Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Reliability On Wireless Sensor Network For Energy Irregularity Models Using Fuzzy Deep Learning Protocol: Fdlp

: Electronic devices are widely used in many engineering and medical fields, such as aviation, power, communications, military and robotic, etc. Usually, the reduced the precision of the device, the more components the system includes. However, part failures are usually conditional. The safety model for conditional electronic circuit failure (WSN) is built in this paper and the impacts of decentralized strength and decentralized strain are discussed. Thanks to a wide range of applications, the Wireless Sensor Networks (WSN) has become the primary technology for universal life and remains an efficient research. The development of a secure and energy-efficient WSN remains to pose a major study task. Merger clustering methods have frequently been used to decrease energy consumption and extend the life of the network. This article presents a deep learning algorithm centered on fusion-level clustering logic, WSN-oriented routing, cross-protocol to eliminate secure, energy-based issues and stretch the life of the network. The simulation findings indicate that the proposed method operates very efficiently than the current strategy in aspects of power, energy consumption, charge interaction, network life, network reliability and client relationship. The suggested approach is intensively tested and contrasted to the current processes, namely LEACH, TEEN and DEEC, and suggested.
Design of 10kW three-phase EV charger with wide output voltage range based on voltage-fed isolated PFC converter

ABSTRACT This article presents a design of the three-phase Electric vehicle (EV) charger with a wide-output voltage range. The proposed charger is based on a voltage-fed/isolated power factor correction (PFC) converter followed by a non-isolated DC/DC converter. The three-phase PFC converter come from asymmetrical dual active bridge (ADAB) converter having zero-voltage- and zero-current-switching (ZV-ZCS) operation in the primary side so that a low-cost device such as Insulated gate bipolar junction transistor (IGBT) is available despite of a high switching frequency. Also, since the PFC converter adopts the harmonic modulation technique that can be implemented in a simple equation, all the controls for the three-phase PFC converter and the non-isolated DC/DC converter can be implemented with a simple digital controller by excluding fast current loops for input current harmonic regulation. The feasibility of the proposed charger has been verified with a 10kW prototype.
The prevalence and nutritional status of adolescent Saudi girls with disordered eating

Abstract This study on adolescents was intended to assess the prevalence of disordered eating attitudes and the nutritional status of adolescent girls in Saudi Arabia. Disordered eating attitudes and behaviour were assessed using the EAT-26. The type of eating disorder (ED) was determined using Diagnostic statistical manual of mental disorders, fifth edition. The nutritional status of the adolescent girls was determined by measuring their weight and height twice using standard protocols. The BMI-for-age and height-for-age were defined using WHO growth charts. Comparisons between adolescent girls with and without EDs were conducted using SPSS version 26. Eating disorders (EDs) were prevalent among 10⋅2 % of these girls. Other specified feeding or EDs were the most prevalent ED (7⋅6 %), followed by unspecified feeding or eating disorder (2⋅4 %). Anorexia nervosa was common among 0⋅3 % of the girls. The eating disordered adolescents were either overweight (7⋅7 %), obese (10⋅3 %), stunted (7⋅7 %) or severely stunted (2⋅6 %). ANOVA revealed that the BMI-for-age was influenced by age (P = 0⋅028), the type of ED (P = 0⋅019) and the EAT-26 (P < 0⋅0001). Pearson's correlation showed that the EAT-26 score increased significantly with the BMI (r 0⋅22, P = 0⋅0001), height (r 0⋅12, P = 0⋅019) and weight (r 0⋅22, P = 0⋅0001). The early detection of EDs among adolescents is highly recommended to reduce the risk associated with future impaired health status. Nutrition professionals must target adolescents, teachers and parents and provide nutritional education about the early signs and symptoms of ED and the benefits of following a healthy dietary pattern.
Depressive symptoms and suicidality by menopausal stages among middle-aged Korean women

