Explore the vast range of titles available.

This study addresses the challenges faced by individuals with upper limb disadvantages in operating power wheelchair joysticks by utilizing the extended Function–Behavior–Structure (FBS) model to identify design requirements for an alternative wheelchair control system. A gaze-controlled wheelchair system is proposed based on design requirements from the extended FBS model and prioritized using the MosCow method. This innovative system relies on the user’s natural gaze and comprises three levels: perception, decision making, and execution. The perception layer senses and acquires information from the environment, including user eye movements and driving context. The decision-making layer processes this information to determine the user’s intended direction, while the execution layer controls the wheelchair’s movement accordingly. The system’s effectiveness was validated through indoor field testing, with an average driving drift of less than 20 cm for participates. Additionally, the user experience scale revealed overall positive user experiences and perceptions of the system’s usability, ease of use, and satisfaction.

To improve the use experience of airport chairs, this study adopted morphological analysis to conduct systematic deconstruction, analysis, and statistics on the modeling features of existing airport chairs. The KJ method was used to determine the design requirements of airport chairs. The study combined the Kano model to classify the functional attributes and prioritize the requirements, and it merged the module and demand analysis to build a Function-Behavior-Structure mapping model. In this way, passengers’ demand for airport chairs can be clearly defined, and airport chair configuration planning can be carried out according to the difference in preferences. Further, different module combination schemes can be formed to improve the functional allocation efficiency of airport chairs, optimize the overall service quality of airports, and provide some basis and reference for the future design of airport chairs.

Although the market share of domestic children’s furniture is increasing annually, some potential problems limit its long-term and stable development, and there is still a gap in China compared with foreign countries. This study focused on the demand preferences for growable children’s beds and examined the design features that influence these preferences. This study introduces a combination of Hierarchical Analyses (AHP), Quality Function Development (QFD), and the Platts Conceptual Decision Matrix (PUGH) into the innovative design of a research model for children’s furniture (AHP-QFD-PUGH). This study screened and classified the decision-making indicators obtained from the research, ranked their importance by quantitative calculation, and finally proposed an optimal design solution. Additionally, to further study the structural characteristics, the function-behavior-structure (FBS) model served as a supplementary analysis tool to effectively circumvent subjective factors in product design. This integrated model accurately explored user needs and product characteristics, providing substantial guidance and new ideas for optimizing the design of growable children’s beds and enhancing growth of the children’s furniture industry.

Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require different division indicators and techniques. The purpose of this study is to provide a set of appropriate modular division techniques for FDM 3D printers. This research offers an ecologically friendly module division index and uses module clustering as the module division principle in accordance with the current industrial development trend and the fundamental requirements of FDM 3D printer consumers in the current market. The K-means algorithm is used to use the Jaccard similarity coefficient as the metric of similarity of the DSM clustering process to realize the module division of the FDM 3D printer after studying the function–behavior–structure mapping model of the 3D printer. Additionally, the elbow method–cluster error variance and average contour coefficient evaluation systems were built, respectively, in order to verify the viability of the FDM 3D printer module division method and obtain the best module division results. By analyzing these two systems, it was discovered that when the FDM 3D printer was divided into three modules, the in-cluster error variance diagram obviously had an inflection point, and the average profile coefficient and other modular approaches that need to be adjusted to their respective goods can use this division method as a theoretical foundation and point of reference.

Three-dimensional (3D) bioprinting is one of the most promising methodologies that are currently in development for the replacement of animal experiments. Bioprinting and most alternative technologies rely on animal-derived materials, which compromises the intent of animal welfare and results in the generation of chimeric systems of limited value. The current study therefore presents the first bioprinted liver model that is entirely void of animal-derived constituents. Initially, HuH-7 cells underwent adaptation to a chemically defined medium (CDM). The adapted cells exhibited high survival rates (85–92%) after cryopreservation in chemically defined freezing media, comparable to those preserved in standard medium (86–92%). Xeno-free bioink for 3D bioprinting yielded liver models with high relative cell viability (97–101%), akin to a Matrigel-based liver model (83–102%) after 15 days of culture. The established xeno-free model was used for toxicity testing of a marine biotoxin, okadaic acid (OA). In 2D culture, OA toxicity was virtually identical for cells cultured under standard conditions and in CDM. In the xeno-free bioprinted liver model, 3-fold higher concentrations of OA than in the respective monolayer culture were needed to induce cytotoxicity. In conclusion, this study describes for the first time the development of a xeno-free 3D bioprinted liver model and its applicability for research purposes.

PurposeThe purpose of this study is to determine the farmers’ behavioural intention (BI) to adopt Internet of things platforms (IoT-P) in the agriculture context by comparing two well-known theories: the united theory of acceptance and use of technology (UTAUT), and the decomposed theory of planned behaviour (DTPB) with the integration of innovation resistance theory (IRT).Design/methodology/approachPurposive sampling was used to get responses from 267 potential farmers to examine their IoT-P adoption intention in Pakistan. The PLS-SEM, PLS model evaluation criterion and PLS model selection criterion were considered to determine the significance of path co-efficient, explanatory power, predictive power and more parsimonious model.FindingsThe findings demonstrate that DTPB is the best model with the extension of functional barriers (FBs) and psychological barriers (PBs). It has more predictive relevance and explanatory power. The results show that farmers’ attitude (ATT), based on the evaluation of three attributes (i.e. perceived usefulness (PU), perceived ease of use (PEOU) and compatibility (COMP)), is the strong predictor of farmers’ BI to adopt IoT-P. In addition, self-efficacy (SEF) and facilitating conditions (FC) peer influence (PI) and superiors’ influence (SPI) are required for adoption of IoT-P devices. Finally, FB and PB significantly and negatively influence the farmers’ BI to adopt IoT-P.Originality/valueThis research is the first to consider the two technology adoption models with the integration of IRT for explaining farmers’ BI in the context of agriculture.

