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Focal boost to dominant intraprostatic lesions (DILs) has recently been proposed for prostate radiation therapy. Accurate and fast delineation of the prostate and DILs is thus required during treatment planning. In this paper, we develop a learning-based method using positron emission tomography (PET)/computed tomography (CT) images to automatically segment the prostate and its DILs. To enable end-to-end segmentation, a deep learning-based method, called cascaded regional-Net, is utilized. The first network, referred to as dual attention network, is used to segment the prostate via extracting comprehensive features from both PET and CT images. A second network, referred to as mask scoring regional convolutional neural network (MSR-CNN), is used to segment the DILs from the PET and CT within the prostate region. Scoring strategy is used to diminish the misclassification of the DILs. For DIL segmentation, the proposed cascaded regional-Net uses two steps to remove normal tissue regions, with the first step cropping images based on prostate segmentation and the second step using MSR-CNN to further locate the DILs. The binary masks of DILs and prostates of testing patients are generated on the PET/CT images by the trained model. For evaluation, we retrospectively investigated 49 prostate cancer patients with PET/CT images acquired. The prostate and DILs of each patient were contoured by radiation oncologists and set as the ground truths and targets. We used five-fold cross-validation and a hold-out test to train and evaluate our method. The mean surface distance and DSC values were 0.666 ± 0.696 mm and 0.932 ± 0.059 for the prostate and 0.814 ± 1.002 mm and 0.801 ± 0.178 for the DILs among all 49 patients. The proposed method has shown promise for facilitating prostate and DIL delineation for DIL focal boost prostate radiation therapy.

Searching for information about a specific person is a frequent online activity. In most cases, users are aided in the search process by queries containing a name in Web search engines. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the synonym suggestion problem by utilizing automated speech generation, and deep learning to produce novel spoken name embeddings. These embeddings capture the way people pronounce names in a particular language and accent. Utilizing a name’s pronunciation can help detect names that sound alike, but are written differently. We demonstrated the proposed approach on a large-scale dataset with more than 250,000 forenames and surnames and evaluated it on two ground truth datasets containing 7400 forenames and 25,000 surnames (including their verified synonyms). The performance of SpokenName2Vec was found superior to the 10 other algorithms evaluated, including phonetic encoding, string similarity, and machine learning algorithms. The results obtained emphasize the potential of spoken name embeddings for improved synonym suggestion.

Event horizon imaging requires a third-generation space Very Long Baseline Interferometer on a 500 GHz band. The interferometer challenges available frequency standards. Namely, the practical coherent integration time is about 45 times shorter than needed. A self-calibration could aid this though a 3rd satellite is needed hence, an increased mission cost. This paper is devoted to experimental verification of a novel concept for two-way frequency transfer.We built a dedicated breadboard, test set-up, and run tests with and without temperature differences between branches of the breadboard. A phase difference between derived oscillators is the white noise-like function of the time with 1-σ of 0.5° at 103.2 GHz over more than 3 hours. Varying the temperature difference between branches ±3 C during 4.5 hours, being the orbit period, confirmed that the phase difference is the function of this temperature difference too. The Allan Deviation demonstrated between two derived 103.2 GHz oscillators is of 1.1×10−14/τ over 10 ms < τ < 1000 s, where τ is the averaging time, and the optical communication link path length is 5 km (17 ms). Degradation of this Allan Deviation is of 2 times at the longest-possible delay of 0.2 s in the interferometer system; the 0.2 s is the biggest sum of geometric and communication delays. This worst-case satisfies the requirement with a margin of 11 times. An obtained coherence is in the range of 0.997−0.9998 which is beneficial for the interferometer, at 557 GHz. This is one of the key technology enablers for black hole imaging from space.

This companion paper is to support the replication of paper "Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding", which was presented at ACM Multimedia 2020. The supported paper's main purpose was to provide a photogrammetry point cloud-based dataset with hierarchical multilabels to facilitate the area of 3D deep learning. Based on this provided dataset and source code, in this work, we build a complete package to reimplement the proposed methods and experiments (i.e., the hierarchical learning framework and the benchmarks of the hierarchical semantic segmentation task). Specifically, this paper contains the technical details of the package, including file structure, dataset preparation, installation package, and the conduction of the experiment. We also present the replicated experiment results and indicate our contributions to the original implementation.

Deep neural networks (DNNs) technique has achieved impressive performance on semantic segmentation,while its training process requires a large amount of pixel-wise labelled data. Domain adaptation,as a promising solution,can break the restriction by training the model on synthetic data,and generalising it in real-world data. However,there is still a lack of attention paid to the imbalance problems on semantic segmentation adaptation,including the imbalance problem between i) source and target data,ii) different classes. To solve these problems,a progressive hierarchical feature alignment method is proposed in this paper. To alleviate the data imbalance problem,the network is progressively trained by the data from multisource domains,so as to obtain domain-invariant features. To address the class imbalance problem,the features are aligned hierarchically across domains. According to the experimental results,our method shows the competitive adapted segmentation performance on three benchmark datasets.

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.

This paper deals with the use of social networks in agricultural enterprises and focuses mainly on their role and share in increasing the competitiveness of agricultural enterprises in the market. Primary data were obtained from an extensive survey of the development of information and communication technologies in agricultural enterprises, which was conducted in the first quarter of 2021 throughout the Czech Republic (“Survey 2021”). The research was primarily focused on capturing current trends in the use of ICT with emphasis on selected key areas (broadband, social networks, communication tools, regional Internet portals, used hardware categories, used software, mobile communications, Internet of Things, data storage and security, social networks, etc.). This survey builds on previous extensive surveys conducted by the Department of Information Technologies, Faculty of Electrical Engineering, CULS in Prague in several phases since 1999, with the last stage being conducted in 2017. Some surveys were conducted in cooperation with the Ministry of Agriculture of Czech Republic.Compared to recent years, the survey includes new domains, such as the use of the Internet of Things in plant and animal production, data storage and security, the impact of the Covid-19 pandemic on the company's core operations, etc. The survey was prepared, conducted and administered by the Department of Information Technology, Faculty of Economics and Management, University of Life Sciences Prague.

INTRODUCTION: Vaccination has significantly reduced morbidity and mortality resulting from rotavirus infection worldwide. However, rotavirus vaccine efficacy (VE) appears to wane over the first 2 years since vaccination, particularly in developing countries. Statistical methods for detecting VE waning and estimating its rate have been used in a few studies, but comparisons of methods for evaluating VE waning have not yet been performed. In this work we present and compare three methods – Durham’s method, Tian’s method, and time-dependent covariate (TDC) method – based on generalizations of the Cox proportional hazard model. METHODS: We developed a new stochastic agent-based simulation model to generate data from a hypothetical rotavirus vaccine trial where the protective efficacy of the vaccine may vary over time. Input parameters to the simulation model were obtained from studies on rotavirus infections in four developing countries. We applied each of the methods to four simulated datasets and compared the type-1 error probabilities and the powers of the resulting statistical tests. We also compared estimated and true values of VE over time. RESULTS: Durham’s method had the highest power of detecting true VE waning of the three methods. This method also provided quite accurate estimates of VE in each period and of the per-period drop in VE. CONCLUSIONS: Durham’s method is somewhat more powerful than the other two Cox proportional hazards model-based methods for detecting VE waning and provides more information about the temporal behavior of VE.

This paper seeks to examine the concept of “Merdeka Belajar” or “Freedom of Learning” which was initiated by the Indonesian Minister of Education and Culture, Nadiem Makarim. The study of freedom of learning uses text study with content analysis method. The concept of “Freedom of Learning” is very interesting when studied with a multicultural education perspective. Moreover, the two concepts have the same relevance and spirit, namely freedom and justice in the human resources development context, particularly through the educational process. Based on the results, the implementation of “MerdekaBelajar” program so far includes four aspects, namely financing, teachers / educators, assessment, and graduates. In a multicultural perspective, problems in management and assessment aspects basically caused by a misunderstanding of the meaning of standards and standardization. Meanwhile, the teacher and graduate aspects lie in the importance of developing multicultural competencies.Integrating a multicultural education perspective in the “Freedom of Learning” policy also transforms an education system that is creative, innovative, and character.

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

Objective: To propose a deep-learning network for the diagnosis of two corneal diseases: Fuchs’ endothlelial dystrophy and keratoconus, based on optical coherence tomography (OCT) images of the cornea. Methods: In this paper, we propose a novel network with parallel resolution-specific encoders and composite classification features to directly diagnose Fuchs’ endothelial dystrophy and keratoconus using OCT images. Our proposed network consists of a multi-resolution input, multiple parallel encoders, and a composite of convolutional and dense features for classification. The purpose of using parallel resolution-specific encoders is to perform multi-resolution feature fusion. Also, using composite classification features enhances the dense feature learning. We implemented other related networks for comparison with our network and performed k-fold cross-validation on a dataset of 16,721 OCT images. We used saliency maps and sensitivity analysis to visualize our proposed network. Results: The proposed network outperformed other networks with an image classification accuracy of 0.91 and a scan classification accuracy of 0.94. The visualizations show that our network learned better features than other networks. Significance: The proposed methods can potentially be a step towards the early diagnosis of corneal diseases, which is necessary to prevent their progression, hence, prevent loss of vision.

In the field of modern bidding, electronic bidding leads a new trend of development, convenience and efficiency and other significant advantages effectively promote the reform and innovation of China’s bidding field. Nowadays, most systems require a strong and trusted third party to guarantee the integrity and security of the system. However, with the development of blockchain technology and the rise of privacy protection, researchers has begun to emphasize the core concept of decentralization. This paper introduces a decentralized electronic bidding system based on blockchain and smart contract. The system uses blockchain to replace the traditional database and uses chaincode to process business logic. In data interaction, encryption techniques such as zero-knowledge proof based on graph isomorphism are used to improve privacy protection, which improves the anonymity of participants, the privacy of data transmission, and the traceability and verifiable of data. Compared with other electronic bidding systems, this system is more secure and efficient, and has the nature of anonymous operation, which fully protects the privacy information in the bidding process.

Energy harvesting technology is a key enabler for the application of sensing systems in remote locations. For condition monitoring of large-scale electrical engines (LSEE) magnetic energy harvesting (MEH) has been suggested as a suitable technique. MEH can provide comparatively high output powers. A critical parameter for the design of a MEH system is the ratio between the maximum and the minimum operating current. In the case of a large ration the design of a MEH system becomes challenging. Protective measures are required to prevent a destruction at high primary currents. In this paper we investigate a miniaturized magnetic energy harvesting design, where the transformer consists of a single sheet of steel. We investigate the output power performance of the design with respect to low power applications and show, that the miniaturized design is intrinsically safe with respect to high primary currents.

Research on ethnic entrepreneurship has generated substantial literature on the development of ethnic businesses among different immigrant groups in North America. Such studies tend to focus on the emergence of immigrant entrepreneurship among earlier immigrant groups by highlighting either the group characteristics or the opportunity structure. Existing studies also tend to overlook the importance of innovation in immigrants’ small businesses due to the marginality of immigrant businesses. Thus, there is a dearth of research on new immigrant communities in the USA that illuminates immigrants’ innovative practices. Drawing on the experiences of 50 Bangladeshi entrepreneurs in New York, this research examined how this emerging immigrant group transformed into immigrant entrepreneurs through the investigation of their innovative practices in small business. This paper determined that immigrant entrepreneurs are embedded within the dynamics of the immigration trajectory and the broader context of American society. Although these new immigrants were driven towards the lower end of the economy, this study found that innovations have expanded the breadth and depth of their businesses and made their businesses different and rewarding.

