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Child abuse is associated with alterations in the hypothalamic-pituitary-adrenal (HPA) axis functioning. However, the unique effects of psychological and physical abuse and protective factors against these effects remain largely unknown. To close these gaps, the present study examined the unique effects of psychological and physical abuse on cortisol stress response and explored the moderating role of psychosocial resources in these associations among a sample of Chinese preadolescent children (N = 150; aged 9-13 years; M

We theoretically and experimentally demonstrate a strong and tunable optical anisotropy in epitaxially-grown VO2 thin films. Using a combination of temperature-dependent X-ray diffraction, spectroscopic ellipsometry measurements and first-principle calculations, we reveal that these VO2 thin films present an ultra-large birefringence (Δn > 0.9). Furthermore, leveraging the insulator-to-metal transition of VO2, we demonstrate a dynamic reconfiguration of optical properties from birefringent to hyperbolic, which are two distinctive regimes of anisotropy. Such a naturally birefringent and dynamically switchable platform paves the way for multi-functional devices exploiting tunable anisotropy and hyperbolic dispersion.

Scene recovery is a fundamental imaging task with several practical applications, including video surveillance and autonomous vehicles, etc. In this paper, we provide a new real-time scene recovery framework to restore degraded images under different weather/imaging conditions, such as underwater, sand dust and haze. A degraded image can actually be seen as a superimposition of a clear image with the same color imaging environment (underwater, sand or haze, etc.). Mathematically, we can introduce a rank-one matrix to characterize this phenomenon, i.e., rank-one prior (ROP). Using the prior, a direct method with the complexity O(N) is derived for real-time recovery. For general cases, we develop ROP

Children exposed to peer victimization are at increased risk for psychopathology. However, the physiological mechanisms linking peer victimization to child psychopathology and the potential gender differences in these links remain inadequately understood. The present study examined whether cortisol reactivity to acute stress mediated the associations between relational and physical victimization and internalizing and externalizing problems and whether these associations differed between boys and girls. A sample of 150 Chinese children (aged 9–13 years; Mage = 10.69 years; 51% boys) reported experiences of relational and physical victimization and participated in a standardized laboratory psychosocial stress task, during which six salivary cortisol samples were collected. Parents or primary caregivers reported their children’s internalizing and externalizing problems. Overall, neither physical nor relational victimization was associated with cortisol reactivity. However, when examined separately by gender, relational victimization was associated with blunted cortisol reactivity for boys but not for girls. Further, among boys but not girls, relational victimization was indirectly associated with internalizing and externalizing problems via blunted cortisol reactivity. Our findings suggest that blunted cortisol reactivity may serve as a physiological pathway linking peer victimization to psychopathology for boys but not for girls.

Objectives: To investigate the clinical effect of posterior nasal nerve combined with anterior ethmoid neurotomy in the treatment of moderate to severe persistent allergic rhinitis. Methods: Thirty patients with moderate to severe persistent allergic rhinitis admitted to Linhai Second People’s Hospital from August 2019 to June 2020 were selected as subjects for prospective study and design. All patients underwent posterior nasal neurotomy and anterior ethmoid neurotomy simultaneously. Subsequently, the efficacy of all patients at 0.5 and one year postoperatively was compared. Their symptom score preoperatively and one year postoperatively as well as their preoperative and postoperative quality of life score were compared, and related adverse reactions were collected. Results: The curative effect ratio was 60% after 0.5 years and 90.0% after one year, showing a significant increase (χ2=12.000, P=0.007

The phenomenon of urban shrinkage is currently occurring worldwide; however, the "growth-oriented" planning paradigm is not suitable for these shrinking cities. Reconsidering the relationship between urban form and PM

With a large amount of domestic sewage and industrial wastewater discharged into the water bodies, sulfur-containing organic matter in wastewater produced volatile organic sulfide, such as dimethyl trisulfide (DMTS) through microorganisms, caused the potential danger of drinking water safety and human health. At present, there is still a lack of technology on the removal of DMTS. In this study, the ultraviolet/peroxymonosulfate (UV/PMS) advanced oxidation processes was used to explore the degradation of DMTS. More than 90% of DMTS (30 µg/L) was removed under the conditions of the concentration ratio of DMTS to PMS was 3:40, the temperature (T) was 25 ± 2℃, and 10 min of irradiation by a 200 W mercury lamp (365 nm). The kinetics rate constant k of DMTS reacting with hydroxyl radical (HO·) was determined to be 0.2477 min

