This paper deals with the performance of a heat pipe solar collector. The solar collector consisted of an INTERCONNECTED heat pipe so as to reduce the production cost by using an INTERCONNECTED heat pipe because all the heat pipes can be evacuated, sealed and tested at once. Performance of a prototype of the heat pipe solar collector was experimentally examined, and the results were compared with those obtained through theoretical analysis. The results shown in this paper seem feasible.

In this paper, the new methodology of interconnection network among the various corporations which are the members of a single financial group is studied and developed. For the first time, each company is observed individually and the cognitive risk factors of each are monitored by new introduced indexes. The risk contagion effect on the main node of the holding is calculated respectively. The new indexes help the management to monitor the entire financial group performance. Also guide the main node of the holdings to make appropriate decisions relating to any investment, profit returning and risk of the financial group nodes. It is shown, if a company defaults to meet its obligations, how much this will affect the other companies of the network and the main node of the holding. With the use of new developed methodological indexes, the topology of the financial group network is displayed graphically for the first time and the mathematical structure is developed as well. Finally, the illustrative results are apparent in a new introduced software which is coded and tested with real data and the results show high accuracy.

This article considers the many developments in technology and practice that are making libraries more connected and interdependent. It looks at new integrated online SERVICES and reviews the increasing importance of both formal and informal standards. Global centralized Web SERVICES are discussed. The relationships between information industry companies and libraries are considered. Virtual reference SERVICES and far-reaching digitization projects are explored. The article concludes that close cooperation is allowing libraries to take their SERVICES to new levels and is a key to the continued innovation of those SERVICES.

In this paper, two new computationally efficient improved stochastic algorithms for solving multi-area DC Optimal Power Flow (DC-OPF) in INTERCONNECTED power systems have been presented. These algorithms are based on the combined application of Fuzzy Logic strategy incorporated in both Evolutionary Programming (EP) and Tabu Search (TS) algorithms, hence termed as Fuzzy Mutated Evolutionary Programming (FMEP) and Fuzzy Guided Tabu Search (FGTS). Multi-area DC-OPF calculations determine optimum generation schedule, optimal control variables and system quantities of each area with due consideration of generation and transmission system limitations for efficient power system operation. The popularity of EP and TS algorithms are due to their significant property of dealing with the optimization problems without any restrictions on the structure or type of the function to be optimized and due to the ease of computation. The proposed methods are tested on single area, two area and four area IEEE 30-bus INTERCONNECTED systems. The optimal solutions obtained using SLP (Successive Linear Programming), EP, TS, FMEP and FGTS are compared and analyzed. The analysis reveals that the proposed algorithms are relatively simple, efficient, reliable and suitable for real-time applications. And these algorithms can provide accurate solution with fast convergence and have the potential to be applied to other power engineering problems.

Climate change is the twenty-first century's most significant threat to human health. Human activity has led to a gradual increase in greenhouse gas concentrations, resulting in global warming and other related hazards. Climate change is significantly impacting ecosystems and biodiversity, leading to a potential increase in zoonotic diseases. Zoonoses are infectious diseases that are transmitted among animals and humans. Vector-borne, foodborne, and waterborne diseases are major infectious diseases associated with climate change. Changes in temperature and precipitation patterns influence the survival, reproduction, and activity of disease-carrying vectors like mosquitoes, ticks, and sandflies. Variations in climate can affect the spread of diseases such as malaria, leishmaniasis, and rodent-borne illnesses like leptospirosis, bartonellosis, plague, and hantavirus infections. Climate change can also impact waterborne illnesses by altering water quality and increasing the risk of contamination during natural disasters. Additionally, higher temperatures and increased humidity can cause transmission of airborne zoonotic diseases, such as aspergillosis, tuberculosis, and influenza. The evolving climate may also contribute to the development of resistance in disease-causing microorganisms, making treatment more challenging. Human behavior influenced by climate change, such as changes in agricultural practices and land use, can indirectly affect disease transmission by altering vector habitats and human-animal interactions. Overall, the complex interplay between climate change, ecological factors, and human behavior underscores the need for comprehensive strategies to mitigate the risks posed by zoonotic diseases and protect public health.

In this paper, a new decentralized observer scheme based on combination of two large scale INTERCONNECTED subsystems is proposed. It discusses design problem of flow measurement by partial differential equation (PDE) observer for state estimation of a quasi linear system without external disturbance and without measurement noise. The measurement system is decomposed into two subsystems according to their locations, condensate and gas pipes, such that each subsystem has interconnection with other. By using matrix form Navier Stocks equations and Lyapunov technique, inequality at each subsystem, a Lyapunov-based design of PDE observer is developed such that the resulting estimation error system is exponentially stable and presented in terms of standard linear matrix inequalities (LMIs). The first mean value theory for integrals is used in the observer design development. The proposed method may be used as a backup for existing systems in the operation field or as a main system for new established plants.

This paper describes an application of Artificial Neural Networks (ANN) to Load ‎Frequency Control (LFC) of nonlinear power systems. Power systems, such as other ‎industrial processes, have parametric uncertainties that for controller design had to ‎take the uncertainties into account. For this reason, in the design of LFC controller the ‎idea of robust control theories are being used. To improve the stability of nonlinear ‎power system, in the various operating point and under different disturbances this ‎controller has been reconstructed with the use of neural network capability based on ‎Radial Basis Function (RBF). The motivation of using the robust control for training ‎of the RBF neural networks controller is taking the large parametric uncertainties into ‎account in such away that both stability of the overall system and good performance ‎have been achieved for all admissible uncertainties. The simulation results on ‎INTERCONNECTED power system show that the proposed Nonlinear Neural Controller ‎‎(NNC) not only is robust to increasing of load perturbations and operating point ‎variations, but also the NNC gives good dynamic response compared with ‎conventional III and robust controllers. It guarantees the stability of the overall system ‎even in the presence of generation rate constraint (GRC‏.(‏

Load changes affect the frequency of electrical networks. Frequency stabilization is very important due to the increasing penetration of renewable energy sources in power systems. Load frequency control (LFC) is used in the power system, so that the frequency of the programmed system and the power of the connection line of an INTERCONNECTED area are within the specified limits. To improve the dynamic performance of the power system and control the frequency stability, LFC is used in power systems. The main task of LFC is to keep the system frequency according to the specified nominal value and to maintain the correct amount of exchange power between the control areas. In this paper, load frequency control in two-area power system is studied and simulated. Each area has a steam generating unit with a reheat steam turbine. The system equations are expressed in the state space and the system model is determined based on the transfer function. The simulation results have been obtained using Matlab software. The simulation results show the effect of Richter's parameters on the transient dynamic behavior of the system.