The Internet of Things combines (IoT) various information sensing devices to form a cross-domain network. In the IoT environment, the devices at the edge need to inter-communication with the server in the cloud. But the different devices use different communication protocols and operating systems, which can cause barriers, such as communication, operation, and registration. Model-Driven System Engineering (MDSE) provides a methodology to support the design, analysis, development, operation of complex systems. For the different characteristics of IoT devices, MDSE can support the modeling of device, task, service, and process. This paper introduces a complex-product-oriented unified modeling language, called X Language, and presents a system model to manage the IoT system based on X language. Block Definition Diagram (BDD) of X language is used to build the device model and server model. State Machine Diagram (SMD) of X language is used to build the process model of device and server operation. This work enables an IoT system to flexible and interoperability by the system-driven capability of X Language.

In order to predict heart failure disease in advance and keep heart failure patients away from pain, this paper combines genetic algorithms (GA) to optimize Extreme Learning Machine (ELM) and builds a network model that predicts heart failure disease based on some of the characteristic factors that may induce heart failure. The ELM model has the characteristics of fast training speed and strong generalization ability, but its randomly generated input weights and hidden layer bias will cause instability in the prediction results. In this paper, we use GA’s principle of survival of the fittest to find the most suitable parameter configuration of the ELM algorithm, and analyzes the simulation results, and compares them with the prediction results of the original ELM model. The results show that the GA-optimized ELM model can better predict the occurrence of heart failure disease and has better predictive performance. The ELM algorithm, which has undergone parameter optimization has made up for its original defects, which may affect clinical practice and become a new assistant for doctors to predict patients with heart failure.

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications towards understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This paper presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, definition extension, and application. We also create a software package for a variety of widely-used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting real-world applications., Comment: ACM Computing Surveys, Volume 55, Issue 1, January 2023, Article No 1, pp 1-35, https://doi.org/10.1145/3479428

The band gap of two-dimensional (2D) materials become a hot issue for photoelectric detection. Recently, public attention is thoroughly aroused as to the remarkable electrical transport characteristic and super photoresponse of 2D graphdiyne. The simulation results show that the photoresponse can be adjusted in various solutions based on the graphdiyne nanosheets with different sizes and thicknesses. Based on few-layered graphdiyne nanosheets prepared by a liquid-phase exfoliation method, a photoelectrochemical (PEC)-type few-layered graphdiyne photodetector is demonstrated in this paper. A group of PEC tests are carried out in neutral solution to verify the simulation results. The as-prepared graphdiyne photodetector possesses high photocurrent density, effective responsivity and excellent cycle stability in condition of KCl electrolyte and solar illuminance. The detectivity of the PEC-type graphdiyne photodetector can be easy to adjust by altering electrolyte concentration and other corresponding parameters, which indicates the proposed equipment can be a good candidate for photoelectric detection.

Internet of Things (IoT) is a scientific advancement that is likely to transfigure our traditional way of life by automating everything connected to the internet. Smart physical devices/objects have become indispensable part of our lives thereby making our being simplified and way more organized. Internet of Things encompass and empower several heterogeneous physical devices/objects for sensing and gathering information from surroundings, consequently exchanging it in a seamless mode to realize smart application goals without human intervention, for instance, home automation, smart cities, smart agriculture, and smart industries etc. The growth in the number of such wired/wireless physical devices/objects and the speed of that growth raises concern over security. To be able to implement a smart world with such billions of physical devices/objects, appropriate mechanisms are required to provide secure end-to-end communications amongst them. In addition to it, a variety of attacks may occur in various layers of Internet of Things. Without a well-founded and congruous Internet of Things environment, budding Internet of Things applications cannot be exploited to their full potential. Hence, reliability, security and recovery from attacks are amongst the most unsparing sides of the Internet of Things. In this paper, we have presented a comprehensive review of Internet of Things security issues followed by security measures in Internet of Things. The focus in this paper is also on research challenges and application domains of Internet of Things.

This paper aims to propose a nonlinear adaptive attitude control method for urban aerial mobility (UAM) to counteract its center of gravity (CG) variations, which is considered one of the challenging issues for UAM development. To this end, we firstly analyze the effect of the dynamic change caused by the CG variations rigorously. The analysis result uncovers that the model uncertainties (it can be regarded as the internal disturbances) due to the CG variations are not rapidly changing during the operation of UAM. Based on this observation, a nonlinear adaptive controller is suggested to attenuate the disturbances by leveraging on a two-stage design procedure with the concept of the disturbance observer-based control (DOBC) and the three-loop control topology in this paper. To be more specific, the baseline controller, based on the feedback linearization control (FBLC) in conjunction with the three-loop control structure, is designed by including the disturbances as exogenous inputs to the system. And then, the nonlinear disturbance observer is separately designed to estimate these disturbances. Finally, the performance of the proposed method is examined through numerical simulations.

In this paper, a frame structural topology optimization methodology for the generation of moment resisting braced frames for tall buildings, considering dynamic seismic loading, is presented. Real-world loading conditions along with standardized steel profiles of Euronorm are employed. In the literature, the studies that employ the ground structure topology optimization method are limited mostly to truss structures under static loading conditions. The dynamic response in the framework of structural topology optimization remains a challenging problem, especially when the ground structure method is used for deriving the structural system of tall buildings. The contribution of this study to the state of the art, relies on the implementation of the proposed methodology in the conceptual design phase of civil engineering frame structures, where both axial and flexural stiffness is considered. Direct time integration methods are used, to implement forced vibrations and real recorded earthquake data in the topology optimization procedure, resulting to novel layouts for lateral resisting systems of tall buildings. Aiming to aid the engineer to the final design phase, the response spectrum modal analysis is employed and the seismic loading is applied according to the Eurocode 8.