Twitter is one of the main information sharing platforms in the world with millions of tweets created daily. To ensure that users get relevant content in their feeds Twitter extensively leverages machine learning-based recommender systems. However, given the large volume of data, all production systems must be both memory and CPU efficient. In the 2021 ACM RecSys challenge Twitter simulates the production environment with a large dataset of almost 1 bilion user-tweet engagements that span a 4 week period. The goal is to accurately predict engagement type, and all models are subject to strict run-time constraints during inference. In this paper we present our approach to the 2021 ACM Recsys challenge. We use a hybrid pipeline and leverage gradient boosting, neural network classifiers and multi-lingual language models to maximize performance. Our approach achieves strong results placing 3’rd on the public leaderboard. We further explore the complexity of language model inference, and show that through distillation it can be possible to run such models in highly constrained production environments.

This study explored the dynamic and complex relationships between air quality and urban form when considering reduced human activities. Applying the random forest method to data from 62 prefecture-level cities in China, urban form-air quality relationships were compared between 2015 (a normal year) and 2020 (which had significantly reduced air pollution due to COVID-19 lockdowns). Significant differences were found between these two years; urban compactness, shape, and size were of prime importance to air quality in 2020, while fragmentation was the most critical factor in improving air quality in 2015. An important influence of traffic mode was also found when controlling air pollution. In general, in the pursuit of reducing air pollution across society, the best urban forms are continuous and compact with reasonable building layouts, population, and road densities, and high forest area ratios. A polycentric urban form that alleviates the negative impacts of traffic pollution is preferable. Urban development should aim to reduce air pollution, and optimizing the effects of urban form on air quality is a cost-effective way to create better living environments. This study provides a reference for decision-makers evaluating the effects of urban form on air pollution emission, dispersion, and concentration in the post-pandemic era.

Children exposed to peer victimization are at increased risk for psychopathology. However, the physiological mechanisms linking peer victimization to child psychopathology and the potential gender differences in these links remain inadequately understood. The present study examined whether cortisol reactivity to acute stress mediated the associations between relational and physical victimization and internalizing and externalizing problems and whether these associations differed between boys and girls. A sample of 150 Chinese children (aged 9-13 years; M

Lipases or triacylglycerol (TAG) lipases belong to the α/β-hydrolases superfamily, which are enzymes capable of catalyzing the hydrolysis of the ester bond between fatty acids and glycerol. Interestingly, some lipases have been found to not only possess hydrolysis activity but also acyltransferase activity in yeasts and microalgae. Our present study reported a novel dual-functional Mucor circinelloides lipase Lip10 with a slight lipolysis activity but a noteworthy phospholipid/diacylglycerol acyltransferase (PDAT) activity. The purified Lip10 mutants prefer to utilize phosphatidyl serine to form TAG over phosphatidyl ethanolamine and phosphatidylcholine. Site-directed mutagenesis indicated that the histidine residue in the acyltransferase motif H-(X)4-D is indispensable for the PDAT activity of Lip10. Overexpression of the acyltransferase motif of Lip10 promoted cell growth by 12% and increased lipid production by 14% compared to the control, whilst overexpression of the lipase motif induced lipid degradation in M. circinelloides.

In this letter, a pulse-amplitude-modulated based hybrid optical orthogonal frequency division multiplexing (PHO-OFDM) scheme is proposed. We use the high order quadrature-amplitude-modulation to replace the one-dimensional pulse-amplitude-modulated on the even subcarriers for compensating the data capacity weakness of the conventional pulse amplitude modulated discrete multitone (PAM-DMT). The combined time-domain PHO-OFDM signal has a larger amplitude range than the original PAM-DMT but with a reduced peak-to-average power ratio. Besides that, a flexible power allocation method is introduced to the proposed PHO-OFDM systems to fully exploit the whole dynamic range of LEDs. The simulation results show that, under the same average bit rate, the proposed PHO-OFDM scheme has a better bit error rate performance compared with the conventional PAM-DMT scheme, thus demonstrating its application potential in visible light communications.

