AT Sciences (United States)

This work investigates the role of the Weyl tensor in the formation of a black hole.We discuss the development of the Weyl tensor and prove its existence in spacetime during the gravitational collapse of cosmic objects, utilizing the Riemannian curvature tensor, Ricci tensor, Kulkarni-Nomizu product, and Schouten tensor.By decomposing the Weyl tensor, we use theorems and proofs that satisfy the exact solutions of the Einstein field equations.We observe that the Riemann curvature tensor and Weyl tensor share the same symmetric identities, as trW (δ, .)σ= 0 such that W δσγτ = 0 when Riemannian curvature tensor, R δσγτ = 0 .Additionally, the Riemann curvature and Weyl scalar tensor invariants are conformally related to each other, asin the Schwarzschild metric.From the Einstein field equations, the Ricci tensor is R στ = 0 ; consequently, the stress-energy tensor, T στ = 0 , indicating that the Einstein field equation is empty space.However, in the Schwarzschild black hole solution, the Ricci tensor vanishes, but the Weyl tensor does not.Additionally, it seems that divergence occurs around the event horizon in a stagnant and uncharged Schwarzschild black hole with proper acceleration.Furthermore, the investigation into the existence of the Weyl tensor in the Schwarzschild black hole reveals its presence.We also explore the Reissner-Nordström, Kerr, and Kerr-Newman black holes by examining the coupling between the Einstein-Maxwell field equations and the Weyl tensor, utilizing small Weyl corrections.We obtain the metric that reduces to the Kerr-Newman black hole solution in Boyer-Lindquist coordinates when α = 0 .The same metric equation obtained reduces to Kerr black hole solutions when the electric charge q = 0 and the coupling parameter α = 0 .Furthermore, when the parameter of the charged rotating black hole a vanishes, we obtain solutions for the static and spherically symmetric black hole with Weyl corrections.When the terms a = q = 0 , the obtained metric reduces to the Schwarzschild black hole solution.

Salt Tolerant Microbes are a group of microorganisms that grow, develop, and survive in extremely high salt concentrations. Based on their tolerance level they generally grow up optimally at pH values beyond 9.0, but the growth is inhibited at the pH value that is most closely associated with neutral 6.5. They have minimal dietary needs and a good salt quantity that is high enough to osmotic pressure. They can produce biological metabolites that have certain actions such as antibacterial, antifungal, antioxidant, and anticancer. We discussed in this article various pharmaceutical formulations of salt-tolerant microbes, every formulation shows the specific pharmacological actions like anti-cancer activity, anti-oxidant activity, and anti-microbial activity, and also discusses methods for the biosynthesis of salt-tolerant microbes’ nanoparticles.

This is an open access chapter distributed under the terms of the CC BY 4.0 license.

Cloud security is vulnerable to insider threats, which can be difficult to detect and defend against. However, there is a novel approach that can help to tackle this problem. By using keylogging to monitor user activity in real-time, combined with cryptography, suspicious behavior can be identified and flagged for security personnel. This approach transforms keylogging from an offensive tool to a defensive one for detecting insider threats in the cloud. With this method, we can give organizations the peace of mind that their cloud systems are safe from malicious activities by authorized users. Insider threats pose a serious challenge to cloud security. These authorized users can leverage their knowledge to bypass traditional defenses. This research explores a novel approach using keylogging to monitor user activity in real-time. By combining this with cryptography, the system can identify suspicious behavior and alert security personnel. This approach aims to reframe keylogging as a defensive tool for insider threat detection in the cloud.

where she is working on her master's degree in fluids, structures

ObjectivePrenatal diagnosis of transposition of great arteries (TGA) is expected to improve postoperative outcomes after neonatal arterial switch operation (ASO); however, published reports give conflicting results. We aimed to determine the association between prenatal diagnosis and early postoperative outcomes after neonatal ASO.MethodsCohort study involving 243 newborns who underwent ASO (70% prenatally diagnosed) between 2010 and 2019. Multivariable regression was used to determine the association between prenatal diagnosis and (a) birth characteristics and (b) postoperative outcomes.ResultsGestational age and birthweight centile were lower and small-for-gestational-age more common (11.8% vs 1.4%) in those diagnosed prenatally. Among births which followed labour induction or prelabour caesarean, prenatal diagnosis was associated with earlier gestation at birth (mean (SD), 38.5 (1.6) vs 39.2 (1.4), p=0.01). Among births which followed spontaneous labour, prenatal diagnosis was associated with earlier gestation at labour onset (38.2 (1.8) vs 39.2 (1.4), p=0.01). Prenatal diagnosis was associated with longer postoperative mechanical ventilation (incidence rate ratio 1.74, 95% CI 1.37 to 2.21), intensive care (1.70, 1.31 to 2.21) and hospital length of stay (1.37, 1.14 to 1.66) after ASO. Gestational age mediated up to 60% of the effect of prenatal diagnosis on postoperative outcomes.ConclusionAmong newborns undergoing ASO for TGA, prenatal diagnosis is associated with poorer early postoperative outcomes. In addition to minimising iatrogenic factors (such as planned births) resulting in earlier births, evaluation of other dynamics following a prenatal diagnosis which may result in poor fetal growth and earlier onset of spontaneous labour is important.

