Library Of Modules Research Articles

FROM VEIN TO BRAIN: ENHANCING TRAffiCKING OF CAR T-CELLS IN DIFFUSE MIDLINE GLIOMA

Abstract AIMS H3K27M-altered diffuse midline glioma (DMG) is the deadliest childhood brain tumour, with treatment limited by anatomical location and chemoresistance; palliative radiotherapy remains the mainstay of treatment. Chimeric antigen receptor (CAR) T-cell therapy produces promising pre-clinical results in treating these tumours whilst leaving interspersed normal brain cells intact. Early clinical outcomes of GD2-CAR T-cell therapy for H3K27M-altered DMG demonstrate short-lived neurological and radiological improvement. Enhancing trafficking to and infiltration into the tumour core may enhance CAR T-cell efficacy. Here, advanced T-cell engineering modules are tested for their ability to improve CAR T-cell trafficking. METHOD A library of T-cell engineering modules was created each with a unique DNA barcode to allow identification of CAR T-cells expressing a given module. The library includes chemokine receptors (CCR) to improve tumour homing by chemotaxis towards tumour-secreted chemokines, adhesion molecules to increase extravasation across the blood-brain-barrier, and molecules to degrade upregulated tumour extracellular matrix (ECM) components to improve CAR T-cell motility through the tumour. Murine T-cells were co-transduced with a CAR and barcoded advanced T-cell engineering modules and administrated intravenously in syngeneic models of high-grade glioma (HGG)/DMG. Tumour, normal brain, bone marrow, spleen and liver tissue were collected at several time points post CAR T-cell administration to assess presence of CAR T-cells using genetic barcodes. RESULTS Co-expression of advanced T-cell engineering modules did not impair CAR T-cell function in vitro. CARs co-expressing selected CCRs were preferentially detected in tumour tissue in mice with GL261-EGFRvIII or H3.3K27Mp53LOFPDGFRAWT-engrafted tumours. Findings were further validated by flow cytometry detection of selected CCR-expressing CAR T-cells as compared to CAR T-cells without additional engineering components. Similar experiments for adhesion molecules and ECM modifiers are planned. CONCLUSION Results to date indicate that CAR T-cells co-expressing selected CCRs enhance their homing to HGG/DMG tumours, encouraging further in vivo research to assess impact on CAR T-cell efficacy.

Enhancing Jakarta Faces Web App with AI Data-Driven Python Data Analysis and Visualization

Python is widely used in artificial intelligence (AI) and machine learning (ML) because of its flexibility, adaptability, rich libraries, active community, and broad environment, which makes it a popular choice for AI development. Python compatibility has already been examined with Java using TCP socket programming on both non-graphical and graphical user interfaces, which is highly essential to implement in the Jakarta Faces web application to grab potential competitive advantages. Python data analysis library modules such as numpy, pandas, and scipy, as well as visualization library modules such as Matplotlib and Seaborn, and machine-learning module Scikit-learn, are intended to be integrated into the Jakarta Faces web application. The research method uses similar TCP socket programming for the enhancement process, which allows instruction and data exchange between Python and Jakarta Faces web applications. The outcome of the findings emphasizes the significance of modernizing data science and machine learning (ML) workflows for Jakarta Faces web developers to take advantage of Python modules without using any third-party libraries. Moreover, this research provides a well-defined research design for an execution model, incorporating practical implementation procedures and highlighting the results of the innovative fusion of AI from Python into Jakarta Faces.

Multiplex Expression Cassette Assembly: A flexible and versatile method for building complex genetic circuits in conventional vectors.

