Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in modelling and analysis of nano structures: a review

Abstract Nanostructures are widely used in nano and micro-sized systems and devices such as biosensors, nano actuators, nano-probes, and nano-electro-mechanical systems. The complete understanding of the mechanical behavior of nanostructures is crucial for the design of nanodevices and systems. Therefore, the flexural, stability and vibration analysis of various nanostructures such as nanowires, nanotubes, nanobeams, nanoplates, graphene sheets and nanoshells has received a great attention in recent years. The focus has been made, to present the structural analysis of nanostructures under thermo-magneto-electro-mechanical loadings under various boundary and environmental conditions. This paper also provides an overview of analytical modeling methods, fabrication procedures, key challenges and future scopes of development in the direction of analysis of such structures, which will be helpful for appropriate design and analysis of nanodevices for the application in the various fields of nanotechnology.
Shallow-cloud impact on climate and uncertainty: A simple stochastic model

Abstract Shallow clouds are a major source of uncertainty in climate predictions. Several different sources of the uncertainty are possible—e.g., from different models of shallow cloud behavior, which could produce differing predictions and ensemble spread within an ensemble of models, or from inherent, natural variability of shallow clouds. Here, the latter (inherent variability) is investigated, using a simple model of radiative statistical equilibrium, with oceanic and atmospheric boundary layer temperatures, To and Ta, and with moisture q and basic cloud processes. Stochastic variability is used to generate a statistical equilibrium with climate variability. The results show that the intrinsic variability of the climate is enhanced due to the presence of shallow clouds. In particular, the on-and-off switching of cloud formation and decay is a source of additional climate variability and uncertainty, beyond the variability of a cloud-free climate. Furthermore, a sharp transition in the mean climate occurs as environmental parameters are changed, and the sharp transition in the mean is also accompanied by a substantial enhancement of climate sensitivity and uncertainty. Two viewpoints of this behavior are described, based on bifurcations and phase transitions/statistical physics. The sharp regime transitions are associated with changes in several parameters, including cloud albedo and longwave absorptivity/carbon dioxide concentration, and the climate state transitions between a partially cloudy state and a state of full cloud cover like closed-cell stratocumulus clouds. Ideas of statistical physics can provide a conceptual perspective to link the climate state transitions, increased climate uncertainty, and other related behavior.
Pelvic Mass in a Post-partum Female

Background : Ovarian cancer is associated with significant morbidity and mortality. It may be asymptomatic in its early phase and even when patients do seek medical attention they often present with mild and non-specific abdominal symptoms including cramping, bloating or subtle urinary symptoms. Case Report : This case presents a rare presentation of ovarian cancer in a postpartum, pre-menopausal female. Conclusion : This case illustrates the importance of having a high index of suspicion for potential ovarian cancer in patients of any age presenting with a pelvic or adnexal mass.
A new methodology for simultaneous comparison and optimization between nanoparticles and their drug conjugates against various multidrug-resistant bacterial strains

Abstract Background Multidrug-resistant bacteria are becoming more hazardous day by day for human health all over the world, and the scientific community is trying hard to resolve this issue by various approaches. One of the very common approaches is to bind drugs to nanoparticles and study enhanced antibacterial properties. Objective To compare simultaneously different types of nanoparticles, their concentration, bacterial strains and their incubation time intervals for each of the selected drug combination. Methods We have selected the most commonly used gold and silver nanoparticles and few examples from fluoroquinolone antibiotics to make their conjugates and study their efficacy against multidrug-resistant E. coli and S. aureus strains simultaneously, at different incubation time intervals and different concentration of nanoparticles. Results Gold nanoparticle hybrids do not show any significant effect. Silver nanoparticle hybrids show far better results, even at extremely low concentrations. Conclusions This unique and simple approach allows us to know the exact time intervals and concentration required for each nanoparticle combination to control the growth for any specific strain. This approach can be extended to any set of nanoparticles, drugs and bacterial strains for comparative purposes.
Multimodal Discourse Analysis of Judges’ Footing Shifts in Criminal Courtroom

Abstract The paper, based on the concept of FOOTING, makes a multimodal discourse analysis of the relationship between the judge’s discourse and his footing shifts in a criminal courtroom. The results show that in the interaction, multimodal resources in judges’ discourse include conversational features (prolonging keywords, interrupting, repeating, taking turns, etc.), acoustic ones (ascending F0 for pitches and dB for intensity, transition tracks between consonants and formants of vowels, duration of some keywords in important sentences, etc.), and visual ones (facing other parties, facing the materials, etc.). The multimodal resources activate different judges’ footings, including ANIMATOR, ANIMATOR + AUTHOR and ANIMATOR + AUTHOR + PRINCIPAL, and identify the judge’s footing shifts in the courtroom. The results also demonstrate that the judge’s footing shifts perform the functions of trial organizing, information confirming, fact investigating, spokesperson of the collegial panel, law educating and so on in criminal trials.
Monte Carlo Simulation Approach to Reliability Analysis of Complex Systems