Abstract Aims There has been increasing evidence of hormonal changes during reproductive events that lead to mood changes. However, studies on the severity of psychological problems according to the menopausal stage are limited. Thus, this study aimed to investigate the association between menopausal stages, depression and suicidality. Methods A total of 45 177 women who underwent regular health check-ups between 2015 and 2018 at Kangbuk Samsung Hospital were included. Participants were stratified into four groups (pre-menopause, early transition, late transition and post-menopause) based on the Stages of Reproductive Aging Workshop Criteria. The Center for Epidemiological Studies-Depression scale (CESD) was used to evaluate depressive symptoms, and the degree of depressive symptoms was classified as moderate (CESD score 16–24) or severe (CESD score ⩾ 25). To measure suicide risk, we administered questionnaires related to suicidal ideation. Results Overall, the prevalence of CESD scores of 16–24 and ⩾ 25 was 7.6 and 2.8%, respectively. Menopausal stages were positively associated with depressive symptoms in a dose-dependent manner. Multivariable-adjusted prevalence ratios (PRs, 95% confidence intervals) for CESD scores of 16–24 comparing the stages of the early menopausal transition (MT), late MT and post-menopause to pre-menopause was 1.28 (1.16–1.42), 1.21 (1.05–1.38) and 1.58 (1.36–1.84), respectively. The multivariable-adjusted PRs for CESD scores ⩾ 25 comparing the stages of the early MT, late MT and post-menopause to pre-menopause were 1.31 (1.11–1.55), 1.39 (1.12–1.72), 1.86 (1.47–2.37), respectively. In addition, the multivariable-adjusted PRs for suicidal ideation comparing the early MT, late MT and post-menopause stages to the pre-menopause stage were 1.24 (1.12–1.38), 1.07 (0.93–1.24) and 1.46 (1.25–1.70) (p for trend <0.001), respectively. Conclusions These findings indicate that the prevalence of depressive symptoms and suicidal ideation increases with advancing menopausal stage, even pre-menopause.
Edinburgh Research Explorer Novel Compounds Targeting the Enterohaemorrhagic Escherichia coli Type Three Secretion System Reveal Insights into Mechanisms of Secretion Inhibition

Summary Anti-virulence (AV) compounds are a promising alternative to traditional antibiotics for fighting bacterial infections. The Type Three Secretion System (T3SS) is a well-studied and attractive AV target, given that it is widespread in more than 25 species of Gram-negative bacteria, including enterohemorrhagic E. coli (EHEC), and as it is essential for host colonization by many pathogens. In this work, we designed, synthesized and tested a new series of compounds that block the functionality of the T3SS of EHEC. Affinity chromatography experiments identified the primary target of the compounds as the T3SS needle pore protein EspD, which is essential for effector protein translocation into host cells. These data were supported by mechanistic studies that determined the coiled-coil domain 1 of EspD as a key compound-binding site, thereby preventing correct assembly of the T3SS complex on the cell surface. However, binding of inhibitors to EspD or deletion of EspD itself did not result in transcriptional down-regulation of effector proteins. Instead, we found the compounds to exhibit dual-functionality by also down-regulating transcription of the entire chromosomal locus encoding the T3SS, further demonstrating their desirability and effectiveness. and sulfonyl groups were incorporated to provide diversity. Similarly, methyl substitutions of the hydroxyl groups on the THP and DHP cores were generated.
User-Propagators for Custom Theories in SMT Solving

We present ongoing work on developing a user-propagator framework in SMT solving. We argue that the integration of user-propagators in SMT solving yields an efficient approach towards custom theory reasoning, without bringing fundamental changes in the underlining SMT architecture. We showcase our approach in the SMT solver Z3, provide practical evidence of our work, and also discuss potential venues for further improvements.
A Difference-In-Difference Approach to Estimate the Price Effect of Market Entry in High-Speed Rail

The effect of competition on prices in the passenger transport sector can be difficult to estimate because of many influencing factors such as state regulation, demand, seasonality, and reactions of indirect competitors. A case of great interest in the rail sector is Italy, where on-track competition in the high-speed segment has been in place for nearly ten years. The paper aims at answering the question whether—and how much—incumbent’s prices are affected when competition starts on a route previously in monopoly. The case is the start of operations on the Turin–Milan–Venice route, where Italo entered in May 2018. Adopting a difference-in-difference approach, we check if Trenitalia changed the price strategy on the route before and after the entry, with respect to two control groups. The addition of placebo tests allows us to understand the ranges of significance of the results and to estimate the noise level of the estimates. Our findings suggest that the start of competition led to lower prices in the short-medium period. In the specific case, the Milano–Venezia route saw Trenitalia’s prices reduced by 21–26% (±2–5%) in a time span of 84–140 days after Italo’s entry and for advanced bookings from 2 to 10 days. Last-day prices remain unchanged, while early bookings are reduced by just 9%. This price reduction does not remain stable in the longer term, when other effects add up and blur the effect of the entry. Nevertheless, we keep observing a smaller, ∼15%, but still negative prices change.
Restimulation Via Dendritic Cells Is Most Efficiently Maintained by Protective Antiviral Cytotoxic T Cell Memory

