Explore the vast range of titles available.

Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.

Driven by ongoing migration for Industry 4.0, the increasing adoption of artificial intelligence, big data analytics, cloud computing, Internet of Things, and robotics have empowered smart manufacturing and digital transformation. However, increasing applications of machine learning and data science (DS) techniques present a range of procedural issues including those that involved in data, assumptions, methodologies, and applicable conditions. Each of these issues may increase difficulties for implementation in practice, especially associated with the manufacturing characteristics and domain knowledge. However, little research has been done to examine and resolve related issues systematically. Gaps of existing studies can be traced to the lack of a framework within which the pitfalls involved in implementation procedures can be identified and thus appropriate procedures for employing effective methodologies can be suggested. This study aims to develop a five-phase analytics framework that can facilitate the investigation of pitfalls for intelligent manufacturing and suggest protocols to empower practical applications of the DS methodologies from descriptive and predictive analytics to prescriptive and automating analytics in various contexts.

Micro-Electro-Mechanical System (MEMS) technology is employed to fabricate surface acoustic wave (SAW)-type and resistive-type ozone sensors on quartz glass (SiO2) substrates. The fabrication process commences by using a photolithography technique to define interdigitated electrodes (IDEs) on the substrates. Electron-beam evaporation (EBE) followed by radio frequency (RF) magnetron sputtering is then used to deposit platinum (Pt) and chromium (Cr) electrode layers as well as a zinc oxide (ZnO) sensing layer, respectively. Finally, annealing is performed to improve the crystallinity and sensing performance of the ZnO films. The experimental results reveal that the ZnO thin films provide an excellent ozone-concentration sensing capability in both sensors. The SAW-type sensor demonstrates a peak sensitivity at a frequency of 200 kHz, with a rapid response time of just 35 s. Thus, it is suitable for applications requiring a quick response and high sensitivity, such as real-time monitoring and high-precision environmental detection. The resistive-type sensor shows optimal sensitivity at a relatively low operating temperature of 180 °C, but has a longer response time of approximately 103 s. Therefore, it is better suited for low-cost and large-scale applications such as industrial-gas-concentration monitoring.

Directional distance function (DDF) has been a commonly used technique for estimating efficiency and productivity over the past two decades, and the directional vector is usually predetermined in the applications of DDF. The most critical issue of using DDF remains that how to appropriately project the inefficient decision-making unit onto the production frontier along with a justified direction. This paper provides a comprehensive literature review on the techniques for selecting directional vector of the directional distance function. It begins with a brief introduction of the existing methods around the inclusion of the exogenous direction techniques and the endogenous direction techniques. The former commonly includes arbitrary direction and conditional direction techniques, while the latter involves the techniques for seeking theoretically optimized directions (i.e., direction towards the closest benchmark or indicating the largest efficiency improvement potential) and market-oriented directions (i.e., directions towards cost minimization, profit maximization, or marginal profit maximization benchmarks). The main advantages and disadvantages of these techniques are summarized, and the limitations inherent in the exogenous direction-selecting techniques are discussed. It also analytically argues the mechanism of each endogenous direction technique. The literature review is end up with a numerical example of efficiency estimation for power plants, in which most of the reviewed directions for DDF are demonstrated and their evaluation performance are compared.

Both atopic dermatitis (AD) and periodontitis are common chronic inflammatory diseases. However, the association between AD and periodontitis remains poorly understood. This study aimed to evaluate the effects of dental scaling (DS) on the risk of AD among patients with periodontitis. In this retrospective cohort study using health insurance data, we identified individuals aged ≥20 years with periodontitis and a matched cohort without a history of periodontitis in Taiwan from 2011 to 2015. Age- and sex-matching was applied to select controls (ratio = 1:1). Both cohorts were followed until the end of 2017 to monitor atopic dermatitis (AD) incidence. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for AD risk associated with periodontitis were estimated using multivariate Cox regression. Among patients with periodontitis, we compared the risk of AD between those who received DS and those who did not. During the follow-up period, patients with periodontitis had an increased risk of AD compared with those without periodontitis (HR 2.47, 95% CI 2.25-2.71). The association between periodontitis and increased risk of AD was significant in men (HR 2.68, 95% CI 2.33-3.08), women (HR 2.35, 95% CI 2.07-2.66), and people in every age group. Among patients with periodontitis (n = 38,943), DS was associated with a reduced risk of AD (HR 0.33, 95% CI 0.30-0.37), and there was a dose-response relationship (p < 0.0001). The beneficial effects of DS on the risk of AD were observed across subgroups. The risk of AD was lowest in patients with periodontitis who received DS more than four times compared with those without DS (HR 0.14, 95% CI 0.08-0.25). In conclusion, our study revealed a significant association between periodontitis and increased risk of atopic dermatitis in Taiwanese adults. Moreover, regular dental scaling may lower this risk, underscoring the value of integrating oral care into managing systemic inflammation.