The manipulation of multiple transcription units for simultaneous and coordinated expression is not only key to building complex genetic circuits to accomplish diverse functions in synthetic biology, but is also important in crop breeding for significantly improved productivity and overall performance. However, building constructs with multiple independent transcription units for fine-tuned and coordinated regulation is complicated and time-consuming. Here, we introduce the Multiplex Expression Cassette Assembly (MECA) method, which modifies canonical vectors compatible with Golden Gate Assembly, and then uses them to produce multi-cassette constructs. By embedding the junction syntax in primers that are used to amplify functional elements, MECA is able to make complex constructs using only one intermediate vector and one destination vector via two rounds of one-pot Golden Gate assembly reactions, without the need for dedicated vectors and a coherent library of standardized modules. As a proof-of-concept, we modified eukaryotic and prokaryotic expression vectors to generate constructs for transient expression of green fluorescent protein and β-glucuronidase in Nicotiana benthamiana, genome editing to block monoterpene metabolism in tomato glandular trichomes, production of betanin in tobacco and synthesis of β-carotene in Escherichia coli. Additionally, we engineered the stable production of thymol and carvacrol, bioactive compounds from Lamiaceae family plants, in glandular trichomes of tobacco. These results demonstrate that MECA is a flexible, efficient and versatile method for building complex genetic circuits, which will not only play a critical role in plant synthetic biology, but also facilitate improving agronomic traits and pyramiding traits for the development of next-generation elite crops.

COMPUTER PRODUCTION OF FACIAL EPITHETS

BACKGROUND: The issue of rehabilitation of patients with facial defects is extremely acute. In addition to the annual increase in the number of patients with cancer of the maxillofacial region, in recent years the number of people with shrapnel and gunshot wounds to the face has increased as a result of local wars and conflicts. Traditional methods of orthopedic rehabilitation of patients and the manufacture of facial epitheses are quite a complex and lengthy process. In the postoperative period, there is a sharp decrease in the quality of life of this category of patients, a violation of the basic body functions necessary for vital activity and poor social adaptation. Direct prosthetics of the face in the postoperative period was impossible due to the lack of necessary digital modeling technologies and structural materials for additive or subtractive production methods. The production of temporary or permanent facial epitheses using digital technologies is an urgent task that can improve the social and functional living conditions of patients. AIMS: Development of 3D modeling technology for additive manufacturing of facial epithets. METHODS: To achieve this goal, the first task was to develop specialized three-dimensional software for modeling defects in the facial area. The functionality of the program should allow you to virtually simulate the missing parts of the face (ear, eye, nose, orbit). To create a digital platform together with IT specialists, it was decided to use the following programming languages: C++ - Writing the software core, writing UI/UX interaction modules, interacting with the Windows operating system; C# - Complex assembly of the entire project; Python is an automated assembly of virtual library modules; OpenGL HSLS is a shader language for graphical visualization of objects; C is the creation of functions for interacting with shaders that require high speed. RESULTS: A specialized computer program has been developed for 3D modeling of prostheses in patients with defects in the middle zone of the face using combined facial scanning and computed tomography data (Computer program. Apresyan S.V., Stepanov A.G. "A program for 3D modeling of facial epitheses. Registration number (certificate) 2023663490, Registration date: 07/04/2023). Instead of obtaining analog impressions with plaster or silicone material, the developed technology uses a special 3D facial scanner, which greatly eases the suffering of patients. A virtual three-dimensional database of ears, noses, orbits, and zygomatic bones of patients of various ages and gender is integrated into the developed program. This allows the specialist to select the most adaptive part of the face to make up for the defect. Built-in modeling tools allow you to personalize a 3D model of a part of the face, based on the structural features of the maxillofacial region of a person. The finished three-dimensional model of a part of the face can be exported in various formats CONCLUSIONS: The developed 3D program for modeling defects allows to avoid invasive prosthetics approaches, to coordinate the shape of future structures with the patient. The built-in library of structures with a database provides remote manufacturing of the prosthesis without the presence of the patient, in case of a need to replace it. Among the undeniable advantages of the technology, it is important to include the fact that prostheses can be made directly on the day of surgery to remove part of the face, completely restoring lost functions and providing rapid social adaptation.

Design and Development of the Space Shooters Game Using Pygame

Space Shooter is a game that aims to shoot all the asteroids emerging from space while keeping the spaceship from colliding with them by maneuvering the spaceship. The main objective of this game is to accumulate as many points as possible by accurately shooting the asteroids. This game is developed using Pygame, a cross-platform Python module library designed for writing video games. Pygame includes computer graphics and sound libraries designed for use with the Python programming language. This study explores the development process of the "Space Shooter" game using Pygame and analyzes the effectiveness and reliability of this module in creating an interactive and engaging game. The results indicate that Pygame is an effective tool for game development, allowing easy integration of graphics and sound, and providing an intuitive interface for both novice and experienced developers.