Abstract The article presents new results concerned with general procedures and algorithms to assess the reliability of complex systems with various reliability structures. The analytical method and based on it the simulation method were used to estimate the reliability characteristics of the port grain transportation system. Finally, the general simulation algorithm was developed to evaluate the reliability characteristics of ageing complex systems. In this case, the systems operating processes were described by any distributions of sojourn times in operation states and the reliability functions of their components were modified in such a way that these components are not characterized by a “lack of memory”. The application of this algorithm has been illustrated by the results for exemplary complex two-state systems.
Leptospira Interrogans Serogroup Sejroe Serovar Hardjo in Aborting Cows: Two Herd Cases in Sicily (Italy)

Abstract Introduction The aim of this study was to present two outbreaks of bovine abortion due to Leptospira infection in cattle herds located in the northern part of Sicily (Italy). The animals were positive for Leptospira interrogans serogroup Sejroe serovar Hardjo in a microscopic agglutination test (MAT). Material and Methods A total of 23 Charolaise cows (farm A) and 75 Limousine bulls and Cinisara and Modicana cows (farm B) were enrolled in this study. The blood samples were collected from all subjects at the following time points: before a cycle of intramuscular treatment with oxytetracycline dihydrate (T0), after 5–6 weeks from the treatment (T1), and every 10 weeks until seronegativisation (T2 in Farm A and T3 in Farm B). A serological test (MAT) was used for the diagnosis of leptospirosis. Results Two samples from farm A (2/23) and 29 samples from farm B (29/75) were positive to Leptospira interrogans, serogroup Sejroe, serovar Hardjo in the MAT. Leptospira spp. DNA was detected by real-time PCR in the urine sample of one positive cow on farm A, and in placenta and brain samples belonging to one aborted foetus on farm B. Conclusion It is important to use serological and molecular diagnostic techniques complementarily to identify infected individuals.
The Interplay of Syntactic and Lexical Salience and its Effect on Default Figurative Responses

Abstract The aim of the paper is to determine how salient and non-salient figurative discourse nouns affect readers’ default response processing and oculo-graphic (eye-movement) reactions. Whereas the theories of the Graded Salience and the Defaultness Hypotheses, developed by R. Giora (Giora, 1999, 2003; Giora, Givoni, & Fein, 2015), have stimulated further research in the area of interpretive salience (Giora et al., 2015; Giora, Jaffe, Becker & Fein, 2018), the resonating influence of syntactic salience on default interpretations has been largely neglected. In this study we provide corpus-based evidence followed by eye-tracking experiment verification, supportive of the synchronized influence of syntactic and lexical salience. The results show that default figurative responses in lexically salient positions may require more cognitive effort (longer fixations) if they are syntactically less salient. Literal responses to figurative nouns may also result from either weak lexical or syntactic salience of nouns. Therefore, apart from exemplifying resonance with lexical salience (in terms of lexical frequency, familiarity, conventionality, and prototypicality), the default figurative interpretations are also syntactically dependent.
BULGARIAN ACADEMY OF SCIENCES

Building a computer system, which can automatically answer questions in the human language, speech or text, is a long-standing goal of the Artificial Intelligence (AI) field. Question analysis, the task of extracting important information from the input question, is the first and crucial step towards a question answering system. In this paper, we focus on the task of Vietnamese question analysis in the education domain. Our goal is to extract important information expressed by named entities in an input question, such as university names, campus names, major names, and teacher names. We present several extraction models that utilize the advantages of both traditional statistical methods with handcrafted features and more recent advanced deep neural networks with automatically learned features. Our best model achieves 88.11% in the F1 score on a corpus consisting of 3,600 Vietnamese questions collected from the fan page of the International School, Vietnam National University, Hanoi.
Data-Driven Personas for Enhanced User Understanding: Combining Empathy with Rationality for Better Insights to Analytics

Abstract Persona is a common human-computer interaction technique for increasing stakeholders’ understanding of audiences, customers, or users. Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades. However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making. In this vision, the persona profile functions as an interface to a fully functional analytics system. With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users. We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes. We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations. We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question defines the unit of analysis for decision-making. The system provides several functionalities for stakeholders within organizations to address this question.
BR2: A Travel Behavioral Approach to Personalized Route Recommendation Based on GPS Trajectories