Dendritic cells (DC) play a key role in the initiation of T cell-mediated immune responses and may therefore be successfully used in antiviral and antitumor vaccination strategies. Because both strength and duration of an immune response determines the outcome of a vaccination protocol, we evaluated the life span of DC-induced antiviral CTL memory against systemic and peripheral challenge infections with lymphocytic choriomeningitis virus (LCMV). We found that expansion and activation of CTL by DC was transient. Protection against systemic LCMV infection after DC immunization was relatively long-lived ( > 60 days), whereas complete protection against peripheral infection via intracerebral infection or infection into the footpad with LCMV, where rapid recruitment of effector T cells to the site of infection and elimination of viral pathogen plays a major role, was short-lived ( < 30 days). Protective immunity was most efﬁciently restored by administration of antigenic peptides via DC, rather than in combination with IFA or in liposomes. These results suggest that Ag presentation by DC may be crucial for both initiation and maintenance of protective CTL-mediated immunity against viruses infecting solid organs or against peripheral mesenchymal or epithelial tumors. The Journal of Immunology, 1999, 163: 1839–1844.
Remote Sensing Image Road Segmentation Detection Method Based on Asymmetric Convolution Net

The feature extraction of convolutional layer of neural network has an important influence on the accuracy of neural network identification, and it is very important to increase the ability of neural network to extract image features. The use of suitable convolutional neural network structure in limited practical applications can not be separated from tens of thousands of human operations, which is time-consuming and labor-intensive and easily leads to resource consumption. Thus, it is difficult to improve the performance of convolutional neural network architecture in research. When processing remote sensing images, it is not difficult to find that the road shapes in remote sensing images are often dense and fine, which limits the model to have a certain receptive field. Therefore, on the basis of integrating attention mechanism, this paper adds asymmetric convolution nets as the building blocks of CNN. By manipulating one-dimensional asymmetric convolution nets, the square convolution kernel is enhanced to show its own characteristics, so as to promote the accuracy of network training. That is, symmetric convolution nets are used to replace the original square kernel convolutional layer to construct the asymmetric convolution net (AC-Net). Then AC-Net is replaced by a similar initial architecture to increase the accuracy of the network and avoid unnecessary calculation. The effectiveness of AC-Net is inseparable from its ability to improve the robustness of the model to rotation distortion and the core skeleton of the square convolution kernel. The simulation results demonstrate the feasibility of this research method.
A meta-model for product configuration ontologies

Conceptual modelling of product configuration is an essential step towards improving reuse and configuration knowledge sharing, systems interoperability, and people communication. Among several approaches proposed, ontology-based approaches are known to provide better support for the conceptualization of product configuration knowledge in terms of precision and expressiveness of reasoning and representation. This paper studies product configuration ontologies and presents a meta-model of concepts and their relationships that are used in those ontologies. The proposed meta-model consists of a generalisation hierarchy of configuration types, the compositional structure of a configurable product, the generalisation hierarchy of a component, and constraint types. The meta-model has been designed with the aim of first understanding the current state of product configuration ontologies and then extending it for integrating new concepts.
Why do zombie firms seldom die or resurrect? The effect of government subsidies on the survival duration of China’s zombie firms

Abstract One of the most important tasks in China’s supply-side structural reform is to effectively clean up zombie firms. Using industrial firm-level data in China from 1998 to 2007, we identified zombie firms in Chinese industrial sectors. Based on the identified zombie firms, we measured the survival duration of zombie firms and empirically studied the effect of fiscal subsidies on the survival duration of zombie firms by using survival analysis. Results revealed that fiscal subsidies significantly lengthened the survival duration of zombie firms. Specifically, fiscal subsidies not only reduced the possibility of zombie firms exiting the market, but more importantly, they reduced the possibility of zombie firms’ resurrection. Thus, fiscal subsidies do not contribute to the “death” or “resurrection” of zombie firms. These results are robust despite changing the identification method and the survival analysis method, and considering the endogeneity of fiscal subsidies. Our findings show that we should adhere to market-oriented mechanism in disposing zombie firms and cut off the channels that government transfuses to zombie firms by fiscal subsidies.
IRnator: A Framework for Discovering Users Needs from Sets of Suggestions