Python Data Analysis and Visualization in Java GUI Applications Through TCP Socket Programming

Python is popular in artificial intelligence (AI) and machine learning (ML) due to its versatility, adaptability, rich libraries, and active community. The existing Python interoperability in Java was investigated using socket programming on a non-graphical user interface (GUI). Python's data analysis library modules such as numpy, pandas, and scipy, together with visualization library modules such as Matplotlib and Seaborn, and Scikit-learn for machine-learning, aim to integrate into Java graphical user interface (GUI) applications such as Java applets, Java Swing, and Java FX. The substantial method used in the integration process is TCP socket programming, which makes instruction and data transfers to provide interoperability between Python and Java GUIs. This empirical research integrates Python data analysis and visualization graphs into Java applications and does not require any additional libraries or third-party libraries. The experimentation confirmed the advantages and challenges of this integration with a concrete solution. The intended audience for this research extends to software developers, data analysts, and scientists, recognizing Python's broad applicability to artificial intelligence (AI) and machine learning (ML). The integration of data analysis and visualization and machine-learning functionalities within the Java GUI. It emphasizes the self-sufficiency of the integration process and suggests future research directions, including comparative analysis with Java's native capabilities, interactive data visualization using libraries like Altair, Bokeh, Plotly, and Pygal, performance and security considerations, and no-code and low-code implementations.

A Modular Robotic Arm Configuration Design Method Based on Double DQN with Prioritized Experience Replay

Abstract: The modular robotic arms can achieve desired performances in different scenarios through the combination of various modules, and concurrently hold the potential to exhibit geometric symmetry and uniform mass symmetry. Therefore, selecting the appropriate combination of modules is crucial for realizing the functions of the robotic arm and ensuring the elegance of the system. To this end, this paper proposes a double deep Q-network (DDQN)-based configuration design algorithm for modular robotic arms, which aims to find the optimal configuration under different tasks. First, a library of small modules of collaborative robotic arms consisting of multiple tandem robotic arms is constructed. These modules are described in a standard format that can be directly imported into the software for simulation, providing greater convenience and flexibility in the development of modular robotic arms. Subsequently, the DDQN design framework for module selection is established to obtain the optimal robotic arm configuration. The proposed method could deal with the overestimation problem in the traditional deep Q-network (DQN) method and improve the estimation accuracy of the value function for each module. In addition, the experience replay mechanism is improved based on the SumTree technique, which enables the algorithm to make effective use of historical experience and prevents the algorithm from falling into local optimal solutions. Finally, comparative experiments are carried out on the PyBullet simulation platform to verify the effectiveness and superiority of the configuration design method developed in the paper. The simulation results show that the proposed DDQN-based method with experience replay mechanism has higher search efficiency and accuracy compared to the traditional DQN scheme.

A Prototype Software Framework for Transferable Computational Health Economic Models and Its Early Application in Youth Mental Health.

We are developing an economic model to explore multiple topics in Australian youth mental health policy. To help make that model more readily transferable to other jurisdictions, we developed a software framework for authoring modular computational health economic models (CHEMs) (the software files that implement health economic models). We specified framework user requirements for: a simple programming syntax; a template CHEM module; tools for authoring new CHEM modules; search tools for finding existing CHEM modules; tools for supplying CHEM modules with data; reproducible analysis and reporting tools; and tools to help maintain a CHEM project website. We implemented the framework as six development version code libraries in the programming language R that integrate with online services for software development and research data archiving. We used the framework to author five development version R libraries of CHEM modules focussed on utility mapping in youth mental health. These modules provide tools for variable validation, dataset description, multi-attribute instrument scoring, construction of mapping models, reporting of mapping studies and making out of sample predictions. We assessed these CHEM module libraries as mostly meeting transparency, reusability and updatability criteria that we have previously developed, but requiring more detailed documentation and unit testing of individual modules. Our software framework has potential value as a prototype for future tools to support the development of transferable CHEMs.Code: Visit https://www.ready4-dev.com for more information about how to find, install and apply the prototype software framework.