Various unknown circumstances may affect users’ travel behaviors between two locations on the road network, hence it is complicated to provide satisfactory personalized route recommendations. In this paper, we believe that users’ travel behaviors are reflected and can be learned from their historical GPS trajectories. The Behavior-based Route Recommendation (BR2) method is proposed to compute personalized routes based exclusively on users’ travel preferences. The concepts of appearance and transition behaviors are used to describe users’ travel behaviors. The behaviors are extracted from users’ past travels and the missing behaviors, of unvisited locations, are estimated with the Optimized Random Walk with Restart technique. Then, the temporal dependency of travel behaviors is considered by constructing a time difference interval histogram. Last, a behavior graph is generated to allow the maximum probability route computation with the shortest path algorithm, resulting in the most likely route to be taken by a user. Experiments conducted on two real GPS trajectory data sets demonstrate the efficiency and effectiveness of the proposed method.
Breast cancer knowledge and awareness among females in Al-Qassim Region, Saudi Arabia in 2018

Aim: This study aimed to measure breast cancer (BC) awareness among women in Al-Qassim and to compare the results to previous studies in Saudi Arabia and international studies. Method: This is a cross-sectional study conducted in Al-Qassim region, Saudi Arabia. All women above 18 years of age were included and those who could not complete the questionnaire for any reason were excluded. The data were collected by using a valid pretested structured questionnaire taken from previous studies. Descriptive statistics were presented using frequency and proportion for all categorical variables and mean ± standard deviation for continuous variable. The relationship between dependent variable versus independent variables had been conducted using Chi-square test. P value of ≤0.05 was considered as statistically significant. Results: Nearly all participants were highly aware of BC (95.4%) and half of them correctly identified that not only females are affected by BC. With regards to personal breast assessment, more than a half of them have done breast self-examination; however, only one out of four females had done clinical breast examination and mammography test. The most common risk factor of BC was family history and the commonest signs and symptoms were the size and shape changes of the breast. The prevalence of poor knowledge was 202 (38.9%) while good knowledge was 317 (61.1%). Age group in years and use of oral contraceptives were the independent significant factors of poor knowledge. Conclusion: The overall knowledge of women about BC in this study was inadequate. While half of the women performed breast self-examination on the contrary, the actual clinical breast examination found to be low. The most common risk factor being identified was family history of BC and smoking. Size and shape changes of breast as well as breast lump were the most common signs and symptoms. Age group in years and the use of contraceptives pills were being identified as the significant factors of knowledge toward BC.
REMA: Graph Embeddings-based Relational Schema Matching

Schema matching is the process of capturing correspondence between attributes of different datasets and it is one of the most important prerequisite steps for analyzing heterogeneous data collections. State-of-the-art schema matching algorithms that use simple schema-or instance-based similarity measures struggle with finding matches beyond the trivial cases. Semantics-based algorithms require the use of domain-specific knowledge encoded in a knowledge graph or an ontology. As a result, schema matching still remains a largely manual process, which is performed by few domain experts. In this paper we present the Relational Em-beddings MAtcher, or rema, for short. rema is a novel schema matching approach which captures semantic similarity of attributes using relational embeddings : a technique which embeds database rows, columns and schema information into multidimensional vectors that can reveal semantic similarity. This paper aims at communicating our latest findings, and at demonstrating rema’s potential with a preliminary experimental evaluation.
High Dimensional Fuzzy Outlier Detection

We propose a novel outlier detection approach, namely high dimensional fuzzy outlier detection (HDFOD), to address the pertinence of outlier results, i.e., to ﬁ nd outliers in high dimensions that lack pertinence and understandability. Our key idea is to use fuzzy constraint technology to prune irrelevant objects for outlier detection, during which the nearness measure theory in fuzzy mathematics is used for detecting similarities between objects and constraint information. HDFOD ﬁ nds outlier by searching sparse subspace, where genetic algorithms can be extended and incorporated into HDFOD such that an optimum solution of an outlier is discovered. While constructing a sparse subspace, we present the sparse threshold concept to describe the sparse levels of data objects in a subspace, where data objects are regarded as outliers. Then, we demonstrate the effectiveness and scalability of our method on synthetic and UCI datasets. The experiment evaluations reveal that our fuzzy constraint-based outlier detection is superior to two existing high dimensional algorithms.
Intended Human Exposure to Non-ionizing Radiation for Cosmetic Purposes

