Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction Database-Supported and BIM-Based Interface Communication and Management: A Pilot Project

Construction projects are subject to numerous interface problems, particularly during the construction phase. The absence of suitable systems or platforms to tackle these issues could hinder the performance of construction management. Thus, the communication and management of interfaces (CMI) are necessary to improve the quality of the management of construction projects. E-mail and generic construction information systems are commonly used communication tools; however, they pose several limitations in recording and managing as well as in responding to interface problems. Building information modeling (BIM), by contrast, saves and delivers information in a digital format in a 3D computer-aided design (CAD) environment. The adoption of BIM technology integrated with web technology for construction projects allows users to communicate interface issues and obtain responses for them effectively. Thus, this study develops a database-supported and BIM-based CMI (DBCMI) system for general contractors to enhance their CMI work efficiency during the construction phase. To confirm the efficacy of the CMI, the DBCMI system was used in a building project in Taiwan. The case study results reveal that the proposed DBCMI system is an effective communication and management platform, particularly for practical CMI work integrated with BIM technology. This study concludes with the benefits of using the proposed system and possible limitations in its further application.
Differences between stereotypical gender and definitional gender in pronominal antecedent retrieval in Brazilian Portuguese

This paper is the second of a research work aimed at investigating the role of gender cues in pronominal antecedent retrieval in Brazilian Portuguese. It was examined how antecedent candidates carrying stereotypical gender and definitional gender are perceived by memory in pronoun resolution. The results of two eye-tracking experiments conducted with native speakers of Brazilian Portuguese showed that gender morphological cues play a role at the very beginning of pronoun resolution processing, and only at a later processing phase, structural constraints of Principle B seemed to help the parser to select the correct antecedent. Like the previous work, the results indicated Principle B does not work as an initial filter blocking structurally unacceptable candidates in Brazilian Portuguese; on the contrary, all candidates seemed to be taken into account despite of violating Principle B. Similarly to the previous work, masculine antecedent candidates were preferable to be retrieved by memory in comparison to feminine candidates. And this time the results provided evidence that antecedent candidates with definitional gender rather than stereotypical gender are preferable to be retrieved by memory since the former is lexically specified, while the latter relies on probabilities based on world knowledge inferences.
A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid

The curtailing of consumers’ peak hours demands and filling the gap caused by the mismatch between generation and utilization in power systems is a challenging task and also a very hot topic in the current research era. Researchers of the conventional power grid in the traditional power setup are confronting difficulties to figure out the above problem. Smart grid technology can handle these issues efficiently. In the smart grid, consumer demand can be efficiently managed and handled by employing demand-side management (DSM) algorithms. In general, DSM is an important element of smart grid technology. It can shape the consumers’ electricity demand curve according to the given load curve provided by the utilities/supplier. In this survey, we focused on DSM and potential applications of DSM in the smart grid. The review in this paper focuses on the research done over the last decade, to discuss the key concepts of DSM schemes employed for consumers’ demand management. We review DSM schemes under various categories, i.e., direct load reduction, load scheduling, DSM based on various pricing schemes, DSM based on optimization types, DSM based on various solution approaches, and home energy management based DSM. A comprehensive review of DSM performance metrics, optimization objectives, and solution methodologies is’ also provided in this survey. The role of distributed renewable energy resources (DERs) in achieving the optimization objectives and performance metrics is also revealed. The unpredictable nature of DERs and their impact on DSM are also exposed. The motivation of this paper is to contribute by providing a better understanding of DSM and the usage of DERs that can satisfy consumers’ electricity demand with efficient scheduling to achieve the performance metrics and optimization objectives.
Recent Advances in Inflammatory Diagnosis with Graphene Quantum Dots Enhanced SERS Detection