Intelligent design of reconfigurable flexible assembly fixture for aircraft panels based on smart composite jig model and knowledge graph

The current demand for aircraft panel assembly fixtures necessitates agile manufacturing solutions. Traditional welding fixtures struggle to meet diverse panel types, high quantities, and short manufacturing cycles. Conversely, reconfigurable flexible assembly fixtures offer universality, efficiency and ease of maintenance but heavily rely on designers' experience, hindering knowledge reuse. Existing research on computer-aided fixture design (CAFD) primarily addresses specialized fixtures, neglecting reconfigurable flexible ones, lacking fixture design knowledge systems. Aiming at the above problems, the study proposes an intelligent design method for the reconfigurable flexible assembly fixture for aircraft panels based on the smart composite jig model (SCJM) and knowledge graph (KG). Firstly, based on the SCJM of the aircraft panel type assembly fixture (PSCJM), a configuration design method is proposed to carry out the multi-level design of reconfigurable assembly fixture to instantiate PSCJM containing fixture configuration rules and a module library based on model definition (MBD). Then, a knowledge retrieval method is proposed to search the suitable fixture design knowledge based on KG, assisting designers in achieving the flexible design of aircraft panel reconfigurable assembly fixtures. Finally, practical engineering design validation confirms the method's feasibility and effectiveness in obtaining the reconfigurable flexible assembly fixture design scheme for aircraft panels.

Research and Design of an Automated Security Event Analysis and Handling Framework Based on Threat Intelligence

In order to deeply explore and utilize the value of threat intelligence, strengthen research on attack organizations, and grasp the correlation between attack organizations, the author proposes the research and design of an automated security event analysis and handling framework based on threat intelligence. The author extracts the behavioral characteristics of the attack organization based on known APT attacks, and uses the machine learning framework Light GBM to establish a multi classification model to complete the analysis of unknown APT attack organizations. Through the study of multi-dimensional analysis of multi-source threat intelligence, attack organization correlation and judgment, an attack organization correlation and judgment system has been designed and implemented. The system includes six modules: threat intelligence collection module, threat intelligence multi-dimensional analysis module, attack organization fingerprint library module, attack organization correlation module, attack organization analysis module, and user module, providing attack organization correlation and judgment services for network security. The test results show that the intelligence reading and search query function can achieve the reading of various information of attack organizations, and achieve visual display of threat intelligence. The intelligence management function can achieve operations such as adding, deleting, and updating intelligence. The user management function of the system can achieve the management of administrator users and ordinary users. After testing, all functions of the system have been implemented and meet expectations.

Continuously Improving IT Service Management in the Pharmaceutical Industry

This paper presents a case study of IT Service Management (ITSM) adoption in the highly regulated pharmaceutical sector. The outcomes in the case company included (1) the integration of regulatory requirements into ITSM workflows, (2) refining IT Infrastructure Library (ITIL) modules, and (3) establishing a comprehensive Configuration Management Database (CMDB). The project was conducted for nearly a year and started with a maturity assessment to identify areas requiring improvement. ITSM case studies in the pharmaceutical industry are scarce, and new guidelines are necessary to assist IT departments in their mission. For practitioners, this paper describes critical steps on how to assess and improve service management using the Plan-Do-Check-Act (PDCA) approach. For academics, our work identifies sector-specific compliance requirements for ITSM and a solution to address them with ITSM tools. Our approach can be crucial to IT departments operating in economic sectors with an impact on human safety. This paper provides a compelling argument for consistently refining ITSM practices, which can lead to relevant improvements in IT service quality.

Innovative growth and development of a neurological surgery residency cadaveric spine-simulation training program: a single-institution experience.

