Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of metal contents in aromatic herbs and spices from Algeria: Chemometric approach

Trace metals (As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr, and Zn) in acid digests of some aromatic herbs and spices (basil, fennel, laurel, mint, oregano, rosemary, thyme, black pepper, cinnamon, coriander, and cumin) were determined quantitatively using ICP‐AES method. The highest concentrations (μg g−1 dry matter) of each of the 11 investigated metals were found as follows: As (0.42) and Pb (1.6) in rosemary samples; Cd (0.1) in basil; Co (0.62), Cu (12.13), and Zn (52.26) in oregano; Cr (2.95) and Mg (3110) in fennel, Fe (494) and Ni (7.61) in cumin; Mn (192) in black pepper; and finally Sr (60.68) in mint. Some chemometric techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least square discriminant analysis (PLS‐DA) were used on metallic concentrations data in an attempt to classify these aromatic herbs and spices. The unsupervised pattern recognition PCA and HCA models gave the same result about similarities and differences between the studied plant samples, and five clusters of similar aromatic herbs and spices samples were formed. In order to verify the results of this clustering, we used a supervised pattern recognition method called partial least square discriminant analysis (PLS‐DA). Classes were groups or clusters of similar plants obtained previously. A hierarchical model builder (HMB) based on four PLS‐DA models was used to simultaneous determination of the class of each sample. It was found that all samples were correctly classified by PLS‐DA in their original groups as determined by PCA and HCA.
Joint Application Placement and Request Routing Optimization for Dynamic Edge Computing Service Management

As mobile edge computing (MEC) hosting applications at the network edge with limited capacities, service providers are facing the new challenge of how to make full use of the scarce edge resources to maximize the system performance. Accommodating this challenge requires careful application placement and request routing to coordinate diverse MEC nodes. However, frequent application re-placement would greatly increase the system reconfiguration cost, indicating a performance-cost trade-off. In response, in this paper, we study the problem of joint optimization on application placement and request routing to maximize the system performance, under a long-term budget of the application reconfiguration cost. Solving this problem is non-trivial since the long-term budget is coupled with the future system states (e.g., user request arrivals) that are typically unpredictable. To address this challenge, we first advocate an approximated dynamic optimization framework to decompose the long-term optimization problem into a series of one-shot problems which do not require the future system states. Moreover, since the decomposed problem is a mixed integer linear program (MILP) which is proven to be NP-hard, we then devise an efficient dependent rounding based approximation algorithm, which can achieve the near-optimal performance in a fast manner. Both rigorous theoretical analysis and extensive trace-driven evaluations demonstrate the proposed framework can achieve superior performance gain over existing schemes.
Extrasolar Planets and Astrobiology

Introduction, Background, and Preview Star Formation and Proto-Stellar Collapse Planet Formation Exoplanets Life: A Brief History and its Boundaries Planetary Radiation, Comparative Planetology, Biosignatures and Daisyworld Cosmochemistry, Dust and Prebiotic Molecules Comets, Meteorites and Proto-Planetary Disk Structure Habitable Zones
Culturable Endophytes Associated with Soybean Seeds and Their Potential for Suppressing Seed-Borne Pathogens

Seed-borne pathogens in crops reduce the seed germination rate and hamper seedling growth, leading to significant yield loss. Due to the growing concerns about environmental damage and the development of resistance to agrochemicals among pathogen populations, there is a strong demand for eco-friendly alternatives to synthetic chemicals in agriculture. It has been well established during the last few decades that plant seeds harbor diverse microbes, some of which are vertically transmitted and important for plant health and productivity. In this study, we isolated culturable endophytic bacteria and fungi from soybean seeds and evaluated their antagonistic activities against common bacterial and fungal seed-borne pathogens of soybean. A total of 87 bacterial isolates and 66 fungal isolates were obtained. Sequencing of 16S rDNA and internal transcribed spacer amplicon showed that these isolates correspond to 30 and 15 different species of bacteria and fungi, respectively. Our antibacterial and antifungal activity assay showed that four fungal species and nine bacterial species have the potential to suppress the growth of at least one seed-borne pathogen tested in the study. Among them, Pseudomonas koreensis appears to have strong antagonistic activities across all the pathogens. Our collection of soybean seed endophytes would be a valuable resource not only for studying biology and ecology of seed endophytes but also for practical deployment of seed endophytes toward crop protection.
Cooperative non-equilibrium phase transition in a dilute thermal atomic gas