Abstract Cosmetic devices using non-ionizing radiation (NIR) are increasingly available for people who wish to modify their appearance for aesthetic purposes. There are a wide range of NIR modalities used for cosmetic procedures, including devices that use optical radiation (laser, intense pulsed light, and light-emitting diode), electromagnetic fields, and ultrasound. Common procedures involving the application of NIR include epilation, skin rejuvenation, body sculpting and contouring, treatment of vascular and skin lesions, tattoo removal, and scar reduction. The majority of research on the use of NIR cosmetic devices has focused on the efficacy of the treatment rather than adverse effects or complications. Studies that assessed safety consisted mostly of case reports and small case series. Common adverse effects on the skin reported include mild and transient pain, erythema, swelling, and changes in pigmentation. Less common, more severe side effects include burns, blisters, scarring, persisting erythema, altered pigmentation, and eye damage. Some of the latter may have resulted from treatment errors. Particular groups of people that may be at greater risk from optical radiation include people with dark skin, with high sun exposure, and taking photosensitizing medications or supplements. There is lack of evidence for the safety profile of cosmetic NIR procedures during pregnancy. Reports of injuries to workers administering treatments with cosmetic NIR devices are rare, but inadvertent damage to the eye from optical devices may occur. Randomized controlled trials are required to fully assess potential adverse effects from the use of NIR cosmetic devices. Regulation varies worldwide and some regions apply the same safety classification and guidance as for medical devices. In order to reduce harm associated with the use of cosmetic devices, ICNIRP considers it important that regulations that cover all types and frequencies of cosmetic NIR devices are adopted worldwide and that there is greater oversight regarding their use.
Dental procedure under opioid-free balanced anaesthesia in a child with Rett syndrome who convulsed on every attempt to feed: Case report

Rett syndrome is a genetic neurodevelopmental disorder which occurs in females and includes autism, spasticity, hypotonia, microcephaly, scoliosis, stereotyping, abnormal respiratory control and seizures. They are at an increased predisposition for QT interval prolongation and cardiac arrhythmias. An 8-year-old severely intellectually impaired girl with Rett syndrome was referred to us for anaesthesia for multiple dental abscess drainage and rehabilitation. Her frequency of convulsions had increased and she convulsed every time an attempt was made to feed her. The pain of chewing exacerbated the convulsions. The cornerstone of our management was to provide adequate pain relief, anaesthesia without muscle relaxant and opioids, and judicious use of local anaesthetics. We discuss the anaesthetic management and its advantages and limitations in this case report.
Community-based home injury risk assessment in rural Nepal

ABSTRACT Background: Unintentional injuries in the home are an important cause of death and disability among young children globally. However, in many parts of the world, particularly in the Low and Middle-Income Countries (LMICs) like Nepal, there is dearth of data regarding home injuries and home hazards to guide the development of effective interventions, and policies for preventing childhood home injuries. Aims: To explore the environmental risks associated with unintentional injuries amongst children aged 0-59 months in the Makwanpur district of Nepal, and to explore the potential for changes to the home environment to prevent injury occurrence. Methods: This study employed a multi-method approach. First, a literature review was undertaken to understand what environmental hazards had previously been identified and whether environmental change interventions are effective in reducing home hazards or home injuries in LMICs. Next, community-based studies were designed to collect both quantitative and qualitative information to best understand the problem of home injury risks in the study area. For this, quantitative data were collected through a community-based household survey (740 households) to understand home injury hazards and the injuries, and qualitative data were collected through five focus groups (FGs) to obtain perceptions on injuries and community-identified solutions to improve the safety of the home environment. Results: The literature review highlighted the limited evidence available from studies exploring the effectiveness of environmental change interventions in reducing childhood home injuries or injury hazards in LMICs. The household survey and home hazard assessment revealed a significant burden of hazards for childhood injuries within the home environment. Total of 242/1042 children
Application of LSI Based On Relevance Feedback

When we used LSI in EEAS (expert evaluation assistant system), we faced with such an issue: the number of dimensions of the semantic space needed must be determined for each collection. The dimensions of semantic space influence the results Seriously. But the users usually do not have the knowledge to adjust the dimensions. So we must provide a way for the user to adjust the dimensions of semantic space dimension for every topic. We propose such an approach which uses the relevance feedback of the user to get the statistic features of our target documents to modify the semantic space. This paper give a idea to complete of this approach by using expectation of the features values of the document vectors. The experiment proves that the retrieval result is improved.