Understanding the Interaction between Older Adults and Soft Service Robots: Insights from Robotics and the Technology Acceptance Model

As the world’s population increasingly ages, we need technological solutions such as robotics technology to assist older adults in their daily tasks. In this regard, we examine soft service robots’ potential to help care for the elderly. To do so, we developed and tested the degree to which they would accept a soft service robot that catered to their functional needs in the home environment. We used embodied artificial to develop an in-house teleoperated human-sized soft service robot that performed object-retrieval tasks with a soft gripper. Using an extended technology acceptance model as a theoretical lens, we conducted a study with 79 older adults to examine the degree to which they would accept a soft service robot in the home environment. We found perceived ease of use, perceived usefulness, and subjective norms as significant predictors that positively influenced older adults’ intention to adopt and use soft service robots. However, we also found that perceived anxiety and perceived likability did not significantly predict older adults’ intention to adopt and use soft service robots. We discuss the implications, limitations, and future research directions that arise from these findings

MoMo Strategy : Learn More from More Mistakes

Training accurate convolutional neural networks (CNNs) is essential for achieving high-performance machine learning models. However, limited training data pose a challenge, reducing model accuracy. This research investigates the selection and utilization of misclassified training samples to enhance the accuracy of CNNs where the dataset is long-tail distributed. Unlike classical resampling methods involving oversampling of tail classes and undersampling of head classes, we propose an approach that allocates more misclassified training samples into the training process to learn more (namely, MoMo strategy), with ratios of 50:50 and 70:30 for the wrongly predicted and correctly predicted samples, respectively. Additionally, we propose incorporating a balanced sample selection method, whereby the maximum training sample per class in an epoch is assigned to address the long-tail dataset problem. Our experimental results on a subset of the current largest plant dataset, PlantCLEF 2023, demonstrate an increase of 1%-2% in overall validation accuracy and a 2%-5% increase in tail class identification. By selectively focusing on more misclassified samples in training, at the same time, integrating a balanced sample selection achieves a significant boost in accuracy compared to traditional training methods. These findings emphasize the significance of adding more misclassified samples into training, encouraging researchers to rethink the sampling strategies before implementing more complex and robust network architectures and modules

How Transferable are Herbarium-Field Features in Few-Shot Plant Identification with Triplet Loss?

Due to the limited tropical plant field photos but increasing digitized herbarium specimens, cross-domain plant identification has been employed to investigate the use of herbarium specimens on automated plant identification. However, the domain shift between herbarium and field images makes identifying plant species across these two domains challenging. One of the recent papers shows the superiority of triplet loss in preserving similarities between the crossed domains. Nevertheless, the impact of data deficiency on the performance of this triplet loss model has yet to be studied in depth in this field. Specifically, the transferability of the model features trained from limited cross-domain data to target images in the field domain. Therefore, this paper investigates the robustness of cross-domain plant features learned using this triplet loss metric learning approach compared to the supervised classification approach under general and few-shot experimental settings. Detailed experiments show that the triplet loss metric approach outperformed the supervised classification approach in the few-shot setting and achieved comparable results in the general experimental setting. In addition, the feature dictionary generation schemes composed of various herbarium field feature combinations we proposed boost our models’ performance significantly compared to a single feature type dictionary strategy

Association of anticardiolipin, antiphosphatidylserine, anti-β2 glycoprotein I, and antiphosphatidylcholine autoantibodies with canine immune thrombocytopenia

β2GPI expression and identification. (PDF 159 kb

Fractionation and extraction of bio-oil for production of greener fuel and value-added chemicals : Recent advances and future prospects

Bio-oil is a highly valuable product derived from biomass pyrolysis which could be used in various downstream applications upon appropriate upgrading and refining. Extraction and fractionation are two promising methods to upgrade bio-oil by separating the complex mixture of bio-oil compounds into distinct fine chemicals and fractions enriched in certain classes of chemical compounds. In this review, various extraction techniques for bio-oil (organic solvent extraction, water extraction, supercritical fluid extraction, distillation, adsorption, chromatography, membrane, electrosorption and ionic liquid extraction), their associated features (extraction mechanisms involved, advantages and disadvantages), the characteristics of bio-oil extracts and their applications are presented and critically discussed. It was revealed that the most promising technique is via organic solvent extraction. Furthermore, the technological gaps and bottlenecks for each separation techniques are disclosed, as well as the overall challenges and future prospects of oil palm biomass-based bio-oil value chain. This review aims to provide key insights on bio-oil upgrading via extraction and fractionation, and a proposed way forward via technology integration in establishing a sustainable palm oil mill-based biorefinery

A high-resolution linkage map for comparative genome analysis and QTL fine mapping in Asian seabass, Lates calcarifer

10.1186/1471-2164-12-174BMC Genomics12-BGME

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049