Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tailless UAV Multidisciplinary Design Optimization Using Global Variable Fidelity Modeling

This paper describes the multidisciplinary design optimization (MDO) process of a tailless unmanned combat aerial vehicle (UCAV) using global variable fidelity aerodynamic analysis. The developed tailless UAV design framework combines multiple disciplines that are based on low-fidelity and empirical analysis methods. An automated high-fidelity aerodynamic analysis is efficiently integrated into the MDO framework. Global variable fidelity modeling algorithm manages the use of the highfidelity analysis to enhance the overall accuracy of the MDO by providing the initial sampling of the design space with iterative refinement of the approximation model in the neighborhood of the optimum solution. A design formulation was established considering a specific aerodynamic, stability and control design features of a tailless aircraft configuration with a UCAV specific mission profile. Design optimization problems with low-fidelity and variable fidelity analyses were successfully solved. The objective function improvement is 14.5% and 15.9% with low and variable fidelity optimization respectively. Results also indicate that low-fidelity analysis overestimates the value of lift-to-drag ratio by 3-5%, while the variable fidelity results are equal to the high-fidelity analysis results by algorithm definition.
Sum of Squares Decomposition for Categorical Data

The decomposition of the sum of squares (SS) to a within-group sum of squares (WSS) and between-group sum of squares (BSS) is often employed as the criterion to judge the quality of clusters. However, the method is applicable only to continuous data. The aim of this paper is to present an extension of this criterion to categorical data. We employ Gini’s definition of the SS for a single categorical variable. The definition is extended to the multivariate case, and its decomposition is conducted in a reasonable manner. The results presented gives a theoretical foundation to the selection of discrimination and characteristic rules from a lattice.
The domination number of the graph deﬁned by two levels of the n -cube, II

Consider all k -element subsets and ℓ -element subsets ( k > ℓ ) of an n -element set as vertices of a bipartite graph. Two vertices are adjacent if the corresponding ℓ -element set is a subset of the corresponding k -element set. Let G k,ℓ denote this graph. The domination number of G k, 1 was exactly determined by Badakhshian, Katona and Tuza. A conjecture was also stated there on the asymptotic value ( n tending to inﬁnity) of the domination number of G k, 2 . Here we prove the conjecture, determining the asymptotic value of the domination number γ ( G k, 2 ) = k +3 2( k − 1)( k +1) n 2 + o ( n 2 ).
Review of Root-Mean-Square Error Calculation Methods for Large Deployable Mesh Reflectors

In the design of a large deployable mesh re ﬂ ector, high surface accuracy is one of ultimate goals since it directly determines overall performance of the re ﬂ ector. Therefore, evaluation of surface accuracy is needed in many cases of design and analysis of large deployable mesh re ﬂ ectors. The surface accuracy is usually speci ﬁ ed as root-mean-square error, which measures deviation of a mesh geometry from a desired working surface. In this paper, methods of root-mean-square error calculation for large deployable mesh re ﬂ ectors are reviewed. Concept of re ﬂ ector gain, which describes re ﬂ ector performance, and its relationship with the root-mean-square error is presented. Approaches to prediction or estimation of root-mean-square error in preliminary design of a large deployable mesh re ﬂ ector are shown. Three methods of root-mean-square error calculation for large deployable mesh re ﬂ ectors, namely, the nodal deviation root-mean-square error, the best- ﬁ t surface root-mean-square error, and the direct root-mean-square error, are presented. Concept of e ﬀ ective region is introduced. An adjusted calculation of root-mean-square error is suggested when the concept of e ﬀ ective region is involved. Finally, these reviewed methods of root-mean-square error calculation are applied to surface accuracy evaluation of a two-facet mesh geometry, a center-feed mesh re ﬂ ector, and an o ﬀ set-feed mesh re ﬂ ector for demonstration and comparison.
Trappability of Low Density Invasive Rats