To tackle complex IR tasks, where users cannot precisely define their needs, interaction is paramount. Both query-reformulation approaches and chatbots are limited for this type of task, since the former only learn to mimic users, while the latter are bounded by the domain they have been trained on. To take a first step towards truly exploratory and interactive IR, we introduce a framework, where users navigate document collections by expressing their preference among sets of queries proposed by the system at each step -- thus refining the knowledge about the user's information need. Our training approach, based on self-supervised and reinforcement learning techniques, aims at minimizing the amount of interactions required to reach relevant queries, and thus documents, for users. We experimentally show that the introduced framework enables efficient learning from interactions with simple user bots, that are demonstrated to generalize well in real-world settings.
Configuration manager: describing an emerging professional figure

The implementation and management of product configurators in enterprises are accompanied by several challenges and it is widely recognized that the organizational ones are among the biggest. In order to overcome such challenges, companies need people with adequate competencies. What are the required individual competencies to successfully implement and use product configurators (both sales and technical ones)? Which are the characteristics of an ideal professional figure that can have all these competencies? How could the needed competencies be developed through training? This paper brings these questions to the scientific discussion on product and sales configurators. However, these questions have a wider scope since they also relate to the enquiry on mass customization and on product variety management: they deepen the perspective on organizational design related to these issues.
The Impact of Query Refinement on Systematic Review Literature Search: A Query Log Analysis

The creation of high-quality medical systematic reviews requires the development of a complex Boolean query to retrieve medical literature. An effective query in this context is critical, as it determines how many documents are to be assessed for inclusion in the resulting systematic review, as all retrieved documents must be screened. Therefore an effective query must balance a reasonable assessment workload with an estimate for how many relevant documents exist for a given topic. Getting this balance correct is naturally a difficult challenge, and there is a certain level of intuition involved in how a query should be formulated and refined. This paper reveals such intuitions and behaviours by analysing the query logs of a specialised tool developed to assist expert searchers in refining complex Boolean queries. These query logs contain unique information that permits a deeper understanding of user behaviour than previous studies. The approximately 6,000 queries collected over one year are available for further analysis at https://github.com/ielab/searchrefiner-logs-collection.
Feature and variability extraction from Agile specifications and their related source code for software product line migration

Migrating a set of similar software products into a Software Product Line is a time-consuming and costly process which, ultimately, provides an important gain in time and customization. Conducting this migration within an agile development process is a complex process which requires discipline and adaptation. We think it can be beneficial to drive the migration by leveraging agile software specifications and the source code versioning platform. Currently, we are working on a method, whose design is explained in this paper, which exploits: (1) Epics and User stories to identify features and variability and (2) the source code associated to code merges related to User stories and Epics to locate them. We plan to extract features and variability inside Epics and User stories using Natural Language Processing (NLP) techniques. Then we plan to investigate how formal concept analysis (FCA) and relational concept analysis (RCA) can assist feature model synthesis and establish mappings between features and source code. These knowledge discovery methods have been chosen for their ability to highlight and hierarchically organize groups of similar artefacts. FCA only considers artefact description to establish groups of similar artefacts. RCA groups similarly described artefacts that, in addition, share similar relationships to other artefact groups. We also plan to evaluate the method within the context of a company (ITK) with which we collaborate, using its code base and the associated project management artifacts. We also will assess how the method can be generalized to public projects in source code versioning platforms.
EarlyBird: automating application signalling for network application integration in datacenters

Many recent studies in datacenter networking have proposed the idea of using information from applications for optimizing and resource planning. These Application-Aware Networks generally assume that applications can provide an accurate view about their requirements from the network and their traffic characteristics in real time. However, relying on the applications and developers to convey the traffic information is not realistic. We believe that automating the process of information extraction from applications is a crucial step towards realizing the idea of Network-Application Integration (NAI). In this paper, we investigate whether we can automatically identify places in the application code that, when executed, lead to predictable changes in the host's network output. By augmenting the application code at these execution places, we can generate explicit signals that can be used to predict local network events (such as changes in rate, bursts, etc.). This creates a mechanism for automatic adjustment of the network based on application signals. We propose a combination of simple heuristics and learning methods to minimize the burden of the application developer for NAI. To the best of our knowledge, this is the first study that attempts to automatically realize NAI on the application. Our experimental evaluation shows a high accuracy (over 87%) of our prototype in predicting local network events such as micro-bursts.
Defining categorical reasoning of numerical feature models with feature-wise and variant-wise quality attributes

Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model. In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE - the state-of-the-art CT tool.