Modular Approach Research Articles

A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

Codebase release 1.0 for TorchGPE

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the novel Improved Eagle Strategy (IES) hybrid model, which uniquely integrates bio-inspired optimization with clustering techniques to drastically reduce computation time while ensuring highly accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model’s application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model, marking a significant advancement in optimizing cloud computing processes, particularly within the IoT ecosystem.

Modular and extendable 1D-simulation for microfluidic devices

Microfluidic devices have been the subject of considerable attention in recent years. The development of novel microfluidic devices, their evaluation, and their validation requires simulations. While common methods based on Computational Fluid Dynamics (CFD) can be time-consuming, 1D simulation provides an appealing alternative that leads to efficient results with reasonable quality. Current 1D simulation tools cover specific microfluidic applications; however, these tools are still rare and not widely adopted. There is a need for a more versatile and adaptable tool that covers novel applications, like mixing and the addition of membranes, and allows easy extension, resulting in one comprehensive 1D simulation tool for microfluidic devices. In this work, we present an open-source, modular, and extendable 1D simulation approach for microfluidic devices, which is available as an open-source software package at https://github.com/cda-tum/mmft-modular-1D-simulator. To this end, we propose an implementation that consists of a base module (providing the core functionality) that can be extended with dedicated application-specific modules (providing dedicated support for common microfluidic applications such as mixing, droplets, membranes, etc.). Case studies show that this indeed allows to efficiently simulate a broad spectrum of microfluidic applications in a quality that matches previous results or even fabricated devices.

Sustainable Future-Proof Healthcare Facilities: Modular and Adaptable Design Approach

Sustainable Future-Proof Healthcare Facilities: Modular and Adaptable Design Approach

Fostering an age-friendly Marketing Education: Integrating IntergenerationalLearning in Marketing Courses

ABSTRACT This manuscript examines the adaptation and integration of intergenerational learning to create an inclusive marketing education environment. The development of innovative course designs to promote intergenerational learning and age-friendly curricula is an opportunity for educational institutions, given a demographic shift in many parts of the world. An innovative course design is featured that addresses a gap in marketing education literature regarding intergenerational classrooms, aligning with AACSB’s societal impact objectives and the UN Sustainable Development Goal #4 – to promote inclusive education and lifelong learning opportunities for all. Guided by the contact hypothesis, we discuss a modular approach to intergenerational classrooms that promises to break down barriers for older adult learners, increase the effectiveness of intergenerational learning, and benefit students of all ages. This manuscript features a three-week intergenerational module integrated into a semester-long (16-week) consumer behavior course consistent with experiential learning theory principles. We use this module to illustrate the potential of integrating intergenerational learning in marketing education and highlight the necessary conditions to adopt similar modules in other marketing classes. This approach fosters environments conducive to inclusivity and societal wellbeing.

Programmable Piperazine Synthesis via Organic Photoredox Catalysis.

Piperazine cores have long been identified as privileged scaffolds in the development of pharmaceutical compounds. Despite this, the facile synthesis of diverse C-substituted piperazines remains a challenge without prefunctionalized substrates/cores. Herein, we describe a programmable approach to highly diversifiable piperazine cores, which circumvents the typical need for radical precursors. The use of organic photoredox catalysis renders this method operationally simple, as direct substrate oxidation followed by 6-endo-trig radical cyclization with in situ generated imines may furnish the product. Additionally, the photoredox-catalyzed anti-Markovnikov hydroamination of readily accessible ene-carbamates provides a modular approach to functionalized diamine starting materials which are shown to generate more complex piperazine cores. A wide range of both carbonyl and amine condensation partners were shown to be compatible with this system in good to excellent yield.

Engineering Supramolecular Nanofiber Depots from a Glucagon-Like Peptide-1 Therapeutic.