A BSTRACT : On Aotea/Great Barrier Island, New Zealand, two invasive rat species (Pacific rats and ship rats) pose risks to the ecosystems and challenge the management in two sanctuaries. At Glenfern Sanctuary (83 ha) an eradication has successfully removed ship rats and a predator-proof fence prevents reinvasion. However, Pacific rats persist in low abundance. At Windy Hill Sanctuary (770 ha) intensive rodent control maintains both species at low abundance despite ongoing reinvasion. A capture-mark-recapture study was conducted between February and April in 2016 and repeated between July and September 2017 to determine population densities, confirm species composition, and analyse the effects of time, population density, and interspecific competition on rat behaviour. Live traps were monitored with camera traps to analyse behaviour of rats around traps. Population density and detection probability of Pacific rats varied between times reflecting seasonality in food abundance and rat reproduction. The detection probability of Pacific rats also differed between sites, being higher at Glenfern Sanctuary than at Windy Hill Sanctuary, presumably due to interspecific competition with ship rats. Where Pacific rats were the sole species they were captured in traps in the first night. However, in coexistence with ship rats, Pacific rat detection was delayed by at least ten days. Population density influenced the number of trap encounters and interactions but did not significantly influence the capture rate. Interspecific competition was identified as problematic for monitoring, controlling, and eradicating Pacific rats. 29 th Vertebrate
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which represents the interaction function. The contour plot of this function (isointeraction diagram) shows lines of equal in teraction. Whereas the first two methods can be applied to cases, in which only a very small number of dose combinations were studied, the other two approaches require an experimental design appropriate for a surface modeling of response data or interaction indices. Note that any of these approaches should yield identical results provided surface modeling is done in an appropriate manner. Finally, it should be noted that in addition to the objections to the universal response surface approach, the claim to have developed a universal method is not correct because the method could be applied only to experimental data which can ade quately be described by the dose-response relation used.
Uni ed Creeping Model Identifying the Critical State of Granular Material

We propose a state evolution model to study the creep of a granular material both below and beyond the yield stress. Three types of creeping curves are observed under both static and dynamic loading conditions. The experiments are analyzed using the proposed model which introduces characteristic strain to account for the influences of historic strain on current state. A criterion to identify the stability of a granular system is obtained based on the proposed model. Two key factors dominating the stability of the granular system are recognized, the shear wave velocity and critical velocity. Our laboratory experiments in combination with the proposed criterion offers a physical explanation for intrinsic triggering mechanism of system evolution from stable to fluidic state: decrease in wave velocity or/and critical velocity.
Conceptualizing Group Dynamics from Our Clients’ Perspective: Conceptualizing Group Dynamics from Our Clients’ Perspective: Development of the Conceptualization of Group Dynamics Development of the Conceptualization of Group Dynamics Inventory Inventory

There is a clear call in group counseling practice and training for evidence-based practice (ACA, 2005; ASGW, 2008; CACREP, 2009). At the same time, group counselors also are asked to keep clients' experience at the center of their work (ASGW, 2012). This article outlines the authors' effort to develop and study an instrument designed to measure group counselors' conceptualization skill using group members' subjective perceptions as a foundation. Descriptions of the instrument's internal reliability, construct validity, and generalizability will be presented, as well as suggestions for conducting research on this instrument in future studies. and in requires The goal of this study was to develop an instrument capable of assessing the abilities of group to accurately conceptualize perceptions of dynamics This done by adapting new The designed to assess group conceptualization of
Live Streaming E-commerce: The Impact of the Intensity, Duration, and Phases of Peak Interaction on Sales Performance

In today’s emerging and competitive live streaming e-commerce, streamers must continually work to improve their sales performance. Past research has highlighted the performance impact of live interaction, treating its effects as enduring based on general or average levels. Live interaction in some moments is more salient than that in others, however. Building on gestalt characteristics and event system theories, this study takes a short-term momentary perspective to examine the intensity and duration of peak interaction during product display and non-product display phases of a live streaming video. Using moving window and computer vision techniques, these variables are extracted from videos data obtained on the Taobao Live platform. Their effects are then empirically quantified with econometric model estimations.
Antibacterial potential of indigenous medicinal plants against methicillin-resistant Staphylococcus aureus isolated from septic wounds

Aims: This study was aimed to screen indigenous medicinal plants for their antibacterial potential against methicillin-resistant Staphylococcus aureus (MRSA). Methodology and results: Three indigenous plants ( Nigella sativa , Zingiber officinale and Calotropis procera ) and thymoquinone were screened for antibacterial activity against MRSA, isolated from septic wounds of patients admitted to Mayo Hospital Lahore, Pakistan. Isolated bacteria were screened for methicillin and cefoxitin resistance by the Kirby-Bauer method, followed by mecA gene-specific polymerase chain reaction (PCR). Confirmed MRSA was processed for antibacterial activity of plant extracts and thymoquinone followed by cytotoxicity assay of plant extract having least minimum inhibitory concentration (MIC) value. Out of total samples (n=100), S. aureus (29%), MRSA (26%) and vancomycin-resistant S. aureus (VRSA) (21.7%) isolates were recovered based on morphology, biochemical profile and antibiotic susceptibility testing. Nigella sativa showed the highest antibacterial activity (10.06 ± 6.53 mm) against MRSA followed by Z. officinale (4.06 ± 3.72 mm) and C. procera (3.65 ± 3.33 mm) in comparison to standard thymoquinone (17.93 ± 10.14 mm). The least MIC value recorded was for Z. officinale at 36.89 ± 3.75 µg/mL. Zingiber officinale was the most effective antibacterial agent, followed by N. sativa and C. procera and non-toxic for eukaryotic cells at all tested concentrations (1500 μg/mL to 2.92 μg/mL). Conclusion, significance and impact of study: It was concluded that Z. officinale may be used as an effective alternative for treating septic wound infection in local or topical preparations. As pathogenic S. aureus is becoming life-threatening among antibiotic-resistant bacteria and traditional plants are in used for centuries to treat septic wound infections.
System-level dynamic phasor models of hybrid AC/DC microgrids suitable for real-time simulation and small signal analysis