Background Respiratory diseases represent a global health burden. Because research on therapeutic strategies of airway diseases is essential, the technique of precision-cut lung slices (PCLS) has been developed and widely studied. PCLS are an alternative ex vivo model and have the potential to replace and reduce in vivo animal models. So far, the majority of studies was conducted with short-term cultivated PCLS (≤ 72 h). As there is large interest in research of chronic diseases and chronic toxicity, feasibility of cultivating human PCLS long-term over 2 weeks and recently over 4 weeks was investigated by another research group with successful results. Our aim was to establish a model of long-term cultivated rat PCLS over a period of 29 days. Methods Rat PCLS were cultured for 29 days and analysed regarding viability, histopathology, reactivity and gene expression at different time points during cultivation. Results Cultivation of rat PCLS over a 29-day time period was successful with sustained viability. Furthermore, the ability of bronchoconstriction was maintained between 13 and 25 days, depending on the mediator. However, reduced relaxation, altered sensitivity and increased respiratory tone were observed. Regarding transcription, alteration in gene expression pattern of the investigated target genes was ascertained during long-term cultivation with mixed results. Furthermore, the preparation of PCLS seems to influence messenger ribonucleic acid (mRNA) expression of most target genes. Moreover, the addition of fetal bovine serum (FBS) to the culture medium did not improve viability of PCLS. In contrast to medium without FBS, FBS seems to affect measurements and resulted in marked cellular changes of metaplastic and/or regenerative origin. Conclusions Overall, a model of long-term cultivated rat PCLS which stays viable for 29 days and reactive for at least 13 days could be established. Before long-term cultivated PCLS can be used for in-depth study of chronic diseases and chronic toxicity, further investigations have to be made.

Biological features of silver nanoparticles in rising the insulin level of diabetic animal models were considered in recent years, which resulted in decreasing hyperglycemia condition. We reviewed the published literature to investigate the possible role of silver nanoparticles (Ag-NPs) throughout the treatment of diabetes mellitus in animal studies. In this systematic review and meta-analysis, we performed a search throughout the English literature of electronic databases, including Scopus, PubMed, and ISI Web of Science, up to the date of May 22, 2020. Primary outcomes and data regarding fast blood sugar (FBS), lipid profile, and liver enzyme were collected from the available articles, while the studies that did not provide sufficient information on the effects of silver nanoparticles through the course of diabetes mellitus were excluded. Our search yielded 1283 results that included five animal studies in the meta-analysis. The comparison between the plasma insulin level of the diabetic group treated by Ag-NPs with the diabetic control group displayed no significant differences with the P values = 0.299. In addition, significant differences were revealed by comparing the FBS level of the diabetic group treated by Ag-NPs with the diabetic control group (P value < 0.001). According to the present meta-analysis, the application of Ag-NPs in animal models resulted in displaying the anti-diabetic effects, which can be applied in future treatments. Furthermore, a correlation was noticed between these nanoparticles and the reduction of serum FBS among diabetic cases.

Breakwaters are used to reduce incoming wave energy at harbors and shorelines. This paper presents a comparison of novel two-dimensional hybrid intelligent models for the idealization of the effects of waves on the performance of moored rectangular floating breakwaters (FBs). Fluid structure interactions (FSIs) were idealized by airy-type monochromatic regular waves generated in a numerical wave tank. The coupled Volume of Fluid-Fast Fictitious Domain (VOF-FFD) interpolation method was used to evaluate FB motions. Different forms of Least Squares Support Vector Machine Methods (LSSVMs) that utilized 183 data streams were used to model FB performance for different wave height-to-water depth ratios, dimensional aspect ratios, and specific length-to-water depth ratios. Of those, 80% were used to train the model and 20% to test it. Parametric studies have shown that during training a Least Squares Support Vector Machine Method-Bat Algorithm (LSSVM-BA) with R2 = 0.8725, MAE = 0.0276, and RMSE = 0.0488 presents the most appropriate model for the evaluation of FB performance. Notwithstanding this, during testing a Least Squares Support Vector Machine Method-Cuckoo Search (LSSVM-CS) Algorithm with corresponding values of 0.6841, 0.0519, and 0.0708 performs better.

We present a novel method for updating the joint properties, including the rotational degrees of freedom (DOFs), of finite element (FE) models of complex structures. Updating is based on a frequency response function (FRF)-based substructuring (FBS) method. An entire structure is divided into substructures, and the properties of the joints that connect substructures are updated. Reference FRFs measured in the assembled structure are defined. Next, an FBS model is constructed using the rotational DOFs. To update all joint properties of the FE model, the differences between the reference and calculated FRFs are represented using newly defined metrics and minimized. We employ an analytical expression based on the FBS method to calculate the design sensitivities of the joint properties in terms of the rotational DOFs. The method is used to update an FE model of a rail.