Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB)

Permeable reactive barrier (PRB) is an important groundwater treatment technology. However, selecting the optimal reactive material and estimating the width remain critical and challenging problems in PRB design. Machine learning (ML) has advantages in predicting evolution and tracing contaminants in temporal and spatial distribution. In this study, ML was developed to design PRB, and its feasibility was validated through experiments and a case study. ML algorithm showed a good prediction about the Freundlich equilibrium parameter (R2 0.94 for KF, R2 0.96 for n). After SHapley Additive exPlanation (SHAP) analysis, redefining the range of the significant impact factors (initial concentration and pH) can further improve the prediction accuracy (R2 0.99 for KF, R2 0.99 for n). To mitigate model bias and ensure comprehensiveness, evaluation index with expert opinions was used to determine the optimal material from candidate materials. Meanwhile, the ML algorithm was also applied to predict the width of the mass transport zone in the adsorption column. This procedure showed excellent accuracy with R2 and root-mean-square-error (RMSE) of 0.98 and 1.2, respectively. Compared with the traditional width design methodology, ML can enhance design efficiency and save experiment time. The novel approach is based on traditional design principles, and the limitations and challenges are highlighted. After further expanding the data set and optimizing the algorithm, the accuracy of ML can make up for the existing limitations and obtain wider applications.

Design and optimization methodology for different 3D processed materials (PLA, ABS and carbon fiber reinforced nylon PA12) subjected to static and dynamic loads

This research presents a methodology for the design and optimization of 3D printed parts with material extrusion (MEX) technology with three different commercial materials: PLA, ABS and N + CF (PA12) subjected to tensile and fatigue stresses, which included three stages: pretreatment, design of experiments and sequential optimization by statistical modeling. In the pretreatment stage, mainly the printing control factors (inner layer and contour height, printing speed, extrusion temperature, nozzle, infill arrangement and printing orientation) were determined; then, factors to optimize tensile strength as a function of printing pattern (linear, 3D, hexagonal), infill percentage (33%, 66%, 100°) and printing orientation (+45°/-45°, 0°/90°) were evaluated. Fatigue analysis was performed as a function of impression orientation using 100% infill, linear impression pattern, 5 Hz and a load range between 90 and 50% UTS. Optimization of tensile strength resulted in parts that exceeded the UTS of their corresponding filament, leading to infinite life relative to fatigue tests. Results were presented for fatigue life prediction based on Weibull analysis, Basquińs model and a multivariate response surface correlation analysis. The best fatigue behavior was related to the optimized tensile strength, the infill pattern applied to the printing orientation and the intrinsic properties of ABS (1 × 107cycles, stress up to 20 MPa). With respect to the other materials, a good fatigue behavior was highlighted at the number of cycles achieved 1 × 106 (stress up to 18 MPa) and 1 × 105 (stress up to 24 MPa) for N + CF and PLA, respectively. This study contributes to a better understanding of how printing parameters correlate with tensile and fatigue properties.

CDFA method: a way to assess assembly and installation performance of aircraft system architectures at the conceptual design

This paper describes an engineering design methodology, called conceptual design for assembly (CDFA) in the context of aircraft development, to assess aircraft systems' installation during conceptual phase, in relation to industrial performance objectives. The methodology is based on a given framework (hierarchical structure) which includes a set of attributes, collected in recognized domains that characterize the aircraft systems installation. The framework of the CDFA methodology enables to analyze product architectures at different levels of granularity, splitting the global analysis into sub-problems (problem discretization) with the aim to help architects and designers to identify product architecture weaknesses in terms of fit for assembly performances. The CDFA methodology was applied on a complex system (the nose-fuselage of a commercial aircraft) presenting a high number of criticalities both for the product and its assembly operations. Results identified the architectural components leading to the less efficient assembly operations and the rationales enabling to elaborate alternative architectures for an improved product industrial efficiency.

