Miracle-dz/UniFAR-FTUs huggingface dataset:
Design for Additive Manufacturing in the Cloud Platform

Additive manufacturing (AM) has experienced a phenomenal expansion in recent years and new technologies and materials rapidly emerge in the market. Design for Additive Manufacturing (DfAM) becomes more and more important to take full advantage of the capabilities provided by AM. However, most people still have limited knowledge to make informed decisions in the design stage. Therefore, an interactive DfAM system in the cloud platform is proposed to enable people sharing the knowledge in this field and guide the designers to utilize AM efficiently. There are two major modules in the system, decision support module and knowledge management module. A case study is presented to illustrate how this system can help the designers understand the capabilities of AM processes and make rational decisions.
An external focusing strategy was beneficial in experienced children but not in novices: The effect of external focus, internal focus, and holistic attention strategies

External focus (attention directed to an intended effect) has been shown to improve motor performance compared to internal focus (attention directed to body movements). Recently, holistic attention (attention directed to the overall feeling) has been discussed as an effective alternative. We hypothesized that a less specific cue (e.g. holistic attention) may be more effective than an external focus in experienced individuals. Therefore, the present study examined the effect of external focus, internal focus, and holistic attention on 14 experienced children-athletes (Mage, 14.35 ± 1.98 years; Mheight, 171.28 ± 9.53 cm; Mweight, 58.28 ± 10.28 kg) and 14 novice children (Mage, 14.21 ± 1.92 years; Mheight, 170.92 ± 12.40 cm; Mweight, 62.14 ± 15.62 kg). Participants completed a 16-meter front crawl swimming task with assigned instructions for three trials per condition. The results showed main effects of skill level (p < 0.001), condition (p = 0.006), and the skill level/condition interaction (p = 0.003). Post hoc tests revealed that external focus was superior to internal focus (p < 0.001), with no difference between holistic attention and internal focus (p = 0.158) or holistic attention and external focus (p = 0.05) in the experienced athletes, while none of the conditions were different in novices. The benefits of attentional focus may be influenced by the level of experience.
Human fetal mesenchymal stem cells secretome promotes scarless diabetic wound healing through heat‐shock protein family

Abstract The high mortality rate of patients with diabetic foot ulcers is urging the appearance of an effective biomedical drug. Senescence is one of the major reasons of aging‐induced decline in the diabetic wound. Our previous studies have demonstrated the anti‐senescence effect of secretomes derived from human fetal mesenchymal stem cells (hfMSC). The present study tends to explore the potential role of hfMSC secretome (HFS) in wound healing through anti‐aging. Meanwhile, we try to overcome several obstacles in the clinical application of stem cell secretome. A verticle bioreactor and microcarriers are employed to expand hfMSC and produce the HFS on a large scale. The HFS was then subjected to lyophilization (L‐HFS). The PLGA (poly lactic‐co‐glycolic acid) particles were used to encapsulate and protect L‐HFS from degradation in the streptozotocin (STZ)‐induced diabetic rat model. Results showed that HFS‐PLGA significantly enhanced wound healing by promoting vascularization and inhibiting inflammation in the skin wound bed. We further analyzed the contents of HFS. Isobaric tag for relative and absolute quantitation (ITRAQ) and label‐free methods were used to identify peptides in the secretome. Bioinformatics analysis indicated that exosome production‐related singling pathways and heat‐shock protein family could be used as bio‐functional markers and quality control for stem cell secretome production.
Diagnostic methods for Plasmodium knowlesi: performance, limitations and recommendations

Malaria is a life-threatening disease which causes an economic and public health burden in endemic regions. Plasmodium knowlesi is a zoonotic malaria caused by the bite of P. knowlesi-infected female Anopheles mosquitoes. The disease poses risks of complications owing to the short replication cycle of the parasite. Sensitive and specific diagnostic methods are needed to detect the presence of P. knowlesi infection in knowlesi-endemic regions. This paper reviews and compares the performance of various diagnostic techniques as well as highlights the limitations and challenges of the current diagnostic tools for P. knowlesi. Recommendations for further improvement of the P. knowlesi diagnostic tools are also included.
Single Cell RNA Sequencing Reveals the Pathogenesis of Aortic Dissection Caused by Hypertension and Marfan Syndrome