Purpose This paper aims to discover why such a public partnership project had been successful with a non-profit third-party alliance such as a smart city consortium (SCC) promoting smart city development. Design/methodology/approach This descriptive case study is primarily based on analysing data collected from various texts, public statements, media interviews and three semi-structured interviews with key members involved in the Covid-19 dashboard project. Findings The data and analysis reviews that both interpersonal and interorganisational trust, dedication and proactiveness of the leaders at SCC were major contributing factors to why SCC was able to partner with the Hong Kong Government in the Covid-19 dashboard in the first place and that the success was also a direct outcome of effective mass collaborative knowledge management activities. Research limitations/implications The research in leadership attributes and activities in the non-profit alliance has been few and this collaborative partnership between the alliance and the government is an example of the importance of further research in smart city leadership. Practical implications In deploying projects for mass collaboration and knowledge sharing in smart city development (which is multi-disciplinary in nature). there are still many new and evolving organisational practices and leadership matters that many business leaders and city managers can learn from. Social implications Smart city development projects involve the notion of sharing data in an open environment enabled by software and mediating tools. Successful projects such as this Hong Kong Covid-19 dashboard which serves a diverse audience can further promote the importance of an open data policy regime for the benefit of the public. Originality/value This case study covers a highly original and unique case study with the leaders at the SCC and representatives from the Hong Kong Government.

Introduction This paper will evaluate the difference between Sleepiness, Loneliness and Social Connectedness between a higher education and mundane sample. Although current literature evaluates the role of Sleepiness within Higher education (Araujo et al, 2021), to the best of our knowledge there are no papers comparing a student population to a non-student sample. A significant difference between the subjective sleepiness scores of a student and non-student population is expected, which would demonstrate that a student sample perceive themselves to be sleepier than their non-student counterparts. Methods An online survey was administered to 202 participants via Qualtrics. 94 were non-students and 108 were higher education students. Sleepiness was measured using the Stanford Sleepiness Scale (Shahid et al, 2011), The UCLA Loneliness Scale was used to measure loneliness (Russell, 1980). Results An independent t-test showed that there was a significant difference between students and non-students subjective sleepiness scores. (t(200) = 2.23, p = 0.02) with students being significantly more like to rate themselves higher on the Stanford Sleepiness Scale. (Figure 1). Further analysis shows that Loneliness and Subjective Sleepiness are significantly correlated for a student sample (r (108) = 0.308, P = 0.001). Discussion This study suggests that students enrolled within higher education are more highly associated with the risks of Excessive daytime Sleepiness and the resultant detrimental effects on social health. Further research needs to be performed to determine the negative effects of Sleepiness on Student’s social health.

Based on panel data from 30 provinces in China from 2008 to 2017, this paper constructs a quantile regression econometric model to analyze whether China’s environmental regulation has an impact on export trade and to verify whether the Porter hypothesis has been valid in China in recent years. The results show that in the short term, environmental regulations have a restraining effect on export trade, while in the long run, due to the existence of innovation efficiency, environmental regulations will change from having a restraining effect to a promoting effect on export trade. Strict environmental regulations will reduce the production cost of Chinese products, further improve the export competitiveness of Chinese enterprises, and promote export trade. The empirical results verify the conclusion that the Porter hypothesis is confirmed in China. The following three suggestions are proposed for China’s exports to promote the win–win of China’s green development and export trade: promote the realization of international and domestic double circulation, avoid becoming “pollution shelters” and support technological innovation in environmental protection industries.

The construction industry is one of the least digitally advanced industries. Although the industry is project-based, a project-level assessment of digitalization is lacking. The aim of this paper is to develop a digitalization index to assess the degree of digitalization of construction projects. Relying on the outcomes of a workshop with 11 participants and questionnaire responses from 113 site managers in Sweden, four activities; visualized drawings and three-dimensional (3D) models on sites; updated drawings, models, and system documents; created and updated work disposition plans; and updated time resource plans were selected, and a digitalization index enabling a simple assessment of the degree of digitalization of construction projects was constructed based on the degree of digitalization of the data management processes involved in these four activities. The approach to determine the digitalization index was demonstrated in a case study of a new construction project. For future studies, an accurate and simple assessment of the degree of digitalization of projects should increase the opportunities to study the association between the degree of digitalization and project performance. With longitudinal assessments, digitalization trends in the construction industry can be reported. Funding: strategic innovation program, Smart Built Environment - Vinnova (Swedish Governmental Agency for Innovation Systems); Formas (Swedish Government Research Council for Sustainable Development); Energimyndigheten (Swedish Energy Agency)Swedish Energy Agency

“Wasan” is the collective name given to a set of mathematical texts written in Japan in the Edo period (1603–1867). These documents represent a unique type of mathematics and amalgamate the mathematical knowledge of a time and place where major advances where reached. Due to these facts, Wasan documents are considered to be of great historical and cultural significance. This paper presents a fully automatic algorithmic process to first detect the kanji characters in Wasan documents and subsequently classify them using deep learning networks. We pay special attention to the results concerning one particular kanji character, the “ima” kanji, as it is of special importance for the interpretation of Wasan documents. As our database is made up of manual scans of real historical documents, it presents scanning artifacts in the form of image noise and page misalignment. First, we use two preprocessing steps to ameliorate these artifacts. Then we use three different blob detector algorithms to determine what parts of each image belong to kanji Characters. Finally, we use five deep learning networks to classify the detected kanji. All the steps of the pipeline are thoroughly evaluated, and several options are compared for the kanji detection and classification steps. As ancient kanji database are rare and often include relatively few images, we explore the possibility of using modern kanji databases for kanji classification. Experiments are run on a dataset containing 100 Wasan book pages. We compare the performance of three blob detector algorithms for kanji detection obtaining 79.60% success rate with 7.88% false positive detections. Furthermore, we study the performance of five well-known deep learning networks and obtain 99.75% classification accuracy for modern kanji and 90.4% for classical kanji. Finally, our full pipeline obtains 95% correct detection and classification of the “ima” kanji with 3% False positives.

