Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in Natural Product Extraction Techniques, Electrospun Fiber Fabrication, and the Integration of Experimental Design: A Comprehensive Review

The present review explores the growing interest in the techniques employed for extracting natural products. It emphasizes the limitations of conventional extraction methods and introduces superior non-conventional alternatives, particularly ultrasound-assisted extraction. Characterization and quantification of bioactive constituents through chromatography coupled with spectroscopy are recommended, while the importance of method development and validation for biomarker quantification is underscored. At present, electrospun fibers provide a versatile platform for incorporating bioactive extracts and have extensive potential in diverse fields due to their unique structural and functional characteristics. Thus, the review also highlights the fabrication of electrospun fibers containing bioactive extracts. The preparation of biologically active extracts under optimal conditions, including the selection of safe solvents and cost-effective equipment, holds promising potential in the pharmaceutical, food, and cosmetic industries. Integration of experimental design into extraction procedures and formulation development is essential for the efficient production of health products. The review explores potential applications of encapsulating natural product extracts in electrospun fibers, such as wound healing, antibacterial activity, and antioxidant properties, while acknowledging the need for further exploration and optimization in this field. The findings discussed in this review are anticipated to serve as a valuable resource for the processing industry, enabling the utilization of affordable and environmentally friendly, natural, and raw materials.
Robust optimal consumption-investment strategy with non-exponential discounting

This paper extends the existing dynamic consumption-investment problem to the case with more general discount functions under the robust framework. The decision-maker is ambiguity-averse and invests her wealth in a risk-free asset and a risky asset. Since non-exponential discounting is considered in our model, our optimization problem is time inconsistent. By solving the extended Hamilton-Jacobi-Bellman equations, the corresponding optimal consumption-investment strategies for sophisticated and naive investors under power and logarithmic utility functions are derived explicitly. Our model and results extend some existing ones and derive some interesting phenomena.
Classification of two-and three-factor time-homogeneous separable LMMs

The flexibility of parametrizations of the LIBOR market model (LMM) comes at a cost, namely the LMM is high-dimensional, which makes it cumbersome to use when pricing derivatives with early exercise features. One way to overcome this issue for short- and medium-term time horizons is by imposing the separability condition on the volatility functions and approximating the model using a single time-step approximation. In this paper, we examine the flexibility of separable LMMs under the relaxed assumption that the driving Brownian motions can be correlated. In particular, we are interested in how the separability condition interacts with time-homogeneity, a desirable property of a LMM. We show that the two concepts can be related using a Levi-Civita equation and provide a characterization of two- and three-factor separable and time-homogeneous LMMs and show that they are of practical interest. The results presented in this paper are also applicable to local-volatility LMMs. These separable volatility structures can be used for the driver of a two- or three-dimensional Markov-functional model — in which case no (single time-step) approximation is needed and the resultant model is both time-homogeneous and arbitrage-free.
The influence of visitors’ specific motor actions on the perception and evaluation of museum exhibits

Background and Study Aim. An important element in the system of museum activities are approaches to attracting visitors. Among these approaches, one can single out the dialogue of the museum with visitors through specially organized active motor actions. Such actions have certain parameters (the nature of physical activity and its age-appropriateness, intensity, duration, etc.), which can influence the perception and evaluation of exhibits and the decision of visitors to return to the museum. The purpose of the study is an attempt to explain how specially organized active motor actions of visitors affect the perception and evaluation of museum exhibits and getting satisfaction from visiting. Material and Methods. Publications from the Web of Science Core Collection (WoS) were selected as the source of information. The search was carried out using keywords that characterize the interaction of visitors with the subject-spatial environment of the museum. The VOSviewer program was used to analyze the relationships between documents retrieved from WoS. Results. It is argued that the sedentary behavior of visitors, typical for many museums, does not contribute to their involvement in the museum space and the creation of meaning in museums. It is noted that an important aspect in the organization of special motor actions of visitors is the orientation to their motivation and interpretation of movements in the museum exhibits. The optimal level of physical activity has been identified. It creates a comfortable feeling and contributes to an adequate perception of the exhibits and corresponds to the main mission of the museum. Conclusions. Specially organized motor actions form the maximum motivation for visitors to study the museum exhibits. Such movements carry a certain semantic character of the theme of the museum.
Pavement contour extraction and material recognition based on landscape camera