An ontology network for Diabetes Mellitus in Mexico

Background

Medical experts in the domain of Diabetes Mellitus (DM) acquire specific knowledge from diabetic patients through monitoring and interaction. This allows them to know the disease and information about other conditions or comorbidities, treatments, and typical consequences of the Mexican population. This indicates that an expert in a domain knows technical information about the domain and contextual factors that interact with it in the real world, contributing to new knowledge generation. For capturing and managing information about the DM, it is necessary to design and implement techniques and methods that allow: determining the most relevant conceptual dimensions and their correct organization, the integration of existing medical and clinical information from different resources, and the generation of structures that represent the deduction process of the doctor. An Ontology Network is a collection of ontologies of diverse knowledge domains which can be interconnected by meta-relations. This article describes an Ontology Network for representing DM in Mexico, designed by a proposed methodology. The information used for Ontology Network building include the ontological resource reuse and non-ontological resource transformation for ontology design and ontology extending by natural language processing techniques. These are medical information extracted from vocabularies, taxonomies, medical dictionaries, ontologies, among others. Additionally, a set of semantic rules has been defined within the Ontology Network to derive new knowledge.

Results

An Ontology Network for DM in Mexico has been built from six well-defined domains, resulting in new classes, using ontological and non-ontological resources to offer a semantic structure for assisting in the medical diagnosis process. The network comprises 1367 classes, 20 object properties, 63 data properties, and 4268 individuals from seven different ontologies. Ontology Network evaluation was carried out by verifying the purpose for its design and some quality criteria.

Conclusions

The composition of the Ontology Network offers a set of well-defined ontological modules facilitating the reuse of one or more of them. The inclusion of international vocabularies as SNOMED CT or ICD-10 reinforces the representation by international standards. It increases the semantic interoperability of the network, providing the opportunity to integrate other ontologies with the same vocabularies. The ontology network design methodology offers a guide for ontology developers about how to use ontological and non-ontological resources in order to exploit the maximum of information and knowledge from a set of domains that share or not information.

A hybrid method of healthcare delivery research and human-centered design to develop technology-enabled support for caregivers of hematopoietic stem cell transplant recipients

Health information technology (HIT) is a widely recognized strategy to encourage cancer patients and caregivers to participate in healthcare delivery in a sustainable and cost-effective way. In the context of autologous hematopoietic cell transplant (HSCT), HIT-enabled tools have the potential to effectively engage, educate, support, and optimize outcomes of patients and caregivers in the outpatient setting. This study sought to leverage human-centered design to develop a high-fidelity prototype of a HIT-enabled psychoeducational tool for HSCT caregivers. Phase 1 focuses on breadth and depth of information gathering through a systematic review and semi-structured interviews to determine optimal tool use. Phase 2 engages in human-centered design synthesis and visualization methods to identify key opportunities for the HIT design. Phase 3 employs human-centered design evaluation, engaging caregivers to respond to low-fidelity concepts and scenarios to help co-design an optimal tool for HSCT. This study outlines a hybrid method of healthcare delivery research and human-centered design to develop technology-enabled support for HSCT caregivers. Herein, we present a design methodology for developing a prototype of HIT-enabled psychoeducational tool which can be leveraged to develop future eHealth innovations to optimize HSCT.

Systematic engineering design helps creating new soft machines

Soft robotics is an emerging field in the robotics community which deals with completely new types of robots. However, often new soft robotic designs depend on the ingenuity of the engineer rather being systematically derived. For this reason, in order to support the engineer in the design process, we present a design methodology for general technical systems in this paper and explain it in depth in the context of soft robotics. The design methodology consists of a combination of state-of-the-art engineering concepts that are arranged in such a way that the engineer is guided through the design process. The effectiveness of a systematic approach in soft robotics is illustrated on the design of a new gecko-inspired, climbing soft robot.

A methodological approach for using high-level Petri Nets to model the immune system response

BACKGROUND

Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN.

RESULTS

We propose a novel methodological approach that is based on high level PN, and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features.

CONCLUSIONS

The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra cellular) models and cellular (population) models built upon the same technique and software.

Understanding Design Tradeoffs for Health Technologies: A Mixed-Methods Approach

We introduce a mixed-methods approach for determining how people weigh tradeoffs in values related to health and technologies for health self-management. Our approach combines interviews with Q-methodology, a method from psychology uniquely suited to quantifying opinions. We derive the framework for structured data collection and analysis for the Q-methodology from theories of self-management of chronic illness and technology adoption. To illustrate the power of this new approach, we used it in a field study of nine older adults with type 2 diabetes, and nine mothers of children with asthma. Our mixed-methods approach provides three key advantages for health design science in HCI: (1) it provides a structured health sciences theoretical framework to guide data collection and analysis; (2) it enhances the coding of unstructured data with statistical patterns of polarizing and consensus views; and (3) it empowers participants to actively weigh competing values that are most personally significant to them.