Medical images play a significant role in modern diagnosis but often suffer from non-uniformity and low-luminance, which always affects the results of diagnosis and even leads to misdiagnosis in real applications. To obtain a clear and accurate view of the medical images, a new method based on luminance-level modulation and gradient modulation (LM&GM) is proposed in this paper. LM&GM is a two-stage approach that, first, increases the visual perception using the luminance-level modulation (LM) operation by compressing the range of luminance levels of the input image, and second, uses the gradient modulation (GM) operation to enhance the details of the previous step result. Experimental results on CT images, X-ray images and MRI images from medical image datasets and quantitative analyses by structural similarity index measurement (SSIM), average gradient (AG), relative enhancement in contrast (REC) and information entropy (IE) demonstrate that the results of the proposed method are competitive and overwhelm those of the existing methods.

The present study examined the mediation effects of emotional intelligence and coping style between child neglect and psychological abuse and smartphone addiction. A sample of 1041 Chinese adolescents from 11 to 15 years of age (mean age = 12.41 years, SD = 0.65) responded to anonymous questionnaires regarding child neglect and psychological abuse, emotional intelligence, coping style and smartphone addiction. The findings revealed that both emotional intelligence and coping style mediated the link between child neglect and psychological abuse and smartphone addition in a parallel fashion. In addition, emotional intelligence and coping style also sequentially mediated the link between child neglect and psychological abuse and smartphone addiction. The present study can contribute to a better understanding of how child abuse and neglect increased the risk of smartphone addiction. It suggests that tailored approaches are necessary for adolescents who are addicted to smartphones.

Human-induced climate change is one of the most pressing challenges of our time. The Helmholtz Association is making essential contributions to curbing the causes of climate change and finding ways to adapt. With the Helmholtz Climate Initiative, research is concentrated on the two focal points, "reduction of net emissions" and "adaptation to climate impacts". In Net-Zero-2050, Cluster I of the Helmholtz Climate Initiative, strategies and ways to reduce carbon emissions are scientifically investigated and evaluated. Furthermore, two digital communication formats are being developed to comprehensively show the research's complex results. Firstly, the web-based National Net-Zero-2050 Atlas informs the user about different methods and technologies for CO2 reduction and possible reduction paths. Secondly, the Soil Carbon App enables actors of the agricultural sector to assess climate mitigation potentials that arise from using different land management methods. A land surface model is used to simulate future scenarios presented in the app via cloud-based, model-driven workflows.Both formats aim to support users in making decisions and developing strategies. The work on the products follows the principles of comprehensibility, transparency and appropriate information presentation. During the work on the products, we identified challenges such as:How to deal with multi-dimensional data sets and uncertainties? How to present scientific results in a user-friendly manner for varied target groups? How to guide the users of varied target groups through the communication formats? With the aid of a critical internal reflection, approaches to overcome these challenges were developed and applied. For example, the atlas introduces different complexity levels to enable users to gain understanding, despite very diverse backgrounds, prior knowledge and information needs. The app, for its part, offers two main sectors that address the users’ different demands and prior knowledge: (1) it features options to choose from the data and subsequently delivers graphical analyses, and (2) it provides respective interpretation, texts, and web links.The article presents the two dissemination products as well as the challenges and solutions from the development work.

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is $O(N)$ where $N$ is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness., Comment: 9 pages, 6 figures, 1 table, Accepted by CVPR 2021

The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O

Hyperbolic spaces offer a rich setup to learn embeddings with superior properties that have been leveraged in areas such as computer vision, natural language processing and computational biology. Recently, several hyperbolic approaches have been proposed to learn robust representations for users and items in the recommendation setting. However, these approaches don’t capture the higher order relationships that typically exist in the recommendation domain. Graph convolutional neural networks (GCNs) on the other hand excel at capturing higher order information by applying multiple levels of aggregation to local representations. In this paper we combine these frameworks in a novel way, by proposing a hyperbolic GCN model for collaborative filtering. We demonstrate that our model can be effectively learned with a margin ranking loss, and show that hyperbolic space has desirable properties under the rank margin setting. At test time, inference in our model is done using the hyperbolic distance which preserves the structure of the learned space. We conduct extensive empirical analysis on three public benchmarks and compare against a large set of baselines. Our approach achieves highly competitive results and outperforms leading baselines including the Euclidean GCN counterpart. We further study the properties of the learned hyperbolic embeddings and show that they offer meaningful insights into the data. Full code for this work is available here: https://github.com/layer6ai-labs/HGCF.