Increasing demands on the power density of gearboxes require a precisive gear design regarding common failure mechanism. Particularly in turbo gearboxes with low-viscosity lubricants, the damage mechanism scuffing is relevant. In this paper an innovative test rig for the experimental investigation of scuffing at pitch line velocities up to 100 m/s is presented. The scuffing load capacity depending on the pitch line velocity of two gear design variants running at constant temperatures and lubricant conditions was investigated. Furthermore, the morphology of scuffing was investigated with regard to the damage location and the surface condition. Based on the experimental results, a simulation approach with an accuracy superior to the existing standards for calculating the scuffing load capacity of highspeed gears has been derived.

In this introduction, we first review the lessons learned in development economics about the ability of randomized control trials to analyse what Duflo refers to as the “plumbing problems” of policy implementation, as opposed to “engineering problems” of policy design. We then examine the papers published within this special issue from a plumbing-based perspective to highlight the benefits of the co-creation of knowledge in corporate sustainability through a field-based experimental agenda. We finally propose that field-based experiments can radically influence the future development of our (and related) fields of inquiry in three ways: (1) focusing on the implementation processes of sustainability strategies, (2) shifting attention from the analysis of past events to the design of future actions, and (3) yielding our role as sole owners of the knowledge development process and agreeing to shared ownership with the organizations we study.

The dc-dc converters with high step-up DC-voltage gain play a vital role in integrating low voltage DC sources. Though several converter topologies are reported in the recent past, attempts have been made to reduce the components, especially the switching devices, passive elements, converter losses, etc., of the converter. A novel DC-DC converter topology, viz., single switched impedance network (SSIN)-based converter with n -stage is proposed in this paper. The operation of the SSIN based converter in continuous and discontinuous conduction modes are discussed. The effect of parasitic elements on DC-voltage gain and efficiency of the SSIN is carried out. The small-signal model of the SSIN converter is derived. The performance of the SSIN converter is compared with the similar converter topologies. The 500 W prototypes of the SSIN with n = 1 and n = 2 are fabricated, and the experimental results are presented in this paper. To improve the converter performance, SiC-based semiconductor devices are used. The SSIN converter with two-stage has low switch loss at 500 W load, improved efficiency during high power and high output voltage operation, the switch stress of Vo /2, etc.

ObjectiveDual-ended readout depth-encoding detectors based on bismuth germanate (BGO) scintillation crystal arrays are good candidates for high-sensitivity small animal positron emission tomography used for very-low-dose imaging. In this paper, the performance of three dual-ended readout detectors based on 15×15 BGO arrays with three different reflector arrangements and 8×8 silicon photomultiplier arrays were evaluated and compared.ApproachThe three BGO arrays, denoted wo-ILG (without internal light guide), wp-ILG (with partial internal light guide), and wf-ILG (with full internal light guide), share a pitch size of 1.6 mm and thickness of 20 mm. Toray E60 with a thickness of 50μm was used as inter-crystal reflector. All reflector lengths in the wo-ILG and wf-ILG BGO arrays were 20 and 18 mm, respectively; the reflectors in the wp-ILG BGO array were 18 mm at the central region of the array and 20 mm at the edge. By using 18 mm reflectors, part of the crystals in the wp-ILG and wf-ILG BGO arrays worked as internal light guides.Main resultsThe results showed that the detector based on the wo-ILG BGO array provided the best flood histogram. The energy, timing and DOI resolutions of the three detectors were similar. The energy resolutions full width at half maximum (FWHM value) based on the wo-ILG, wp-ILG and wf-ILG BGO arrays were 27.2±3.9%, 28.7±4.6%, and 29.5±4.7%, respectively. The timing resolutions (FWHM value) were 4.7±0.5 ns, 4.9±0.5 ns, and 5.0±0.6 ns, respectively. The DOI resolution (FWHM value) were 3.0±0.2 mm, 2.9±0.2 mm, and 3.0±0.2 mm, respectively. Over all, the wo-ILG detector provided the best performance.

With widespread deployment of virtualization technologies in datacenter networks, traditional tools used for network monitoring, such as hardware taps, become unfit. This is due to the inability of hardware solutions for dynamic deployment and virtual network monitoring. This paper presents the design and evaluation of SoftTap, a scalable alternative to hardware taps which is capable of operating over both physical and virtual switches. SoftTap is based on port and flow mirroring capabilities of commodity OpenFlow switches and is not limited to a specific network architecture or topology. A key design challenge in SoftTap is the fast computation of switch mirroring configurations in large-scale deployments. Our design is based on novel polynomial time approximation algorithms that are shown to achieve bounded approximation ratios compared to optimal solutions. We evaluate SoftTap using model-driven simulations as well as realistic Mininet experiments. Specifically, our simulations consider large networks to show the scalability of SoftTap. Mininet experiments, on the other hand, consider its real-world utility by implementing an intrusion detection system (IDS) and a VoIP metering application on top of SoftTap. In our experiments, under SoftTap, IDS achieves up to 25% higher detection recall, while VoIP metering achieves up to 23% less packet loss compared to existing mirroring-based traffic monitoring approaches.