Aortic dissection (AD) is mainly caused by hypertension and Marfan syndrome. However, it is unclear whether the cellular components and functions are different between the two causes. A total of 11 aortic samples were collected for single-cell RNA analysis and 20 clusters were disclosed, including VSMCs, fibroblasts, endothelial cells, T cells, B cells, monocytes, macrophages, mast cells, and neutrophils components. There were differences in cell subclusters and function between hypertension and Marfan patients. The cells also had different differentiations. Cellchat identified cell ligand–receptor interactions that were associated with hypertension and Marfan-induced AD involving SMC, fibroblast, mo-macrophages, and T-cell subsets. This study revealed the heterogeneity of cellular components and gene changes in hypertension and Marfan-induced AD. Through functional analysis and the changes in intercellular communication, the possible mechanisms of different causes of AD were explained from a new perspective, so we can better understand the occurrence and development of diseases.
GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.
Learning experiences in a giant interactive environment: insights from The Cube

Abstract In November 2012, Queensland University of Technology in Australia launched a giant interactive learning environment known as The Cube. This article reports a phenomenographic investigation into visitors’ different experiences of learning in The Cube. At present very little is known about people’s learning experience in spaces featuring large interactive screens. We observed many visitors to The Cube and interviewed 26 people. Our analysis identified critical variation across the visitors’ experience of learning in The Cube. The findings are discussed as the learning strategy (in terms of absorption, exploration, isolation and collaboration) and the content learned (in terms of technology, skills and topics). Other findings presented here are dimensions of the learning strategy and the content learned, with differing perspectives on each dimension. These outcomes provide early insights into the potential of giant interactive environments to enhance learning approaches and guide the design of innovative learning spaces in higher education.
Principal Component Analysis and Related Methods for Investigating the Dynamics of Biological Macromolecules

Principal component analysis (PCA) is used to reduce the dimensionalities of high-dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high-dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods and their applications for investigating protein dynamics.
Technical vocational education and training research center as a center of excellence in Universitas Pendidikan Indonesia

This research aims to provide an overview of the Center for Excellence in Higher Education Science and Technology (PUI-PT) in Indonesia, one of which is the Technical Vocational Education and Training Research Center (TVET-RC) belonging to the Universitas Pendidikan Indonesia (UPI). In this research, UPI became the locus and focus of the research center of excellence in the form of TVET-RC, as part of PUI-PT in Indonesia. The results showed that TVET-RC UPI as the leading part of PUI-PT in Indonesia was closely related to the findings in the form of four things: (1) Vocational Teacher Professional Education; (2) Redesign of the vocational expertise spectrum; (3) Mixed learning preparation guide; (4) HR capacity and governance of TVET-RC. The four findings are at the same time the development of TVET-RC at UPI which is able to contribute to the improvement of good and productive human resources.
The RVP Method—From Real Ab-Initio Calculations to Complex Energies and Transition Dipoles

The purpose of this review is to describe the rationale behind the RVP (resonance via Padé) approach for calculating energies and widths of resonances, while emphasizing a solid mathematical ground. The method takes real input data from stabilization graphs, where quasi-discrete continuum energy levels are plotted as a function of a parameter, which gradually makes the employed basis functions more diffuse. Thus, input data is obtained from standard quantum chemistry packages, which are routinely used for calculating molecular bound electronic states. The method simultaneously provides the resonance positions (energies) and widths (decay rates) via analytical continuations of real input data into the complex plane (via the Padé approximant). RVP holds for isolated resonances (in which the energy-gap between resonance states is smaller than their decay rates). We focus also on the ability to use an open-source “black-box” code to calculate the resonance positions and widths as well as other complex electronic properties, such as transition dipoles.
The Relationship Among Expectancy Belief, Course Satisfaction, Learning Effectiveness, and Continuance Intention in Online Courses of Vocational-Technical Teachers College Students