Most of the oleaginous microorganisms cannot assimilate xylose in the presence of glucose, which is the major bottleneck in the bioconversion of lignocellulose to biodiesel. Our present study revealed that overexpression of xylose isomerase (XI) gene xylA or xylulokinase (XK) gene xks1 increased the xylose consumption by 25 to 37% and enhanced the lipid content by 8 to 28% during co-fermentation of glucose and xylose. In xylA overexpressing strain Mc-XI, the activity of XI was 1.8-fold higher and the mRNA level of xylA at 24 h and 48 h was 11- and 13-fold higher than that of the control, respectively. In xks1 overexpressing strain Mc-XK, the mRNA level of xks1 was 4- to 11-fold of that of the control strain and the highest XK activity of 950 nmol min−1 mg−1 at 72 h which was 2-fold higher than that of the control. Additionally, expression of a translational fusion of xylA and xks1 further enhanced the xylose utilization rate by 45%. Our results indicated that overexpression of xylA and/or xks1 is a promising strategy to improve the xylose and glucose co-utilization, alleviate the glucose repression, and produce lipid from lignocellulosic biomass in the oleaginous fungus M. circinelloides. • Overexpressing xylA or xks1 increased the xylose consumption and the lipid content. • The xylose isomerase activity and the xylA mRNA level were enhanced in strain Mc-XI. • Co-expression of xylA and xks1 further enhanced the xylose utilization rate by 45%.

Der durch den Menschen verursachte Klimawandel ist eine der drängendsten Herausforderungen der Gegenwart. Die Helmholtz-Gemeinschaft leistet wichtige Beiträge um die Ursachen der klimatischen Veränderung einzudämmen und Anpassungsmöglichkeiten zu finden. Mit der Helmholtz-Klima-Initiative konzentriert sich die Forschung auf die beiden Schwerpunkte „Vermeidung von Emissionen“ und „Anpassung an Klimafolgen“. In Netto-Null-2050, Cluster I der Helmholtz-Klima-Initiative, werden Strategien und Wege zur Minderung von Kohlenstoff Emissionen wissenschaftlich untersucht und bewertet. Im Clusters I werden zwei Formate entwickelt, mit denen die komplexen Ergebnisse der Forschung verständlich gezeigt werden können. Mit dem web-basierten Netto-Null-Atlas wird der Nutzer zu verschiedenen Methoden und Technologien zur CO2-Verminderung, sowie zu möglichen Reduzierungspfaden informiert. Die native Boden-Kohlenstoff-App ermöglicht Akteuren in der Landwirtschaft, Potentiale für Klimaschutz durch landwirtschaftliche Managementmethoden abzuschätzen. Mit dem Landoberflächenmodell CLM werden Zukunftsszenarien simuliert, die über cloud-basierte, modell-gesteuerte Workflows in der App dargestellt werden. Beide Formate haben das Ziel die Nutzer in Entscheidungen und bei der Entwicklung von Strategien zu unterstützen. Wichtige Ziele während der Arbeit an den Produkten sind u.a. eine gute Verständlichkeit, Transparenz und eine angemessene Informationsaufbereitung. Dabei werden beispielsweise im Atlas verschiedene Abstraktionslevel eingeführt, die den Nutzern mit sehr unterschiedlichem Vorwissen und Informationsbedarf Erkenntnisgewinne ermöglichen. Daneben ergibt sich unter dem Aspekt der Transparenz z.B. die Frage, wie in der App mit der Thematik „Unsicherheiten“ umgegangen werden kann. Der Beitrag stellt die beiden Disseminations-Produkte sowie die Herausforderungen und Lösungsansätze aus der Entwicklungsarbeit vor.

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences. Previous graph-based recommendation approaches process the observed user-item interaction graph as a ground-truth depiction of the relationships between users and items. However, especially in the implicit recommendation setting, all the unobserved user-item interactions are usually assumed to be negative samples. There are missing links that represent a user's future actions. In addition, there may be spurious or misleading positive interactions. To alleviate the above issue, in this work, we take a first step to introduce a principled way to model the uncertainty in the user-item interaction graph using the Bayesian Graph Convolutional Neural Network framework. We discuss how inference can be performed under our framework and provide a concrete formulation using the Bayesian Probabilistic Ranking training loss. We demonstrate the effectiveness of our proposed framework on four benchmark recommendation datasets. The proposed method outperforms state-of-the-art graph-based recommendation models. Furthermore, we conducted an offline evaluation on one industrial large-scale dataset. It shows that our proposed method outperforms the baselines, with the potential gain being more significant for cold-start users. This illustrates the potential practical benefit in real-world recommender systems.