We demonstrate a non-equilibrium phase transition in a dilute thermal atomic gas. The phase transition is induced by dipole-dipole interactions between Rydberg atoms which are separated by less than the transition wavelength to neighbouring states. In the frequency domain we observe a cooperative shift of the Rydberg state which results in intrinsic optical bistability above a critical number of excited atoms. In the time domain we observe critical slowing down where the correlation length diverges with critical exponent alpha = -0.53+/-0.10. The atomic emission spectrum in the many- body phase provides strong evidence for a superradiant cascade.
Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).
Elucidating the Impact of behavioral biases in Pakistan Stock Market: Moderating Impact of Financial Literacy

The current study focuses on some of the most commonly relied upon biases in decision making. The study aimed at understanding the influence of herding, overconfidence, anchoring, and loss aversion on the decision-making style of investor besides it also investigates the role of financial literacy, since the traditional paradigm of finance is of the view that the knowledge of finance is directly associated with the degree of irrational outcomes. To explore this linkage data from investors trading at Lahore, Karachi, and Islamabad is gathered. Structural equation modeling is used for establishing the proposed associations. The results revealed that behavioral biases significantly impact the decision-making of investors. The results of moderation analysis presented that financial literacy plays a major role in de-biasing decision making. These findings can be extremely useful for investors, policymakers, and investment professionals. Not only to make optimal decision-making but also by providing a deeper understanding of the daily life stock market behavior.
Freeze-Dried Tuna Pepsin Powder Stabilized by Some Cryoprotectants: In Vitro Simulated Gastric Digestion toward Different Proteins and Its Storage Stability

The impact of maltodextrin (10%) in combination with trehalose or glycerol at different levels (2.5% and 5%) and their mixture on the stability of freeze-dried pepsin from skipjack tuna stomach was studied. Addition of 5% trehalose and 10% maltodextrin yielded the powder (TPP-T5) with highest relative pepsin activity (p < 0.05). TPP-T5 had different shapes and sizes, with mean particle size of 65.42 ± 57.60 μm, poly-dispersity index of 0.474, and zeta potential of −19.95. It had bulk density of 0.53 kg m−3 and possessed fair flowability. The wetting time for TPP-T5 was 16.36 ± 0.73 min, and solubility was 93.58%. TPP-T5 stored at room temperature under different relative humidities could maintain proteolytic activity up to 4 weeks. Commercial porcine pepsin (CP) and crude tuna pepsinogen had molecular weights of 35.2 and 43.3 kDa, respectively, when analyzed using gel filtration (Sephadex G-50) and SDS-PAGE. Tuna pepsin had comparable hydrolysis toward threadfin bream muscle protein, whey protein isolate, and kidney bean protein isolate to commercial pepsin, especially at a higher level (15 units/g protein). Digested proteins contained peptides with varying molecular weights as determined by MALDI-TOF. Therefore, pepsin from skipjack tuna stomach could replace commercial porcine pepsin and was beneficial supplement for patients with maldigestion, particularly the elderly.
Inconsistency is the Consistency: The Title IX Reporting Process for Sexual and Gender-Based Misconduct Within Maryland Public Universities

Title IX is a primary federal legal approach to address campus sexual and gender-based misconduct, yet few students utilize Title IX reporting as a formal campus support, and those that do frequently report negative experiences. In this study, we interviewed 11 student survivors at four Maryland public universities who engaged with the Title IX reporting and response process. Our aims were to (a) examine how Title IX functions in a state public education system with a robust Title IX policy; (b) describe commonalities and differences in experiences; and (c) use theories of institutional betrayal and support to understand aspects of the process most helpful or harmful for survivors, especially minoritized survivors. Results reflected several common themes, but also an inconsistent Title IX process both within and across institutions beholden to the same Title IX policy, representing potential policy deviations. Further, institutional betrayal was reflected in the experiences of minoritized survivors who described Title IX staff microaggressions and invalidations and survivors who unknowingly disclosed to mandatory reporters. Overall, experiences contributed to a perception of the Title IX office and reporting process as unhelpful and untrustworthy. Results identify the need to reduce inconsistencies in Title IX reporting and response processes to ameliorate process harms. Online slides for instructors who want to use this article for teaching are available on PWQ's website at http://journals.sagepub. com/doi/suppl/10.1177/03616843221115340.
The Mathematics of Signal Processing

1. Introduction 2. Normed vector spaces 3. Analytic tools 4. Fourier series 5. Fourier transforms 6. Compressive sensing 7. Discrete transforms 8. Linear filters 9. Windowed Fourier transforms, continuous wavelets, frames 10. Multiresolution analysis 11. Discrete wavelet theory 12. Biorthogonal filters and wavelets 13. Parsimonious representation of data Bibliography Index.
Toward a Theory of Nature Experience and Health