Inflammatory diseases are some of the most common diseases in different parts of the world. So far, most attention has been paid to the role of environmental factors in the inflammatory process. The diagnosis of inflammatory changes is an important goal for the timely diagnosis and treatment of various metastatic, autoimmune, and infectious diseases. Graphene quantum dots (GQDs) can be used for the diagnosis of inflammation due to their excellent properties, such as high biocompatibility, low toxicity, high stability, and specific surface area. Additionally, surface-enhanced Raman spectroscopy (SERS) allows the very sensitive structural detection of analytes at low concentrations by amplifying electromagnetic fields generated by the excitation of localized surface plasmons. In recent years, the use of graphene quantum dots amplified by SERS has increased for the diagnosis of inflammation. The known advantages of graphene quantum dots SERS include non-destructive analysis methods, sensitivity and specificity, and the generation of narrow spectral bands characteristic of the molecular components present, which have led to their increased application. In this article, we review recent advances in the diagnosis of inflammation using graphene quantum dots and their improved detection of SERS. In this review study, the graphene quantum dots synthesis method, bioactivation method, inflammatory biomarkers, plasma synthesis of GQDs and SERS GQD are investigated. Finally, the detection mechanisms of SERS and the detection of inflammation are presented.
Gynecological Cancers in Lynch Syndrome: A Comparison of the Histological Features with Sporadic Cases of the General Population

Introduction: About 5% of endometrial cancers (ECs) are attributed to an inherited predisposition, for which Lynch syndrome (LS) accounts for the majority of cases. Women with LS have a 40–60% predicted lifetime risk of developing EC, in addition to a 40–80% lifetime risk of developing colorectal cancer and other cancers. In this population, the lifetime risk of developing ovarian cancer (OC) is 10–12%. Object: to compare the histopathological features of LS-associated EC and OC with sporadic cancers in order to evaluate whether there are differences in terms of age at diagnosis, site of occurrence in the uterus, histological type, stage at diagnosis, and tumor grading. Materials and methods: we compared data obtained from 96 patients with LS-associated gynecological cancers (82 with EC and 14 with OC) to a control group (CG) of 209 patients who developed sporadic EC, and a CG of 187 patients with sporadic OC. Results: The mean age at diagnosis of LS-associated EC and OC was much lower than in the control groups. In both groups with EC, the endometrioid histotype was the most frequently occurring histotype. However, among LS women there was a significantly higher incidence of clear cell tumors (11% versus 2.4% in the CG, p = 0.0001). Similar to the sporadic cancer cases, most of the LS-associated ECs presented at an early stage (89% of cases at FIGO I-II stage). In the LS group, the tumor frequently involved only the inner half of the endometrium (77% of cases, p < 0.01). In the LS group, 7.3% of ECs were localized to the lower uterine segment (LUS), whereas no cancer developed in the LUS in the CG. No serous OCs were diagnosed in the LS group (versus 45.5% in the CG, p = 0.0009). Most of the LS-associated OCs presented at an early stage (85% of cases at FIGO I-II stages, p < 0.01). Conclusion: LS-associated EC and OC seem to have peculiar features, occurring at a younger age and at an earlier stage. In LS, EC less frequently involves the outer half of the endometrium, with a more frequent occurrence in the LUS. The presence of clear cell EC was more frequently observed, whereas in OC, the predominant histotype was endometrioid.
Effect of Vitamin C on Tendinopathy Recovery: A Scoping Review

Tendinopathies represent 30–50% of all sports injuries. The tendon response is influenced by the load (volume, intensity, and frequency) that the tendon support, resulting in irritability and pain, among others. The main molecular component of tendons is collagen I (60–85%). The rest consist of glycosaminoglycans-proteoglycans, glycoproteins, and other collagen subtypes. This study’s aim was to critically evaluate the efficacy of vitamin C supplementation in the treatment of tendinopathies. At the same time, the study aims to determine the optimal conditions (dose and time) for vitamin C supplementation. A structured search was carried out in the SCOPUS, Medline (PubMed), and Web of Science (WOS) databases. The inclusion criteria took into account studies describing optimal tendon recovery when using vitamin C alone or in combination with other compounds. The study design was considered, including randomized, double-blind controlled, and parallel designs in animal models or humans. The main outcome is that vitamin C supplementation is potentially useful as a therapeutic approach for tendinopathy recovery. Vitamin C supplementation, alone or in combination with other products, increases collagen synthesis with a consequent improvement in the patient’s condition. On the other hand, vitamin C deficiency is mainly associated with a decrease in procollagen synthesis and reduced hydroxylation of proline and lysine residues, hindering the tendon repair process.
An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