Diabetes and obesity have emerged as major global health concerns. Glucagon-like peptide-1 (GLP-1), a natural incretin hormone, stimulates insulin production and suppresses glucagon secretion to stabilize and reduce blood glucose levels and control appetite. The therapeutic use of GLP-1 receptor agonists (e.g., semaglutide) has transformed the standard of care in recent years for treating type 2 diabetes and reversing obesity. The native GLP-1 sequence has a very short half-life, and therapeutic advances have come from molecular engineering to alter the pharmacokinetic profile of synthetic GLP-1 receptor agonists to enable once-weekly administration, reduce the frequency of injection, and improve adherence. Efforts to further extend this profile would offer additional convenience or enable entirely different treatment modalities. Here, an injectable GLP-1 receptor agonist depot is engineered through integration of a prosthetic self-assembling peptide motif to enable supramolecular nanofiber formation and hydrogelation. This supramolecular GLP-1 receptor agonistic (PA-GLP1) offers sustained release in vitro for multiple weeks, supporting long-lasting therapy. Moreover, in a rat model of type 2 diabetes, a single injection of the supramolecular PA-GLP1 formulation achieved sustained serum concentrations for at least 40 days, with an overall reduction in blood glucose levels and reduced weight gain, comparing favorably to daily injections of semaglutide. The general and modular approach is also extensible to other next-generation peptide therapies. Accordingly, the formation of supramolecular nanofiber depots offers a more convenient and long-lasting therapeutic option to manage diabetes and treat metabolic disorders.

Drosophila Eye Gene Regulatory Network Inference Using BioGRNsemble: An Ensemble-of-Ensembles Machine Learning Approach

Background: Gene regulatory networks (GRNs) are complex gene interactions essential for organismal development and stability, and they are crucial for understanding gene-disease links in drug development. Advances in bioinformatics, driven by genomic data and machine learning, have significantly expanded GRN research, enabling deeper insights into these interactions. Methods: This study proposes and demonstrates the potential of BioGRNsemble, a modular and flexible approach for inferring gene regulatory networks from RNA-Seq data. Integrating the GENIE3 and GRNBoost2 algorithms, the BioGRNsemble methodology focuses on providing trimmed-down sub-regulatory networks consisting of transcription and target genes. Results: The methodology was successfully tested on a Drosophila melanogaster Eye gene expression dataset. Our validation analysis using the TFLink online database yielded 3703 verified predicted gene links, out of 534,843 predictions. Conclusion: Although the BioGRNsemble approach presents a promising method for inferring smaller, focused regulatory networks, it encounters challenges related to algorithm sensitivity, prediction bias, validation difficulties, and the potential exclusion of broader regulatory interactions. Improving accuracy and comprehensiveness will require addressing these issues through hyperparameter fine-tuning, the development of alternative scoring mechanisms, and the incorporation of additional validation methods.

ModXNA: A Modular Approach to Parametrization of Modified Nucleic Acids for Use with Amber Force Fields.

Modified nucleic acids have surged as a popular therapeutic route, emphasizing the importance of nucleic acid research in drug discovery and development. Beyond well-known RNA vaccines, antisense oligonucleotides and aptamers can incorporate various modified nucleic acids to target specific biomolecules for various therapeutic activities. Molecular dynamics simulations can accelerate the design and development of these systems with noncanonical nucleic acids by observing intricate dynamic properties and relative stability on the all-atom level. However, modeling these modified systems is challenging due to the time and resources required to parametrize components outside default force field parameters. Here, we present modXNA, a tool to derive and build modified nucleotides for use with Amber force fields. Several nucleic acid systems varying in size and number of modification sites were used to evaluate the accuracy of modXNA parameters, and results indicate the dynamics and structure are preserved throughout the simulations. We detail the protocol for quantum mechanics charge derivation and describe a workflow for implementing modXNA in Amber molecular dynamics simulations, which includes updates and added features to CPPTRAJ.

