Design Models Research Articles

Healthy Placemaking in the Public Space in Sana'a City: A Case Study (The Main Square of Qaa Al-Olfi)

The public space with its components (streets and public parks) occupied an important place in the history of urban design and community health, as they are the determinants and resilient elements of health within cities. Modern concepts and design methods have been developed in response to urban health pandemics, including the creation of a healthy placemaking that achieves the health of the population within the urban context; Due to the lack of applications by principles healthy placemaking in streets and public parks in Sanaa, it has negatively impacted the daily health activities of residents, The research problem is that Sanaa's streets and public parks do not have any urban design to promote the health of Yemenis in the face of the pandemics; he study object is to create guided design models for the streets and public park in the study area, which simulate the applications of healthy placemaking to help planners provide solutions to face the spread of diseases and pandemics in urban areas, The importance of this research comes in the form of the first Yemeni urban study that deals exclusively with the issue of healthy placemaking in the streets and public parks during of urban health pandemics. The research followed the descriptive inductive approach in studying the theoretical concepts and the practical approach in creating design models the applications of the health placemaking during health pandemics on the study area.

Designing a marine systems design specialization track at NTNU

In this paper we describe and discuss the current and future MSc specialization programme in marine systems design at the Norwegian University for Science and Technology (NTNU). We follow the structure of a design process. First, we identify relevant stakeholders. That includes industry, society and the students themselves, and we discuss what are their key needs and requirements for a master level education. Further, we outline what can be considered a conceptual solution for the marine systems design specialization programme´s structure and content. We apply two basic types of learning elements. First, a set of focused topic blocks covering central systems design models and methods to develop the required theoretical competence platform. Second, the students perform a series of creative, 'use case'-oriented collaborative development projects based on CDIO (Conceive, Design, Implement, Operate) principles. We discuss the “educational design solution” in terms of learning objectives vs. achieved results and evaluate the impact both from an academic and an industrial perspective.

Closing the gap between early and detailed ship design models

Conventionally, ship design and engineering are segregated activities, carried out with different software packages that thus each have their own place, qualities and tools. And consequently, a different data model. As a report on ongoing work to bridge that gap, this paper first explores existing neutral data models and standards employed or considered in maritime applications and concludes that none of these is directly applicable. It continues with describing the requirements and derived abstract data model of the SEUS project and its design and engineering applications. A graph database is identified as a potentially useful tool for SEUS data modelling, and a hands-on experiment confirms this presumption.

Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient

The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.

Effects of the interaction between cold spells and fine particulate matter on mortality risk in Xining: a case-crossover study at high altitude

BackgroundWith global climate change, the health impacts of cold spells and air pollution caused by PM2.5 are increasingly aggravated, especially in high-altitude areas, which are particularly sensitive. Exploring their interactions is crucial for public health.MethodsWe collected time-series data on meteorology, air pollution, and various causes of death in Xining. This study employed a time-stratified case-crossover design and conditional logistic regression models to explore the association between cold spells, PM2.5 exposure, and various causes of death, and to assess their interaction. We quantitatively analyzed the interaction using the relative excess odds due to interaction (REOI), attributable proportion due to interaction (AP), and synergy index (S). Moreover, we conducted stratified analyses by average altitude, sex, age, and educational level to identify potential vulnerable groups.ResultsWe found significant associations between cold spells, PM2.5, and various causes of death, with noticeable effects on respiratory disease mortality and COPD mortality. We identified significant synergistic effects (REOI>0, AP > 0, S > 1) between cold spells and PM2.5 on various causes of death, which generally weakened with a stricter definition of cold spells and longer duration. It was estimated that up to 9.56% of non-accidental deaths could be attributed to concurrent exposure to cold spells and high-level PM2.5. High-altitude areas, males, the older adults, and individuals with lower educational levels were more sensitive. The interaction mainly varied among age groups, indicating significant impacts and a synergistic action that increased mortality risk.ConclusionOur study found that in high-altitude areas, exposure to cold spells and PM2.5 significantly increased the mortality risk from specific diseases among the older adults, males, and those with lower educational levels, and there was an interaction between cold spells and PM2.5. The results underscore the importance of reducing these exposures to protect public health.