This paper proposes the optimal design for fast electric vehicle charging stations for the capacity configuration of components and the power scheduling strategy. The proposed approach is the artificial transgender longicorn algorithm, which is a simple and powerful controller. The optimal capacity of the system components and the optimal power scheduling approach are determined. First, an electric vehicle charging simulation model is built considering the demand response, which dynamically modified the charging expectation based on the electricity price at the time of use. Second, based on the system design, a multi‐objective optimization model is proposed with minimal objectives for the electricity cost and polluting emissions. Finally, a scenario analysis is carried out and it is concluded that renewable energy supplies, connection to the utility grid and demand response may help improve the performance of the optimization objectives. By then, the performance of the proposed technique is implemented in the MATLAB platform and compared with several existing approaches.
Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography

Purpose: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. Methods: Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. Results: Machine learning models were designed and optimized, specifically decision trees, based on the values of proposed asymmetry metrics. The use of these models on the dataset provided good classification of the patients (accuracy 88%, sensitivity 70%, specificity 93% and precision 75%). Conclusions: The obtained machine learning models based on retinal nerve fiber layer asymmetry are simple but effective methods which offer a good trade-off in classification of patients and simplicity. The fast binary classification relies on a few asymmetry values of the retinal nerve fiber layer thickness, allowing their use in the daily clinical practice for glaucoma screening.
Supporting Teachers in Improving Formative Decision-Making: Design Principles for Formative Assessment Plans

Formative assessment is considered as one of the most effective interventions to support teacher decision-making and improve education and student learning. However, formative assessment does not always meet these expectations. In order to be effective, formative assessment activities should be consciously and coherently planned aligned with other aspects of the curriculum and the decisions teachers wish to make based on these activities. While there is sufficient support for teachers to design formative assessment activities, no guidelines exist to help them tie these different activities together in an effective way. To support teachers in designing formative assessment plans informing formative decision-making, this study focused on the creation of a set of design principles. These design principles for formative assessment plans were formulated based on expert interviews and subsequently evaluated by future users. The result is a set of eight design principles that can be used and validated in educational practice.
DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment Analysis of transcriptomics data

We are at a time of considerable growth in transcriptomics studies and subsequent in silico analysis. RNA sequencing (RNA-Seq) is the most widely used approach to analyse the transcriptome and is integrated in many studies. The processing of transcriptomic data typically requires a noteworthy number of steps, statistical knowledge, and coding skills, which are not accessible to all scientists. Despite the development of a plethora of software applications over the past few years to address this concern, there is still room for improvement. Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values. The intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, and statistical comparisons of expression profile levels between groups. Further meta-analysis such as enrichment analysis is also possible, without the need for prior bioinformatics expertise. DEVEA performs a comprehensive analysis from multiple and flexible data sources representing distinct analytical steps. Consequently, it produces dynamic graphs and tables, to explore the expression levels and statistical results from differential expression analysis. Moreover, it generates a comprehensive pathway analysis to extend biological insights. Finally, a complete and customizable HTML report can be extracted to enable the scientists to explore results beyond the application. DEVEA is freely accessible at https://shiny.imib. es/devea/ and the source code is available on our GitHub repository https://github.com/MiriamRiquelmeP/DEVEA.
Effects of Nano-Aerators on Microbial Communities and Functions in the Water, Sediment, and Shrimp Intestine in Litopenaeus vannamei Aquaculture Ponds

