Coming Out, Going Home

Despite the historical centrality of Western cities as sites of queer cultural settlement, larger global economic and political forces have vociferously shaped, dispersed, and altered dreams of mobility for gay Taiwanese millennials in the age of globalization. While Taiwanese gay millennials follow a seemingly universal “rural-to-urban,” “East-to-West” movement trajectory, this study also explicates local nuanced ramifications running against the common trend. Drawn upon five-year ethnographic studies in Taiwan, this study examines how parents could to some extent conform to societal pressures by co-creating a life narrative to the society. Parents/family appear to contribute to how participants' decision on spatial movement but gay male millennials with supportive parents are eventually “going home.” However, the concept of home is configured by multiple economic and social forces involving (1) the optimal distance with the biological family and (2) the proper performances of consumption policed and imposed by the gay community in the neoliberal Taiwanese society.

Optimization Design on a High-performance Magnetic Shielding Barrel for Atomic Magnetometer Measurement Application

The ultra-high-precision measurement of the atomic magnetometer is largely restricted by the size of its working magnetic field. In order to reduce the residual magnetic field as much as possible, this article carried out the research on the methods to improve the shielding performance. Firstly, the axial shielding factor that limits the shielding performance of the magnetic shielding barrel was derived with various parameters including the radius, length, thickness, number of layers, distance between adjacent layers, etc. of the magnetic shielding barrel. Secondly, simulation was carried out to verify the correctness of the formula. Simulation shows that the shielding performance of the magnetic shielding barrel decreases with the size of magnetic shielding barrel increase. Besides, with the increase of the distance between two adjacent spacing layers, the shielding performance first increases rapidly and then gradually decreases, indicating that the optimal distance between adjacent layers is 9mm. Especially, the performance of the magnetic shielding barrel improves significantly as the layer thickness and number of layers increase. Experimental results show that the internal remanence of the three-layer magnetic shielding barrel is less than 1nT, and the available axial length of homogeneity range is greater than 200mm.

Minimizing uncertainty in complete automation of Douglas-Peucker Algorithm for geospatial mapping

It is qualitatively evident that the greater the map scale change, the greater the optimal distance threshold of the Douglas-Peucker Algorithm, which is used in polyline simplification. However, no specific quantitative relationships between them are known by far, causing uncertainties in complete automation of the algorithm. To fill this gap, the current paper constructs quantitative relationships based on the spatial similarity theories of polylines. A quantitative spatial similarity relationship model was proposed and evaluated by setting two groups of control experiments and taking <C, T> as coordinates. In order to realize the automatic generalization of the polyline, we verified whether these quantitative relationships could be fitted using the same function with the same coefficients. The experiments revealed that the unary quadratic function is the best, whether the polylines were derived from different or the same geographical feature area(s). The results also show that using the same optimal distance threshold is unreasonable to simplify all polylines from different geographical feature areas. On the other hand, the same geographical feature area polylines could be simplified using the same optimal distance threshold. The uncertainties were assessed by evaluating the automated generalization results for position and geometric accuracy perspectives using polylines from the same geographic feature areas. It is demonstrated that in addition to maintaining the geographical features, the proposed model maintains the shape characteristics of polylines. Limiting the uncertainties would support the realization of completely automatic generalization of polylines and the construction of vector map geodatabases.

An Efficient Data-Balancing Cyber-Physical System Paradigm for Quality-of-Service (QoS) Provision over Fog Computing

Cyber-physical systems (CPSs) have greatly contributed to many applications. A CPS is capable of integrating physical and computational capabilities to interact with individuals through various new modalities. However, there is a need for such a paradigm to focus on the human central nervous system to provide faster data access. This paper introduces the CPS paradigm that consists of CPS enabled human brain monitoring (CPS-HBM) and efficient data-balancing for CPS (EDB-CPS). The CPS-HBM provides architectural support to make an efficient and secure transfer and storage of the sensed data over fog cloud computing. The CPS-HBM consists of four components: physical domain and data processing (PDDP), brain sensor network (BSN), Service-oriented architecture (SOA), and data management domain (DMD). The EDB-CPS module aims to balance data flow for obtaining better throughput and lower hop-to-hop delay. The EDB-CPS accomplishes the goal by employing three processes: A node advertisement (NA), A node selection and recruitment (NSR), and optimal distance determination with mid-point (ODDMP). The processes of the EDB-CPS are performed on the PDDP of the CPS-HBM module. Thus, to determine the validity of EDB-CPS, the paradigm was programmed with C++ and implemented on a network simulator-3 (NS3). Finally, the performance of the proposed EDB-CPS was compared with state-of-the-art methods in terms of hop-to-hop delay and throughput. The proposed EDB-CPS produced better throughput between 443.2–445.2 KB/s and 0.05–0.078 ms hop-to-hop delay.

Building Plane Segmentation Based on Point Clouds

Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane’s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage’s (pantry’s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage’s and pantry’s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes.