Stereolithographic Rapid Prototyping of Clear, Foldable, Non-Refractive Intraocular Lens Designs: A Proof-of-Concept Study

Purpose A cataract is a cloudy area in the crystalline lens. Cataracts are the leading cause of blindness and the second cause of severe vision impairment worldwide. During cataract surgery, the clouded lens is extracted and replaced with an artificial intraocular lens, which restores the optical power. The fabrication of intraocular lenses using existing molding and lathing techniques is a complex and time-consuming process that limits the development of novel materials and designs. To overcome these limitations, we have developed a stereolithography-based process for producing models of clear lens designs without refractive function, serving as a proof of concept. This process has the potential to contribute toward new lens development, allowing for unlimited design iterations and an expanded range of materials for scientists to explore. Methods Lens-like 3D objects without refractive function were fabricated by using stereolithography. A photopolymerizable resin containing 2-phenoxyethyl acrylate, poly (ethylene glycol) dimethacrylate, and a suitable photoinitiator was developed for the production of lens-like 3D object prototypes. The morphology of the printed devices was characterized by scanning electron microscopy. The transparency and thermal properties were analyzed using spectrophotometry and differential scanning calorimetry, respectively. The biocompatibility of the devices was investigated in a cultured human lens cell line (FHL-124), using a standard lactate dehydrogenase assay, and the lenses were folded and implanted in the human capsular bag model. Results One-piece lens-like 3D objects without refractive function and with loop-haptic design were successfully fabricated using Stereolithography (SLA) technique. The resulting 3D objects were transparent, as determined by UV spectroscopy. The lactate dehydrogenase test demonstrated the tolerance of lens cells to the prototyping material, and apparent foldability and shape recovery was observed during direct injection into a human capsular bag model in vitro. Conclusions This proof-of-principle study demonstrated the potential and significance of the rapid prototyping process for research and development of lens-like 3D object prototypes, such as intraocular lenses.

Interactive reverse engineering of CAD models

Reverse engineering Computer-Aided Design (CAD) models based on the original geometry is a valuable and challenging research problem that has numerous applications across various tasks. However, previous approaches have often relied on excessive manual interaction, leading to limitations in reconstruction speed. To mitigate this issue, in this study, we approach the reconstruction of a CAD model by sequentially constructing geometric primitives (such as vertices, edges, loops, and faces) and performing Boolean operations on the generated CAD modules. We address the complex reconstruction problem in four main steps. Firstly, we use a plane to cut the input mesh model and attain a loop cutting line, ensuring accurate normals. Secondly, the cutting line is automatically fitted to edges using primitive information and connected to form a primitive loop. This eliminates the need for time-consuming manual selection of each endpoint and significantly accelerates the reconstruction process. Subsequently, we construct the loop of primitives as a chunked CAD model through a series of CAD procedural operations, including extruding, lofting, revolving, and sweeping. Our approach incorporates an automatic height detection mechanism to minimize errors that may arise from manual designation of the extrusion height. Finally, by merging Boolean operations, these CAD models are assembled together to closely approximate the target geometry. We conduct a comprehensive evaluation of our algorithm using a diverse range of CAD models from both the Thingi10K dataset and real-world scans. The results validate that our method consistently delivers accurate, efficient, and robust reconstruction outcomes while minimizing the need for manual interactions. Furthermore, our approach demonstrates superior performance compared to competing methods, especially when applied to intricate geometries.

Enhancing Furniture Manufacturing with 3D Scanning

Product design and manufacturing leverage 3D scanning for various applications. This study aims to investigate the effectiveness of 3D scanning in furniture production by surveying the literature and showcasing four real-world case studies. The literature review reveals that 3D data acquired from real-world objects have applications in research, rapid prototyping, restoration, and preservation of antique furniture, optimizing CNC machining processes, and measuring furniture components for quality control. The case study descriptions demonstrated the circumstances, rationale, and methodology for 3D scanning. All the case studies analyzed stem from the collaboration between the Laboratory for Product Development and Design at the Faculty of Mechanical Engineering at the University of Sarajevo and various furniture production enterprises from Bosnia and Herzegovina. The conclusions highlight that 3D scanning in the furniture sector is advantageous for developing computer-aided design models from early-stage design prototypes, validating the dimensional accuracy of manufactured components by comparing with CAD models, safeguarding and reconstructing vintage furniture, and remanufacturing formerly produced goods that lack complete technical records (reverse engineering).

