets of interest. Finally, a modular software tool is developed for industrial applications. The developed Microstructure Segmentation, Quantification, and Fusion (+) tool or MicroSegQ+ enables a weighted pairing of predictions from different semantic segmentation models. The pairing strategy helps to exploit the complimentary performance of convolutional and transformer models on multi-class metallographic images from a Metal Matrix Composite (MMC) material system. In addition to developing models on this MMC dataset, the proposed strategy is evaluated on an open-source metal AM dataset. We also evaluate the generalizability of the best-performing model on a second MMC dataset from a different industrial setting that shows an overall accuracy of 93 % without fine-tuning. The proposed tool enables rapid quantification under 1 min, unlike the existing semi-automatic approach that takes 1–2 h of segmentation effort on single bead cross-sections. The tool is wrapped into a single executable file for industrial deployment. The work also contributes to two annotated datasets from a direct energy deposition (DED) AM process. The datasets can be used to develop and validate new semantic segmentation models of AMed microstructures, whereas the functionality of the tool can be expanded upstream to include data and modeling steps, enabling a no-code pipeline for the industry.

Fluorogenic Chemical Probes for Wash-free Imaging of Cell Membrane Damage in Ferroptosis, Necrosis, and Axon Injury.

Selectively labeling cells with damaged membranes is needed not only for identifying dead cells in culture, but also for imaging membrane barrier dysfunction in pathologies in vivo. Most membrane permeability stains are permanently colored or fluorescent dyes that need washing to remove their non-uptaken extracellular background and reach good image contrast. Others are DNA-binding environment-dependent fluorophores, which lack design modularity, have potential toxicity, and can only detect permeabilization of cell volumes containing a nucleus (i.e., cannot delineate damaged volumes in vivo nor image non-nucleated cell types or compartments). Here, we develop modular fluorogenic probes that reveal the whole cytosolic volume of damaged cells, with near-zero background fluorescence so that no washing is needed. We identify a specific disulfonated fluorogenic probe type that only enters cells with damaged membranes, then is enzymatically activated and marks them. The esterase probe MDG1 is a reliable tool to reveal live cells that have been permeabilized by biological, biochemical, or physical membrane damage, and it can be used in multicolor microscopy. We confirm the modularity of this approach by also adapting it for improved hydrolytic stability, as the redox probe MDG2. We conclude by showing the unique performance of MDG probes in revealing axonal membrane damage (which DNA fluorogens cannot achieve) and in discriminating damage on a cell-by-cell basis in embryos in vivo. The MDG design thus provides powerful modular tools for wash-free in vivo imaging of membrane damage, and indicates how designs may be adapted for selective delivery of drug cargoes to these damaged cells: offering an outlook from selective diagnosis toward therapy of membrane-compromised cells in disease.

Esra (elevator simulation, research & analysis): an open-source software tool for elevator traffic simulation, research, and analysis

ABSTRACT Today, although elevator companies typically have their own elevator simulation and analysis tools, these remain inaccessible to the public due to commercial concerns and rivalry in the sector. Consequently, due to the lack of modular open-source software tools, interest in the design and development of elevator systems in academia is quite limited, contrary to the vast potential of the field. This paper addresses this gap introducing an open-source software tool, the Elevator Simulator for Research and Analysis (ESRA), developed using the MATLAB programming language and an object-oriented approach. ESRA features built-in control methods, and visualization options with the capability to save and reuse traffic data, facilitating in-depth comparative analysis. Moreover, its modular architecture allows a design and development basis for further research in the field. The proposed software aims to enable developers to design, implement, analyse, and compare their control algorithms with other methods.