The association between oral health and cancer outcomes remains unclear. The purpose of this study was to evaluate the complications and mortality after admission of head and neck cancer (HNC) in patients with and without dental scaling (DS). We used data from public health insurance and identified 121,973 patients with admission of HNC aged ≥ 18 years who received inpatient care in 2006-2020. The outcomes during the admission of HNC were compared between patients who had received DS or not within the previous 24 months before admission. The adjusted odds ratios (HRs) and 95% confidence intervals (CIs) of complications and mortality associated with DS were analyzed in the multivariate Cox proportional regression models. We found that DS was significantly associated with reduced risks of septicemia (OR 0.84, 95% CI 0.81-0.88), stroke (OR 0.87, 95% CI 0.80-0.95), pneumonia (OR 0.88, 95% CI 0.84-0.91), urinary tract infection (OR 0.88, 95% CI 0.80-0.97), and 30-day in-hospital mortality (OR 0.88, 95% CI 0.85-0.92). Compared with HNC patients without DS, HNC patients with DS had a shortened length of hospital stay (p < 0.0001), decreased medical expenditures (p < 0.0001), and reduced risks of intensive care (OR 0.92, 95% CI 0.89-0.95) after admission of HNC. In conclusion, we suggested that HNC patients who received DS had reduced complications and mortality compared with those without DS. It is essential to interpret this association with caution due to the confounding factors involved. Our study implied the possibility that clinical physicians may encourage HNC patients to receive regular DS.

Infrared sensors incorporating suspended zinc oxide (ZnO) pyroelectric films and thermally insulated silicon substrates are fabricated using conventional MEMS-based thin-film deposition, photolithography, and etching techniques. The responsivity of the pyroelectric film is improved via annealing at 500 °C for 4 h. The voltage response of the fabricated sensors is evaluated experimentally for a substrate thickness of 1 µm over a sensing range of 30 cm. The results show that the voltage signal varies as an inverse exponential function of the distance. A positioning system based on three infrared sensors is implemented in LabVIEW. It is shown that the position estimates obtained using the proposed system are in excellent agreement with the actual locations. In general, the results presented in this study provide a useful source of reference for the further development of MEMS-based pyroelectric infrared sensors.

In the registration of medical images, nonrigid registration targets, images with large displacement caused by different postures of the human body, and frequent variations in image intensity due to physiological phenomena are substantial problems that make medical images less suitable for intensity-based image registration modes. These problems also greatly increase the difficulty and complexity of feature detection and matching for feature-based image registration modes. This research introduces an automatic image registration algorithm for infrared medical images that offers the following benefits: effective detection of feature points in flat regions (cold patterns) that appear due to changes in the human body’s thermal patterns, improved mismatch removal through coherent spatial mapping for improved feature point matching, and large-displacement optical flow for optimal transformation. This method was compared with various classical gold standard image registration methods to evaluate its performance. The models were compared for the three key steps of the registration process—feature detection, feature point matching, and image transformation—and the results are presented visually and quantitatively. The results demonstrate that the proposed method outperforms existing methods in all tasks, including in terms of the features detected, uniformity of feature points, matching accuracy, and control point sparsity, and achieves optimal image transformation. The performance of the proposed method with four common image types was also evaluated, and the results verify that the proposed method has a high degree of stability and can effectively register medical images under a variety of conditions.

Differential characteristics of the production function represent elasticity measures and marginal rates of production technologies; in particular, marginal productivity (MP) plays an important role in economic theory and applications. This study provides a theoretical foundation of directional marginal productivity (DMP) supporting the meta-data envelopment analysis (meta-DEA) which measures the efficiency via marginal-profit-maximized orientation. In addition, the segmented marginal rate of technical substitution is developed based on DMP. In fact, DMP is developed to address finding the improving direction of the efficient firm on the frontier towards the marginal profit maximization. This approach, which emphasizes “planning” over “efficiency evaluation”, forms the basis for transforming a typical “ex-post” DEA into an “ex-ante” DEA study. Two case studies show that the DMP provides an explicit span of directions for productivity improvement via a trade-off between these distinct directions.

The trend toward a more competitive electricity market has led to efforts by the electric power industry to develop advanced efficiency evaluation models that adapt to market behavior operations management. The promotion of the operational performance management of the electric power industry plays an important role in China’s efforts toward energy conservation, emission control and sustainable development. Traditional efficiency measures are not able to distinguish sales effects from productive efficiency and thus are not sufficient for measuring the operational performance of an electricity generation system for achieving its specific market behavior operations management goals, such as promoting electricity sales. Effectiveness measures are associated with the capacity of an electricity generation system to adjust its input resources that influence its electricity generation and, thus, the capacity to match the electricity demand. Therefore, the effectiveness measures complement the efficiency measures by capturing the sales effect in the operational performance evaluation. This study applies a newly developed data envelopment analysis-based effectiveness measurement to evaluate the operational performance of the electric power industry in China’s 30 provincial regions during the 2006–2010 periods. Both the efficiency and effectiveness of the electricity generation system in each region are measured, and the associated electricity sales effects and electricity reallocation effects are captured. Based on the results of the effectiveness measures, the alternative operational performance improvement strategies and potentials in terms of input resources savings and electricity generation adjustments are proposed. The empirical results indicate that the current interregional electricity transmission and reallocation efforts are effective in China overall, and a moderate increase in electricity generation with a view to improving the effect on sales is more crucial for improving effectiveness.