With the rapid development of autonomous driving, road monitoring and other fields, traffic management departments are actively promoting the digital transformation of roads and intelligent maintenance applications. In this context, it is particularly important to study the technology of extracting road width, road surface material and other basic road attribute information from road images automatically. Therefore, this paper proposes a multi-branch network based on convolutional neural network for pavement contour extraction and material recognition, firstly, CNN (convolutional neural network) is used as the backbone network to extract image features, then a branch for pavement material recognition is added after the backbone network to distinguish the type of pavement, and a branch for semantic segmentation is executed in parallel to extract pavement contour, to achieve the purpose of recognizing pavement material and extracting pavement contour at the same time The automatic extraction of pavement material and pavement contour is realized, which lays a solid foundation for the automatic extraction of pavement width, automatic evaluation of pavement technical condition and intelligent road maintenance. The experimental results show that the accuracy of pavement material recognition reaches 99.2% and the IoU of pavement profile extraction reaches 96.1% on the self-collected 20822 image sets, which proves the effectiveness of the proposed network in this paper.
A Qualitative and Quantitative Analysis of Research in Mobility Technologies for Visually Impaired People

Assistive technology in rehabilitation programs is vital for people with vision impairments worldwide. The term “blind assistive technology” refers to mobility devices specifically designed to provide position, orientation and mobility assistance for visually impaired individuals during indoor and outdoor activities. The paper presents a comprehensive evaluation of 140 research articles published over the past 75 years (1946 to 2022). This research analyses the evolution of assistive technology aids in depth, in terms sensing technique followed, algorithms employed for obstacle detection, localization, object recognition, depth estimation and scene understanding. It also includes, the functional attributes of the aid, feedback type, and assistive solutions embedded in aid. It evaluates the assistive aids for their usability index, portability, battery life, feedback type, and aesthetics. The survey findings reveal that optical and sonic sensor-based aids prioritize speed, weight, and battery life but lack major functionalities, achieving an average performance score of 62%. Stereo, monocular, SLAM, and 3-D point cloud-based aids excel in obstacle distance estimation and avoidance but require greater memory resources, with a lower performance score of 41%. Artificial intelligence and cloud-based aids offer comprehensive scene details but demand complex computational capabilities, achieving a performance score of 44%. However, the most suitable technology for developing state-of-the-art solutions for blind individuals is the multisensor fusion-based and guide robot-based aids, providing a majority of the essential assistive functions with a performance score of 51%. The study highlights possible challenges associated with implementing assistive technology aids, emphasizes the importance of user acceptability, and stresses the need for real-time evaluation of blind aids. The paper lays a concrete foundation and direction for future development, emphasizing the critical challenges faced by blind users, including boarding trains, traveling on public transport, shopping in a supermarket, avoiding dynamic obstacles, and real-time understanding of the surrounding scene. Addressing these key concerns is crucial for the continued development and improvement of assistive technology aids for the visually impaired, leading to enhanced independence, mobility, and ultimately, a higher quality of life.
Constructing maps by clustering trajectories

We propose a new approach for constructing the underlying map from trajectory data. This algorithm is based on the idea that road segments can be identified as stable subtrajectory clusters in the data. For this, we consider how subtrajectory clusters evolve for varying distance values, and choose stable values for these, in this way avoiding a global proximity parameter. Within trajectory clusters, we choose representatives, which are combined to form the map. We experimentally evaluate our algorithm on hiking and vehicle tracking data. These experiments demonstrate that our approach can naturally deal with different road widths, and differences in density of the data. It can also to an extent separate roads that run close to each other and can deal with outliers in the data, two issues that are notoriously difficult in road network reconstruction. Constructing Maps by Clustering Trajectories iii
Theory and Transport of Nearly Incompressible Magnetohydrodynamic Turbulence

The theory of nearly incompressible magnetohydrodynamics (NI MHD) was developed largely in the early 1990s, together with an important extension to inhomogeneous flows in 2010. Much of the focus in the earlier work was to understand the apparent incompressibility of the solar wind and other plasma environments, and the relationship of density fluctuations to apparently incompressible manifestations of turbulence in the solar wind and interstellar medium. Further important predictions about the “dimensionality” of solar wind turbulence and its relationship to the plasma beta were made and subsequently confirmed observationally. However, despite the initial success of NI MHD in describing fluctuations in the solar wind, a detailed application to solar wind turbulence has not been undertaken. Here, we use the equations of NI MHD to describe solar wind turbulence, rewriting the NI MHD system in terms of Elsässer variables. Distinct descriptions of 2D and slab turbulence emerge naturally from the Elsässer formulation, as do the nonlinear couplings between 2D and slab components. For plasma beta order 1 or less regions, predictions for 2D and slab spectra result from the NI MHD description, and predictions for the spectral characteristics of density fluctuations can be made. We conclude by presenting a NI MHD formulation describing the transport of majority 2D and minority slab turbulence throughout the solar wind. A preliminary comparison of theory and observations is presented.
Safe, Efficient, and Comfortable Reinforcement-Learning-Based Car-Following for AVs with an Analytic Safety Guarantee and Dynamic Target Speed

Over the last decade, there has been rising interest in automated driving systems and adaptive cruise control (ACC). Controllers based on reinforcement learning (RL) are particularly promising for autonomous driving, being able to optimize a combination of criteria such as efficiency, stability, and comfort. However, RL-based controllers typically offer no safety guarantees. In this paper, we propose SECRM (the Safe, Efficient, and Comfortable RL-based car-following Model) for autonomous car-following that balances traffic efficiency maximization and jerk minimization, subject to a hard analytic safety constraint on acceleration. The acceleration constraint is derived from the criterion that the follower vehicle must have sufficient headway to be able to avoid a crash if the leader vehicle brakes suddenly. We critique safety criteria based on the time-to-collision (TTC) threshold (commonly used for RL controllers), and confirm in simulator experiments that a representative previous TTC-threshold-based RL autonomous-vehicle controller may crash (in both training and testing). In contrast, we verify that our controller SECRM is safe, in training scenarios with a wide range of leader behaviors, and in both regular-driving and emergency-braking test scenarios. We find that SECRM compares favorably in efficiency, comfort, and speed-following to both classical (non-learned) car-following controllers (intelligent driver model, Shladover, Gipps) and a representative RL-based car-following controller.
Analysis of Oplegnathus Punctatus Body Parameters Using Underwater Stereo Vision

Due to the loss of underwater light resources, monitoring fish situations in underwater surveillance has led to fish distortion. Monitoring and observing fish situations in culture ponds around the world is becoming more and more important to prevent them from suffering uncertain damage. In this study, we propose a stereo underwater surveillance system for Oplegnathus punctatus by developing an underwater depth prediction model and an underwater fish skeleton model based on deep learning. The underwater depth prediction model is a convolutional neural network-based method for extracting underwater depth spatial features. The fish skeleton prediction model is for extracting 9 keypoints on the fish body. Additionally, since there is no established underwater Oplegnathus punctatus dataset for fish body analysis in culture ponds, we have collected and proposed a depth and skeleton Oplegnathus punctatus dataset, which contains underwater information on Oplegnathus punctatus bodies. The experimental results on our self-collected dataset show that the fish body information measurement achieves 94% accuracy in weight. We also compared our proposed method with Mask-RCNN and stereo-matching methods, and our method proved to be the most effective.
Analysis of Several Discharges – Durations- Drip Line Placements Under Mango Trees “Tommy Atkins” (Magnifera Indica L.) In Zabid Valley, Tihama- Yemen.

Jamil Ahmed. The wetted volume of root area is one of the most important parameters for irrigation efficiency in the drip irrigation. Three durations (short – moderatelong duration) were compared with supplied 40, 60 and 80 L respectively. Three discharges (8, 16, and 32 l/h) were used and four treatments were tested for each discharge: one dripper, two drippers, four drippers with a looping of 20 cm and 40 cm around the trunk. The case of discharge 8 L/h with irrigation duration (10 h) in the treatment four drippers with a looping 40 cm around the trunk was superior and given the best percentage of root wetted area, 100% of the effective root area, as well as, an average volumetric water content and a coefficient of uniformity were 26% and 87%, respectively. Also, the saved water was 65.5 % compared to surface irrigation.
توافق المعايير المحاسبية الدولية مع قياس رأس المال البشري Compatibility International Accounting Standards with the Human Capital Measurement

: The aims of this study are to determine the appropriateness of international accounting standards and international financial reporting standards (IAS/IFRS) to build a special basis to measure human capital in business organizations. Researcher adopted scientific methodology research, and propose the conceptual theoretical framework for measuring human capital on the basis of IAS and IFRS. The main recommendations were to determine the quantitative indicators of human performance to measure the added value contributed by human resources, to measure their contributions to maximize productivity and achieve the organization’s objectives, in addition, researcher recommends the necessity to adopt the separate accounting standard of human capital
Sleep Quality and Screen Time as The Most Influential Factor of Computer Vision Syndrome

Background: Since the Covid-19 Pandemic hit Indonesia, college students have had many eye health complaints; one is Computer Vision Syndrome (CVS). Factors that can increase the risk of CVS are decreased sleep quality, high computer/laptop use duration, and a non-ergonomic position. This study aimed to prove the relationship between sleep quality, visibility, and screen time on the incidence of CVS in medical students of Diponegoro University.Method: This cross-sectional study, including 147 medical students was conducted using primary data from questionnaires of Wendy Strouse Watt, Thomas H Murphy, Hospitality Eye Care Center, and vsp.com to gain data on respondent identity, visibility, and screen time. Sleep quality data were taken from the PSQI and CVS data through the CVS-Q questionnaire. Chi-square, Mann-Whittney, and logistic regression of multivariate analysis were used to analyze the data.Results: From 147 subjects, 108 were CVS (73.5%), and 39 were Non-CVS (26.5%). Variables of sleep quality and screen time (total) showed a significant relationship with CVS, which had p-values of p=0.001 and p=0.009 (p<0.05). As for the visibility to CVS, there was no relationship with p=0.863. In the multivariate test, sleep quality (p=0.000; ExpB=4.342) and total screen time (p=0.006; ExpB=3.913) were the most influential variables. The results showed that Computer Vision Syndrome is ubiquitous among Diponegoro University's final-year medical students. Sleep quality and total screen time can increase the risk of CVS by four times. This study also shows that CVS still appears in the post-pandemic era.
The lower and upper solution method for three-point boundary value problems with integral boundary conditions on a half-line