Cadaveric and dry 3D model-based simulation training is a valuable educational tool for neurosurgical residents. Such simulation training is an opportunity for residents to hone technical skills and decision-making and enhance their neuroanatomy knowledge. The authors describe the growth and development of the Oregon Health & Science University Department of Neurological Surgery resident-focused, hands-on, spine-simulation surgery courses and provide details of course evaluations, layout, and setup. A four-part spine surgical simulation series, including two human cadaveric and two dry 3D model-based courses, was created to provide resident spine procedure training. Residents participated in the spine simulation series (2017-2021) and completed annual course curriculum and anonymous post-course evaluations. Evaluations included both Likert scale items and free-text responses. Responses to Likert scale items were analyzed in Python. Free-text responses were quantified using the Valence Aware Dictionary for Sentiment Reasoner. Descriptive statistics were calculated and plotted using Python's seaborn and matplotlib library modules. The analysis included 129 spine (occipitocervical, thoracolumbar, and spine model fusion I and II) simulation course evaluations. Likert responses demonstrated high average responses for evaluation questions (4.67 ± 0.90 and above). The average compound sentiment value was 0.58 ± 0.28. This is the first time Likert responses and sentiment analysis have been used to demonstrate how neurosurgical residents positively value a hands-on spine simulation training. Simulation is an essential component of neurosurgical resident education training. The authors encourage other neurosurgical education programs to develop and leverage spine simulation as a teaching tool.

Applying shape grammar and BIM for generating the mass modular housing design in ShanLiChenJia village, ZhaoYuan, China

ABSTRACTThis paper proposes a method for floor plan designs that combines shape grammar with Building Information Modeling (BIM) technology to create plan module libraries by using the mass modular housing project in ShanLiChenJia village as an example. The modular building is composed of several component modules, and shape grammar can manipulate their graphics and functions to enrich the combination relationship between component modules. The rule-making of the combination process can code the size relationship that affects the module form by adding space and size constraints. In the rule system of shape grammar, geometric dimensions, and functional attributes are first embedded in the shape graph. Then, the mapping relationship is established between the shape graph and the attributes of the modules. Finally, the BIM platform can establish the module library using Autodesk Revit. In addition, a spatial relationship in the scheme is selected in Revit software for generative experiments, which confirms the feasibility of shape grammar in generative design and spatial diversity of modular buildings.

A new perspective of wind speed forecasting: Multi-objective and model selection-based ensemble interval-valued wind speed forecasting system

Wind-speed forecasting is pivotal for optimizing resource utilization and promoting sustainable development. Previous studies have predominantly focused on point and interval forecasts derived from single-valued wind speed series, failing to capture the inherent fluctuations and uncertainties in wind speed. This study introduces a novel approach: a multi-objective and model selection-based ensemble interval-valued wind speed forecasting system. The primary objective is to empower decision-makers with enhanced decision support and improved risk management. The proposed system comprises four integral components: an interval-valued wind-speed preprocessing module, a model library module, a model selection module, and a multi-objective ensemble module. First, the preprocessing module decomposes the complex interval-valued wind speed forecasting task into multiple simpler forecasting subtasks. Subsequently, a model library is constructed for different forecasting subtasks. The model selection module utilizes a comprehensive evaluation metric to select the best-suited prediction model for each forecasting subtask. Finally, the multi-objective ensemble module integrates the subtask prediction results to yield the interval-valued wind speed forecasting outcomes. The most significant findings of this study are: (1) When faced with different forecasting subtasks, the choice of the optimal prediction model varies depending on the nature of the task. Therefore, the results of the model selection also differ with changes in the data characteristics. (2) In the ensemble stage, the importance and contribution of different subtasks may differ. The proposed ensemble module considers the interrelations and interactions between subtasks, resulting in varying weights for each subtask. For the two different datasets from the Chengde wind farm, the interval mean absolute percentage error values were 2.0862 % and 3.3632 %, and the interval standard deviation values of the error were 0.1383 and 0.3206, respectively. The experimental results clearly demonstrate the exceptional performance of the proposed system in interval-valued wind speed forecasting, which provides better decision support and risk management for wind speed forecasting.