There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the graph uncertainty into account. Various existing techniques either rely on restrictive assumptions, fail to preserve topological properties within the samples or are prohibitively expensive for larger graphs. In this work, we introduce the node copying model for constructing a distribution over graphs. Sampling of a random graph is carried out by replacing each node’s neighbors by those of a randomly sampled similar node. The sampled graphs preserve key characteristics of the graph structure without explicitly targeting them. Additionally, sampling from this model is extremely simple and scales linearly with the nodes. We show the usefulness of the copying model in three tasks. First, in node classification, a Bayesian formulation based on node copying achieves higher accuracy in sparse data settings. Second, we employ our proposed model to mitigate the effect of adversarial attacks on the graph topology. Last, incorporation of the model in a recommendation system setting improves recall over state-of-the-art methods.

Concrete is an open composite system, which allows the incorporation of various cementitious or inert materials as cement or aggregate components, thereby offering the possibility of creating a smart concrete. This study investigated the autogenous healing of concrete blended with ground granulated blast furnace slag (GGBS) and polyvinyl alcohol (PVA) fiber in terms of flexural strength recovery and crack sealing behavior. It was found that 50% GGBS-blended concrete showed good autogenous healing ability and the addition of PVA fibers indirectly further increased the effectiveness of autogenous healing in concrete. The hydration of cement paste was investigated for its relationship with autogenous healing using isothermal calorimetry, X-ray diffraction (XRD), and thermogravimetric analysis (TGA). The exterior of GGBS-blended concrete was prone to carbonation which could affect its autogenous ability. The hydration rate and ultimate degree of hydration of GGBS-blended concrete were lower than those of pure Portland cement concrete, suggesting that the use of GGBS provides a greater potential for autogenous healing both in the short and long term.

In this letter, an improved linear nonsymmetrical transform (ILNST) is proposed to reduce the peak-to-average power ratio (PAPR) for asymmetric clipping optical orthogonal frequency division multiplexing-based visible light communication (VLC) systems. The emission power conservation criteria are implemented to calculate the enlarge coefficient with a given compression coefficient. The transfer characteristic of a 5 mm commercial light-emitting diodes is introduced to estimate the VLC systems performance. Bit error ratio (BER) and complementary cumulative distribution function simulation results show that the proposed ILNST outperforms the classical linear nonsymmetrical transform method in PAPR reduction and BER enhancement.

The 25 November 2016 Aketao, Xinjiang earthquake occurred on the northeastern margin of the Pamir plateau, rupturing the Muji fault on the northern segment of the Kongur Extensional System. We collected coseismic offsets at 7 GPS sites, which show that the fault experienced significate dextral slip with a near-field geodetic displacement of up to 12 cm along the strike. The joint inversion of GPS data and teleseismic P waveforms suggests a complex rupture pattern characterized by the unilateral propagation slip from the epicenter to the southeast for 60 km with a total seismic moment of ~ 1.3 × 1019 Nm, corresponding to a magnitude of Mw 6.7 earthquake. Our model of slip distribution shows two major slip patches with a slip amplitude up to ~ 0.6 m, one located at shallow depths of 0–8 km close to the hypocenter with apparent surface breaks and the other, ~ 40 km to the southeast, buried at a greater depth of ~ 12 km. The rupture is dominated by a right-lateral strike slip with significant normal-slip components. The near-field GPS data enhances the spatial resolution of source model. Based on the preferred slip model, the static Coulomb Failure Stress change caused by 2016 Aketao earthquake suggests that the unzipped western and eastern ends of Muji fault and the northern segment of Kungai Fault are significantly promoted.

This paper proposes an optimal driving strategy to improve the ride comfort performance of intelligent vehicles, which is different from the general researches that focus on the driving safety. Firstly, based on the establishment of a 7-DOF vehicle model and a road excitation model, the relationship between vehicle velocity and the comfort index is analyzed and described by a non-monotonic polynomial. Then, a velocity control requirement considering driving performance and comfort performance is constructed and transformed into a multi-objective optimization problem. By using the Pareto optimization theory, a set of Pareto optimal solutions is obtained. Finally, to choose a velocity from the Pareto optimal set, a strategy considering the subjective and objective comfort indexes from ISO2631 is given. This strategy can be applied to the velocity planning of intelligent vehicles on uneven roads. The simulation results show that the proposed strategy is effective and feasible.