A 3D printing assisted microfluidic absorbance-based measurement system for biological assay

Development of rapid analytical systems utilizing 3D printing is an emerging area of interest with the potential to provide efficient solutions by integrating multidisciplinary technology without compromising the quality of the system. In this study we report the fabrication of a 3D printing assisted microfluidic based absorbance measurement system, leveraging 3D printing along with integrating miniature optical components for the accurate measurement of biological assays. The developed system is rapid, affordable, and compact, through set of computer-aided design models and fusion deposition modeling 3D printing along with relevant electronic circuitry involving optical components like surface mounting devices. The handheld device features a capacitive touchscreen display, programmed to seamlessly perform MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. The device was employed for assessing the cell viability using Michigan cancer foundation-7 (MCF-7) cell lines over varying concentrations of tamoxifen, reciprocating the MTT assay analysis conducted by using spectrophotometer. The device achieved excellent results which upon comparison with the conventional spectrophotometer-based results have shown a correlation coefficient of 0.98. This compact and rapid absorbance measurement system holds significant potential for evaluating the cytotoxicity of drugs, and further development of innovative analytical devices.

Design, simulation, and additive manufacturing performance of three lightweight FBG acceleration sensors

Low-weight, high-performance accelerometers are sought-after objectives in aerospace structural design. This paper presents three design schemes for lightweight FBG accelerometers: lattice-based (FBGL), reinforced (FBGR), and lattice-reinforced (FBGL-R) accelerometers. The influence of the design’s filling factor ( F d ) on the first-order frequency and sensitivity in three directions of the accelerometers is investigated. Additionally, the Additive Manufacturing (AM) performance of the designed accelerometers is explored and compared with the original model. The results demonstrate that the first-order natural frequencies of the three design models decrease linearly with increasing F d , with FBGL-R exhibiting a larger adjustable range of low-frequency response under the same mass. FBGL shows the greatest sensitivity variation with F d in all three directions, while FBGR exhibits the least sensitivity variation. Under the same mass fraction, FBGR demonstrates higher sensitivities in all three directions, with FBGL-R's performance falling between the other two designs. AM simulations indicate that the porous structures can reduce the deformation amplitude during the AM of FBG accelerometers. FBGL, FBGR, and FBGL-R exhibit approximately 7% lower deformations compared to the original sensor. The deformation distribution is similar among the four models, with larger residual deformations occurring at the outer edges of the components, while differences in deformation distribution exist within the porous regions.

Decentralized Clinical Trials in Early Drug Development—A Framework Proposal

ABSTRACT The COVID-19 pandemic has led to a rethinking of clinical trial design to maintain clinical research activity, with regulatory changes allowing for the wider implementation and development of decentralized design models. Evidence of the feasibility and benefits associated with a remote design comes mainly from observational studies or phase 2 and 3 clinical trials, in which implementation is easier with a better-established safety profile. Early drug development is a slow and expensive process in which accrual and safety are key aspects of success. Applying a decentralized model to phase 1 clinical trials could improve patient accrual by removing geographic barriers, improving patient population diversity, strengthening evidence for rare tumors, and reducing patients’ financial and logistical burdens. However, safety monitoring, data quality, shipment, and administration of the investigational product are challenges to its implementation. Based on published data for decentralized clinical trials, we propose an exploratory framework of solutions to enable the conceptualization of a decentralized model for phase 1 clinical trials.

InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding.

Multi-task scene understanding aims to design models that can simultaneously predict several scene understanding tasks with one versatile model. Previous studies typically process multi-task features in a more local way, and thus cannot effectively learn spatially global and cross-task interactions, which hampers the models' ability to fully leverage the consistency of various tasks in multi-task learning. To tackle this problem, we propose an Inverted Pyramid multi-task Transformer, capable of modeling cross-task interaction among spatial features of different tasks in a global context. Specifically, we first utilize a transformer encoder to capture task-generic features for all tasks. And then, we design a transformer decoder to establish spatial and cross-task interaction globally, and a novel UP-Transformer block is devised to increase the resolutions of multi-task features gradually and establish cross-task interaction at different scales. Furthermore, two types of Cross-Scale Self-Attention modules, i.e., Fusion Attention and Selective Attention, are proposed to efficiently facilitate cross-task interaction across different feature scales. An Encoder Feature Aggregation strategy is further introduced to better model multi-scale information in the decoder. Comprehensive experiments on several 2D/3D multi-task benchmarks clearly demonstrate our proposal's effectiveness, establishing significant state-of-the-art performances. The codes are publicly available https://github.com/prismformore/Multi-Task-Transformer/tree/main/InvPT.

