Design of Beamforming, Transparent Metasurfaces Using Integral Equations

An accurate method for designing transmissive metasurfaces is presented that provides perfect transmission while transforming the amplitude and phase of the wavefront. The designed metasurfaces consist of three spatially-varying, electric impedance sheets separated by two dielectric substrates. The design method uses integral equations to account for interactions within and between the impedance sheets, allowing for accurate design. In this paper, a comparison between the integral equation method and the local periodicity approximation is presented. The comparison includes one design example for a transmitted field of uniform phase and amplitude. The design using integral equations provides better collimation. Two other examples involving an amplitude tapered transmitted field are reported to show the versatility of the proposed design technique. In all the examples, the metasurface is $7.35\lambda_0$ wide, the focal length is $4\lambda_0$, and has an overall thickness of $0.1355 \lambda_0$ at the operating frequency of 5GHz. The designs are verified using a commercial finite element electromagnetic solver

Phasor Measurement Unit Data-Based Steady State and Dynamic Model Estimation

Phasor measurement units (PMUs) have been put into power grid for real-time monitoring. This research investigates the PMU data for steady state estimation and dynamic model estimation. It focuses on three main research areas to enhance the security of the power system monitoring. First, optimal PMU placement (OPP) problem is developed to minimize the number of PMUs required for the system to be completely observable using mixed integer linear programming and nonlinear programming. Second, PMU measurements are ranked for oscillation monitoring based on two approaches: oscillation mode observability and Prony analysis. Further, the principles, multi-channel data handling, and noise resilience techniques of three eigenvalue identification methods used in power systems: Prony analysis, Matrix Pencil (MP), and Eigensystem Realization Algorithm (ERA) are examined. The first part of this research discusses the optimal PMU placement (OPP) problem to find the optimal number of PMUs to make the system fully observable. Two different formulations are presented for modeling power grid observability to solve the OPP problem: mixed integer linear programming (MILP) and nonlinear programming (NLP). For each formulation, modeling of power flow measurements, zero injection, limited communication facility, single PMU failure, and limited channel capacity is studied. MILP zero injection formulation is improved to solve the redundant observability and optimality limitations. A new formulation for nonlinear programming-based PMU placement considering zero injection measurement is proposed. A comparison between MILP and NLP formulations is conducted to show the advantages and disadvantages of each formulation. The second part of this research is to rank PMU measurements for oscillation monitoring based on two approaches: oscillation mode observability and Prony analysis. In the first approach, the system model is assumed known and the critical oscillation mode observability of different measurements are compared. In the second approach, the dynamic model of the system is not known. Prony analysis is employed to identify critical oscillation modes based on PMU measurements. Measurements at different locations are compared for their characteristics in Prony analysis. Specifically, singular values of Hankel matrices are compared. The two approaches lead to the same conclusion. Their internal connection is presented in this research. As a step further, sensitivity analysis of model order assumption and noise level in Prony analysis is conducted to show singular values of Hankel matrices can indeed serve as indicators of the quality of oscillation monitoring. In addition, power system eigenvalues from PMU measurement data are identified using Prony analysis, matrix Pencil (MP), and Eigensystem Realization Algorithm (ERA). This part sheds insight on the principles of the three methods: eigenvalue identification through various Hankel matrix formulation. Further, multiple channel data handling and noise resilience techniques are investigated. In the literature, singular value decomposition (SVD)-based rank reduction technique has been applied to MP and resulted in a reduced-order system eigenvalue estimation and an excellent noise resilient feature. In this part of the research, ERA is refined using the SVD-based rank reduction to achieve a superior performance. Moreover, a reduced-order Prony analysis method is invented. With the proposed technique, Prony analysis can not only give reduced-order system eigenvalues, but also become noise resilient. This dissertation has been resulted in three conference papers (two published and one accepted) and two journal papers (one published and one in revision process). The future work of this dissertation will examine the dynamic parameter estimation technique using the measurement-based methods. Using the PMU data and measurement-based methods of the system identification can provide an accurate dynamic parameter estimation without prior information of the system transfer function. Generator parameters such as inertia constant, damping coefficients, and regulation speed constant can be estimated

Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques

Renewable energy (RE) sources, such as wind, geothermal, bioenergy, and solar, have gained interest in developed regions. The rapid expansion of the economies in the Middle East requires massive increases in electricity production capacity, and currently fossil fuel reserves meet most of the power station demand. There is a considerable measure of unpredictability surrounding the locations of the concerned regions where RE can be used to generate electricity. This makes forecasting difficult for the investor to estimate future electricity production that could be generated in each area over the course of a specific period. Energy production forecasting with complex time-series data is a challenge. However, artificial neural networks (ANNs) are well suited for handling nonlinearity effectively. This research aims to investigate the various ANN models capable of providing reliable predictions for sustainable sources of power such as wind and solar. In addition to the ANN models, a state-of-the-art ensemble learning approach is used to improve the accuracy of predictions further. The proposed strategies can forecast RE generation accurately over short and long time frames, relying on historical data for precise predictions. This work proposes a new hybrid ensemble framework that strategically combines multiple complementary machine learning (ML) models to improve RE forecasting accuracy. The ensemble learning (EL) methodology outperforms long short-term memory (LSTM), light gradient boosting machine (LightGBM), and sequenced-GRU in predicting wind power (MAE: 0.782, MAPE: 0.702, RMSE: 0.833) and solar power (MAE: 1.082, MAPE: 0.921, RMSE: 1.055). It achieved an impressive R2 value of 0.9821, indicating its superior accuracy

HKMA Project Portfolio

HKMA is a well-known IT company in the Middle East. HKMA has big portfolio that involves in the areas of networking, web-site development and maintenance, customized applications, E-Commerce, and E-Government. The company’s financial performance is doing very well. However, in the last fiscal year, the CEO was not satisfied about the plummeted performance and asked for investigation. HKMA has many problems with its portfolio management. The portfolio of projects does not support HKMA’s strategy. There are 97 projects which is too many. Therefore, resources are spread very thinly. We can say the HKMA’s portfolio is unbalanced and unfocused. The PMO General Manager took advantage of the low performance of HKMA’s last fiscal year, and proposed a plan to the CEO in order to leverage the company’s business value. He presented a strategic plan that is mainly focused on fixing the company’s portfolio system. The plan was very successful in implementation with the support of designed portfolio model and emphasizing the role of PMO which brought a heavy weight matrix organization. Implementing the plan was not easy. However, the hardest part was dealing with human issues. Many units resisted the change in the new system especially functional managers who used to have full authorization of employees, but not any more after the new strong project matrix. in general, the PMO GM was professional and experienced enough handle soft factors as well as hard factors. Political marketing and clear communication were essential keys for him

Risk Management System for Brooks Cooperation

Brooks Corporation (BC) is a technology company that specializes in the design and manufacturing of wireless communication chips with a variety of clients such as Nokia, Motorola, Intel, Samsung and many others. Since its foundation, BC has striven to provide the best customer satisfaction by delivering its projects within the desired schedule and quality. A recent downturn in operations led BC’s main stakeholders to request from senior management an identification of the causes and a cost effective solution. This prompted the formation of an internal team with the purpose of identifying the reasons for the current downturn. The team identified three problems that some of their projects had experienced: reduction of profit margin, negative payoff, and unsuccessful project outcomes. The conclusion of the analysis was that these problems stemmed from the fact that project managers and project teams did not use any risk analysis tools during the development of their projects. For this reason, senior management entrusted the team with the task of developing a company-wide risk management system (RMS) for BC

Dental Anxiety Among Physicians: Relationship with Oral Problems, Dental Visits, and Socio-Demographic Factors

Elaf Alkuwaiti,1 Rand Alshubaili,1 Nada AlZahrani,1 Sarah Khusheim,1 Raghad AlMunif,1 Nawal Alharbi,1 Muhammad Nazir2 1College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia; 2Department of Preventive Dental Science, College of Dentistry Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi ArabiaCorrespondence: Muhammad Nazir, Tel +966-543579615, Email [email protected]: The purpose of study was to investigate dental anxiety (DA) and its relationship with oral health problems, dental visits, and socio-demographic factors among physicians.Patients and Methods: This cross-sectional study was conducted on physicians working in Dhahran, Khobar, Dammam, and Qatif cities of the kingdom of Saudi Arabia. The study included physicians (general practitioners, residents, specialists, and consultants) working in the public and private sectors. Modified Dental Anxiety Scale and World Health Organization’s Oral Health Questionnaire for Adults were used to evaluate DA, oral health problems, and dental attendance.Results: The study included data from 355 participants with a mean age of 40.13 ± 10.45 years. There were 57.2% of non-Saudi and 42.8% of Saudi participants in the study. Bad dental experience in the previous dental visit was reported by 40% of participants, which was significantly related to DA (P = 0.002). Only 9.60% of participants had no DA, whereas 41.10% demonstrated low DA, 23.4% moderate DA, 18.9% high DA, and 7% extreme DA. Common oral problems included tooth sensitivity (65.40%), tooth cavities (45.90%), bleeding gums (43.10%), and bad breath (36.90%). More than half of participants (58.3%) visited the dentist during the last year and dental pain was the most common reason for dental visits (31.3%). Saudi participants demonstrated significantly increased DA than non-Saudis (P = 0.019). DA was significantly related to tooth sensitivity (P = 0.001), tooth cavities (P = 0.002), dry mouth (P = 0.044), and bad breath (P = 0.005). The participants with difficulty in biting foods (P > 0.001) and feeling embarrassed due to the appearance of teeth (P < 0.001) demonstrated significantly higher DA.Conclusion: This sample of physicians showed a high prevalence of DA, oral problems, and dental visits due to pain. DA was significantly related to physicians’ negative dental experience, tooth sensitivity, dental decay, dry mouth, and bad breath.Keywords: physicians, dental anxiety, dental attendance, oral healt

Implementation of Companywide Project Management System: Beyond Brain, An Artificial Intelligence Company

Beyond Brain, Inc. is an Artificial Intelligence (AI) Research and Development Company that provides consulting and custom Product development services to the world. It develops linguistic and artificial intelligence (AI) software providing a full line of document recognition, document conversion, and data capture technologies and products. Beyond Brain, Inc. is an US based firm which was formed on January 2006 as research focused company with 10 smart people. In mid 2007 the company transitioned from a research focus to practical implementation and now (as of November 2008) has 40 full‐time members who in a relatively short time ‐ operating on a modest budget ‐ have made significant headway towards company goals. The annual revenue of the company hits around 2 million dollars. Along with good establishment, the company is still in an intense engineering and development phase geared towards better commercialization all over the world. The overall number of users of Beyond Brain’s product and technology exceeds 1 million people all over the world (according to the internal business research). Most Beyond Brain’s products are developed in Beyond Brain, Inc. Headquarters in Portland, Oregon

Bioactive hard protein coronas on poly(propylene sulfone) surfaces enable mast cell nanotherapy

Contact between nanomaterials and biomolecules such as serum proteins leads to “soft” or “hard” corona formation via dynamic or irreversible adsorption, respectively. While soft coronas of antibodies can temporarily retain the ability for immunological recognition, preserving protein function within hard coronas has remained an elusive goal due to the unfolding of protein at the nano-bio interface. Here, we show that poly(propylene sulfone) nanoparticles efficiently and stably adsorb proteins, unexpectedly forming bioactive hard coronas using a facile methodology. This process is permitted by site-specific hydrophobic-hydrophobic interactions between nanoparticle surfaces and proteins, allowing stable simultaneous pre-adsorption of multiple proteins such as enzymes and antibodies. For therapeutic validation, a nanotherapy for enhanced antibody-based targeting of mast cells and inhibition of anaphylaxis was demonstrated in a humanized mouse model. Protein immobilization on the poly(propylene sulfone) surface therefore provides a simple and rapid platform for the design, fabrication, and optimization of bioactive and targeted nanomedicines