Since the beginning of year 2020, when the whole world were undergoing the COVID-19 epidemic, all schools were lockout and classes were suspended until e-learning was rushed to be online for students to continue their learning, including the students in China. Although many studies had discussed the effectiveness of online learning from many different points of views, it still remained many uncertainties on the qualities of distance learning, especially when under the circumstances of rush and students’ involuntary learning. This manuscript attempted to determine whether students’ learning expectation reach the qualities of distance learning or not. In this manuscript, the snowball sampling method was adopted to have 356 students who studied at vocational-technical teachers’ college in China. Based on the expectation confirmation theory and its model, five hypotheses were proposed to construct a research model to determine relationship between student’s expectancy belief, course satisfaction, learning effectiveness, and continuous learning intention when facing the dilemma of classes suspended but learning continues, learning online during this ongoing pandemic. The results of this study showed that: (1) The expectancy value belief were positively related with theoretical course satisfaction, but negatively related with practical course satisfaction; (2) Theoretical course satisfaction and practical course satisfaction were positively related with learning effectiveness; and (3) Learning effectiveness was positively related with continuance to learn. In addition, three factors influencing the most on the qualities of theoretical course were environmental interference such as surrounding noises, poor internet connection, and poor absorption of learning contents, respectively, while three factors influencing the most on the qualities of practical course were inaccessible to practice, poor absorption of learning content, and lack of practical materials, respectively. Based on the results analyzed, this study suggested schools and teachers focused on how to improve the qualities and to reduce or prevent any disturbances to the class given to fulfill students’ class expectation first and then to ensure students’ learning effectiveness and intention to continuous learning.
COAP: Compositional Articulated Occupancy of People Supplementary Material

Sampling Strategy in the Local Shape Decomposition (Sec. 4.1). Each local articulated part in Figure 2 is temporarily represented as a point cloud by sampling points on the mesh surface. Each point is sampled by first selecting a mesh face with probability proportional to the face area and then randomly sampling barycentric coordinates in order to calculate a point on the selected face. To further balance the overlap among local articulated body parts, the kth point cloud allocates one half of its capacity to encode the central component corresponding to bone Gk, whereas the other half covers the whole local articulated body part region. This design guides the neural networks to properly learn localized occupancy fields, where the largest part is reserved to represent the core bone component, while fewer samples for the non-central parts encourage smooth interpolation between connected occupancy fields. In all experiments, we used a total of 1000 samples per body part which are encoded as local body codes zk with 128 dimensions.
Body Temperature Patterns and Energy Balance Hormones in Free-Living Thirteen-Lined Ground Squirrels (Ictidomys tridecemlineatus) from Different Latitudes

This article examines hormone concentrations and body temperature (Tb) patterns of free-living thirteen-lined ground squirrels (TLGSs) across the majority of their latitudinal range in the United States (from Texas to Minnesota). Free-living TLGSs (n=40) were implanted with Tbdata loggers in 2019 before they entered hibernation. Three adult female TLGSs, one each from Oklahoma (low latitude), Iowa (middle latitude), and Minnesota (high latitude), were recaptured in 2020 after the hibernation season. Although this provides an n of 1 for each location and therefore no statistically supported conclusions can be drawn, the hibernation season was longest in the animal from the highest latitude with coldest winter soil temperatures (Minnesota) and shortest in the animal retrapped at the lowest latitude (Oklahoma). Torpor bouts were generally longer when soil temperatures were lower. The Iowa and Minnesota squirrels had a prolonged period of short torpor bouts with Tb near 20°C at the beginning of the hibernation season. Concentrations of the orexigenic hormone ghrelin and the sex hormones estradiol and testosterone were also compared in populations from different latitudes. In general, Minnesota males had higher testosterone than males from other populations, possibly due to a later breeding season relative to other squirrel populations. Animals trapped in early summer had significantly lower concentrations of ghrelin than those captured in midsummer, potentially driving the fat-storing period before the hibernation season. Together, these results suggest latitudinal variation in physiological regulation of circannual rhythms.
Energy-Constrained UAV Flight Scheduling for IoT Data Collection With 60 GHz Communication