This paper deal with the following second-order three-point boundary value problem with integral boundary condition on a half-line \begin{eqnarray*} \left. \begin{array}{ll} u''(x)+q(x)f(x, u(x), u'(x))=0, \ \ \hbox{$x\in(0,+\infty)$,} \\ u(0)=\lambda\ds\int_{0}^{\eta}u(s)ds,\ \ u'(+\infty)=C, \end{array} \right. \end{eqnarray*} where $\lambda>0, \ 0<\lambda\eta<1$ and $f:[0,+\infty)\times \mathbb{R}^2\rightarrow\mathbb{R}$ satisfies a Nagumo's condition which plays an important role in the nonlinear term depend on the first-order derivative explicitly. By using Schauder's fixed point theorem, the upper and lower solution method and topological degree theory, first we give sufficient conditions for the existence of at least one solution and next at least three solutions of the above problem. Moreover, an example is included to demonstrate the efficiency of the main results.
Generalized linear models with non monotonic link function

In this paper, a general model is proposed to extend generalized linear models to non-monotonic link functions. In order to determine the best model, different link function families are analyzed, and through AIC, the best model is chosen. The Maximum Likelihood estimation method is used. By using the estimated Fisher Information Matrix a confidence interval for the parameters is obtained. Lastly, one example of regression extension is proposed for the Poisson distribution on a study of Salmonella Ames.
Assessment of illumination conditions in a single-pixel imaging configuration

Single-pixel imaging based on multiplexing is a promising technique, especially in applications where 2D detectors or raster scanning imaging are not readily applicable. With this method, Hadamard masks are projected on a spatial light modulator to encode an incident scene and a signal is recorded at the photodiode detector for each of these masks. Ultimately, the image is reconstructed on the computer by applying the inverse transform matrix. Thus, various algorithms were optimized and several spatial light modulators already characterized for such a task. This work analyses the imaging quality of such a single-pixel arrangement, when various illumination conditions are used. More precisely, the main comparison is made between coherent and incoherent ("white light") illumination and between two multiplexing methods, namely Hadamard and Scanning. The quality of the images is assessed by calculating their SNR, using two relations. The results show better images are obtained with "white light" illumination for the first method and coherent one for the second.
Computational and experimental investigation of spiral separator hydrodynamics

Spirals play an important role in fine coal and mineral processing. To date, almost all spiral designs have been empirically based. Although many operational units have evolved utilizing this approach, it does not necessarily lead to optimal spiral designs. This paper presents the preliminary results of a collaborative computational and experimental research program to understand the detailed separation process which will ultimately lead to a parametric assessment of alternative spiral designs through computational simulation. Unlike the empirical approach, a computational simulation is only subject to the fundamental physics of the fluid flow. No prior knowledge of the generic spiral flow is necessary. The computer code implements a robust finite volume technique to predict the three dimensional fluid motion. A height function method is used to model free surfaces and the code is capable of handling arbitrary spiral geometries. Preliminary validation of the computational simulation is carried out through analysis of an existing spiral design--the Mineral Technologies LD9 unit. Computational flow predictions are compared with experimental measurements obtained using a number of techniques, including sampling and flow visualization, for flow test cases where no solid particulate is present.
Markov-Chain Based Performance Analysis and Evaluation of Harvest-Then-Access Scheduling for Wireless Powered Communication Network

Wireless powered communication network (WPCN) is a novel structure of the network that integrates wireless information communication (WIT) and wireless energy transfer(WET). The scheduling design is a key factor for performance improvement for WPCN. As an effective scheduling design approach, “Harvest-then-Access” has been proposed. This approach requires optimizing control parameters in the scheduling. Therefore, some approaches, such as machine learning or simulation, have been used. This paper proposes a Markov-chain model-based analytical model of Harvest-then-Access scheduling for WPCN. We propose a new Markov-chain model, which includes the essential operation in WPCN with Harvest-then-Access. From the analytical model, we derive the optimal time interval of WET in the network with Harvest-then-Access, which simultaneously provides throughput improvement and fairness among devices. The validity of the proposed analytical model is demonstrated by comparing the analytical and simulation result. Then, we evaluate the performance of Harvest-then-Access scheduling through comparison to the other scheduling.