Health information system assessments: Opportunities for international collaboration

Abstract Since the late 1990s, with the development of European Core Health Indicators (ECHI) and the European Health Interview Survey (EHIS), the EU aims to strengthen its continental database for evidence-informed public health policy and research. BRIDGE Health (2015-2017), InfAct (Information for Action 2018-2021) and PHIRI (Population Health Information Research Infrastructure 2020-2023) projects prepared for the establishment of a sustainable European health information infrastructure. Country health information system (HIS) assessments were core project activities, performed on the basis of the WHO Euro ‘Support Tool to strengthen health information systems’. To serve project objectives, the original top-down Support Tool was modified and implemented in a peer-to-peer approach among project countries. InfAct and PHIRI conducted 9 country peer-assessments each. InfAct assessments were carried out through country visits, targeting overall health information inequalities and good practices. PHIRI performed virtual country visits, focusing on COVID-19 health information flows. The WHO Tool was further adapted by the Joint Action TEHDAS (2021-2023) to assess the readiness of HIS in Europe for the European Health Data Space. Experience with these different iterations led to the revision of the Support Tool in 2021 to include a more expanded and modular approach which enables HIS assessments to opt to focus on specific aspects. To date, a sustainable European health information infrastructure has not been set up. Currently, the EU funds a joint Health Information Network with WHO Euro. Building regional infrastructures for public health and NCDI/MH monitoring and reporting depend on structures and on the political will of supranational organizations and their members. Meanwhile, HIS assessments to improve surveillance, population health and to reduce health information inequalities lend themselves to increased international collaboration and knowledge exchange.

Design and Development of a Wearable Device for Multi-Parametric Human Health Monitoring

INTRODUCTION: Wearable technology has emerged as a promising tool for continuous health monitoring, offering potential disease prevention and management benefits. However, existing devices often provide limited physiological data, hindering comprehensive health assessments.OBJECTIVES: This study aims to design and develop a wearable device capable of multi-parametric human health monitoring, focusing on vital indicators such as heart rate, body temperature, and physical activity.METHODS: A modular design approach was employed to integrate multiple biosensors into a compact wearable device. The collected data was processed by a microcontroller and transmitted wirelessly to a server.RESULTS: The developed wearable device demonstrated accurate and reliable measurement of target health parameters, with results comparable to existing prototypes in terms of functional capabilities.CONCLUSION: The proposed wearable device holds significant potential for enhancing personal health management by providing comprehensive and real-time health data. Future research will focus on expanding the device's capabilities, improving battery life, and conducting long-term user trials.

Raspberry-Like Plasmonic Nanoaggregates with Programmable Hierarchical Structures for Reproducible SERS Detection of Wastewater Pollutants and Biomarkers.

Conventional solid-based SERS substrates often face challenges with inconsistent sample distribution, while liquid-based SERS substrates are prone to aggregation and precipitation, resulting in irreproducible signals in both cases. In this study, we tackled this dilemma by designing and synthesizing raspberry-like plasmonic nanoaggregates that exhibit a high density of hotspots and are colloidally stable at the same time. In particular, the nanoaggregates consist of a core made of functionalized polystyrene (PS) microspheres, which act as a template for rapid self-assembly of Au@Ag core-shell nanoparticles to form raspberry-like hierarchical nanoaggregates within 5 min of mixing. The optimized nanoaggregates can be used as reproducible and stable SERS substrates for a range of wastewater pollutants (e.g., rhodamine 6G (R6G) and malachite green (MG)) and nucleobases (e.g., adenine and uracil), with the detection limits as low as 1 × 10-10, 1 × 10-16, 3 × 10-8, and 3 × 10-7 M, respectively. Additionally, the trace detection of adenine in clinical urine samples has been successfully demonstrated. Our modular assembly approach opens up new possibilities in SERS substrate design and advanced trace-chemical detection technologies.

Chemo-Click: Receptor-Controlled and Bioorthogonal Chemokine Ligation for Real-Time Imaging of Drug-Resistant Leukemic B Cells.