This article presents an integrated theoretical framework to study the socioenvironmental attributes of the nature experience as a basic health behavior. After first reviewing existing literature on theories behind nature exposure, we discuss social cognitive theory (SCT) to explain individual nature experience through the model's triadic dynamic of environment, cognitions, and behaviors. We then expand beyond SCT's focus on the individual to examine structural and societal spheres of influence on nature experience found in ecological systems theory and ecosocial theory. In moving from proximal to distal influences, we identify the core constructs of each theory that may reinforce or deter decisions inclining individuals toward nature engagement. In synthesizing aspects of these three theories, we propose an integrated theoretical framework of nature experience distinguished by three ideas. First, individual-level formative influences in nature pervade higher level ecologies as a learned social behavior. Second, nature experience happens within multiple systems and timepoints. Third, social relationships within historical processes shape contextual factors of the nature experience, resulting in disparities in nature access and nature responses that manifest heterogeneously. Theorizing behind nature experience can inform why this occurs. We offer suggestions for further research to build on the groundwork put forth here: for hypothesizing around present observations, for collecting data to confirm and/or refute parts of the theory, and for further hypothesis generation inspired by the theory to inform the research agenda. In conclusion, we consider the practical implications of theory underlying nature experience as a health behavior relevant to research, interventions, and policy.
Estimation of losses and efficiency of double-stator switched flux permanent magnet machines

Losses such as rotor iron and stator iron losses, magnet eddy current loss, as well as efficiency of a double stator permanent magnet machine having varying rotor pole numbers is estimated and presented in this study. This current investigation would be vital for electrical machine designers in quantifying the amount of losses in the various sections of a given double stator electric machine and as well provide better insight on resulting output efficiency of the machine. Time-stepping finite element analysis (TS-FEA) procedure is adopted in the calculations using ANSYS-MAXWELL simulation software. The compared machines having the same stator teeth/pole number (Ps) and different rotor pole (Pr) numbers are designated as: 6Ps/10Pr, 6Ps/11Pr, 6Ps/13Pr and 6Ps/14Pr. The predicted total loss values at rated current and operating base speed is 13.02 Watts, 13.13 Watts, 13.539 Watts, 13.537 Watts, from the 6Ps/10Pr, 6Ps/11Pr, 6Ps/13Pr and 6Ps/14Pr machine types, respectively. The corresponding efficiency of the machine types at rated working conditions is: 81.76%, 88.17%, 86.95% and 78.61%, respectively. The results show that magnitude of losses in a given machine would largely depend upon factors such as number of rotor poles and hence, the machine’s operating speed, electric loading and angular rotor position of the machine, etc. It is also found that the number of loss waveform cycles (Nc) in an electric revolution of the investigated machine would depend upon its stator and rotor arrangements. The most performing compared machine types are the ones that have 11- and 13-rotor pole numbers, while the least amount of efficiency is obtained from the machine type that has 14-rotor pole number. Above all, the 6Ps/10Pr and 6Ps/14Pr are characterized with high amount of pulsations or ripples and this is detrimental to the overall performance of the machines.
Study on Distribution of Lubricating Oil Film in Contact Micro-Zone of Full Ceramic Ball Bearings and the Influence Mechanism on Service Performance

Compared with metal ball bearings, full ceramic ball bearings have more outstanding service performance under extreme working conditions. In order to reveal the lubrication mechanism and improve the operation performance and service life of full ceramic ball bearings, in this paper, the friction, vibration, and temperature rise characteristics of 6208 silicon nitride full ceramic deep groove ball bearing, under the condition of oil lubrication, are studied experimentally. Based on the test results, and through theoretical calculation and simulation analysis, the distribution of the lubricating oil film in bearing contact micro-zone under different working conditions was simulated. After that, the surface of contact micro-zone of full ceramic ball bearing was analyzed. It was found that there is an optimal oil supply for full ceramic ball bearing oil lubrication in service. Under the optimal oil supply lubrication, full film lubrication can be achieved, and the bearing exhibits the best characteristics of friction, vibration, and temperature rise. Compared with the load, the rotational speed of the bearing has a decisive influence on the optimal oil supply. When the rotational speed and load are constant, the minimum oil film thickness and oil film pressure in the contact area of the rolling body decrease with the increase of angle ψ from the minimum stress point of the rolling body. Under the action of high contact stress, thin oil film will be formed in the bearing outer ring raceway. In the field of full ceramic ball bearings, the research content of this paper is innovative. The research results of this paper have an important guiding significance for revealing the oil lubrication mechanism of full ceramic ball bearing and enriching its lubrication theory and methods.
The influence of trust and commitment on free-to-play gamers co-creation intentions