Analysis of asymptomatic Drosophila models for ALS and SMA reveals convergent impact on functional protein complexes linked to neuro-muscular degeneration

BackgroundSpinal Muscular Atrophy (SMA) and Amyotrophic Lateral Sclerosis (ALS) share phenotypic and molecular commonalities, including the fact that they can be caused by mutations in ubiquitous proteins involved in RNA metabolism, namely SMN, TDP-43 and FUS. Although this suggests the existence of common disease mechanisms, there is currently no model to explain the resulting motor neuron dysfunction. In this work we generated a parallel set of Drosophila models for adult-onset RNAi and tagged neuronal expression of the fly orthologues of the three human proteins, named Smn, TBPH and Caz, respectively. We profiled nuclear and cytoplasmic bound mRNAs using a RIP-seq approach and characterized the transcriptome of the RNAi models by RNA-seq. To unravel the mechanisms underlying the common functional impact of these proteins on neuronal cells, we devised a computational approach based on the construction of a tissue-specific library of protein functional modules, selected by an overall impact score measuring the estimated extent of perturbation caused by each gene knockdown.ResultsTranscriptome analysis revealed that the three proteins do not bind to the same RNA molecules and that only a limited set of functionally unrelated transcripts is commonly affected by their knock-down. However, through our integrative approach we were able to identify a concerted effect on protein functional modules, albeit acting through distinct targets. Most strikingly, functional annotation revealed that these modules are involved in critical cellular pathways for motor neurons, including neuromuscular junction function. Furthermore, selected modules were found to be significantly enriched in orthologues of human neuronal disease genes.ConclusionsThe results presented here show that SMA and ALS disease-associated genes linked to RNA metabolism functionally converge on neuronal protein complexes, providing a new hypothesis to explain the common motor neuron phenotype. The functional modules identified represent promising biomarkers and therapeutic targets, namely given their alteration in asymptomatic settings.

The 3D Modules of Enzyme Catalysis: Deconstructing Active Sites into Distinct Functional Entities

Enzyme catalysis is governed by a limited toolkit of residues and organic or inorganic co-factors. Therefore, it is expected that recurring residue arrangements will be found across the enzyme space, which perform a defined catalytic function, are structurally similar and occur in unrelated enzymes. Leveraging the integrated information in the Mechanism and Catalytic Site Atlas (M-CSA) (enzyme structure, sequence, catalytic residue annotations, catalysed reaction, detailed mechanism description), 3D templates were derived to represent compact groups of catalytic residues. A fuzzy template-template search, allowed us to identify those recurring motifs, which are conserved or convergent, that we define as the “modules of enzyme catalysis”. We show that a large fraction of these modules facilitate binding of metal ions, co-factors and substrates, and are frequently the result of convergent evolution. A smaller number of convergent modules perform a well-defined catalytic role, such as the variants of the catalytic triad (i.e. Ser-His-Asp/Cys-His-Asp) and the saccharide-cleaving Asp/Glu triad. It is also shown that enzymes whose functions have diverged during evolution preserve regions of their active site unaltered, as shown by modules performing similar or identical steps of the catalytic mechanism. We have compiled a comprehensive library of catalytic modules, that characterise a broad spectrum of enzymes. These modules can be used as templates in enzyme design and for better understanding catalysis in 3D.

Identification of a β-arrestin-biased negative allosteric modulator for the β2-adrenergic receptor

Catecholamine-stimulated β2-adrenergic receptor (β2AR) signaling via the canonical Gs-adenylyl cyclase-cAMP-PKA pathway regulates numerous physiological functions, including the therapeutic effects of exogenous β-agonists in the treatment of airway disease. β2AR signaling is tightly regulated by GRKs and β-arrestins, which together promote β2AR desensitization and internalization as well as downstream signaling, often antithetical to the canonical pathway. Thus, the ability to bias β2AR signaling toward the Gs pathway while avoiding β-arrestin-mediated effects may provide a strategy to improve the functional consequences of β2AR activation. Since attempts to develop Gs-biased agonists and allosteric modulators for the β2AR have been largely unsuccessful, here we screened small molecule libraries for allosteric modulators that selectively inhibit β-arrestin recruitment to the receptor. This screen identified several compounds that met this profile, and, of these, a difluorophenyl quinazoline (DFPQ) derivative was found to be a selective negative allosteric modulator of β-arrestin recruitment to the β2AR while having no effect on β2AR coupling to Gs. DFPQ effectively inhibits agonist-promoted phosphorylation and internalization of the β2AR and protects against the functional desensitization of β-agonist mediated regulation in cell and tissue models. The effects of DFPQ were also specific to the β2AR with minimal effects on the β1AR. Modeling, mutagenesis, and medicinal chemistry studies support DFPQ derivatives binding to an intracellular membrane-facing region of the β2AR, including residues within transmembrane domains 3 and 4 and intracellular loop 2. DFPQ thus represents a class of biased allosteric modulators that targets an allosteric site of the β2AR.