Spectroscopic ellipsometry characterization of coatings on polymeric substrates can be challenging due to the substrate optical anisotropy. We compare four characterization strategies for thin coating layers on anisotropic polymeric substrates with regard to accuracy of the resulting layer thickness and coating optical constants. Each strategy differs in measured data type, model construction, implementation complexity, and inherent capabilities and sensitivity to the coating properties. Best practices and limitations are discussed for each strategy.

We investigated the optical behaviors of vanadium dioxide (VO 2 ) films through the semiconductor-to-metal (STM) phase transition using spectroscopic ellipsometry. Correlations between film thickness and refractive index were observed resulting from the absorbing nature of these films. Simultaneously analyzing data at multiple temperatures using Kramers-Kronig consistent oscillator models help identify film thickness. Nontrivial variations in resulting optical constants were observed through STM transition. As temperature increases, a clear increase is observed in near infrared absorption due to Drude losses that accompany the transition from semiconducting to metallic phases. Thin films grown on silicon and sapphire substrate present different optical properties and thermal hysteresis due to lattice stress and compositional differences.

It is a well-known challenge to determine refractive index (n) from ultra-thin films where the thickness is less than about 10 nm [1,2]. We discovered an interesting exception to this issue while characterizing spectroscopic ellipsometry (SE) data from isotropic, free-standing polymer films. Ellipsometry analysis shows that both thickness and refractive index can be independently determined for free-standing films as thin as 5 nm. Simulations further confirm an orthogonal separation between thickness and index effects on the experimental SE data. Effects of angle of incidence and wavelength on the data and sensitivity are discussed. While others have demonstrated methods to determine refractive index from ultra-thin films [3,4], our analysis provides the first results to demonstrate high-sensitivity to the refractive index from ultra-thin layers.

Lipases or triacylglycerol (TAG) lipases belong to the α/β-hydrolases superfamily, which are enzymes capable of catalyzing the hydrolysis of the ester bond between fatty acids and glycerol. Interestingly, some lipases have been found to not only possess hydrolysis activity but also acyltransferase activity in yeasts and microalgae. Our present study reported a novel dual-functional

In this paper, we present a food-specialized detection1 deep learning architecture with knowledge transferred from a pretrained food/non-food classification model. Existing approaches in object detection all separate it from image classification due to their incompatible outputs, whereas our work bridges the gap between the two most fundamental computer vision topics by making use of transferred features, and as such we contend that our work provides a new perspective in object detection. Experiments are conducted in two parts. First, transfer learning quantification experiments show that initializing a network with transferred features from classification task can surprisingly produce a boost to generalization for the detection task. Second, experiments on three state-of-the-art neural network backbones show that our approach enables rapid progress and improved performance. The results significantly surpass all original plain networks with more than 10% precision improvement. In addition, our scheme can be easily generalized to any CNN-based architecture.

The local binary pattern with H function (H-LBP) technique enables fast and efficient edge extraction in digital radiography. In this paper, we reformulate the model of H-LBP and propose a novel sparsity-based shrinkage approach, in which the threshold can be adapted to the data sparsity. Using this model, we upgrade fast H-LBP framework and apply it to real digital radiography. The experiments show that the method improved using the new shrinkage approach can avoid elaborately artificial modulation of parameters and possess greater robustness in edge extraction compared with the other current methods without increasing processing time.

For the digital neutron radiography applications, low contrast is inevitable due to the neutron and photon statistics limited or high transmission (and low attenuation) in the sample. In this paper, we introduce a total variation-based l 0 minimization model and solve it by an alternating minimization approach. The proposed model is applied to base & detail decomposition for the dynamic range modification and detail magnification. Experimental results on several real neutron images are presented to demonstrate the effectiveness of the proposed method.