The Psychosocial Adjustment of Kidney Recipients in Canada's Kidney Paired Donation Program

Introduction: Kidney paired donation programs have been implemented globally. The involvement of at least 2 donors in these programs might exacerbate recipients’ debt of gratitude and guilt, worries about the donor's health, and worries about graft failure documented by previous studies. However, there is an absence of research on the psychosocial implications of kidney paired donation. This study aimed to provide an in-depth examination of recipients’ experience of kidney paired donation, with a focus on psychosocial adjustment. Methods/Approach: Individual interviews were conducted with 8 recipients who received a transplant through Canada's Kidney Paired Donation program. Data was analyzed using Interpretative Phenomenological Analysis. Findings: Four themes emerged: (a) an emotionally charged relationship with the known donor, (b) optimal distance regulation in the relationship with the anonymous donor, (c) kidney paired donation as a series of ups and downs, and (d) multilayered gratitude. Discussion: Findings are considered in relation to extant literature. Issues relevant to the transplant community's clinical and research efforts to provide kidney recipients responsive care are discussed.

An approach to partial occlusion using deep metric learning

<span>The human face can be used as an identification and authentication tool in biometric systems. Face recognition in forensics is a challenging task due to the presence of partial occlusion features like wearing a hat, sunglasses, scarf, and beard. In forensics, criminal identification having partial occlusion features is the most difficult task to perform. In this paper, a combination of the histogram of gradients (HOG) with Euclidean distance is proposed. Deep metric learning is the process of measuring the similarity between the samples using optimal distance metrics for learning tasks. In the proposed system, a deep metric learning technique like HOG is used to generate a 128d real feature vector. Euclidean distance is then applied between the feature vectors and a tolerance threshold is set to decide whether it is a match or mismatch. Experiments are carried out on disguised faces in the wild (DFW) dataset collected from IIIT Delhi which consists of 1000 subjects in which 600 subjects were used for testing and the remaining 400 subjects were used for training purposes. The proposed system provides a recognition accuracy of 89.8% and it outperforms compared with other existing methods.</span>

Reconstruction of Lamb Wave Dispersion Curves in Different Objects Using Signals Measured at Two Different Distances

The possibilities of an effective method of two adjacent signals are investigated for the evaluation of Lamb waves phase velocity dispersion in objects of different types, namely polyvinyl chloride (PVC) film and wind turbine blade (WTB). A new algorithm based on peaks of spectrum magnitude is presented and used for the comparison of the results. To use the presented method, the wavelength-dependent parameter is proposed to determine the optimal distance range, which is necessary in selecting two signals for analysis. It is determined that, in the range of 0.17–0.5 wavelength where δcph is not higher than 5%, it is appropriate to use in the case of an A0 mode in PVC film sample. The smallest error of 1.2%, in the distance greater than 1.5 wavelengths, is obtained in the case of the S0 mode. Using the method of two signals analysis for PVC sample, the phase velocity dispersion curve of the A0 mode is reconstructed using selected distances x1 = 70 mm and x2 = 70.5 mm between two spatial positions of a receiving transducer with a mean relative error δcph=2.8%, and for S0 mode, x1 = 61 mm and x2 = 79.7 mm with δcph=0.99%. In the case of the WTB sample, the range of 0.1–0.39 wavelength, where δcph is not higher than 3%, is determined as the optimal distance range between two adjacent signals. The phase velocity dispersion curve of the A0 mode is reconstructed in two frequency ranges: first, using selected distances x1 = 225 mm and x2 = 231 mm with mean relative error δcph=0.3%; and second, x1 = 225 mm and x2 = 237 mm with δcph=1.3%.

Large-scale identification of RBP-RNA interactions by RAPseq refines essentials of post-transcriptional gene regulation

Over the past decade, thousands of putative human RNA binding proteins (RBPs) have been identified and increased the demand for specifying RNA binding capacities. Here, we developed RNA affinity purification followed by sequencing (RAPseq) that enables in vitro large-scale profiling of RBP binding to native RNAs. First, by employing RAPseq, we found that vertebrate HURs recognize a conserved RNA binding motif and bind predominantly to introns in zebrafish compared to 3'UTRs in human RNAs. Second, our dual RBP assays (co-RAPseq) uncovered cooperative RNA binding of HUR and PTBP1 within an optimal distance of 27 nucleotides. Third, we developed T7-RAPseq to discern m6A-dependent and -independent RNA binding sites of YTHDF1. Fourth, RAPseq of 26 novel non-canonical RBPs revealed specialized moonlighting interactions. Last, five pathological IGF2BP family variants exhibited different RNA binding patterns. Overall, our simple, scalable and versatile method enables to fast-forward RBP-related questions.

Using high refractive index nanoparticles to inhibition of spontaneous emission

The article discusses the issues of inhibition of spontaneous emission of molecules by using silicon spherical nanoparticles and dimers made from them. It is shown that at different wavelengths of the visible spectral range, the value of the total spontaneous transitions rate in a molecule located at an optimal distance with respect to the structure with silicon nanospheres and at an optimal size of the structure can be up to 5–10 times lower than the transition rate in the case when the nanoparticles are absent.