Integration of the Power Corridor Concept in the Early-Phase Design of Electric Naval Ships using Mathematical Design Models

The aim of this paper is to illustrate the process of identifying the ‘best compromise’ solution for an all-electric destroyer at the concept design level. The design strategy reflects a paradigm shift from a sequential approach towards a holistic multicriterial approach. The destroyer is required for an extensive range and endurance, fully operable in rough sea states. A mathematical design model (MDM) that includes a set of metamodels, is implemented to evaluate the overall performance of feasible, then non-dominated designs. The power corridor concept is integrated into the MDM to optimize the location and functionality of the individual units of the power train. The fuzzy sets theory is used for normalizing and weighing incommensurable properties of candidate designs, so resolving many of the ill-defined requirements and criteria. The final result of this study is a top-level specification for the destroyer with enhanced performance and reduced power demand.

Isogeometric Shape Optimization of Reissner–Mindlin Shell with Analytical Sensitivity and Application to Cellular Sandwich Structures

Structural shape optimization plays a significant role in structural design, as it can find an appropriate layout and shape to improve structural performance. Isogeometric analysis provides a promising framework for structural shape optimization, unifying the design model and analysis model in the optimization process. This paper presents an adjoint-based analytical sensitivity for isogeometric shape optimization of Reissner–Mindlin shell structures. The shell structures are modeled by multiple NURBS surfaces and design variables are associated with the position of control points. A multilevel approach is performed with a coarse mesh for the design model and a dense mesh for the analysis model. The sensitivity propagation is achieved through a transformation matrix between the design and analysis models. Structural compliance minimization problems with and without constraints are studied and the optimization history shows that the optimization can converge quickly within fewer iterations. The developed formulations are validated through several numerical examples and applied to the optimization of cellular sandwich structures, which are widely used in engineering applications. Numerical results show that optimized sandwich panels can achieve better performance in bending resistance.

Optimization of pediatric vaccines distribution network configuration under uncertainty

Millions of young people are not immunized in low income (LI) and lower middle income (LMI) countries because of low vaccine availability resulting from inefficiencies in cold supply chains. We create supply chain network design and distribution models to address the unique characteristics and challenges facing vaccine supply chains in LI and LMI countries. The models capture the uncertainties of demand for vaccinations and the resulting impacts on immunization, the unique challenges of vaccine administration (such as open vial wastage), the interactions between technological improvements of vaccines and immunizations, and the trade-offs between immunization coverage rates and available resources. The objective is to maximize both the percentage of fully immunized children and the vaccine availability in clinics. Our research examines how these two metrics are affected by three factors: number of tiers in the supply chain, vaccine vial size, and new vaccine technologies. We tested the model using Niger’s Expanded Program on Immunization, which is sponsored by the World Health Organization. We make many observations and recommendations to help LMI countries increase their immunization coverage.

First-principles calculation on electronic properties of hydrogen evolution reaction of Ni-based electrode surfaces with different monatomic doping

At present, the hydrogen evolution reaction (HER) of Ni-based electrode has an important influence on water electrolysis hydrogen production technology, involving complex electrochemical process of electrode. In this project, Materials Studio (MS) software was used to design and construct Ni-based electrode surface (NES) models with monatomic Mo, Co, Fe, Cr doping, and the NES models attached 1 H atom and 2H atoms were denoted as the NES-H models and NES-2H model, respectively. Then the first-principles calculation was carried out.The results showed that the doping of different atoms can effectively change the work function of the pure Ni. In the charge transfer process of the four NES-2H models, the distance between the two H atoms is most affected by Mo doping, and they leave the Ni electrode surface as a single H ion, respectively, while the effect on Co, Fe and Cr doping is relatively consistent, and they leave the Ni electrode surface with H2 molecules, respectively. The doping of four single atoms changes the distance of valence band (VB) top and conduction band (CB) bottom from Fermi level in NES, NES-H and NES-2H models, and affects the HER, in which Mo doping has the greatest effect. The TDOS of the above models is mainly derived from the PDOS of the d orbitals of the doped atoms and Ni atoms. The results will provide a theoretical basis for the research and development of Ni-based electrode materials in HER.

