Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrared and visible image fusion via rolling guidance filter and weight map

Abstract. The main task of image fusion is to extract the advantage information from the source images. Multiscale transform (MST) is commonly used in image fusion, but it is difficult to integrate detail information and structural information from source images. Therefore, it is important to integrate the information in each image patch group. Different from MST, edge preserving technology obtains more edge information than MST by extracting spatial information. Based on this superior feature, we propose an infrared and visible image fusion method via rolling guidance filter (RGF) and weight map. First, source images are decomposed into multiscale detail layers and structural base layers by RGF; second, to avoid the loss of local information, the modified Laplacian energy weight matrix is used as detail weight maps to merging the detail layers; then to reduce the loss of contours of edge objects, a structural weight maps are used to merging the base layers; finally, these two different weight maps are applied to reconstruct the fused image. Simulation results indicate that our fusion approach performs better in merging detail information and structural information than some classic MST fusion algorithms and the latest fusion methods.
Selenium Induces Pancreatic Cancer Cell Death Alone and in Combination with Gemcitabine

Survival rate for pancreatic cancer remains poor and newer treatments are urgently required. Selenium, an essential trace element, offers protection against several cancer types and has not been explored much against pancreatic cancer specifically in combination with known chemotherapeutic agents. The present study was designed to investigate selenium and Gemcitabine at varying doses alone and in combination in established pancreatic cancer cell lines growing in 2D as well as 3D platforms. Comparison of multi-dimensional synergy of combinations’ (MuSyc) model and highest single agent (HSA) model provided quantitative insights into how much better the combination performed than either compound tested alone in a 2D versus 3D growth of pancreatic cancer cell lines. The outcomes of the study further showed promise in combining selenium and Gemcitabine when evaluated for apoptosis, proliferation, and ENT1 protein expression, specifically in BxPC-3 pancreatic cancer cells in vitro.
Fuzzy Logic Prediction Model for Clearance Time of Freeway Traffic Incidents

As one part of the duration time of freeway traffic incidents,clearance time is closely connected with incident duration and incidence.A new definition of clearance time which has more correlations with incident duration regarding to emergency vehicles is put forward.The prediction model based on TSK fuzzy logic is constructed,according to data training by artificial neural network(ANN). The test results indicate that the proposed model can predict the clearance time of common traffic incidents quite well when the actual clearance time is no less than 10 minutes.
The Business of Improving Neighborhoods. A Critical Overview of Neighborhood-Based Business Improvement Districts (NBIDs) in Sweden

This article offers an overview of neighbourhood-based BIDs (NBIDs) in Sweden. Swedish NBIDs tend to appear in stigmatized residential areas engaging with pressing sets of urban issues that have been longstanding concern of social policy. Their overarching goal is raising property values in neighborhoods on the edge between urban decline and (re)development potential. Emerging in a neoliberalizing institutional context, NBIDs present themselves as correctives to public-policy failures by promoting property-oriented solutions. The adaptation of the BID model in the Swedish ‘post-welfare’ landscape, however, exhibits, and arguably exacerbates, the shortcomings found in BID elsewhere. Their opaque institutional structure and lack of accountability contribute to curbing democratic influence over local development, thus reinforcing spatial inequalities. We argue that the growing political advocacy for the institutionalization of the BID model in Sweden presents a new milestone in the neoliberalization of urban governance, as private actors are promoted to legitimate co-creators of urban policy.
A Short–Term Load Forecasting Model Based on ARIMA and BPNN

According to its features a short-term load forecasting model is built in which the autoregressive integrated moving average (ARIMA) is integrated with back propagation neural network (BPNN). By use of the strong approaching capacity to linear time series of ARIMA, the proposed model forecasts the load of the predicted day at first, then the load forecasted by ARIMA is modified by BPNN, thus the insufficiency of single algorithm is overcome. The forecasted results of an actual regional power system by the proposed model show that this method can offer better load forecasting results.
Electric bus charging station supplied by urban electrical DC railway network

The increasing charging stations for electric buses in urban areas bring critical challenges to the electricity distribution network. One potential solution is to interconnect a charging station to the urban Electrical DC Railway Network (EDCRN) to design an electrical smart grid. This work develops a charging strategy to ensure that the electric bus can circulate without stop at the bus depot for recharging during the daytime. To minimize the power demand from the EDCRN, the braking energy of urban trains is recovered to recharge electric bus. The battery is added to store more instantaneous braking energies for recharging the bus later, and its storage capacity is properly sized. Moreover, renewable energies are integrated to achieve an environment-friendly urban electric mobility system. The energy management system is formulated to ensure a reliable and efficient operation of the DC railway micro-grid. An energetic simulation model is built for a period of 24 hours by using the Energetic Macroscopic Representation. Critical results are provided to verify the proposed charging strategy.
Study on Pulse Receiver Technology in Pulsed Laser Rang Finding