An important performance determinant in wheelchair sports is the power exchanged between the athlete-wheelchair combination and the environment, in short, mechanical power. To monitor the mechanical power during wheelchair sports practice, inertial measurement units (IMUs) might be used. However, a well-founded and unambiguous theoretical framework that follows the dynamics of manual wheelchair propulsion is required to validly apply IMUs for mechanical power assessment in wheelchair sports. Such a framework does not yet exist. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to create a set of guidelines on how to use IMUs to monitor mechanical power during wheelchair propulsion supported by experimental data. After verifying the theoretical framework, an IMU model was defined. Subsequently, the validity of the IMU model and underlying assumptions was determined. Therefore, power was estimated from IMU data during wheelchair propulsion and was subsequently compared to gold standard optical motion capture data. Data was collected from eleven participants without wheelchair experience propelled an all-court sports wheelchair on a large treadmill. At the same time, kinematics were measured using two IMUs and an optical motion capture system. The results reveal that, with a proper drag or deceleration test, one IMU on the wheelchair frame and one IMU on the wheel axis, decent power estimations can be obtained in daily wheelchair (sports) practice. To conclude, the theoretical framework and the resulting IMU-based power is thus well suitable to estimate mechanical power during straight-line wheelchair propulsion in wheelchair court sports and daily wheelchair practice, and it is an important first step towards feasible power estimations in all wheelchair (sports) situations.

The Khmer ethnic minority civil servants in the Mekong Delta have an important role. They are the ones who deploy and implement the state's policies and laws for the people, and they directly manage the society in the region with order, stability, and development. Cadres and civil servants of Khmer ethnic minorities are the bridge between the state and the people, they reflect the people's legitimate thoughts and aspirations for the state. The study aims to explore the factors affecting the policy of training and fostering civil servants of Khmer ethnic minorities in the Mekong Delta, Vietnam. Research data were collected from survey results by questionnaires from 875 survey samples in the Mekong Delta, Vietnam. Research results in the SEM model show that there are six factors affecting the policy of training and fostering civil servants of ethnic Khmer people in the Mekong Delta, Vietnam, including Objectives and content of the policy; Resources for policy implementation; Coordinate policy implementation; The capacity of policy enforcement agencies; Economic, political, cultural, and social environment; Awareness and capacity of policy beneficiaries. From the research results, a number of contents are discussed and proposed to contribute to improving the quality of implementation of policies on training and fostering civil servants of Khmer ethnic minorities in the Mekong Delta, Vietnam.

There is a long-standing debate about the extent to which economic theory should inform econometric modeling and estimation. This debate is particularly evident in the program/policy evaluation literature, where reduced-form (experimental or quasi-experimental) and structural modeling approaches are often viewed as rival methodologies. Reduced-form proponents criticize the assumptions invoked in structural applications. Structural modeling advocates point to the limitations of reduced-form approaches in not being able to inform about program impacts prior to implementation or about the costs and benefits of program designs that deviate from the one that was implemented. In this paper, we argue that there is a new emerging view of a natural synergy between these two approaches, that they can be melded to exploit the advantages and ameliorate the disadvantages of each. We provide examples of how data from randomized controlled trials (RCTs), the exemplar of reduced form practitioners, can be used to enhance the credibility of structural estimation. We also illustrate how the structural approach complements experimental analyses by enabling evaluation of counterfactual policies/programs. Lastly, we survey many recent studies that combine these methodologies in various ways across different subfields within economics. (JEL C21, C52, C53, H24, I38, J13, R38)

Machine learning (ML) has seen a substantial increase in its role in improving operations for staff and customers in different industries. However, there appears to be a somewhat limited adoption of ML by farm businesses, highlighted by a review of the literature investigating innovative behaviors by rural businesses. A review of the literature identified a dearth of studies investigating ML adoption by farm businesses in rural regions of the United Arab Emirates (UAE), especially in the context of family-owned farms. Therefore, this paper aims to investigate the drivers and barriers to ML adoption by family/non-family-owned farms in rural UAE. The key research questions are (1) what are the drivers and barriers for rural UAE farms adopting ML? As well as (2) is there a difference in the drivers and barriers between family and non-family-owned farms? Twenty semi-structured interviews were conducted with farm businesses across several rural regions in the UAE. Then, through a Template Analysis (TA), drivers and barriers for rural UAE-based farm owners adopting ML were identified. Interview findings highlighted that farms could benefit from adopting ML in daily operations to save costs and improve efficiency. However, 16 of 20 farms were unaware of the benefits related to ML due to access issues (highlighted by 12 farms) in incorporating ML operations, where they felt that incorporating ML into their operations was costly (identified by 8 farms). It was also identified that non-family-owned farms were more likely to take up ML, which was attributed to local culture influencing family farms (11 farms identified culture as a barrier). This study makes a theoretical contribution by proposing the Machine Learning Adoption Framework (MLAF). In terms of practical implications, this study proposes an ML program specifically targeting the needs of farm owners in rural UAE. Policy-based implications are addressed by the findings aligning with the United Nations’ Sustainability Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities).