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Background Mesenchymal stem cell (MSC)-derived exosomes have been recognized as new candidate agents for treating critical-sized bone defects; they promote angiogenesis and may be an alternative to cell therapy. In this study, we evaluated whether exosomes derived from bone marrow-derived MSCs (BMSCs) preconditioned with a low dose of dimethyloxaloylglycine (DMOG), DMOG-MSC-Exos, exert superior proangiogenic activity in bone regeneration and the underlying mechanisms involved. Methods To investigate the effects of these exosomes, scratch wound healing, cell proliferation, and tube formation assays were performed in human umbilical vein endothelial cells (HUVECs). To test the effects in vivo, a critical-sized calvarial defect rat model was established. Eight weeks after the procedure, histological/histomorphometrical analysis was performed to measure bone regeneration, and micro-computerized tomography was used to measure bone regeneration and neovascularization. Results DMOG-MSC-Exos activated the AKT/mTOR pathway to stimulate angiogenesis in HUVECs. This contributed to bone regeneration and angiogenesis in the critical-sized calvarial defect rat model in vivo. Conclusions Low doses of DMOG trigger exosomes to exert enhanced proangiogenic activity in cell-free therapeutic applications.

The proportion of pet keeping has increased significantly. According to the survey results of Business Next, the proportion of Taiwan families keeping pets was 70% in 2020. Among them, the total number of fish pets was close to 33% of the overall pet proportion. Therefore, aquarium pets have become indispensable companions for families. At present, many studies have discussed intelligent aquarium systems. Through image recognition based on visual sensors, we may be able to detect and interpret the physiological status of the fish according to their physiological appearance. In this way, it can help to notify the owner as soon as possible to treat the fish or isolate them individually, so as to avoid the spread of infection. However, most aquarium pets are kept in groups. Traditional image recognition technologies often fail to recognize each fish’s physiological states precisely because of fish swimming behaviors, such as grouping swimming, shading with each other, flipping over, and so on. In view of this, this paper tries to address such problems and then proposes a practical scheme, which includes three phases. Specifically, the first phase tries to enhance the image recognition model for small features based on the prioritizing rules, thus improving the instant recognition capability. Then, the second phase exploits a designed fish-ID tracking mechanism and analyzes the physiological state of the same fish-ID through coherent frames, which can avoid temporal misidentification. Finally, the third phase leverages a fish-ID correction mechanism, which can detect and correct their IDs periodically and dynamically to avoid tracking confusion, and thus potentially improve the recognition accuracy. According to the experiment results, it was verified that our scheme has better recognition performance. The best accuracy and correctness ratio can reach up to 94.9% and 92.67%, which are improved at least 8.41% and 26.95%, respectively, as compared with the existing schemes.

Due to the popularity of smart devices, traditional one-way teaching methods might not deeply attract school students’ attention, especially for the junior high school students, elementary school students, or even younger students, which is a critical issue for educators. Therefore, we develop an intelligent interactive education system, which leverages wearable devices (smart watches) to accurately capture hand gestures of school students and respond instantly to teachers so as to increase the interaction and attraction of school students in class. In addition, through multiple physical information of school students from the smart watch, it can find out the crux points of the learning process according to the deep data analysis. In this way, it can provide teachers to make instant adjustments and suggest school students to achieve multi-learning and innovative thinking. The system is mainly composed of three components: (1) smart interactive watch; (2) teacher-side smart application (App); and (3) cloud-based analysis system. Specifically, the smart interactive watch is responsible for detecting the physical information and interaction results of school students, and then giving feedback to the teachers. The teacher-side app will provide real-time learning suggestions to adjust the teaching pace to avoid learning disability. The cloud-based analysis system provides intelligent learning advices, academic performance prediction and anomaly learning detection. Through field trials, our system has been verified that can potentially enhance teaching and learning processes for both educators and school students.

Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments.

This study aimed to investigate consumers’ visual image evaluation of wrist wearables based on Kansei engineering. A total of 8 representative samples were screened from 99 samples using the multidimensional scaling (MDS) method. Five groups of adjectives were identified to allow participants to express their visual impressions of wrist wearable devices through a questionnaire survey and factor analysis. The evaluation of eight samples using the five groups of adjectives was analyzed utilizing the triangle fuzzy theory. The results showed a relatively different evaluation of the eight samples in the groups of “fashionable and individual” and “rational and decent”, but little distinction in the groups of “practical and durable”, “modern and smart” and “convenient and multiple”. Furthermore, wrist wearables with a shape close to a traditional watch dial (round), with a bezel and mechanical buttons (moderate complexity) and asymmetric forms received a higher evaluation. The acceptance of square- and elliptical-shaped wrist wearables was relatively low. Among the square- and rectangular-shaped wrist wearables, the greater the curvature of the chamfer, the higher the acceptance. Apparent contrast between the color of the screen and the casing had good acceptance. The influence of display size on consumer evaluations was relatively small. Similar results were obtained in the evaluation of preferences and willingness to purchase. The results of this study objectively and effectively reflect consumers’ evaluation and potential demand for the visual images of wrist wearables and provide a reference for designers and industry professionals.

Background Traditional Chinese medicine (TCM) has been hailed as a rich source of medicine, but many types of herbs and their functions still need to be rapidly discovered and elucidated. HerboChip, a target-based drug screening platform, is an array of different fractions deriving from herbal extracts. This study was designed to identify effective components from TCM that interact with vascular endothelial growth factor (VEGF) as a target using HerboChip. Methods Selected TCMs that are traditionally used as remedies for cancer prevention and wound healing were determined and extracted with 50% ethanol. Biotinylated-VEGF was hybridized with over 500 chips coated with different HPLC-separated fractions from TCM extracts and straptavidin-Cy5 was applied to identify plant extracts containing VEGF-binding fractions. Cytotoxicity of selected herbal extracts and their activities on VEGF-mediated angiogenic functions were evaluated. Results Over 500 chips were screened within a week, and ten positive hits were identified. The interaction of the identified herbal extracts with VEGF was confirmed in cultured endothelial cells. The identified herbs promoted or inhibited VEGF-mediated cell proliferation, migration and tube formation. Results from western blotting analysis demonstrated the identified herbal extracts significantly affected VEGF-triggered phosphorylations of eNOS, Akt and Erk. Five TCMs demonstrated potentiating activities on the VEGF response and five TCMs revealed suppressive activities. Conclusions The current results demonstrated the applicability of the HerboChip platform and systematically elucidated the activity of selected TCMs on angiogenesis and its related signal transduction mechanisms.

Coordinated Multi-Point (CoMP) is an important technique in B4G/5G networks. With CoMP, multiple base stations can be clustered to compose a cooperating set to improve system throughput, especially for the users in cell edges. Existed studies have discussed how to mitigate overloading scenarios and enhance system throughput with CoMP statically. However, static cooperation fixes the set size and neglects the fast-changing of B4G/5G networks. Thus, this paper provides a full study of off-peak hours and overloading scenarios. During off-peak hours, we propose to reduce BSs’ transmission power and use the free radio resource to save energy while guaranteeing users’ QoS. In addition, if large-scale activities happen with crowds gathering or in peak hours, we dynamically compose the cooperating set based on instant traffic requests to adjust base stations’ BSs’ transmission power; thus, the system will efficiently offload the traffic to the member cells which have available radio resources in the cooperating set. Experimental results show that the proposed schemes enhance system throughput, radio resource utilization, and energy efficiency, compared to other existing schemes.

For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB-IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB-IoT provides discontinuous reception operation to save devices’ energy. But, how to further reduce the transmission energy while ensuring the required ultra-reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient, ultra-reliable, and low-complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

In 5G mobile communications, Cloud-RAN (C-RAN) (Camps-Mur et al. in IEEE Commun Mag 57:99–105, 2019) is proposed to provide broadband services. It separates computation entities, i.e., baseband units (BBUs), from base stations and puts all BBUs in a centralized cloud. Existing researches have investigated how to enhance user equipments (UEs) to receive data from multiple collaborative cells by leveraging the dynamic point selection(DPS) technology. Collaborative transmission may cause higher energy consumption for UEs. Although 3GPP has defined the discontinuous reception (DRX) mechanism by regulating UEs to turn off their radio interfaces in a periodical manner, how to well coordinate DRX with DPS under the C-RAN architecture is still left as an open issue. This paper is the first one addressing this resource optimization problem in C-RAN with DPS, which asks how to optimize DRX parameters of UEs by considering their quality-of-service (QoS). We prove this problem to be NP-complete, and then propose two effective and efficient DPS solutions, called serving-ratio(SR) scheme and cost-aware (CA) scheme. SR serves UEs based on a special ‘serving ratio’ to ensure UEs receiving continuous subframes, especially for those in cell intersections. On the other hand, CA exploits the strategies of minimal scheduling costs to balance energy and throughput efficiency in a perspective way. Extensive simulation results validate that our schemes can successfully achieve higher system throughput, provide better resource utilization, and serve more UEs while guaranteeing their QoS and saving considerable energy as compared to existing schemes.

In recent years, several cashier-less convenience stores have appeared, including Amazon’s. A store without cashiers will become a trend in the near future. To substitute the employee in traditional stores, this research proposes and designs an autonomous shopping cart robot to guide customers’ purchase according to the requested shopping list to enhance their shopping experience. The core techniques of the system are the autonomous driving robot, the traffic control center for robots and the dynamic route planning algorithm. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving via image recognition, April tag identification and driving direction guidance from the path planning and traffic control services. This enables the robot to lead customers to find their commodities following the preplanned route. In conjunction with the vocal service, the robot can notify the customer when arriving at each commodity, he or she plans to buy. We also design a light APP for customers to easily set up and manage their shopping list, call for the robotic shopping cart’s help, and interact with the shopping cart robot. To enhance the shopping experience of customers, we design the dynamic route planning genetic algorithm to dynamically plan the shopping route according to the customer’s request and the traffic condition. Experiments show that our genetic algorithm can provide the most stable performance and always get efficient shopping route planning in a limited time compared to other methods.