DATA-DRIVEN ASSESSMENT OF SHEAR STRENGTH OF SINUSOIDAL CORRUGATED STEEL WEBS

The construction industry increasingly recognizes the value of steel beams with corrugated webs for their enhanced resistance to buckling, superior shear capacity, and the elimination of the need for web stiffeners. While a wealth of research focuses on beams with trapezoidal corrugated webs, investigations into sinusoidal corrugated webs (SCWs) remain comparatively sparse. This work investigates the variability in the prediction of the various models and assesses the interactions among different influencing parameters affecting the shear strength of sinusoidal corrugated steel web beams. A comprehensive database of 66 experimental data on SCW beams is assembled and rigorously examined to facilitate this analysis. The collected database is used to test the prediction accuracy of the existing design models provided by codes or proposed models in the literature. The Eurocode3 and DASt-Ri. 015 code models are considered in this study. The results of the evaluated prediction models showed considerable scatters, emphasizing the ongoing need for research to enhance the accuracy of these predictive models for better application in construction practices.

Experimental study of in-fire and post-fire material response of high-strength aluminium alloys

This paper presents an experimental study on the residual material properties of high-strength aluminium alloys in and after fire. A testing programme was firstly conducted on grade 7075-T6 high-strength aluminium alloy, consisting of 13 in-fire and 24 post-fire coupon tests, to derive the material stress–strain responses at and after exposure to elevated temperatures ranging from 20 oC to 550 oC. The key temperature-dependent material properties including stiffness and strengths were determined from the measured stress–strain curves and normalised by their room-temperature counterparts, resulting in a series of in-fire and post-fire retention factors. These experimentally obtained retention factors were used to analyse the effects of elevated temperatures on the residual stiffness and strengths of high-strength aluminium alloys. The design in-fire retention factors, as given in the European, American and Chinese standards, and the existing predictive models in previous studies for the post-fire retention factors, were also quantitatively and qualitatively assessed based on the test data. They were found to be inapplicable due to less accuracy; especially when used for predicting the in-fire Young’s moduli, a 25% over-prediction can be yielded, leading to unsafe design. To address the shortcoming, a series of predictive models were developed to offer accurate predictions of the in-fire and post-fire residual stiffness and strengths of high-strength aluminium alloys, with the mean test-to-prediction ratios ranging from 0.995 to 1.074 for different in-fire and post-fire material properties and the design accuracy improved by at least 15% than existing design models. The Then, a two-stage Ramberg–Osgood material model that was proposed for describing the room-temperature stress–strain curves of structural aluminium alloys was considered in this study, with its applicability to high-strength aluminium alloys assessed. The considered Ramberg–Osgood model was found to well predict the in-fire and post-fire stress–strain curves of high-strength aluminium alloys. The proposed models for retention factors and stress–strain curves can be further assessed based on more test data on other new high-strength aluminium alloy grades. Future research can focus on developing new coating technologies and optimising existing treatments to improve the fire performance of high-strength aluminium alloy structures.

Analysis of bullet impact test on aluminum alloy plates at varying thickness and velocities

Analysis of bullet impact test on aluminum alloy plates at varying thickness and velocities

Unsupervised Object-Centric Learning From Multiple Unspecified Viewpoints.

Visual scenes are extremely diverse, not only because there are infinite possible combinations of objects and backgrounds but also because the observations of the same scene may vary greatly with the change of viewpoints. When observing a multi-object visual scene from multiple viewpoints, humans can perceive the scene compositionally from each viewpoint while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold. This ability is essential for humans to identify the same object while moving and to learn from vision efficiently. It is intriguing to design models that have a similar ability. In this article, we consider a novel problem of learning compositional scene representations from multiple unspecified (i.e., unknown and unrelated) viewpoints without using any supervision and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. During the inference, latent representations are randomly initialized and iteratively updated by integrating the information in different viewpoints with neural networks. Experiments on several specifically designed synthetic datasets have shown that the proposed method can effectively learn from multiple unspecified viewpoints.