77 research outputs found
Telehealth technology: Potentials, challenges and research directions for developing countries
Telehealth has been developed and successfully applied in clinical practices, gained a strong interest and demonstrated its usefulness for medical diagnosis, treatments and rehabilitation worldwide. The advent of high speed communication technology and complex signal processing techniques, and recent advancements in cloud and cognitive computing, has created a new wave of opportunities for delivering remote healthcare applications and services, where the cost-effective diagnosis and treatment solutions as well as healthcare services are important and need to be deployed widely. Nevertheless, there is still a significant challenge in fully adopting this technology due to asymmetry among the healthcare centers, hospitals and the user-ends, especially in developing countries. This paper provides an overview of the telehealth, then to addresses the possible telehealth technologies and applications that could be applied to improve the healthcare service performance, with the focus on the developing countries. The incorporation of different technologies in telehealth including, Internet of Things (IoT), cloud and cognitive computing, medical image processing and effective encoding is introduced and discussed. Finally, the possible research directions, challenges for the efficient telehealth, and potential research and technology collaborations are outlined
Inverse kinematic control algorithm for a welding robot - positioner system to trace a 3D complex curve
The welding robots equipped with rotary positioners have been widely used in several manufacturing industries. However, for welding a 3D complex weld seam, a great deal of points should be created to ensure the weld path smooth. This is a boring job and is a great challenge - rotary positioner system since the robot and the positioner must move simultaneously at the same time. Therefore, in this article, a new inverse kinematics solution is proposed to generate the movement codes for a six DOFs welding robot incorporated with a rotary positioner. In the algorithm, the kinematic error is minimized, and the actual welding error is controlled so that it is always less than an allowable limit. It has shown that the proposed algorithm is useful in developing an offline CAD-based programming tool for robots when welding complex 3D paths. The use of the algorithm increases the accuracy of the end-effector positioning and orientation, and reduces the time for teaching a welding robot - positioner system. Simulation scenarios demonstrate the potency of the suggested method
Quantum ChemistryāMachine Learning Approach for Predicting Properties of Lewis AcidāLewis Base Adducts
Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics
Effects of salinity and alkalinity on growth and survival of all-male giant freshwater prawn (Macrobrachium rosenbergii De Man, 1879) juveniles
All-male giant freshwater prawns (AMGFPs) have been a popular crop cultivated in the Mekong Delta, Vietnam, due to their proven production efficiency compared to all-female or mixed-sex prawn cultures. However, the crucial water quality factors impacting AMGFP aquaculture efficiency have yet to be elaborately investigated. Two separate experiments were randomly arranged with three replicates to evaluate the effects of salinity or alkalinity on the growth and survival of AMGFP juveniles during the grow-out period. The results show that the prawn survival rate in the salinity range of 0ā15ā° varied from 66.1 to 74.8ļ¼
and in a salinity range of 0ā5ā° was relatively low compared to the range of 10-15ā°; however, the difference was not significant among salinities after 90 days of culture (p > 0.05). All the prawn growth performance parameters significantly decreased with increasing salinities of 0, 5, 10, and 15ā° after 30, 60, and 90 days of culture (p 0.05), and both were significantly higher than those at salinities of 10 and 15ā° (p < 0.05) after 90 days of culture. In addition, the survival rate reached 82.5ā84.4ļ¼
and did not significantly differ among alkalinities of 80, 100, 120, 140, and 160 mgCaCO3 Lā1. However, the growth performance parameters and yield of AMGFPs at an alkalinity of 160 mg Lā1 were significantly higher than those at lower alkalinities (80, 100, 120, and 140 mg CaCO3 Lā1) after 90 days of culture. Therefore, it is recommended that a salinity range of 0ā5ā° and alkalinity of 160 mgCaCO3 Lā1 is optimal for the growth-out culture of AMGFP juveniles
TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval
3D object retrieval is an important yet challenging task, which has drawn
more and more attention in recent years. While existing approaches have made
strides in addressing this issue, they are often limited to restricted settings
such as image and sketch queries, which are often unfriendly interactions for
common users. In order to overcome these limitations, this paper presents a
novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D
animal models. Unlike previous SHREC challenge tracks, the proposed task is
considerably more challenging, requiring participants to develop innovative
approaches to tackle the problem of text-based retrieval. Despite the increased
difficulty, we believe that this task has the potential to drive useful
applications in practice and facilitate more intuitive interactions with 3D
objects. Five groups participated in our competition, submitting a total of 114
runs. While the results obtained in our competition are satisfactory, we note
that the challenges presented by this task are far from being fully solved. As
such, we provide insights into potential areas for future research and
improvements. We believe that we can help push the boundaries of 3D object
retrieval and facilitate more user-friendly interactions via vision-language
technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573
Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies
Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cellsā (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivoāderived senescence signature (SenSig) using a foreign body responseādriven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and ācartilage-likeā fibroblasts as senescent and defined cell typeāspecific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34āCSF1RāTGFĪ²R signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p
Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies
Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cellsā (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivoāderived senescence signature (SenSig) using a foreign body responseādriven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and ācartilage-likeā fibroblasts as senescent and defined cell typeāspecific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34āCSF1RāTGFĪ²R signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p
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