Based on the analysis of photodiode in pulsed laser rangfinding, a theory model is setup to describe the reason of drift error, and the approximate relation functions between the photodiode response time, its distortion and the laser pulse power projected on the detector are further obtained respectively. The corresponding self-triggering pulsed laser rangfinding with automatic gain control is proposed, which can not only reduce the measurement tolerance caused by signal rising edge but also reduce the drift error brought by the variable signal amplitude.
Benchmarking missing-values approaches for predictive models on health databases

Abstract Background As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values. These large databases are well suited to train machine learning models, e.g., for forecasting or to extract biomarkers in biomedical settings. Such predictive approaches can use discriminative—rather than generative—modeling and thus open the door to new missing-values strategies. Yet existing empirical evaluations of strategies to handle missing values have focused on inferential statistics. Results Here we conduct a systematic benchmark of missing-values strategies in predictive models with a focus on large health databases: 4 electronic health record datasets, 1 population brain imaging database, 1 health survey, and 2 intensive care surveys. Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning. We investigate prediction accuracy and computational time. For prediction after imputation, we find that adding an indicator to express which values have been imputed is important, suggesting that the data are missing not at random. Elaborate missing-values imputation can improve prediction compared to simple strategies but requires longer computational time on large data. Learning trees that model missing values—with missing incorporated attribute—leads to robust, fast, and well-performing predictive modeling. Conclusions Native support for missing values in supervised machine learning predicts better than state-of-the-art imputation with much less computational cost. When using imputation, it is important to add indicator columns expressing which values have been imputed.
Hidden Markov Model-based Clustering for Malware Classification

Hidden Markov Model-based Clustering for Malware Classification by Shamli Singh Automated techniques to classify malware samples into their respective families are critical in cybersecurity. Previously research applied k-means clustering to scores generated by hidden Markov models (HMM) as a means of dealing with the malware classification problem. In this research, we follow a somewhat similar approach, but instead of using HMMs to generate scores, we directly cluster the HMMs themselves. We obtain good results on a challenging malware dataset.
Research on International Standard Architecture for Smart Grid Dispatching and Control Technology

A survey on the overall situation of IEC standard, Chinese national standard and industrial standard related to the dispatching and control technologies for smart grid is given, and experiences and lessons of implementing relevant standards to smart grid dispatching and smart substation are summarized. The problems existing in such mainstream standards as IEC 61970, IEC 61850 and so on are analyzed in depth, and combining with the actual demands of power grid operation and control as well as the development trend of computer and communication technologies the basic idea and main measures to cope with the problems are proposed. Besides, the standard architecture of smart grid dispatching and control technologies, which is based on the unified security structure, unified communication protocol, unified model description and unified service interfaces, is demonstrated.
Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model

The Madden–Julian oscillation (MJO) is the dominant component of tropical intraseasonal variability, with wide‐reaching impacts even on extratropical weather and climate patterns. However, predicting the MJO is challenging. One reason is the suboptimal state estimates obtained with standard data assimilation (DA) approaches. These are typically based on filtering methods with Gaussian approximations and do not take into account physical properties that are important specifically for the MJO. In this article, a constrained ensemble DA method is applied to study the impact of different physical constraints on the state estimation and prediction of the MJO. The quadratic programming ensemble (QPEns) algorithm utilized extends the standard stochastic ensemble Kalman filter (EnKF) with specifiable constraints on the updates of all ensemble members. This allows us to recover physically more consistent states and to respect possible associated non‐Gaussian statistics. The study is based on identical twin experiments with an adopted nonlinear model for tropical intraseasonal variability. This so‐called skeleton model succeeds in reproducing the main large‐scale features of the MJO and closely related tropical waves, while keeping adequate simplicity for fast experiments on intraseasonal time‐scales. Conservation laws and other crucial physical properties from the model are examined as constraints in the QPEns. Our results demonstrate an overall improvement in the filtering and forecast skill when the model's total energy is conserved in the initial conditions. The degree of benefit is found to be dependent on the observational setup and the strength of the model's nonlinear dynamics. It is also shown that, even in cases where the statistical error in some waves remains comparable with the stochastic EnKF during the DA stage, their prediction is improved remarkably when using the initial state resulting from the QPEns.
Antioxidant and antifungal activities in vitro of essential oils and extracts of twelve Algerian species of Thymus against some mycotoxigenic Aspergillus genera

The aim of the study was to determine the phenolic and flavonoid content of essential oils (EOs), chloroform and ethanolic extracts of 12 Algerian Thymus species and evaluate their antioxidant and antifungal activities. EOs (1.73 ± 0.30–15.00 ± 1.24 μg/mg), chloroform extracts (33.8 ± 2.42–160.93 ± 3.88 μg/mg) and ethanol extracts (27.01 ± 3.56 –148.46 ± 4.40 μg/mg) showed considerable phenolic content. Flavonoids values of chloroform extracts ranged between 3.39± 0.17 and 20.27 ± 0.29 μg/ml while ethanolic extracts values ranged between 2.81 ± 0.11 and 26.64 ± 0.18 μg/mg. Results of DPPH showed that EOs, chloroform and ethanolic extracts exhibited strong radical scavenging activity (IC50 = 21.75 ± 6.54–338.22 ± 2.99 μg/ml, 22.91 ± 5.59–90.93 ± 1.36 μg/ml, and 33.51 ± 5.72–103.80 ± 4.54 μg/ml, respectively). Inhibition of β-carotene bleaching was potentially performed by all EOs (66.48 ± 2.41–94.06 ± 2.68 %), chloroform extracts (68.98± 1.58–95.30± 1.99%), and ethanolic extracts (62.15 ± 2.51–92.36± 1.15%). The antifungal activity of EOs and extracts was tested using the minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC). The EOs (0.1 ± 0.00 mg/ mL – 1.06 ± 0.46 mg/mL), chloroform (0.1 ± 0.00 mg/ mL –1.06 ± 0.46 mg/mL) and ethanol (0.1 ± 0.00 mg /mL–1.6 ± 0.00 mg/mL) showed remarkable antifungal activity against mycotoxigenic Aspergillus genera. The MFC of EOs (1.0 ± 0.34 mg/mL and > 4.8 mg/mL) , chloroform (0.26 ± 0.11 mg/mL and > 1.6 mg/mL) and ethanol (0.2 ± 0.00 mg/mL and > 1.6 mg/mL) were fungicidal in nature higher than MICs. The findings of the study indicated that Thymus spp. EOs and extracts could be used as natural alternatives for food industry.
Regional Integration in the West African Economic and Monetary Union (WAEMU): Complementarity or Competition?

This paper analyzes and characterizes the nature of the interactions between countries of the West African Economic and Monetary Union (WAEMU) over the period 1995–2015. The analysis uses sigma-convergence on the one hand and the Dendrinos-Sonis spatial competition model estimated by the SUR method on the other hand. The results show a lack of convergence of living standards and support the idea of income polarization in space; these results also support the idea of a very poorly integrated region with relatively competitive interrelationships. The paper suggests the acceleration of regional integration in the WAEMU region combined with the implementation of inclusive integration policies that promote each member’s comparative advantage.
Implications from Responsible Human-Robot Interaction with Anthropomorphic Service Robots for Design Science

Accelerated by the COVID-19 pandemic, anthropomorphic service robots are continuously penetrating various domains of our daily lives. With this development, the urge for an interdisciplinary approach to responsibly design human-robot interaction (HRI), with particular attention to human dignity, privacy, compliance, and transparency, increases. This paper contributes to design science, in developing a new artifact, i.e., an interdisciplinary framework for designing responsible HRI with anthropomorphic service robots, which covers the three design science research cycles. Furthermore, we propose a multi-method approach by applying this interdisciplinary framework. Thereby, our finding offer implications for designing HRI in a responsible manner. © 2022 IEEE Computer Society. All rights reserved.
Dimensions of Digital B2B Platforms in Logistics - A White Spot Analysis

Digital platforms have not only transformed entire B2C market segments but also created new markets benefiting from indirect network effects by providing technological building blocks and infrastructure. Digital platforms and according business models can also be found in the B2B context. Especially, logistics seems to be an adequate application for digital, platform-based business models. The present article focuses on B2B logistics platforms and questions whether principles of B2C platforms can be transferred to the domain of logistics. In order to assess the transferability of B2C platform characteristics, a white spot analysis is conducted along a sample of 54 digital platforms. The goal of the white spot analysis is to provide insights into the characteristics of digital B2B platforms in logistics. Moreover, the analysis provides a basis for the discussion whether B2C platform principles can be adopted in an industrial context.
User Experience of Training Pediatric Students on Interactive Simulator During COVID-19 Pandemic

Background COVID-19 pandemic is a reason to revise traditional approaches to medical education. The disadvantage of online education is inability to train practical skills. Purpose To evaluate the effectiveness of training students on an interactive pediatric simulator during distance learning. Patients and Methods An online survey was conducted during quarantine COVID-19 to clear out the attitude to distance learning of third-year students of the Sechenov First Moscow State Medical University (n = 150). During training the level of students’ motivation to learn decreased, and what is most important their practical skills deteriorated. Eighty-four out of 150 students had the effectiveness of their training on the multimedia simulator “Filatov. Pediatrics” analyzed. Results The simulator reproduces clinical tasks using three-dimensional models of children of different ages and genders and has access to personal computers of students. The cases include imitation of a dialogue with a virtual patient, methods of physical examination, the appointment of laboratory and instrumental methods of research, treatment. The use of a pediatric simulator showed greater efficiency in mastering practical skills compared to a group of students who did not use the simulator in class. Summing up the results of the intermediate objective structured clinical exam (OSCE) on physical examination on dummies of the Simulation and Accreditation Center showed better results in the main group (n = 48) compared to the control (n = 36). The students who additionally studied on the simulator completed the tasks on the checklist on a cardiology dummy by 98.3 ± 1.5%. In the control group, the percentage of completed tasks was 94.3 ± 1.3 (p = 0.05). The OSCE for examining the respiratory system on an auscultative dummy revealed 97.3 ± 1.5 and 93.1 ± 1.4%, respectively (p = 0.05). Conclusion The interactive pediatric simulator is a tool for mastering practical skills of medical students during the COVID-19 pandemic, with the use of which the OSCE results are significantly higher.
Fault Resilient Soft Switching DC–DC Converter for Modular Solid State Transformer Applications

Soft-switched isolated converters are preferred for dc–dc stage in solid state transformer applications, wherein fault resilience is highly desirable. This article presents a fault resilient, soft-switching dc–dc converter and associated integrated controller. The proposed integrated controller ensures smooth and reliable fault resilient operation with simplified reconfiguration of employed voltage restorer rectifier during fault. The integrated controller promotes converter design for operation above resonance to avail soft transition of all power devices, reduced EMI, and deployment of body diode integrated switches. Further, the reconfigurable multiplier-type rectifier increases overall converter gain without increased transformer turns ratio. The converter also favors minimized resonant inductance due to lower equivalent resistance and reduced transformer size resulting from above resonant operation. The converter characteristic curves during normal and fault states are presented. Experimental investigations for 1.25-kW prototype in both dynamic and steady state are presented to demonstrate fault resilient capabilities and performance efficacy. Also, impact of postfault reconfiguration on operation frequency, efficiency, and loss distribution is demonstrated.
Combination therapy with pioglitazone/exenatide/metformin reduces the prevalence of hepatic fibrosis and steatosis: The efficacy and durability of initial combination therapy for type 2 diabetes (EDICT)

To compare the efficacy of triple therapy (metformin/exenatide/pioglitazone) versus stepwise conventional therapy (metformin → glipizide → glargine insulin) on liver fat content and hepatic fibrosis in newly diagnosed, drug‐naïve patients with type 2 diabetes.
Study on Convection Heat Transfer of End-Winding for a 10 kW External Rotor PMSM With Open End Cap

The convective heat transfer characteristics of end-winding for a 10 kW external rotor PMSM with open end cap are studied in this paper. A coupling iterative method of CFD and thermal network (TN) is proposed to solve the motor temperatures and convective heat transfer coefficients (CHTC). The variation laws of the CHTCs and heat dissipation proportions of 10 kW motor walls with speed are analyzed. The heat dissipation capacities of the end-winding for motors with open and closed end cap are compared. Based on the CFD results, a fitting formula of end-winding CHTC for external rotor motor with open end cap is constructed, in which the Nusselt number is calculated by the dimensionless parameters composed of the geometric features of the open end cap. The validities of the proposed coupling iterative method and the fitting formula are verified by the temperature rise experiment of the 10 kW prototype.
Electronic nose system based on integrated gas sensors array

A hardware method of the electronic nose system realized based on integrated gas sensors array is (provided.) Its constitutes, integrated gas sensors and the method of obtaining sensors signal are introduced. The system can realize automatically data collection for detected gas and communicate with PC,it has high (sensitivity),reliability and certain common in use for sensor series.Five kinds of wines are detected by this (system.)The results show that the electronic nose system can be used to qualitatively identify wines.Accurate rate has achieved 100%.
Studies on Image Processing of Maize Leaf Diseases

IIn order to identify the disease by using computer, digital image processing technique and pattern recognition technique were used in the paper, which was composed of image acquisition, image preprocessing, feature extraction and matching. The image is converted from RGB model to HIS. A gray level co-occurrence matrix was used to capture the texture features. The particle swarm optimization algorithm was used to improve the bp neural network algorithm. A maize leaf disease features' extraction and recognition system was constructed at last. The rationality and validity were validated experimentally.