This study develops a system-level dynamic phasor (DP) model of a hybrid AC/DC microgrid. A new definition of DP, based on the multiple frequency shift transformation, is proposed to extend the models of a variety of AC/DC and DC/DC converters. The comprehensive control system is considered in developing the DP model of microgrid. Furthermore, a computational framework for applying this DP model into system-level simulation and small signal analysis (SSA) is given. The DP-based simulation is tested under conditions of environmental disturbances and unbalanced supply voltages. The SSA is performed on the developed DP model to evaluate the voltage stability and harmonic modes of the microgrid. Besides, this DP model shows high potential when implemented in the real-time simulation with larger scale system.
The Effectiveness of Transfer Learning for Arabic Handwriting Recognition using Deep CNN

: Recognition of handwritten words is a widely used system in the modern world that remains a significant challenge. Traditional machine learning techniques require creative engineering and considerable domain expertise to transform the raw data into a feature vector from which the classifier could rank the input model. To deal with this problem, the popular convolutional neural networks (CNNs), and Its Derivatives introduced recently, have effectively replaced handcrafted descriptors with network features and have been found to provide significantly better results than traditional methods. It is one of the fastest growing fields in machine learning, promising to reshape the future of artificial intelligence. However, the problem with deep learning is that it requires large data sets for training due to the large number of parameters needed to be tuned by a learning algorithm. In our proposed approach, Deep Convolutional Neural Networks (DCNN) is adapted to perform the classification phase. Notably the architectures Inception-v3, ResNet, and VGG16, using an enriched dataset containing images extracted from the IFN/ENIT database. To refine pre-trained models for deep characteristics extraction, we utilize Transfer Learning technique. This technique has shown very good performance for the frequent recognition problems. According to the results obtained, the developed system gives very interesting recognition rates.
Object Detection in a Maritime Environment: Performance Evaluation of Background Subtraction Methods

This paper provides a benchmark of the performance of 23 classical and state-of-the-art background subtraction (BS) algorithms on visible range and near infrared range videos in the Singapore Maritime dataset. Importantly, our study indicates the limitations of the conventional performance evaluation criteria for maritime vision and proposes new performance evaluation criteria that is better suited to this problem. This paper provides insight into the specific challenges of BS in maritime vision. We identify four open challenges that plague BS methods in maritime scenario. These include spurious dynamics of water, wakes, ghost effect, and multiple detections. Poor recall and extremely poor precision of all the 23 methods, which have been otherwise successful for other challenging BS situations, allude to the need for new BS methods custom designed for maritime vision.
Action Sequencing in Construction Accident Reports using Probabilistic Language Model

– Construction remains among the most hazardous workplaces, thus a significant amount of time and effort in reporting and investigating the accident occurrences has been done in the past decades by government agencies. In light of construction safety, analyzing textual information in construction accident records may assist in our comprehension of past data and be used to minimize future risks. Many attempts have been made in previous studies to identify causes and related entities but yet consider worker activities and behaviors. This study presents a framework that adopts a Probabilistic Language Model to sequence actions taken by workers that depict construction scenarios from unstructured accident narrative reports. The proposed approach achieved outstanding performances with the highest sequence accuracy and pairwise sequence accuracy of 84.81 % and 89.12%, respectively. Moreover, an action sequence database that can explain the relationship between workers’ actions was created. This research is anticipated to contribute to enhancing understanding and establishing safety management systems to actively forecast and prevent accidents.
Ketoprofen Loaded Solid Self Emulsifying Drug Delivery System (SEDDS): Development and Optimization.