Based on compensatory satisfaction theory, the present study examined the relation between body dissatisfaction and adolescent smartphone addiction, as well as the roles of positive self-presentation on social networking sites and friendship quality. A sample of 1036 Chinese adolescents from 11 to 15 years of age (mean age = 12.41 years, SD = 0.65) responded to anonymous questionnaires regarding body dissatisfaction, friendship quality, positive self-presentation on social networking sites and smartphone addiction. The results indicated that body dissatisfaction could positively predict adolescent smartphone addiction. In addition, positive self-presentation on social networking sites mediated the relation between body dissatisfaction and smartphone addiction. Moreover, the first part of the mediating effect (i.e., the link from body dissatisfaction to positive self-presentation on social networking sites) was moderated by friendship quality, with the effect being significant only for adolescents with low levels of friendship quality. The present study can advance our understanding about smartphone addiction and how human interact with technology. Limitations and implication about the present study are also discussed.

In this paper, a logistic-function-based nonlinear companding transform (LNCT) is proposed to reduce the peak-to-average power ratio of asymmetrical hybrid optical orthogonal frequency division multiplexing (AHO-OFDM) signals in visible light communications (VLC). First, the positive and negative amplitudes of the bipolar AHO-OFDM signal are divided into two groups as the positive and nonnegative groups. Then, a peak detection is employed frame-by-frame to locate the group where the maximum peak occurs. Finally, the piecewise companding transform consisting of the nonlinear logistic function and a linear function is performed on the amplitude groups with and without the maximum peak, respectively. The simulation results show that the proposed LNCT scheme outperforms the classical linear nonsymmetrical transform method in terms of the complementary cumulative distribution function with a competing bit error rate performance, thus demonstrating its application potential in AHO-OFDM-based VLC systems.

With the goal of achieving real time and efficient edge extraction for digital radiography, an accelerated H-LBP-based edge extraction method (AH-LBP) is presented in this paper by improving the existing framework of local binary pattern with the H function (H-LBP). Since the proposed method avoids computationally expensive operations with no loss of quality, it possesses much lower computational complexity than H-LBP. Experimental results on real radiographies show desirable performance of our method.

Nowadays, it is a matter of survival for companies to keep their ability for adapting rapidly to changes. The causes that drive to them are related to external and internal factors. One of the main reasons for these factors is the new possibility that Industry 4.0 brings with it. One of the visions of Industry 4.0 is the production of highly customized products within a mass production line. In addition to it, an intelligent interaction happens between machine–objects–people and this occurs vertically and horizontally within the company. However, most industrial parks are not technologically prepared for the concepts of Industry 4.0; thereby this paper proposes the concept of Smart Retrofitting regarding these challenges. As a result, the methodology proposes to take aspects of Industry 4.0 relevant to a specific process, since this could be the first step for a company to implement the concepts of Industry 4.0. Once these aspects are selected, they will be put into practice with the help of cyber-physical systems. With the application of Smart Retrofitting, it will be possible for companies to adhere in a short time to the requirements of Industry 4.0. In this way, it will be possible to remain competitive in the market. Regarding this paper, there will be a case study for the implementation of Smart Retrofitting. As case study, a drilling process was chosen in a thyssenkrupp manufacturing plant in Brazil to start implementing the Smart Retrofitting, and for this first step it was possible to achieve an improvement of the feed rate reducing it by approximately 9% without reducing the lifetime of the tooling.

The new concept of manufacturing, called as Industry 4.0, must transform the paradigm of the industries much faster than any other technological revolution known at the moment. This new paradigm must require the intensive use of different machines, mechatronic systems and electronic devices that will be integrated through protocols which must allow a wide sharing of data among the devices that compose the manufacturing system. Although this new paradigm has been changing the design of new machines, the current systems are not prepared to be integrated by these new concepts. Therefore, a new demand for retrofitting of these current systems is emerging nowadays in order to update them for the context of Industry 4.0. Hence, this paper proposes and studies a methodology based on the concepts of design and systems engineering for retrofitting existing Computer Numeric Control (CNC) machines. As result, the proposed methodology must depict in details the functional requirements (FRs), the design parameters (DPs), data model and the system architecture for updating CNC machines in use, enabling the engineers to create personalized projects for real industries.

The technology of wireless powers transfer is a potential method of electrical energy transmission in the future. We made a Wireless Power Transmission (WPT) device via strongly magnetic coupling resonant, and explained the multiple basic physical phenomena such as frequency splitting and skin effect etc. Furthermore, we used mathematical software to calculate our WPT model, which can offer us a theoretical support. We have set experiments courses for both undergraduates and junior students in Beijing.