Nanobubble technology has promising development and application prospects in the fields of sewage treatment, soil and groundwater remediation, animal and plant growth, and biomedicine. However, few studies have investigated its effect on shrimp aquaculture. In this study, we investigated the effect of nano-aerators on microbial communities of the water, sediment, and shrimp gut in a Litopenaeus vannamei aquaculture pond using 16S rRNA high-throughput sequencing. The results indicated that the nano-aerator significantly increased the microbial community diversity and species abundance in the pond, and the microbial community diversity of the pond sediment increased under short-term aeration conditions. Compared to that with ordinary aerators, nano-aerators increased the proportion of beneficial bacteria, such as Exiguobacterium and Acinetobacter, in the water and sediment microbial communities. Moreover, the proportions of beneficial bacteria in the gut, including Rhodobacter, Oscillospira, and Faecalibacterium, were all increased by using the nano-aerator. Therefore, our findings suggest that nano-aerators could promote the activity of beneficial bacteria in aquaculture ecosystems, thereby regulating water quality, reducing disease incidence, and improving aquaculture efficiency and benefits. Our findings provide new insights into the effects of nano-aerators on microbes in crustacean culture ponds.
Environmental safety assessment of a printing plant in cairo, egypt

The workplace environment impacts workers health, morale, productivity and engagement - both positively and negatively. The workplace environment in most of industry is unsafe and unhealthy. During the past ten years, the printing services have met a large development. Technological changes and the introduction of new work processes in the matter of printing have had a deep impact on labor, security and health. to describe the environmental features of a printing press in Cairo. A descriptive observational study was carried out in a big printing press in Cairo, Egypt. Assessment of the work environment (prepress department, printing department, bookbinding section & office) was done using checklist modified from that of occupational safety team, 2012. The majority of the study participants (79.5%) in the printing press were working in the printing and bookbinding department. which complains from poor ventilation, poor housekeeping, presence of spills & tripping hazards, lack of personal protective equipments, presence of noisy machines, significant manual handling & awkward body positions, lack of first aid kit & hazardous containers (ink containers in the printing department & adhesives in the bookbinding department) were left open. By comparing the departmental total score of the items in the environmental checklist; the prepress department were the highest in fulfilling the positive items with score (86.66%) and the printing & bookbinding department were the least with total score (40.09%). The study concluded that some areas of the printing press (printing & bookbinding department) need urgent attention to the work environment and recommend periodic environmental examination, good housekeeping, training, health education & provision of protective measures. Further work to confirm these findings in other study settings & population is also needed.
Borrower- and Lender-Based Macroprudential Policies: What Works Best Against Bank Systemic Risk?

This paper investigates the complementarity between the different macroprudential policies to contain bank systemic risk. We use a newly updated version of the IMF survey on Global Mac-roprudential Policy Instruments (GMPI). By disentangling the aggregate macroprudential policy index, we assess the complementarity between borrower-targeted and lender-targeted instruments in mitigating systemic risk arising from intra-financial system vulnerabilities. We investigate the effect of boom-bust cycle on such a relationship by analyzing the financial upturns and downturns and show the effectiveness of the macroprudential policies during calm period. We also show that their efficacy in mitigating instability is quite heterogeneous and may vary depending on the set of tools implemented, as well as bank ’ size, TBTF, leverage, liquidity and concentration. Our results bear critical policy implications for implementing optimal macro-prudential tools and provide insights into the trade-off between financial vis-` a-vis price stability.
Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms

Background Lumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms. Methods We utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed. Results In the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy. Conclusions The RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.
BUILDING A COLLABORATIVE RELATIONSHIP WITH AN INVOLUNTARY PARENT IN CHILD PROTECTIVE SERVICES

Building a collaborative relationship with an involuntary parent is challenging; however, the worker-client relationship is essential to promoting the wellbeing of the child and family. This article describes a small-scale qualitative study conducted with child protective workers in one region of Estonia. Findings indicate that in order to encourage involuntary parents to participate, it was crucial to learn what was provoking their resistance. Recognition was used to establish a collaborative relationship with involuntary parents. To reduce the unequal distribution of power, the parent was recognised as an equal partner and negative feelings were validated and understood as a normal reaction to intervention. Keywords: Child Protective Services, collaborative relationship, Estonia, involuntary parent, recognition, worker-client relationship