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Individual humor tendencies or behaviors may change with age, but most studies have focused on school-aged children to college students and have rarely explored differences in humor style tendencies from adulthood to older age. The present study examines humor style differences among participants of different ages. This study hypothesizes that older adults are more likely to prefer positive and self-focused humor styles (self-enhancing humor style). A total of 407 participants of different ages and genders were recruited to investigate their humor style tendencies. The results show that men tend to prefer aggressive humor, while women tend to prefer affiliative humor. Interactions between age and humor styles were found. Further analyses show that adolescent to college-aged participants tend to use affiliative humor, adult participants tend to prefer both affiliative and self-enhancing humor, and older adults tend to use self-enhancing humor. In addition, individuals increasingly tend to prefer self-enhancing humor with age, and this tendency was strongest among the older adults, supporting our hypothesis. This study reveals not only significant gender differences but also age differences in humor style tendencies. Our findings clarify the relationship between humor and age and can serve as a basis for future research.

For a time-reversal symmetric system, the quantum spin Hall phase is assumed to be the same as the Z2 topological insulator phase in the existing literature. The spin Chern number Cs is supposed to yield the same topological classification as the Z2 invariant. Here, by investigating the topological electronic structures of monolayer α-phase group V elements, we uncover the presence of a topological phase in α-Sb, which can be characterized by a spin Chern number Cs=2, even though it is Z2 trivial. Although both being classified as trivial insulators by the existing topological classification schemes, we demonstrate the existence of a phase transition between α-As and Sb, which is induced by band inversions at two generic k points. In the absence of spin-orbit coupling (SOC), α-As is a trivial insulator, while α-Sb is a Dirac semimetal with four Dirac points (DPs) located away from the high-symmetry lines. Inclusion of the SOC gaps out the Dirac points and induces a nontrivial Berry curvature, endowing α-Sb with a high spin Chern number of Cs=2. We further show that monolayer α-Sb exhibits either a gapless band structure or a gapless spin spectrum on its edges as expected from topological considerations.

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fields’ practical use. In this paper, we propose a supervised THz deep learning computed tomography (THz DL-CT) framework based on time-domain information. THz DL-CT restores superior THz tomographic images of 3D objects by extracting features from spatio-temporal THz signals without any prior material information. Compared with conventional and machine learning based methods, THz DL-CT delivers at least 50.2%, and 52.6% superior in root mean square error (RMSE) and structural similarity index (SSIM), respectively. Additionally, we have experimentally demonstrated that the pretrained THz DL-CT model can generalize to reconstruct multi-material systems with no prerequisite information. THz CT through the DL data fusion approach provides a new pathway for non-invasive functional imaging in object investigation.

This study examines the influence of green creativity to the green new product performances. Creativity is a key source of organization’s competitive advantage (Barney, 1991) and increases the likelihood of new product success by providing effective product differentiation (Song, 2018). Building on the thesis of Natural Resource-based view (Hart, 1995), we study the impact of green creativity on the performances of green new products. This study also shows that family involvement plays a role in the green performances of family businesses. We pay particular attention to family firms because of two reasons. First, family businesses represent a significant proportion of the corporate sector in both developed and developing countries (Faccio and Lang 2002). Second, family firms have different behavioral patterns when reacting to stakeholders’ pressures (Huaang, Ding, and Kao, 2009; Sharma and Sharma, 2011) for better environmental management practices. This study surveyed 134 family-owned, high-tech manufacturers in Taiwan. The findings show that the green creativity is positively and significantly related to green new product performances. Our analytical results also show that family involvement moderates the relationship between green creativity and green new product performances.

The impact of demoralization among the general population has received little attention due to the lack of an appropriate measurement. Three studies involving 1,143 high school and undergraduate students in Taiwan were thus conducted to develop and validate a tool to assess demoralization. A pool of 50 items was first developed and administered to high school students. Exploratory factor analysis results supported a 5-factor solution with 15 items (Study 1). Study 2 compared the potential models using confirmatory factor analysis and found the 5-factor second-order model with 15 items the best fit model. The 15-item Mandarin version of Demoralization Scale (DS-M-15) was also found to have good internal consistency, test–retest reliability, and (concurrent and predictive) validity in a sample of undergraduate students (Study 3). Taken together, the converging findings show that the DS-M-15 is a promising tool for assessing demoralization among Chinese adolescents and emerging adults.

This paper presents a novel tensor-based compressive sensing model for terahertz single-pixel imaging system, which features extremely low computational complexity for the image reconstruction and facilitates the realization of highresolution terahertz imaging systems.

Weyl nodes and Fermi arcs in type-II Weyl semimetals (WSMs) have led to lots of exotic transport phenomena. Recently, ${\mathrm{Mo}}_{0.25}{\mathrm{W}}_{0.75}{\mathrm{Te}}_{2}$ has been established as a type-II WSM with Weyl points located near Fermi level, which offers an opportunity to study its intriguing band structure by electrical transport measurements. Here, by selecting a special sample with the thickness gradient across two- (2D) and three-dimensional (3D) regimes, we show strong evidence that ${\mathrm{Mo}}_{0.25}{\mathrm{W}}_{0.75}{\mathrm{Te}}_{2}$ is a type-II Weyl semimetal by observing the following two dimensionality-dependent transport features: (1) a chiral-anomaly-induced anisotropic magnetoconductivity enhancement, proportional to the square of in-plane magnetic field (${B}_{\mathrm{in}}^{2}$); and (2) an additional quantum oscillation with thickness-dependent phase shift. Our theoretical calculations show that the observed quantum oscillation originates from a Weyl-orbit-like scenario due to the unique band structure of ${\mathrm{Mo}}_{0.25}{\mathrm{W}}_{0.75}{\mathrm{Te}}_{2}$. The in situ dimensionality-tuned transport experiment offers an alternative strategy to search for type-II WSMs.

We presented a supervised deep learning method to decrease six-fold data acquisition time and to suppress terahertz image artifact levels based on sinogram from terahertz time-domain spectroscopy (THz- TDS). Our method delivered superior image quality than conventional bicubic interpolation method with 0.9612 and 0.0155 in mean square error (MSE) and structural similarity index (SSIM), respectively.

We presented an unsupervised method to separate mixtures of penetrating hyperspectral THz signals, using Lowner-John ellipsoid and THz time-domain spectroscopy. Its efficacy was demonstrated in the substance system composed of chemical pellets. Compared with contemporary THz spectral unmixing methods, we blindly extracted broadband spectral signatures of pure components with significantly higher accuracy of material identification without prior knowledge/modeling of the signature.

Terahertz imaging system has aroused great attentions in recent years due to its unique applications in security screening, industrial inspection and biomedical evaluation. Most conventional terahertz PCA imaging setup is based on raster scan method, hence, image acquisition time is severely limited by the speed of mechanical movement. Typically, image acquisition time of terahertz PCA tomography systems costs hours to days depending on the size of observed objects, which severely limits their practical feasibility to real-world applications. Here, we propose a terahertz least absolute shrinkage and selection operator (LASSO) compressed sensing (CS) tomography system to reduce more than one order of magnitude of data acquisition time. Our approach replaces slow-moving mechanical raster scanning method by the highspeed spatial modulation of terahertz radiation with designed patterns, resulting in a compact, fast and noise-reduced way to obtain terahertz 3D image dataset. Based on the measured dataset, modified Hadamard and LASSO algorithms are designed to reconstruct high-quality 3D image (voxel number: 128x128x128) in 120s at 10% compressed rate. The reconstructed LASSO 3D terahertz image offers a less than 0.002 mean squared error and 80% structural similarity index compared with the ground truth image. This paves the way toward real-time terahertz 3D imaging in near future, which opens the door for varies of exciting applications in non-destructive sensing, imaging and material inspection.

We present a supervised terahertz deep learning computed tomography (THz DL-CT) system, which offers superior image quality compared with conventional THz CT method evaluated by 3D printed objects. To the best of our knowledge, this is the first demonstration of a THz DL-CT system capable of visualizing hidden objects with multi-material systems.

We present supervised terahertz deep learning models for high-precision terahertz tomography. To investigate the performance of terahertz deep learning models, comprehensive characterization of kernel size in first convolution layers is further studied. By utilizing the length of beam diameter, the optimized kernel size can be designed to deliver much spatially accurate images, which achieves 2.5% on mean square error (MSE), exhibiting 46.8% improvement on MSE than other kernel sizes.

The age and risk level that warrants hepatocellular carcinoma (HCC) screening remains to be defined. To develop risk scores for stratifying average-risk population for mass HCC screening, we conducted a pooled analysis using data from three cohorts involving 12,377 Taiwanese adults 20-80 years of age. During 191,240.3 person-years of follow-up, 387 HCCs occurred. We derived risk scores from Cox's model in two thirds of participants and used another one third for model validation. Besides assessing discrimination and calibration, we performed decision curve analysis to translate findings into public health policy. A risk score according to age, sex, alanine aminotransferase, previous chronic liver disease, family history of HCC, and cumulative smoking had good discriminatory accuracy in both model derivation and validation sets (c-statistics for 3-, 5-, and 10-year risk prediction: 0.76-0.83). It also performed well across cohorts and diverse subgroups. Decision curve analyses revealed that use of the score in selecting persons for screening improved benefit at threshold probabilities of >2% 10-year risk, compared with current guidelines and a strategy of screening all hepatitis B carriers. Using 10-year risk 2% as a threshold for initiating screening, the screening age ranged from 20 to ≥60 years, depending on the tertile of risk scores and status of hepatitis B/C virus infection. Combining risk-score tertile levels and hepatitis virus status to stratify participants was more sensitive than current guidelines for HCC detection within 10 years (89.4% vs. 76.8%), especially for young-onset HCCs

The feature of central projection will result the pixels offset for UAV aerial photos. This problem can be solved by way of correcting relief displacement of DEM that was constructed by dense point cloud. During the image processing procedure, the creation of dense point cloud spent the most time. By cutting the image into blocks to reduce the creating time of high dense point cloud is the main issue of this paper. The coordinate difference between the whole area and the block cutting are another discussion. All in the whole area, 16 feature points were selected to be the check points. When image blocks were merging back, if the difference between two points is less than 1 pixel, the image-block cutting method will be considered workable. By cutting an image into blocks, it can be performed in parallel and distributed way. It can reduce the hardware requirement and make the difference of points matched. This is the contribution of the paper.

This study aimed to identify noninvasive protein markers capable of detecting the presence and prognosis of esophageal squamous-cell carcinoma (ESCC). Analyzing microarray expression data collected from 17-pair ESCC specimens, we identified one protein, matrix metalloproteinase-1 (MMP1), as a possibly useful marker. Plasma MMP1 was then measured by enzyme-linked immunosorbent assay (ELISA) in 210 ESCC patients and 197 healthy controls. ESCC patients had higher mean levels of MMP1 than controls (8.7 ± 7.5 vs. 6.7 ± 4.9 ng/mL, p 9.67 ng/mL had a 48% increase in the risk of ESCC death (adjusted hazard ratio = 1.48; 95% CI = 1.04–2.10). In conclusion, plasma MMP1 may serve as a noninvasive marker of detecting the presence and predicting the survival of ESCC.