BACKGROUND: Care partners of people with serious illness experience significant challenges and unmet needs during the patient\'s treatment period and after their death. Learning from others with shared experiences can be valuable, but opportunities are not consistently available.
OBJECTIVE: This study aims to design and prototype a regional, facilitated, and web-based peer support network to help active and bereaved care partners of persons with serious illness be better prepared to cope with the surprises that arise during serious illness and in bereavement.
METHODS: An 18-member co-design team included active care partners and those in bereavement, people who had experienced serious illness, regional health care and support partners, and clinicians. It was guided by facilitators and peer network subject-matter experts. We conducted design exercises to identify the functions and specifications of a peer support network. Co-design members independently prioritized network specifications, which were incorporated into an early iteration of the web-based network.
RESULTS: The team prioritized two functions: (1) connecting care partners to information and (2) facilitating emotional support. The design process generated 24 potential network specifications to support these functions. The highest priorities included providing a supportive and respectful community; connecting people to trusted resources; reducing barriers to asking for help; and providing frequently asked questions and responses. The network platform had to be simple and intuitive, provide technical support for users, protect member privacy, provide publicly available information and a private discussion forum, and be easily accessible. It was feasible to enroll members in the ConnectShareCare web-based network over a 3-month period.
CONCLUSIONS: A co-design process supported the identification of critical features of a peer support network for care partners of people with serious illnesses in a rural setting, as well as initial testing and use. Further testing is underway to assess the long-term viability and impact of the network.

The TrollLabs Open dataset includes comprehensive information that offers a comparison of design practices and outcomes between human participants and Generative AI during a hackathon event. The dataset was curated through the running of a prototyping hackathon designed to assess the abilities and performance of generative AI, specifically ChatGPT, in the early stages of engineering design. This assessment involved comparing ChatGPT\'s performance to that of experienced engineering students in a hackathon setting, where participants competed by making a prototype that fires a NERF dart as far as possible. In this setup, all ideas, concepts, strategies, and actions undertaken by the AI-controlled team were autonomously generated by the ChatGPT, without human intervention or guidance, but implemented by two participants. Five self-directed baseline teams competed against the AI team. The dataset comprises 116 prototype entries and 433 edges (connection) that enable comparative analysis of design practices and performance between the team instructed solely by generative AI and baseline teams of experienced engineering design students. Prototypes and their attribute data were captured using Pro2booth, an online prototype capture platform running on participants\' phones and computers. The dataset includes a transcript of the conversation between ChatGPT and the team responsible for implementing its recommendations, featuring 97 exchanges of prompts and responses. It contains the initial prompt used to instruct the AI, the objective and rules of the hackathon and the objective performance of teams, showing the ChatGPT team finishing 2nd among six teams. To the authors\' knowledge, the TrollLabs Open dataset is the first and only open resource that directly compares the performance of generative AI with human teams in an engineering design context. Thus, it is intended to be a valuable resource to design researchers, engineering and design students, educators, and industry professionals seeking to find strategies for implementing generative AI tools in their design processes. By offering a comprehensive data collection, the dataset enables external researchers to conduct in-depth analyses that could highlight the practical implications of integrating generative AI in design practices, possibly providing an overview of its limitations and presenting recommendations for improved integration in the design process.

Thermal phase separation technology is a new comprehensive treatment technology, which heats oil-based cuttings to a certain temperature to vaporize oil and water components. Based on a large oil-based drilling cuttings comprehensive utilization project, the engineering design and application effect of thermal phase separation technology were analysed. The practice shows that thermal phase separation technology can reduce the oil content of purified residue to 0.1-0.2%, the average recovery rate of base oil is 94.12% and the annual recovery of base oil is about 4800 t; the purified residue does not have corrosive, leaching toxicity and other dangerous characteristics, and can be used for making bricks or building materials. Thermal phase separation technology is a comprehensive utilization and treatment technology with excellent engineering and environmental benefits, which has a high promotion value.

In K-12 STEM education, engineering design is emphasized, as demonstrated by the bridge-design project. Due to the iterative nature of engineering design, engineering practice is frequently complicated and requires pedagogical guidance. As an emerging pedagogy in STEM education, REP (Reverse Engineering Pedagogy) is showing, but not enough, some benefits in several cases. This paper aims to explore the effects of REP in a bridge-design course. A comparison experiment, REP versus PBL (Project-Based Learning), was conducted by randomly forming two groups of fourth-grade students from a primary school in China. Results indicated that REP was more advantageous than PBL in terms of decreasing students\' cognitive load, boosting their scientific knowledge level and engineering design skills. However, REP and PBL have the same effect on the students\' learning attitude and engagement. The key findings, possible reasons, and suggestions for practice are also discussed.