Cross-modal retrieval aims to find the similarity between different modal data, while the hash retrieval method improves retrieval efficiency. This paper proposes a cross-modal hash retrieval method based on multiple similarity matrices. This paper proposes an unsupervised cross-modal hash retrieval method based on multiple similarity matrices. This paper uses a weighted combination method to construct fusion features through hash features and original features. Based on the three features, the auxiliary similarity matrix of each of the three features is established. Finally, the fusion matrix is constructed by a weighted combination of the similarity matrix of the original features and the hash features. These four different matrices include similarity matrices with varying forms of features and similarity matrices with varying construction methods, which concentrate the similarity information of other modalities. The loss function between different similarity matrices and the loss function between different modalities are calculated through these four different matrices. Considering that most models have a single method for extracting text features, this paper uses text self-attention to strengthen the effect of text feature extraction so that the final performance of this paper is effectively improved. In order to verify the impact of this article, the results are tested on the Wikipedia, MIRFlickr, and NUS-WIDE datasets, and the results prove that the effect of this article has certain advantages compared with the latest methods.

Software development is a complex process which is divided into many phases. According to the software type and industries the development process is restructured. During the entire development what are the main factors which is influencing the process and affecting the quality. The main objective of this study is to focus on factors influencing the development process and how it affects the small scale industries after coming in to the real practice. Entire Software development is a layered process in which different factors are responsible to get the best products. This paper is focused on different technical and non-technical influencing factors which give major impact on the software quality. With influencing factors, their applicability in small scale industries also studied. Three important technical factors i.e. SDLC model and its principles, Cost estimation and Risk parameter whereas two important influencing factors in non-technical.i.e. success factors and environmental factors. Non-technical factors more influencing than technical factors. All technical and non-technical factors have their own role but to apply all these quality parameters in small scale industries we need to make them more easy for their applicability. If quality development process and its parameters are tuned to easy and affordable level more businessmen will shift from manual working environment to the digital working environment.

Object tracking is a challenging task in computer vision based intelligent transportation systems. Recently, Siamese based object tracking methods have attracted significant attention due to their highly efficient performance. These tracking methods usually train a Siamese network to match the initial target patch of the first frame with candidates in a new frame. In these methods, the offline training of the deep neural network and the online instance searching are effectively combined. However, these methods usually do not include template update or object re-identification, which easily results in the drift problem. In this paper, we propose a novel real-time object tracking method to overcome the above problems by effectively combining a multi-stream Siamese network and a region-based convolutional neural network. Specifically, a novel multi-stream Siamese network is proposed to search the target and update the instance template in a new frame. In addition, a faster region-based convolutional neural network detector is used to perform object re-identification in order to improve the tracking performance by making full use of the object category information. These two networks are tightly coupled to ensure that the proposed tracking method has high efficiency and strong discriminative capability. Experimental results on several object tracking benchmarks show that our tracking method can effectively track vehicles and pedestrians in video sequences by exploiting the object category information. The proposed tracking method achieves real-time operations and outperforms several other state-of-the-art methods.

In the recent years, the level and nature of the ground vibrations has been more concerned in worldwide. Vibration affected on surrounding building is often associated with the vibration from the ground that is mainly caused by internal and external sources. One of the external sources is construction activities. Identify the effects of vibration caused by piling works in construction sites was the purpose of this paper. It is also aiming to determine the vibration criteria due to piling works in Klang Valley construction site. In addition, the objective of this study is to compare the level of vibration with Department of Environment (DOE) guideline between both Kajang MRT and Klang Valley MRT construction sites. The data used for this study is obtained from past researchers and field testing is performed by using Polytec Laser Doppler Vibrometer and Rion VM-55. The data has been analyzed by using ModalV of MATLAB software. Based on the results, it can be concluded that the vibration amplitude for three distance includes 5m, 10m and 20m are located above the ISO level which stated that the area within the distances not suitable for placement of sensitive equipment. The highest value of root mean square velocity is occurred in the distance of 5m and the reading is 80000 µm/s. According to Department of Environment (DOE) guidelines, the vibration at distance of 1m and 3m at Kajang MRT will cause major damage to surrounding buildings while minor damage was produced by the vibration at 5m, 10m and 20m distance from bored piling point which located around the area of Klang Valley MRT.

In this paper, a circularly polarized highly efficient broadside antenna based on spoof surface plasmon polaritons (SSPPs) using polarization dependent artificial ground plane (AGP) is proposed. Printed monopole is used to excite even mode of SSPPs in 45° rotated corrugated strip relative to ground plane. The field from the resonating even mode and reflected field from the polarization dependent artificial ground plane contribute for circular polarization in broadside direction. The proposed antenna offers 10-dB input impedance bandwidth and 3-dB axial ratio bandwidth (ARBW) of 15.3% and 11.7% (at 3.42 GHz center frequency) in broadside direction with right hand circular polarization (RHCP), respectively. The minimum axial ratio achieved is 0.39 dB. The total efficiency remains above 92.4% over 3-dB ARBW, while maximum total efficiency achieved is 98.4%. The proposed antenna provides very low cross polar component with maximum gain value of 7.17 dBic in broadside direction.

Many an ethically minded translator would think twice or thrice prior to translating an ideologically loaded text as they need to reflect the encoded ideologies of the original narrative in the target narrative. Yet, some translators decide, for different reasons, to undermine and challenge the narratives in question, thus applying various reframing strategies to superimpose certain directionality on the original narratives. This paper, therefore, examines the English translations of the Arabic editorials on Daesh (ISIS), published by the Washington-based Middle East Media Research Institute (MEMRI) that has a large impact on the US policy and the public. In this paper, we explain how media translation contributes to (re)framing the current civil war in Syria on the one hand, and how this promotes the narrative of ‘Arab and Muslim terrorists’ on the other. The study finds that by relying on the narrativity feature of selective appropriation, MEMRI extensively employs the textual reframing tools, i.e., addition and omission, thus promoting different narratives of the civil war. We propose that rival narratives could be circulated through translation where the translators reframe the original narratives and reconstruct the embedded narrativity features that, in turn, renegotiate the original arguments.

