Miracle-dz/UniFAR-FTUs huggingface dataset:
On a fair distribution of consumer's flexibility between market parties with conflicting interests

Summary The present work deals with social welfare maximization for energy scheduling. The parties involved in this study are a utility company, a distribution system operator and residential end-users. It allows the different participants to share a common electrical grid, without violating network constraints. A smart grid, equipped with a two-way communication system on a district level, is considered. For this purpose, each participant exchanges information, in order to establish a power consumption schedule that optimizes its own objectives, within the grid constraints. This strategy is illustrated on a district level to avoid overloading the distribution network. The consumption profiles of end-users consist of background consumption; some end-users are equipped with a heat pump and/or an electric vehicle, each of which is capable of responding to price variations. The impact of several tariff structures on the stability of the grid is explored. First of all, flat and day/night tariff structures are considered, and it is shown that price variation provokes load synchronization and leads to network overloads. Next, a real-time pricing tariff structure is used. If each end-user optimizes its behaviour to minimize personal costs, the grid is unable to supply all power during peak moments. In conclusion, if power constraints are imposed, the same grid can be stabilized, even if power demands increase by a factor of three or four. In a final example, the impact of imbalance costs is examined. The cluster can be guided to maximize profits for the utility company from the imbalance mechanism. Copyright © 2016 John Wiley & Sons, Ltd.
ASSOCIATION BETWEEN AGE, PRACTICE TIME AND THE DEVELOPMENT OF LIFE SKILLS IN BRAZILIAN ADAPTED SPORT PARTICIPANTS: A STUDY BY TYPE OF SPORT

Life skills are acquired skills necessary to cope with the demands and challenges of everyday life. These life skills are particularly important, as through these life skills learned through sport young people can transfer them to other domains of life (education, workplace, and personal relationships). The aim of the study was to identify the predictive role of age and length of practice in the development of life skills among 210 participants of adapted sports. The sample included 170 boys and 40 girls aged 11-26 years (M = 24.05, SD = 1.59) from collective and individual sports. The instruments used were the Life Skills Scale for Sport and a semi-structured questionnaire. Data analysis was performed using Multivariate Analysis of Variance, Pearson's Correlation and Multiple Regression (p <0.05). The results showed a significant difference (p <0.05) in all life skills according to the type of sport, evidencing higher scores for team sports. Multiple regression revealed that age and time of practice showed a positive prediction regarding life skills in both groups. In practice, the results suggest that coaches and parents should encourage practitioners of adapted sports to develop their life skills through sport, especially practitioners of individual modalities and that age and time of practice had a predictor role for the development of life skills. Received on: 2021/12/06 Reformulated on: 2022/03/31 Accepted: 2022/04/01
Does egg carotenoid improve larval quality in Arctic charr (Salvelinus alpinus)?

Abstract Females in mutually ornamented species are often less conspicuously ornamented than their male conspecifics. It has been hypothesized that offspring quality may decrease if females invest more resources into ornaments at the expense of resources in eggs. An experiment was carried out to test whether natural variation in carotenoid in the eggs from a wild population of Arctic charr (Salvelinus alpinus) was associated with survival and growth of their offspring until hatching. Wild Arctic charr were caught at a spawning ground during the spawning period. Eggs from two different females, one female with yellowish carotenoid‐rich eggs and one with paler eggs, were fertilized by sperm from the same male. This was repeated until gametes were collected from 42 females and 21 males, giving a total of 21 groups. After fertilization, the zygotes from each of the two females were reared in four replicated groups. These 168 groups were reared separately until hatching when the surviving larvae were counted and their body length measured. For the two response variables survival and body length at hatching, no effect was demonstrated of any of the predictors (i) amount of carotenoid in the unfertilized eggs, (ii) the mothers' body condition, or (iii) ornament intensity of their red carotenoid‐based abdominal ornament. Thus, this study gives no support for the hypothesis that females investing less carotenoid into their eggs suffer from decreased offspring quality until hatching. This lack of association between female ornament intensity and their fitness is not as expected if female ornaments evolved due to direct sexual selection from males on the more ornamented females (“direct selection hypothesis”).
Calculation Method of Logistics Energy Consumption in Agricultural Product Supply Chain Based on Structural Equation Model

The ecological value chain agricultural products in China are mainly produced by scattered farmers. Due to the lack of modern planting and breeding techniques for small-scale scattered farmers, it is difficult to obtain scale effects, and the benefit-cost ratio is generally low. Their own production, supply, and marketing integration organization can only enter the market separately, which is difficult to adapt to market needs, and often bear great market risks. In the circulation link, the agricultural product logistics management level in China is low and logistics losses are huge. This research attempts to conduct a theoretical analysis and discussion on the integration of the ecological agricultural product supply chain in China based on the enterprise theory. At the same time, we conduct an empirical analysis of the relationship between supply chain integration and performance from the perspective of logistics and build agricultural product supply chain integration with the company’s logistics capabilities as the center. We perform hypothesis model, collect data through survey methods, use quantitative analysis and path analysis methods to test the hypothesis model, explore the relationship between supply chain management and agricultural product logistics capabilities, and corporate competitiveness, to provide the empirical support for the introduction of supply chain management in the agriculture in China.
Convolutional Neural Networks for Structural Damage Identification in Assembled Buildings

This paper investigates the migration learning AlexNet-based algorithm for the recognition of assembly building structures and the recognition based on an improved algorithm, and gives an analysis of the results. The structure of AlexNet convolutional neural network is introduced and the basic principles of migration learning are analysed. The optimal model for the ceiling damage recognition task was obtained through parameter adjustment, with a test accuracy of 96.6%. The maximum improvement in test accuracy is about 4%, with 82.6% and 79.7% for beam and column damage recognition and inﬁll wall damage recognition respectively.
The Canadian Social Work Review: A Canadian Character of Social Work?

ABSTRACT Noting that scholarly journals represent a particular repository of knowledge, we use content analysis to explore the constructions of social work represented in the Canadian Social Work Review – Revue canadienne de service social over 2010–2019. This journal is the only formal bilingual (French-English), peer-reviewed social work journal in the country. Rather than broadly reflecting Canadian realities and contexts, the emerging trends imply specific regional and social work program dominance, both in terms of authorship and issues explored. In part this is related to English-French language parity having been achieved, though this has led to other unintended consequences. While the published articles represent critical discourses and qualitative approaches are preferred, many articles do not address power, oppression and representation, particularly with regard to Indigenous, racialized and gendered groups. We conclude that the journal, whilst leaning toward a critical representation of social work, also reflects mainstream, dominant views of Canadian social work, the journal thus portraying the contested nature of Canadian social work. Mechanisms that add to existing Editorial Board efforts for further strengthening a critically interrogative lens may be required. Other social work journals may want to consider the story they are telling the profession and the ways in which
Software Misconfiguration Troubleshooting Based on State Analysis

Misconfiguration has become one of the dominant causes of service failure and software abnormality. It will not only affect the user experience and cause economic losses, but also require a lot of manpower for troubleshooting. Therefore, an automated tool to quickly diagnose misconfiguration is essential. This paper introduces a tool called ConfDetect, which treats system as data to derive the troubleshooting clues using state analysis. ConfDetect ranks logs based on heuristics to give problem-related messages, uses NLP for Part-Of-Speech tagging to filter out suspicious words which are compared with configuration key-value pairs and environment information, or searches the knowledge base to find misconfiguration items. The knowledge base realizes self-learning by analyzing the configuration changes of the sick system after it has been healed, and predicts the configuration item resulting in the error log. Misconfiguration tests on MySQL, Ngnix and an autonomous vehicle simulation program on ROS show that ConfDetect is able to deliver error message with 91% accuracy and find specific misconfiguration item with 74% accuracy in general. Besides, the time cost of ConfDetect is in proportion to the volume of logs and it takes less than a few seconds to process thousands of lines log. Experiment results prove that ConfDetect is effective in diagnosing misconfiguration and runs fast.
Antibiotic prescribing for respiratory tract infection across a national primary care network in 2019.

Background Respiratory tract infection (RTI) is the leading reason for avoidable antimicrobial use in primary care, yet provider-level feedback on its use is only available in some provinces. The aim of this study was to validate case definitions for RTI across the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) and determine baseline provider-level variability in antimicrobial prescribing in 2019. Methods The RTI case definitions were developed using demographic, diagnostic coding and keywords in electronic medical record. Manual chart abstraction was performed to identify cases of acute otitis media. Remaining RTI definitions were validated using a random sample of 5,164 patients with encounters in 2019. The proportion of patients with an RTI treated with antibiotics was determined by provider, per patient, per episode and per patient encounter. Results Negative predictive value, positive predictive value and prevalence were as follows: 1.00 (0.99-1.00), 0.99 (0.96-0.99) and 4.14% (4.10-4.19) for common cold; 1.00 (0.99-1.00), 0.94 (0.88-0.98) and 1.09% (1.07-1.12) for acute otitis media; 0.98 (0.96-1.00), 0.93 (0.87-0.97) and 1.2% (1.18-1.22) for acute pharyngitis; 0.99 (0.99-1.00), 0.88 (0.81-0.93) and 1.99% (1.96-2.02) for sinusitis; 0.99 (0.97-0.99), 0.95 (0.89-0.98) and 4.01% (3.97-4.05) for acute bronchitis/asthma. By provider, median (interquartile range [IQR]) proportion treated with antibiotics (per patient) was 6.72 (14.92) for common cold, 64.29 (40.00) for acute otitis media, 20.00 (38.89) for pharyngitis, 54.17 (38.09) for sinusitis, 8.33 (20.00) for acute bronchitis/asthma and 21.10 (20.56) for overall RTI. Conclusion The CPCSSN can provide national surveillance of antimicrobial prescribing practices for RTI across primary care. Baseline variability underscores the need for provider feedback and quality improvement.
TI : LINBO3 ACOUSTO-OPTIC TUNABLE FILTER (AOTF)

We have developed the following new elements for an AOTF: an intersecting waveguide PBS which has a high extinction ratio, low loss, and wavelength independent characteristics; a film-loaded SAW guide (SAWG) which has a strong confinement, excellent filter characteristics, and provides a high design flexibility; and a 0-gap directional coupler type reflector which enables high-performance waveguide integration. These elements were combined to construct an AOTF consisting of five film-loaded SAWGs. The through and drop lights in this AOTF are filtered by three stages of the new SAWG. The 3 dB bandwidth and extinction ratio are 0.37 nm and less than -27 dB, respectively. An optical ADM system that incorporated the new AOTF has demonstrated 10 Gb/s × 32-channel transmission with a 0.8 nm wavelength spacing.
A Comprehensive Review of Deep Learning Algorithms

Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.
Digital Marketing Plan: Naked Wines Australia

This paper develops a digital marketing plan for Naked Wines Australia over a 12-month period based on situation analysis of the current marketing environment. Naked Wines’ current digital marketing strategy relies strongly on their proficient SEO capabilities, their engaged online community of existing customers and their unique online proposition that combines online wine retailing and crowd funding. The overall objectives that the paper identifies include: Sell Objective: To increase total sales revenue from new customers by 20% within the next 12 months Sizzle Objective: To increase brand awareness of new customers by 15% within the next 12 months The paper outlines specific strategies and tactics that aim to satisfy the achievement of these objectives with a focus on reaching new customers. The key strategies to be implemented as part of this plan include an integrated social media, content marketing and offline communication strategies. The report identifies the associated tactics as viral campaign, YouTube content optimisation, as well as print ads and flyers. The underlying focus of this paper is to extend the current reach of Naked Wines Australia within its competitive online market. Simply relying on their current market positioning and the customers they service within it limits Naked Wines Australia opportunities for growth and development. The report sets the overall budget at $400,000 based on past performance, whereby the largest proportion of this budget has been allocated to the social media, content marketing and offline strategies. To evaluate the plan, the report highlights the main methods of control as Google Analytics, short surveys, and a balance scorecard.
Pulse plethysmograph signal analysis method for classification of heart diseases using novel local spectral ternary patterns

Cardiac disorders are one of the prime reasons for an increasing global death rate. Reliable and efficient diagnosis procedures are imperative to minimize the risk posed by heart disorders. Computer‐aided diagnosis, based on machine learning and biomedical signal analysis, has recently been adopted by researchers to accurately predict cardiac ailments. Multi‐channel Electrocardiogram signals are mostly used in scientific literature as an indicator to diagnose cardiac disorders. Recently pulse plethysmograph (PuPG) signal got attention as an evolving biosignal and promising diagnostic tool to detect heart disorders since it has a simple sensor with low cost, non‐invasive, reliable, and easy to handle technology. This article proposes a computer‐aided diagnosis system to detect Myocardial Infarction, Dilated Cardiomyopathy, and Hypertension from PuPG signals. Raw PuPG signal is first preprocessed through empirical mode decomposition (EMD) by removing the redundant and useless information content. Then, highly discriminative features are extracted from preprocessed PuPG signal through novel local spectral ternary patterns (LSTP). Extracted LSTPs are input to a variety of classification methods such as support vector machines (SVM), K‐nearest neighbours, decision tree, and so on. SVM with cubic kernel yielded the best classification performance of 98.4% accuracy, 96.7% sensitivity, and 99.6% specificity with 10‐fold cross‐validation. The proposed framework was trained and tested on a self‐collected PuPG signals database of heart disorders. A comparison with previous studies and other feature descriptors shows the superiority of the proposed system. This research provides better insights into the contributions of PuPG signals towards reliable detection of heart disorder through low‐cost and non‐invasive means.
Analysis and Research on Image Factors of Digital RMB Acceptance Willingness Based on Big Data Analysis Algorithm

Based on the e background of digital RMB, the team takes the digital RMB as the survey object to investigate and analyze the willingness of the people to accept. This survey adopts simple random sampling, the sampling design effect (Deff) is set to 2, and the total number of theoretical samples should be 937. By using K-means clustering, the people were divided into three groups: “DC/EP active trial type”, “DC/EP willing to explore type” and “DC/EP conservative wait-and-see type”, and their willingness to accept was understood respectively. Then use exploratory factor analysis and structural equation model in-depth analysis to draw the following conclusions: the overall willingness of the people to accept the digital RMB is good, and the people prefer safe and simple payment methods. The three characteristics of “security”, “double offline payment” and “legal compensation” have become the main advantages of digital RMB.
MODEL-BASED CONTROL OF HYBRID ELECTRIC POWERTRAINS INTEGRATED WITH LOW TEMPERATURE COMBUSTION ENGINES