In recent years, the data from Internet of Things (IoT) devices is growing at a rapid pace, and the data collection issues have attracted more and more attention. Distinct from existing solutions which usually adopted traditional wireless technologies achieving low-bandwidth data connections towards IoT devices, this paper adopts the 60 GHz communication technology which is with increasing maturity, providing high-bandwidth wireless transmission for data-intensive IoT devices, such as high-definition (HD) cameras. However, 60 GHz links are subject to line-of-sight short communication range, therefore, we propose to use unmanned aerial vehicles (UAVs) to fly over data-intensive IoT devices and achieve short-range high-throughput 60 GHz transmission in this paper. Moreover, for a set of HD cameras deployed in the linear scenario, multiple UAV flights are assigned to collect data by 60 GHz communication. To this end, we investigate the UAV flight scheduling (UFS) problem which aims to minimize the number of UAV flights while satisfying the data requirements of all ground cameras (GCs) with limitations of UAV’s energy and data storage. We prove that the UFS problem is NP-hard and design efficient algorithms with constant theoretical approximation ratios. Specifically, we first study a special case of the UFS problem where all the cameras are on the same direction of the UAV ground station, and propose two algorithms NF_SUFS and FF_SUFS, whose approximation ratios are both proved to be 2 by theoretical analysis. Then, we extend the algorithms to a more general case with the cameras on both directions of the ground station along the road, and put forward the FF_UFS algorithm that achieves an approximation ratio of 3. Finally, we conduct experiments to validate the effectiveness and efficiency of our algorithms.
Impact of Agile Learning on Innovative Behavior: A Moderated Mediation Model of Employee Engagement and Perceived Organizational Support

This study analyzed learning agility, employee engagement, perceived organizational support (POS), and innovative behavior related to the development of innovative environment and the mental and psychological health of employees. A substantial body of research has examined the antecedents of innovative behavior of employees in their work environment, but our current understanding of how learning and motivational aspects of employees synthetically influence the innovative behavior remains incomplete. To address this gap, we developed and tested a moderated mediation model of the relationship between learning agility and employee engagement, POS, and innovative behavior. Following the job-demand resource model, componential theory, and social exchange theory, our postulated model predicted that the mediating effect of employee engagement on the relationship between learning agility and innovative behavior would be moderated by POS. The result of the analysis of the data on 331 corporate employees in South Korea supported this model. Specifically, learning agility was related to innovative behavior, while employee engagement mediated the relationship between learning agility and innovative behavior; POS strengthened the positive effect of learning agility on innovative behavior via employee engagement. We also discuss the implications of the results, future direction, and limitations of this study based on these findings.
Characteristics of Jetters and Little Boxes: An Extensibility Study Using the Neighborhood Connectivity Survey

Individuals connect to sets of places through travel, migration, telecommunications, and social interactions. This set of multiplex network connections comprises an individual’s “extensibility,” a human geography term that qualifies one’s geographic reach as locally‐focused or globally extensible. Here we ask: Are there clear signals of global vs. local extensibility? If so, what demographic and social life factors correlate with each type of pattern? To answer these questions, we use data from the Neighborhood Connectivity Survey conducted in Akron, Ohio, State College, Pennsylvania, and Philadelphia, Pennsylvania (global sample N = 950; in model n = 903). Based on the location of a variety of connections (travel, phone call patterns, locations of family, migration, etc.), we found that individuals fell into one of four different typologies: (a) hyperlocal, (b) metropolitan, (c) mixed‐many, and (d) regional‐few. We tested whether individuals in each typology had different levels of local social support and different sociodemographic characteristics. We found that respondents who are white, married, and have higher educational attainment are significantly associated with more connections to a wider variety of places (more global connections), while respondents who are Black/African American, single, and with a high school level educational attainment (or lower) have more local social and spatial ties. Accordingly, the “urban poor” may be limited in their ability to interact with a variety of places (yielding a wide set of geographic experiences and influences), suggesting that wide extensibility may be a mark of privileged circumstances and heightened agency.