Estimating parameters of plate heat exchanger for condensation of steam from mixture with air as a component of heat exchanger network

The condensation of vapours in the presence of non-condensable gas is happening in a number of industries. Its efficient realisation must be considered in optimisation of Heat Exchanger Networks (HENs) to increase efficiency of energy usage. In these processes, Plate Heat Exchanger (PHE) with enhanced heat and mass transfer can be effectively used. The method of preliminary selection of the PHE heat transfer area, plate size and corrugation geometry is proposed for the case of steam condensing from its mixture with air. The method uses the developed earlier one-dimensional mathematical model, modernized for PHE consisting from commercially produced plates. The heat transfer area of PHE is minimised by proper selection of plate sizes and geometrical characteristics of its corrugation. The optimisation variables are the corrugation angle to the main flow direction inside PHE channel, the corrugations depth and the length of plate. The case study illustrates the application of the method in case of utilisation low grade heat from exhaust gases after process of drying by superheated steam. The method is implemented as a computer program which can be included as a Dynamic Link Library module in software for HEN optimisation.

Efficient Error Estimation for High-Level Design Space Exploration of Approximate Computing Systems

This article presents an error estimation technique for a data-flow graph (DFG) representation of an approximate computing (AC) circuit. The technique, which may be used during the high-level design of digital circuits, estimates error metrics for the outputs of the approximate circuit. The proposed technique receives as an input, output error characterizations of arithmetic modules in a high-level library. The error modeling of a library module is accomplished by dividing the input (operand) ranges into intervals and then characterizing the output error for different combinations of these input intervals. Subsequently, the error for each combination is stored in a lookup table (LUT). The module error models are integrated into a design space exploration (DSE) framework to evaluate different combinations of exact and approximate realizations of various operations in the DFG. The DSE, which performs error calculation and propagation from inputs to outputs of the target DFG, may be used to explore trade-offs between the output error and other design metrics of the approximate circuit, e.g., energy efficiency. The efficacy of the proposed method is assessed for three image processing benchmarks. Results for these benchmarks demonstrate that the framework can efficiently generate the Pareto frontier (PF) in the trade-off space of accuracy versus energy efficiency for the targeted benchmarks. Compared to a purely simulation-based exploration, the proposed technique provides an average of 92 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> speed improvement.

HVLV ENGINEERING WITH MODULE SYSTEM(S), ETO AND LEAN DESIGN – STUDY ON PRACTITIONER INFORMATION NEEDS

AbstractThis research elaborates the engineering design of high value low volume (HVLV) artefacts (aka Capital goods, investment goods). Our goal is to describe what information needs the practitioners have when doing sales engineering and engineering in HVLV projects. The research approach uses Design Research Methodology with four company cases.Our findings are that engineering design of HVLV artefacts reuses several module systems, module libraries, technology catalogues, engineering-to-order and variety of design support systems, configurators, design guidelines, parametric models and lean-based design reasoning patterns etc. This poses major challenges for the engineers; how to use all relevant information and how to find it from different IT-systems.This study indicates that in HVLV context such engineering strategy is required, which guides and drives tactical and operational engineering decisions not only within a project delivery but across project deliveries. Operative and tactical engineering is done during the delivery project and value capture is not achieved in full potential if the engineering strategy is neglected or overruled. This is challenge for current modularisation and ETO-methods and tools.