Energy is one of the components of the national security of countries and is of particular importance to the industrialized countries, including Germany. Energy policy includes many areas and has an impact on various sectors such as the environment, climate, agriculture and others. During the past few years, Germany has witnessed many transformations, the most important of which is the energy transition towards renewable energy, and it was strengthened in the strategy that was It was developed in 2010, which aims to achieve a long-term energy transformation, and sales of the German energy technology sector have evolved from 2010 to 2020, and this issue is related on the other hand to the concept of energy security and because of its strategic importance to European countries that are characterized by the poverty of their resources in traditional energy and depend On the imports of other countries, and accordingly, these countries are trying to search for alternative sources. Germany was one of the first countries that paid attention to the energy transition in terms of its interest in building projects for energy stations with a production capacity of more than 20 megawatts.
The paradigm and domain of data security is the key point as per the current era in which the data is getting transmitted to multiple channels from multiple sources. The data leakage and security loopholes are enormous and there is need to enforce the higher levels of security, privacy and integrity. Such sections incorporate e-administration, long range interpersonal communication, internet business, transportation, coordinations, proficient correspondences and numerous others. The work on security and trustworthiness is very conspicuous in the systems based situations and the private based condition. This examination original copy is exhibiting the efficacious use of security based methodology towards the execution with blockchain
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solid state reaction technique (SSR) was used to prepare high-Tc phase in superconductors the effect of additional Pb to was investigated it has been found
It was found that there was a significant correlation between all tests of the mechanical and electrical activity of the heart (systolic force FC, stroke volume SV, end-diastolic volume, EF volume, and left ventricular volume during diastole LVDD) with the test of the oxygen-phosphating energy system (Markaria). - As safe (Margaria-Kalamen( It was found that there is a significant correlation between all tests of the mechanical and electrical activity of the heart (myocardial systolic force FC, stroke volume SV, end-diastolic volume EDV, and the percentage of heart pumpingEF blood, and left ventricular volume during diastole (LVDD) with the Lactational Oxygen Energy System Test (Wingate Test 30 Second(
The melting duration in the photovoltaic/phase-change material (PV/PCM) system is a crucial parameter for thermal energy management such that its improvement can realize better energy management in respect to thermal storage capabilities, thermal conditions, and the lifespan of PV modules. An innovative and efficient technique for improving the melting duration is the inclusion of an exterior metal foam layer in the PV/PCM system. For detailed investigations of utilizing different metal foam configurations in terms of their convective heat transfer coefficients, the present paper proposes a newly developed mathematical model for the PV/PCM–metal foam assembly that can readily be implemented with a wide range of operating condition
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Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo
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Refrigerant R134a has been widely utilized in automotive air conditioning systems (AACSs); R134a has a high global warming potential (GWP) of 1430 despite having zero ozone depletion potential (ODP). Coming refrigeration systems must include refrigerants with low GWP and zero ODP. The aim of this experimental study is to evaluate the thermal performance of an (AAC) with different values of compressor speeds, i.e., (1000, 1700, and 2400 rpm) and two thermal loads, i.e., (500 and 1000 Watt) with the absence and presence of liquid suction heat exchanger (LSHX) using R134a. The results showed that adding LSHX enhanced the COP cycle by 7.18%, 10.7%, and 3.09% for the first, second, and third speed, respectively, at 500 Watt, while the en
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Today’s world confronts various threats from different sources. Similar to deprivation of energy, economic facilities, or political deposition, educational poisoning is one of the dangerous phenomena that result from distorting and corrupting the ethical and educational components of teaching by various material and non – material means.This paper sheds light on the concept of the educational system which is not a mere process of teaching, but rather an endless process of socialization that begins in the family and develops into religious, ethical, scientific and mythological systems, all of which form the cognitive component. It also defines the necessary means by which it is transmitted from one generation into another. The educati
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This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K
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