The incipient directive concepts that human being obliged to submission it, configured in the form of "Ethics". Although, conscionable assurance of thought AND act is not effective always; but it can be a prelude, justification or measure for legal rules. Again, the actualization of ethics in the context of humorless problems of human being, namely rights AND duties, has required definition of universal AND acceptable measures AND principles for bioethics. For prosperity AND practicability, these principles shall be draft in light of HARMONIES of NATIONS AND have consideration to propensity AND international impetus to protecting environment as whole. As an important fact, just having legal sanctions will fulfill "pragmatic ethics".

Face recognition from digital images is used for surveillance AND authentication in cities, organizations, AND personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors AND one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region AND a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer AND CONVERGENCE time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time AND model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer AND CONVERGENCE time, leading to improved performance in face recognition accuracy. This research provides insights for researchers AND practitioners to enhance the efficiency of federated learning in real-world applications.

Purpose: The present study aims to identify AND explain the futures of Iranian provinces in the competition for the development of NBIC technological CONVERGENCE (synergy between nanotechnology, biotechnology, information technology AND cognitive sciences) with a focus on Yazd province. Method: This research, which has been conducted with a qualitative AND descriptive approach, is classified as an exploratory futures-study. In this way, the relevant research records were first reviewed to compile a list of drivers in the development of convergent technologies. The list was then refined AND prioritized to identify two key drivers, with the Delphi process attended by fourteen experts. By intersecting the uncertainties of these two drivers, four scenarios of the province's future in technological CONVERGENCE competition emerged. Findings: The two key drivers affecting the success of the province in the competition for convergent technologies are: the rate of development of the innovation ecosystem AND the amount of financial resources allocated to this process. Based on this, four scenarios of the future situation of the province in this field were identified, which have been named as "Vanguard", "Buyer", "Remnant" AND "Hired". Conclusion: In order for the provinces to be able to succeed in the forthcoming competitions in the production AND operation of convergent technologies, it is necessary to plan for strengthening the ecosystem infrastructure from now; at the same time, it is necessary to make efficient efforts to provide sufficient financial resources for the research AND commercialization of these technologies.

United NATIONS has taken some serious measures in order to distribute good AND desirable values. The article tries to prepare an answer to this key question that how far the issue has led to positive achievements for international community to avoid from immoral behavior regarding international relations, AND if not, what are its reasons? According to the relevant research done here examined the hypothesis that indicates the United NATIONS has not finally done its best as it ought to.United NATIONS has not attempted to distribute high values AND has shown inefficiency regarding to the issue. However, it should not also be denied some positive actions taken by the United NATIONS such as the Secretary General’s millennium declaration, U.N. reforms, humanitarian AND human rights actions.By such measures, United NATIONS has been able to increase its future efficiency in order to achieve a better result.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, AND so far, some swarm intelligence-based strategies AND have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization AND a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate AND the relationship between the output AND the local search strategy to increase the CONVERGENCE speed. The first group features have more similarity to class AND less similarity to other features, AND the second is redundant AND less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features AND keep relevant ones among each solution. The aim of this operator leads to enhance the CONVERGENCE speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution AND the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification AND outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

We address the throughput maximization problem for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information AND power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay AND secondary user (SU) equipment are designed to hANDle both radio frequency (RF) signal energy harvesting AND SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to hANDle this complexity. First, consider the problem of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation algorithm that satisfies the combinatorial constraints with short CONVERGENCE times. The performance of the optimal proposed algorithm (OPA) is evaluated against benchmark algorithms via numerical results on optimality, CONVERGENCE time, constraints’ compliance, AND imperfect channel state information (CSI) on the CBS-PU link.

Background: Breast cancer is the most common cancer among women in Canada, with rising incidence in First NATIONS (FN) women. This study confirms persistent survival disparities between FN AND non-FN women, with updated findings over an extended follow-up period. Methods: Data from the Ontario Cancer Registry, linked to the Indian Registration System, included 282 FN AND 670 frequency matched non-FN women diagnosed with invasive breast cancer from 1995-2004. Survival outcomes were assessed up to 2019, analyzing 10-year survival rates by cancer stage AND other factors. Results: Unadjusted Kaplan-Meier curves showed lower 10-year survival rates for FN women (76%) compared to non-FN women (87%) for stage I breast cancer. After adjusting for age, comorbidity, detection method, AND surgery severity, no significant difference in mortality hazard was found. Among FN women diagnosed with stage I breastcancer, diabetes (HR = 3. 10, 95% CI = 1. 09-8. 81) AND chemotherapy (HR = 4. 05, 95% CI = 1. 46-11. 20) were associated with increased mortality hazard, while hormonal therapy was associated with reduced mortality hazard (HR = 0. 38, 95% CI = 0. 14-0. 996). Among FN women with advanced-stage breast cancer (stages II-IV), diabetes (HR = 1. 74, 95% CI = 1. 02-2. 98) AND radiotherapy (HR = 1. 88, 95% CI = 1. 07-3. 29) were associated with increasedmortality hazard. Conclusion: Disparities in survival rates between FN AND non-FN women may be influenced by factors such as age, comorbidity, detection method, AND surgery type. This study shows encouraging improvement over time AND advocates for actionable changes to close the survival gap AND enhance treatment outcomes for FN women.

Our purpose in this paper is researching about characteristics of convergent in probability AND almost surely convergent in Serstnev space. We prove that if two sequences of rANDom variables are convergent in probability (almost surely), then, sum, product AND scalar product of them are also convergent in probability (almost surely). Meanwhile, we will prove that each continuous function of every sequence convergent in probability sequence is convergent in probability too. Finally, we represent that for independent rANDom variables, every almost surely convergent sequence is convergent in probability. In this paper, we conclude results in Serstnev space are similar to probability space.