Drug resistance in B cell leukemia is characterized by the coexpression of CXCR5 and CXCR3 chemokine receptors, making it a valuable biomarker for patient stratification. Herein, we report a novel platform of activatable chemokines to selectively image drug-resistant leukemic B cells for the first time. The C-terminal derivatization of the human chemokines CXCL13 and CXCL10 with bioorthogonal tetrazine-BODIPY and BCN groups retained binding and internalization via their cognate CXCR5 and CXCR3 receptors and enabled rapid fluorescence labeling of CXCR5+ CXCR3+ resistant B cells─but not drug-susceptible leukemic cells─via intracellular chemokine ligation. This modular chemical approach offers a versatile strategy for real-time immunophenotyping of cell populations with distinct chemokine profiles and will accelerate the design of new precision medicine tools to advance personalized therapies in blood tumors.

MultiRegionFoam: A Unified Multiphysics Framework for Multi-Region Coupled Continuum-Physical Problems

Abstract This paper presents a unified framework, called multiRegionFoam, for solving multiphysics problems of the multi-region coupling type within OpenFOAM (FOAM-extend). It is intended to supersede the existing solver with the same name. The design of the new framework is modular, allowing users to assemble a multiphysics problem region-by-region and coupling conditions interface-by-interface. The present approach allows users to choose between deploying either monolithic or partitioned interface coupling for each individual transport equation. The formulation of boundary conditions is generalised in the sense that their implementation is based on the mathematical jump/transmission conditions in the most general form for tensors of any rank. The present contribution focuses on the underlying mathematical model for interface coupled multiphysics problems, as well as on the software design and resulting code structure that enable a flexible and modular approach. Finally, deployment for different multi-region coupling cases is demonstrated, including conjugate heat, multiphase flows and fuel-cells. Source code: multiRegionFoam v1.1 [1], repository https://bitbucket.org/hmarschall/multiregionfoam/. Article highlights A novel multiphysics framework, called multiRegionFoam, has been developed for solving multi-region coupled problems in OpenFOAM. The design of the framework allows for a modular multiphysics setup with freedom of choice on the coupling strategy (partitioned vs. monolithic). Extension of the general transport equation by interface conditions enables a unified coupling approach.

Replicable and extensible spatial data acquisition

ABSTRACT Having the ability to reproduce empirical results is foundational to the scientific process. Increasingly, there is an emphasis within the research community to create workflows which are not only reproducible but also replicable in that analytical approaches can be applied to new data. However, a disproportionate number of technical solutions designed to foster reproducibility and replicability have focused on data analysis, particularly within geography. This paper argues for greater attention on a phase of the research process which precedes analysis and is often taken for granted – that of data acquisition. Through code examples using the R Project for Statistical Computing, this paper demonstrates a path toward replicable spatial data acquisition for both secondary and primary data acquisition, with a focus on crowdsourced geographic information. The modular approach demonstrated is flexible in that it allows for straightforward replication but also enables extension to new spatial questions. Such work is valuable because it conserves labor, promotes research provenance, and allows for more robust analyses.

The modular approach to therapy for youths with anxiety, depression, trauma, and conduct problems (MATCH): results from the Norwegian randomized-controlled trial

BackgroundA randomized controlled trial was conducted to examine the effectiveness of the Modular Approach to Therapy for Youths with Anxiety, Depression, Trauma, and Conduct Problems (MATCH) for Norwegian youths referred to seven Child and Adolescent Psychiatric Outpatient Clinics. MATCH addresses comorbid problems that are common in children and youth, and its transdiagnostic design may therefore be more effective compared to standard treatments that often address single problems. MATCH has, however, never been evaluated in a Nordic context, and the present study aimed to fill this gap.MethodsA sample of 121 Norwegian youths (Mage = 9.83, 58.7% boys) was randomly assigned to MATCH (n = 73) or treatment as usual (TAU, n = 48). Primary treatment outcomes were youths’ externalizing and internalizing problems as reported by parents, using the Child Behavior Checklist, the Behavior and Feelings Survey. In addition, the study included assessments of parent-reported Top Problems.ResultsOverall, youths showed significant improvements in both externalizing and internalizing problems from intake to post-test. Results did not provide evidence that MATCH reduces symptoms of these problems compared to TAU.ConclusionsThe findings were inconclusive regarding whether MATCH was more effective than TAU in reducing youth internalizing and externalizing problems.Trial Registration IdentifierISRCTN24029895. Registration date: 8/8/2016.