In 5G mobile communication systems, mm wave communication has become one of the most attractive techniques reason being a huge increase in wireless data traffic. Also, the mobile terminals have become thinner and in functionality, more versatile and continue to become more. A mobile terminal, nowadays consists 2G-4G antennas, NFC antennas, Wi-Fi antennas, and now 5G antennas as well. Thus, compact antenna arrays are required and are a very hot topic today for future mobiles. optimization technique has been used in this paper since it includes the searching and obtaining of the perfect solution possible for a given problem. In this project we’ve first proposed a rectangular patch antenna of size 3.237 mm $\times 2.8$ mm $\times 0.5$ mm with which a $1\times 4$ antenna array has been designed of the dimensions 9.787 mm $\times 34.76$ mm $\times 0.5$ mm. This $1\times 4$ antenna array was optimized using Particle Swarm optimization (PSO) and the parameters have been analysed and discussed. Dimensions are 13.237 mm $\times$ 47.76 mm $\times 0.5$ mm.

Based on the perspective of ITT, the development of cross border e-commerce (hereinafter referred to as CBEC) is directly related to many industries, which promotes the employment and development of many industries, such as logistics, it, production, etc. Therefore, CBEC accounts for a large proportion in economic development. With the improvement of e-commerce platform, the traditional international trade (hereinafter referred to as ITT) has gradually changed to CBEC. In the perspective of ITT, the “Internet plus foreign trade” CBEC is a modern sales way, which caters to the characteristics of the fragmentation of sales orders and the individualization of product demand. Therefore, the average annual growth rate of CBEC in China exceeds 30%, which has become a new direction of ITT transformation and upgrading. Through CBEC, ITT has broken through the limitation of time and space, which has gradually reduced transaction costs. Therefore, computer CBEC has a positive impact on ITT. First of all, this paper analyzes the role of CBEC development. Then, this paper puts forward some problems. Finally, some suggestions are put forward.

Multi-level AC-AC converter has the advantages of reducing switch voltage stress, adapting to high voltage input and reducing waveform distortion. In this paper, a symmetrical switched capacitor multi-level (SSCML) AC-AC converter is studied. The switched capacitor (SC) technology is introduced into the AC-AC converter, which has the advantages of small size, high efficiency and high power density. Firstly, the topology of SSCML AC-AC converter and its control strategy are introduced. Then, the working principle of the topology is analyzed, and the relationship between input voltage and output voltage is derived in detail, as well as the expression of switch voltage stress. Finally, a 300W prototype is built to verify the proportional relationship between input voltage and output voltage. The prototype and experimental results prove that the multi-level AC-AC converter using SC technology has the advantages of small size and low voltage stress. The experimental results also prove that the voltage stress is proportional to the modulation ratio, which is the key to reducing the voltage stress.

A Cyber-Physical System (CPS) refers to the interconnection of control (actuation), computational nodes and sensors, in order to manage physical processes. In recent years, the CPS design methodology has been adopted in several large-scale infrastructures such as smart power grids. Given the application criticality, sustaining the security of these systems is of utmost importance. One of the major security goals is to protect CPS against impersonation, where an adversary intends to manipulate the system state by sending erroneous data that appears to be reported by one of the system nodes, e.g. PMUs of a power grid. This paper proposes a novel hardware-assisted authentication scheme to counter such a threat, by exploiting imperfections that occur in the manufacturing process of integrated circuits. In essence, the proposed scheme associates a fingerprint for each system node so that the authenticity of the data source could be verified. In addition, the paper tackles the threat of message replay where the adversary re-transmits a legitimate message so that the system factors in outdated rather than fresh sensor measurements. This paper thwarts such a replay attack by leveraging the synchronized clocks across the CPS nodes, e.g., based on GPS; the idea is to employ a combination of time-stamp signatures and hardware fingerprints. Our proposed schemes can also detect and prevent data forgery, and Sybil attacks. The viability and performance of the proposed schemes are validated through analysis and prototype implementation.

With the wide application of micro-nano structures in various fields, micro-nano technology has been developed continuously. Among them, micro-nano surface morphology detection technology has become a research hotspot, because the surface geometry and morphology of micro-nano devices determine the functional level and service life of devices. With the increasing complexity of micro nano device surface morphology and the increasing sharp edges, the detection technologies are facing severe challenges. Among various detection methods, structured illumination microscopy (SIM) has attracted many research interests due to the characteristics of high accuracy, strong adaptability and high efficiency. The existing SIM are mainly based on phase-shift technique, Hilbert transform technique and global Fourier transform technique. However, for complex-surface and steep-edge measurements, it is difficult for traditional SIM to achieve both high accuracy and high efficiency. In this paper, a method based on SIM which combines vertical scan and phase-shift is proposed. In this proposed measurement system, vertical scanning of the object is synchronized with the switching of the phase-shifted fringe pattern and only one fringe pattern needs to be projected, which enables a point-to-point processing defined as local Fourier transform method in this paper to be utilized to extract the modulation information which will reserve high-frequency information of the image so it can be applied to both smooth and rough surfaces. Simulation and experiment are carried out to demonstrate that the proposed method can successfully realize fast and accurate detection of complex-surface and steep-edge.

Current data-driven intelligent transportation systems are mainly reliant on IEEE 802.11p to collect and exchange information. Despite promising performance of IEEE 802.11p in providing low-latency communications, it is still vulnerable to jamming attacks due to the lack of a PHY-layer countermeasure technique in practice. In this paper, we propose JammingBird, a novel receiver design that tolerates strong constant jamming attacks. The enablers of JammingBird are two MIMO-based techniques: Jamming-resistant synchronizer and jamming suppressor. Collectively, these two new modules are able to detect, synchronize, and recover desired signals under jamming attacks, regardless of the PHY-layer technology employed by the jammers. We have implemented JammingBird on a vehicular testbed and conducted extensive experiments to evaluate its performance in three common vehicular scenarios: Parking lots (0~15 mph), local traffic areas (25~45 mph), and highways (60~70 mph). In our experiments, while the jamming attacks degrade the throughput of conventional 802.11p-based receivers by 86.7%, JammingBird maintains 83.0% of the throughput on average. Experimental results also show that JammingBird tolerates the jamming signals with 25 dB stronger power than the desired signals.

In the construction sector, the new Industry 4.0 and circular economy paradigms are currently playing an increasingly important role in order to respond to demands for energetic efficiency, optimisation of resource usage, automation and production activities monitoring. Therefore, this article aims to at illustrate industrial research and an experimental development activity that led to the creation of an innovative product named MULTIFId. It consists of an intelligent, economical and low-emission panel, made with waste from the paper and cardboard industrial manufacturing process which intends to contribute to the development of the fundamental enabling technology of “Advanced Materials” for Intelligent Factories.

BackgroundBalloon sinuplasty is a relatively new surgical technology proposed as a less invasive way of treating chronic rhinosinusitis and frontal sinus disease. It is crucial to monitor safety and efficacy to allow informed clinical decisions and appropriate counselling of patients. There are few papers with long-term follow up.MethodPatients were selected for surgery based on symptoms of frontal pain and pressure or headache, pre-operative Sino-Nasal Outcome Test-22 scores, and computed tomography findings. All were resistant to appropriate medical therapy. Balloon sinuplasty was performed followed by a Sino-Nasal Outcome Test-22 questionnaire at two months and then two years.ResultsAll patients underwent successful operations with no failure of technology or technique. There were no significant complications. The average Sino-Nasal Outcome Test-22 score reduced from 46 pre-operatively (standard error: +/− 5.7, n = 9) to 23 at 2 months (standard error: +/− 7.6, n = 7). At 2 years, the average Sino-Nasal Outcome Test score was 23 (standard error: +/− 4.3, n = 6). There was an even larger lasting benefit when pain or pressure symptoms were analysed in isolation.ConclusionThis study adds to the evidence of technique safety and feasibility. The procedure resulted in a significant long-term improvement in symptoms.

Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease threatening the health of the elderly, but the available therapeutic and preventive drugs remain suboptimal. Loganin, an iridoid glycoside extracted from Cornus officinalis, is reported to have anti-inflammatory and memory-enhancing properties. This study is aimed to explore the influence of loganin on cognitive function in 3xTg-AD mice and the underlying mechanism associated with its neuroprotection. According to the results of behavioral tests, we found that administration of loganin could significantly alleviate anxiety behavior and improve memory deficits of 3xTg-AD mice. Furthermore, immunohistochemical analysis displayed that there were decreased Aβ deposition in the hippocampus and cortex of 3xTg-AD mice treated with loganin compared with the control mice. Importantly, the Aβ-related pathological change was mainly involved in altering APP expression and processing. And loganin was also found to reduce the levels of phosphorylated tau (i.e. pTauS396 and pTauS262) in 3xTg-AD mice. By performing 2D-DIGE combined with MALDI-TOF-MS/MS, we revealed 28 differentially expressed proteins in the 3xTg-AD mice treated with loganin compared with the control mice. Notably, 10 proteins largely involved in energy metabolism, synaptic proteins, inflammatory response, and ATP binding were simultaneously detected in 3xTg-AD mice compared to WT mice. The abnormal changes of energy metabolism (PAGM1 and ENO1), synaptic proteins (SYN2 and Cplx2), inflammatory response (1433Z) were verified by western blot. Overall, our study suggested that loganin could be used as a feasible candidate drug to ameliorate molecular deficits, pathologies and cognitive impairment for prevention and treatment of AD.

In this paper, a novel mobile platform for assistive robotics tasks is presented. The machine is designed for working in a home environment, un-structured and possibly occupied by people. To work in this space, the platform must be able to get rid of all the consequent difficulties: to overpass small objects as steps and carpets, to operate with an as-high-as-possible dynamics, to avoid moving obstacles, and to navigate autonomously to track persons for person monitoring purposes. The proposed platform is designed to have an omni-directional mobility that improves the manoeuvrability with respect to state-of-the-art differential drive robots. It also will have a non-axisymmetric shape to easily navigate narrow spaces, and real-time edge computing algorithms for navigation. This work shows the design paradigm adopted for the realization of a novel mobile robot, named Paquitop. For a robust output, the design process used a modular approach which disjointed the several sub-systems which compose the machine. After a brief analysis of the expected features, a set of basic requirements are drawn to guide the functional and executive design. The overall architecture of the platform is presented, together with some details on the mechanical and electrical systems.

This invited paper demonstrated an ultra-highly sensitive ammonia (NH3) sensor based on the light-induced thermoelastic spectroscopy (LITES) technique for the first time. A quartz tuning fork (QTF) with a resonance frequency of 32.768 kHz was employed as a detector. A fiber-coupled, continuous wave (CW), distributed feedback (DFB) diode laser emitting at 1530.33 nm was chosen as the excitation source. Wavelength modulation spectroscopy (WMS) and second-harmonic (2f) detection techniques were applied to reduce the background noise. In a one scan period, a 2f signal of the two absorption lines located at 6534.6 cm−1 and 6533.4 cm−1 were acquired simultaneously. The 2f signal amplitude at the two absorption lines was proved to be proportional to the concentration, respectively, by changing the concentration of NH3 in the analyte. The calculated R-square values of the linear fit are equal to ~0.99. The wavelength modulation depth was optimized to be 13.38 mA, and a minimum detection limit (MDL) of ~5.85 ppm was achieved for the reported NH3 sensor.