The purpose of this study was to formulate a solid self emulsifying drug delivery system (SEDDS) containing Ketoprofen as an intermediate in the development of sustained release solid dosage form. Oleic acid (an oil), Tween 80 (a surfactant), and ethanol (a cosurfactant) were used to formulate SEDDS. Silicon dioxide was used as an adsorbing agent, which may aid in solidification and retardation of drug release. The solubility of Ketoprofen (BCS Class II), as a model drug in this study, was determined in several oils, surfactants and co-surfactants using an UV method. The self emulsifying system of Ketoprofen was constructed by using oil, surfactant and cosurfactant. The ratio of these components in the formulation was 15:28.3:56.7 (w/w) and optimized by a pseudo ternary phase diagram. The droplet size of the optimized liquid with drug was 185.2nm and solid SEDDS was 399nm. Effect of adsorbing agent on emulsification process and in vitro drug release was studied. A liquisolid system is formed by converting a liquid formulation into a dry, free-flowing and compressible powder mixture with selected carrier material and coating material. This technique has industrial applications for low dose insoluble drugs. The liquid crystal phase (LC) viscosity increased significantly with increasing amount of silicon dioxide, which in turn caused an increase in average droplet size of resultant emulsion and slower drug release. Drug release from the formulation increased with increasing amount of cosurfactant
pressure inevitably with age?: Longitudinal evidence among forager-horticulturalists

: The rise in blood pressure with age is a major risk factor for cardiovascular and renal disease, stroke, and type 2 diabetes mellitus. Age-related increases in blood pressure have been observed in almost every population, except among hunter-gatherers, farmers, and pastoralists. Here we tested for age-related increases in blood pressure among Tsimane forager-farmers. We also test whether lifestyle changes associated with modernization lead to higher blood pressure and a greater rate of age-related increase in blood pressure. We measured blood pressure longitudinally Abstract —The rise in blood pressure with age is a major risk factor for cardiovascular and renal disease, stroke, and type 2 diabetes mellitus. Age-related increases in blood pressure have been observed in almost every population, except among hunter-gatherers, farmers, and pastoralists. Here we tested for age-related increases in blood pressure among Tsimane forager-farmers. We also test whether lifestyle changes associated with modernization lead to higher blood pressure and a greater rate of age-related increase in blood pressure. We measured blood pressure longitudinally on 2248 adults age (cid:1) 20 years (n (cid:1) 6468 observations over 8 years). Prevalence of hypertension was 3.9% for women and 5.2% for men, although diagnosis of persistent hypertension based on multiple observations reduced prevalence to 2.9% for both sexes. Mixed-effects models revealed systolic, diastolic, and pulse blood pressure increases of 2.86 ( P (cid:2) 0.001), 0.95 ( P (cid:2) 0.001), and 1.95 mmHg ( P (cid:2) 0.001) per decade for women and 0.91 ( P (cid:2) 0.001), 0.93 ( P (cid:2) 0.001), and (cid:3) 0.02 mmHg ( P (cid:1) 0.93) for men, substantially lower than rates found elsewhere. Lifestyle factors, such as smoking and Spanish fluency, had minimal effect on mean blood pressure and no effect on age-related increases in blood pressure. Greater town proximity was associated with a lower age-related increase in pulse pressure. Effects of modernization were, therefore, deemed minimal among Tsimane, in light of their lean physique, active lifestyle, and protective diet. ( Hypertension . 2012;60:00.) ●
Smart charging solutions for electric mobility

Transport electrification is an accelerating reality which should be complemented by the respective charging technologies and services for its successful deployment. While the electric vehicle (EV) market is now fully developed, users still have concerns about key areas of the electric vehicle charging infrastructure. The transition from conventional vehicles to electric ones dictates the design and development of user-centric charging solutions aiming to facilitate the accessibility to as well as the usability of the charging network and improve the user’s charging experience. This paper aims to highlight the critical issues of the electric mobility sector and analyse smart charging approaches supporting the large-scale implementation of electric cars.
Weakly supervised object localization with multiple instance learning network on industrial inspection images

Weakly supervised object localization aims at learning to locate objects with image-level category labels, whereas fully supervised object detection employs instance level labels with bounding boxes for the same task. Such ﬁne-grained annotation is more costly compared to image-level labels, however with the help of stronger supervision, fully supervised object detection models perform signiﬁcantly better than the weakly supervised object detection models on benchmark datasets. This fact raises doubts whether weakly supervised approaches for object localization can be utilized in industrial applications where high localization accuracy is essential. However, there are fundamental diﬀerences between benchmark datasets and industrial inspection datasets. In this thesis work, we will explore the feasibility of weakly supervised object detection in industrial applications with a special focus on industrial inspection datasets from semiconductor industry by exploiting these diﬀerences. We show that high localization accuracy is achievable with weakly supervised object detection. However, the localization accuracy is very sensitive to dataset properties. We exhibit and discuss the dataset properties for a successful weakly supervised object localization implementation. Despite that, we conclude that further challenges remain for broader success of weakly supervised object localization tasks in industrial applications.