ABSTRACT Through the lens of trust–commitment and co-creation theories, this study explores the effect of trust on commitment and co-creation intentions within the free-to-play (F2P) gaming industry, to extend our understanding of co-creation within a service context. Responses from an online survey were used as inputs into a structural equation model (SEM). The findings reveal that trust in a F2P game influences commitment to continue playing the F2P game and elicit behaviour in the form of co-creation intentions. The findings also found that involvement and achievement motivations partially mediate the relationship between trust and commitment and escapism significantly moderates the relationship between trust and involvement.
IoT Based Accident Prevention System for Hairpin Bend Roads

According to survey, 10% of the total vehicle accidents happen on curved segments in hill stations. It is risky while driving in bends and curves. In order to provide solution, an IoT based Accident Prevention System for Hairpin Bend Roads has been proposed in this work. The objectives of the proposed work are to provide a safe and comfort hill travel by avoiding accidents mainly at the Hairpin bends and U curves, and to alert the drivers of both the vehicles approaching the bend, by generating traffic warning signal that indicates the arrival of a vehicle ahead on the opposite side of the bend. The proposed system includes ultrasonic sensor to detect any vehicle reaching the hairpin bend and alert immediately the vehicles on the other side by enabling an yellow signal. In the proposed work, NodeMCU is used as a processing element which supports ESP-NOW communication technology to enable two-way communication. Therefore, this system reduces the incidence of vehicle collision especially in the forest or hilly region which has poor internet connection. To solve connectivity issues, it has been implemented with machine-to-machine communication protocol without internet.
Smooth indirect adaptive sliding mode control

In this paper, an indirect approach to the dual‐mode adaptive robust controller (DMARC) is proposed, which combines the typical transient and robustness properties of variable structure systems with a smooth control signal in steady state, typical of conventional adaptive controllers, as model reference adaptive controller. The aim of this indirect version, here named indirect DMARC, is to provide a more intuitive controller design, based on physical plant parameters, as resistances, inertia moments, capacitances, and so on, maintaining DMARC properties. In this paper, a stability analysis for the proposed controller and simulations to an unstable second‐order plant will be presented. Copyright © 2013 John Wiley & Sons, Ltd.
Model quality evaluation in identification for H∞ control

A mixed parametric-nonparametric approach to H/sub /spl infin// identification is proposed, aimed at estimating a low-order approximating model and an identification error giving a measure of the model perturbation in a form well suited for H/sub /spl infin// control methodologies. It is shown how to estimate the modeling errors of the identified parametric model and how to evaluate the performance values that can be guaranteed when the H/sub /spl infin// controller is designed and applied to the real system. This performance value is then used for selecting the most "suitable" order of the parametric model class.
A 3D LiDAR odometry for UGVs using coarse-to-fine deep scene flow estimation

Light detection and ranging (LiDAR) odometry plays a crucial role in autonomous mobile robots and unmanned ground vehicles (UGVs). This paper presents a deep learning–based odometry system using two successive three-dimensional (3D) point clouds to estimate their scene flow and then predict their relative pose. The network consumes continuous 3D point clouds directly and outputs their scene flow and uncertain mask in a coarse-to-fine fashion. A pose estimation layer without trainable parameters is designed to compute the pose with the scene flow. We also introduce a scan-to-map optimization algorithm to enhance the robustness and accuracy of the system. Our experiments on the KITTI odometry data set and our campus data set demonstrate the effectiveness of the proposed deep learning–based point cloud odometry.
Monkeypox Clinical Features and Differential Diagnosis: First Case in Campania Region

As of 15 June, there have been, globally, a total of 2103 laboratory-confirmed cases and one probable case of Monkeypox, including one death. We report two cases of vesicular infectious diseases, one of those is the first case of Monkeypox in the Campania Region. The report, therefore, highlights a recrudescent infection disease that could represent a challenge in differential diagnosis with other vesicular infectious diseases such as Varicella Zoster Virus, during a pandemic season that does not seem to end. Indeed, varicella should be carefullu considered in differential diagnosis according to its vesicular or pustular rash to have a prompt diagnosis and public health response in case of monkeypox infection.