Optimizing economic feasibility of CO2 capture processes with rotating packed bed (RPB): Strategies for scale and modularization

Rotating Packed Bed (RPB) technology is increasingly recognized in the field of carbon capture for its potential to broaden application scopes and significantly reduce capital expenditures by minimizing packing volume. However, the commercialization of RPB-based CO2 capture presents several challenges at the process level, including enhancing energy efficiency, refining equipment design, and achieving scalability. Given the mechanical constraints, designing excessively large RPB units may be impractical. Thus, the economic scalability of RPB-based CO2 capture under practical constraints remains an unresolved issue. This study explores the cost-optimal CO2 capture scale through rigorous process simulation using 30-70 wt% monoethanolamine (MEA) solvents. It optimizes the process design and operating parameters to minimize the specific CO2 avoidance costs, within the bounds of practical RPB design limitations. The findings suggest that a capture scale of 100–200 Tons Per Day (TPD) is cost-optimal when using 50 wt% MEA for flue gases with CO2 concentrations above 14.5 mol%. However, cost-effective RPB design becomes challenging at CO2 concentrations as low as 4 mol%. The analysis suggests that the optimal scale might increase with future advancements in RPB design technologies. Current design constraints necessitate a modular approach as the most economically viable strategy for scaling up RPB-based CO2 capture. This modularization strategy supports the wider adoption of CO2 capture technologies at small- to medium-scales, facilitating industry-wide implementation and sustainable progress by reducing initial investment and allowing for phased scaling.

Navigating Ultralarge Virtual Chemical Spaces with Product-of-Experts Chemical Language Models.

Ultralarge virtual chemical spaces have emerged as a valuable resource for drug discovery, providing access to billions of make-on-demand compounds with high synthetic success rates. Chemical language models can potentially accelerate the exploration of these vast spaces through direct compound generation. However, existing models are not designed to navigate specific virtual chemical spaces and often overlook synthetic accessibility. To address this gap, we introduce product-of-experts (PoE) chemical language models, a modular and scalable approach to navigating ultralarge virtual chemical spaces. This method allows for controlled compound generation within a desired chemical space by combining a prior model pretrained on the target space with expert and anti-expert models fine-tuned using external property-specific data sets. We demonstrate that the PoE chemical language model can generate compounds with desirable properties, such as those that favorably dock to dopamine receptor D2 (DRD2) and are predicted to cross the blood-brain barrier (BBB), while ensuring that the majority of generated compounds are present within the target chemical space. Our results highlight the potential of chemical language models for navigating ultralarge virtual chemical spaces, and we anticipate that this study will motivate further research in this direction. The source code and data are freely available at https://github.com/shuyana/poeclm.

An Introduction to the Modular Forensic Handwriting Method

ABSTRACTThe forensic examination of handwriting utilizes complex human perceptual and cognitive processes that can be difficult to accurately and validly describe since, for the most part, these processes are unseen. The series of articles published in this WIREs special issue present the agreed general approach that government practitioners in Australia use in the majority of the handwriting comparisons that they carry out. The systematic modular approach is informed by research and developments in the fields of motor control of handwriting, human factors, validation of handwriting examination, and evidence evaluation and reporting. The method is based around a process outline which structures the comparison process and provides the reader with a guide to the tasks to undertake and issues to consider at the different stages commonly worked through in practical handwriting examinations. Where decision or assessment points occur within the course of the method, a series of modules have been developed which describe the nature of the matter under consideration and address relevant theoretical and practical issues. Each module (published as an article in this special issue) is, as far as is practical, independent of other modules in the method. This will facilitate changes in the process over time that may result from theoretical, practical or technological advances in the field. The document as a whole can be used by practitioners actively involved in casework, or individuals such as researchers, academics, and legal professionals with an interest in understanding or exploring aspects of the practice of forensic handwriting examination.