Inching-locomotion caterpillars (ILAR) show impressive environmental adaptation, having high dexterity and flexibility. To design robots that mimic these abilities, a novel bioinspired robotic design (BIROD) method is presented. The method is composed by an algorithm for geometrical kinematic analysis (GEKINS) to standardize the proportional dimensions according to the insect\'s anatomy and obtain the kinematic chains. The approach is experimentally applied to analyze the locomotion and kinematic chain of these specimens:Geometridae-two pair of prolegs (represents 35 000 species) andPlusiinae-three pair of prolegs (represents 400 species). The obtained data indicate that the application of the proposed method permits to locate the attachment mechanisms, joints, links, and to calculate angular displacement, angular average velocity, number of degrees of freedom, and thus the kinematic chain.Geometridaein contrast toPlusiinae, shows a longer walk-stride length, a lower number of single-rotational joints in 2D (3 DOF versus 4 DOF), and a lower number of dual-rotational joints in 3D (6 DOF versus 8 DOF). The application of BIROD and GEKINS provides the forward kinematics for 35 400 ILAR species and are expected to be useful as a preliminary phase for the design of bio-inspired arthropod robots.

The design and construction of genetic systems, in silico, in vitro, or in vivo, often involve the handling of various pieces of DNA that exist in different forms across an assembly process: as a standalone \"part\" sequence, as an insert into a carrier vector, as a digested fragment, etc. Communication about these different forms of a part and their relationships is often confusing, however, because of a lack of standardized terms. Here, we present a systematic terminology and an associated set of practices for representing genetic parts at various stages of design, synthesis, and assembly. These practices are intended to represent any of the wide array of approaches based on embedding parts in carrier vectors, such as BioBricks or Type IIS methods (e.g., GoldenGate, MoClo, GoldenBraid, and PhytoBricks), and have been successfully used as a basis for cross-institutional coordination and software tooling in the iGEM Engineering Committee.

The use of lattice structure in the Design for Additive Manufacturing (DfAM) engineering practice offers the ability to tailor the properties (and therefore the response) of an engineered component independent of the material and overall geometry. The selection of a lattice topology is critical in maximizing the value of the lattice structure and its unique properties for the intended application. To support this, we have compiled a catalog of lattice structures from the literature that includes all Triply Periodic Minimal Surfaces (TPMS) for which a low-order Fourier series fit is known (so that they can be modeled and manufactured). We also include equations that do not directly correspond to known TPMS but do produce a triply periodic structure without sharp corners that would give rise to stress concentrations. This catalog includes images, elastic mechanical property data, and CAD models useful for the visualization, selection, and implementation of these lattice structures for any engineered structure.

Immersion experiences for undergraduate students in biomedical engineering are key contributors to their ability to identify medical needs. Despite this, as few as 25% of surveyed programs report providing such opportunities. Since 2010 when the National Institute of Health began its R25 grant mechanism to support curricular development toward team-based design, several institutions have established programs for immersion experiences, which provide precedent for their implementation. Published results from such immersion experiences highlight successes in structure and changes in student perspectives after these experiences. As more institutions expand their biomedical engineering curriculum with new immersion-focused programs, it is important to learn from these precedents while also considering opportunities to improve. For newly funded groups that are developing and implementing programs, they may find improved success by strategic use of unique partnerships. However, these partnerships may not be immediately evident to program organizers. Our objective is to discuss two institutions that recently established programs for immersion experience. In the comparison of our two immersion programs, we found five overlapping core features that include: immersion partner collaboration, team-based immersion experiences, needs-finding emphasis, team-based engineering design experiences, and immersion assessment and evaluation. Both programs developed collaborative partnerships with nearby medical schools. Additionally, one program partnered with a community resource (i.e., Human Development Institute). Despite nuanced program differences, we found that students at both programs self-reported increased knowledge or confidence in aspects of the design process (e.g., identifying and refining user needs, concept generation). Our results also highlight student gains unique to their programs - UK students self-reported gains on disability topics and IUPUI students self-reported gains on socioeconomic awareness. In summary, immersion partner collaboration, or partnership, surfaced as a core feature for both programs, and students in both immersion programs endorsed enhanced knowledge or confidence in engineering design.

Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm\'s calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues.

Nowadays, a strong demand exists for printable materials with multifunctionality and proper rheological properties to overcome the limitations to deposit layer-by-layer in additive extrusion. The present study discusses rheological properties related to the microstructure of hybrid poly (lactic) acid (PLA) nanocomposites filled with graphene nanoplatelets (GNP) and multiwall carbon nanotubes (MWCNT) to produce multifunctional filament for 3D printing. The alignment and slip effects of 2D-nanoplatelets in the shear-thinning flow are compared with the strong reinforcement effects of entangled 1D-nanotubes, which govern the printability of nanocomposites at high filler contents. The mechanism of reinforcement is related to the network connectivity of nanofillers and interfacial interactions. The measured shear stress by a plate-plate rheometer of PLA, 1.5% and 9% GNP/PLA and MWCNT/PLA shows an instability for high shear rates, which is expressed as shear banding. A rheological complex model consisting of the Herschel-Bulkley model and banding stress is proposed for all considered materials. On this basis, the flow in the nozzle tube of a 3D printer is studied by a simple analytical model. The flow region is separated into three different regions in the tube, which match their boundaries. The present model gives an insight into the flow structure and better explains the reasons for printing enhancement. Experimental and modeling parameters are explored in designing printable hybrid polymer nanocomposites with added functionality.