Level I lymph node involvement in patients with N1b papillary thyroid carcinoma: a prospective study.

Most of the studies on the incidence, pattern, and predictive factors of lymph node (LN) metastasis with papillary thyroid carcinoma (PTC) have been performed retrospectively and no common consensus has been reached regarding the predictors for the involvement of level I LNs. This study was conducted prospectively to determine the incidence and the possible predictors of level I involvement in N1b PTC patients. The study included 30 consecutive patients with N1b stage of PTC. All the patients underwent neck dissection (ND) including level I. The relation between involvement of level I LNs and various clinicopathological variables was studied. Unilateral neck dissection was performed in 24 patients and bilateral neck dissection in six patients leading to 36 NDs. Level I was excised in all patients, with five specimens (14%) positive for metastasis. Levels II, III, IV, V, VI, and VII were positive in 52.8, 58.3, 58.3, 33.3, 63, and 22.2%, respectively. Level I involvement was significantly related to the number of lymph node levels affected (p = 0.003) and macroscopic extranodal invasion (p = 0.04). It was not related to the involvement of other individual levels, gender, age, size of the largest thyroid nodule, size of the largest LN involved, or histo-pathological variant of the tumor. This study suggests that including level I in therapeutic neck dissection for N1b PTC patients might be recommended in selected cases of multiple level involvement and macroscopic extranodal invasion requiring sacrifice of internal jugular vein, spinal accessory nerve, or sternomastoid muscle.

Comprehensive analysis of optimal power flow using recent metaheuristic algorithms.

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently.

Effective hybrid search technique based constraint mixed-integer programming for smart home residential load scheduling.

In this paper, the problem of scheduling smart homes (SHs) residential loads is considered aiming to minimize electricity bills and enhance the user comfort. The problem is addressed as a multi-objective constraint mixed-integer optimization problem (CP-MIP) to model the constrained load operation. As the CP-MIP optimization problem is non-convex, a novel hybrid search technique, that combines the Relaxation and Rounding (RnR) approach and metaheuristic algorithms to enhance the accuracy and relevance of decision variables, is proposed. This search technique is implemented through two stages: the relaxation stage in which a metaheuristic technique is applied to get the optimal rational solution of the problem. Whereas, the second stage is the rounding process which is applied via stochastic rounding approach to provide a good-enough feasible solution. The scheduling process has been done under time-of-use (ToU) dynamic electricity pricing scheme and two powering modes (i.e., powering from the main grid only or powering from a grid-tied photovoltaic (PV) residential power system), in addition, four metaheuristics [i.e., Binary Particle Swarm Optimization (BPSO), Self-Organizing Hierarchical PSO (SOH-PSO), JAYA algorithm, and Comprehensive Learning JAYA algorithm (CL-JAYA)] have been utilized. The results reported in this study verify the effectiveness of the proposed technique. In the 1st powering mode, the electricity bill reduction reaches 19.4% and 20.0% when applying the modified metaheuristics, i.e. SOH-PSO and CL-JAYA, respectively, while reaches 56.1%, and 54.7% respectively in the 2nd powering scenario. In addition, CL-JAYA superiority is also observed with regard to the user comfort.

Finite-time and sampled-data synchronization of complex dynamical networks subject to average dwell-time switching signal.

This study deals with the finite-time synchronization problem of a class of switched complex dynamical networks (CDNs) with distributed coupling delays via sampled-data control. First, the dynamical model is studied with coupling delays in more detail. The sampling system is then converted to a continuous time-delay system using an input delay technique. We obtain some unique and less conservative criteria on exponential stability using the Lyapunov-Krasovskii functional (LKF), which is generated with a Kronecker product, linear matrix inequalities (LMIs), and integral inequality. Furthermore, some sufficient criteria are derived by an average dwell-time method and determine the finite-time boundedness of CDNs with switching signal. The proposed sufficient conditions can be represented in the form of LMIs. Finally, numerical examples are given to show that the suggested strategy is feasible.

Optimal sizing of hybrid solar/wind/hydroelectric pumped storage energy system in Egypt based on different meta-heuristic techniques.

Providing access to clean, reliable, and affordable energy by adopting hybrid power systems is important for countries looking to achieve their sustainable development goals. This paper presents an optimization method for sizing a hybrid system including photovoltaic (PV), wind turbines with a hydroelectric pumped storage system. In this paper, the implementation of different optimization techniques has been investigated to achieve optimal sizing of grid-connected hybrid renewable energy systems. A comprehensive study has been carried out between Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA), Salp Swarm Algorithm (SSA), and Grey Wolf optimizer (GWO) to validate each one. Moreover, the optimal sizing of the system's components has been studied using real-time information and meteorological data of Ataka region located in Egypt. The purpose of the optimization process is to minimize the cost of energy from this hybrid system while satisfying the operation constraints including high reliability of the hybrid power supply, small fluctuation in the energy injected to the grid, and high utilization of the photovoltaic and wind complementary properties. MATLAB software package has been used to evaluate each optimization algorithm for solving the considered optimization problem. Simulation results proved that WOA has the most promising performance over other techniques.