Powertrain electrification including hybridizing advanced combustion engines is a viable cost-effective solution to improve fuel economy of vehicles. This will provide opportunity for narrow-range high-efficiency combustion regimes to be able to operate and consequently improve vehicle’s fuel conversion efficiency, compared to conventional hybrid electric vehicles (HEV)s. Low temperature combustion (LTC) engines offer the highest peak brake thermal efficiency reported in literature, but these engines have narrow operating range. In addition, LTC engines have ultra-low soot and nitrogen oxides (NOx) emissions, compared to conventional compression ignition and spark ignition (SI) engines. This dissertation concentrates on integrating the LTC engines (i) in series HEV and extended range electric vehicle (E-REV) architectures which decouple the engine from the drivetrain and allow the ICE to operate fully in a dedicated LTC mode, and (ii) a parallel HEV architecture to investigate optimum performance for fuel saving by utilizing electric torque assist level offered by e-motor. An electrified LTC-SI powertrain test setup is built at Michigan Technological University to develop the powertrain efficiency maps to be used in energy management control (EMC) framework. Three different types of Energy Management Control (EMC) strategies are developed. The EMC strategies encompass thermostatic rule-based control (RBC), offline (i.e.,
Family 1 Glycosyltransferase UGT706F8 from Zea mays Selectively Catalyzes the Synthesis of Silibinin 7-O-β-d-Glucoside

Regioselective glycosylation is a chemical challenge, leading to multistep syntheses with protecting group manipulations, ultimately resulting in poor atom economy and compromised sustainability. Enzymes allow eco-friendly and regioselective bond formation with fully deprotected substrates in a single reaction. For the selective glucosylation of silibinin, a pharmaceutical challenged with low solubility, enzyme engineering has previously been employed, but the resulting yields and kcat were limited, prohibiting the application of the engineered catalyst. Here, we identified a naturally regioselective silibinin glucosyltransferase, UGT706F8, a family 1 glycosyltransferase from Zea mays. It selectively and efficiently (kcat = 2.1 ± 0.1 s–1; KM = 36.9 ± 5.2 μM; TTN = 768 ± 22) catalyzes the quantitative synthesis of silibinin 7-O-β-d-glucoside. We solved the crystal structure of UGT706F8 and investigated the molecular determinants of regioselective silibinin glucosylation. UGT706F8 was the only regioselective enzyme among 18 glycosyltransferases found to be active on silibinin. We found the temperature optimum of UGT706F8 to be 34 °C and the pH optimum to be 7–8. Our results indicate that UGT706F8 is an efficient silibinin glycosyltransferase that enables biocatalytic production of silbinin 7-O-β-d-glucoside.
Development of argument based opinion mining model with sentimental data analysis from twitter content

In present scenario, social networks have developed massive in practice and society impact. Specifically, micro‐blogging is on trend in various platforms, such as Twitter, Instagram to evaluate public opinions for various issues. In recent times, some methods are developed for evaluating Twitter messages, based on the sentiment and opinions presented in tweets, corresponding to the hash‐tags and keywords. However, these models have some issues in handling the contradictory content and inconsistent data. Considering with this, this article presents an argument based opinion mining model with sentimental data analysis, for extracting specific argument, which is assessed in bottom‐up manner from the content from society emotion's reflects on the messages. Moreover, this model makes the user to pull out the arguments from a document set, which contains content from commercial sites, to extract the mostly argued positive and negative content. This model use natural language processing techniques, extraction of argument words for defining the decisions. The classification Naive Bayes classification is used for categorizing the results widely under agreed or disagreed. The experimental results prove that the proposed model provides feasible and appropriate results in argument analysis from Twitter content.
An End-to-End No-Reference Video Quality Assessment Method With Hierarchical Spatiotemporal Feature Representation

In this paper, we propose a deep neural network-based no-reference (NR) video quality assessment (VQA) method with spatiotemporal feature fusion and hierarchical information integration to evaluate the perceptual quality of videos. First, a feature extraction model is proposed by using 2D and 3D convolutional layers to gradually extract spatiotemporal features from raw video clips. Second, we design a hierarchical branching network to fuse multiframe features, and the feature vectors at each hierarchical level are comprehensively considered during the process of network optimization. Finally, these two modules and quality regression are synthesized into an end-to-end architecture. Experimental results obtained on benchmark VQA databases demonstrate the superiority of our method over other state-of-the-art algorithms. The source code is available online.1