An Efficient Adaptive and Steep-Convergent Sidelobes Simultaneous Reduction Algorithm for Massive Linear Arrays

Antenna arrays have become an essential part of most wireless communications systems. In this paper, the unwanted sidelobes in the symmetric linear array power pattern are reduced efficiently by utilizing a faster simultaneous sidelobes processing algorithm, which generates nulling sub-beams that are adapted to control and maintain steep convergence toward lower sidelobe levels. The proposed algorithm is performed using adaptive damping and heuristic factors which result in learning curve perturbations during the first few loops of the reduction process and is followed by a very steep convergence profile towards deep sidelobe levels. The numerical results show that, using the proposed adaptive sidelobes simultaneous reduction algorithm, a maximum sidelobe level of −50 dB can be achieved after only 10 iteration loops (especially for very large antenna arrays formed by 256 elements, wherein the processing time is reduced to approximately 25% of that required by the conventional fixed damping factor case). On the other hand, the generated array weights can be applied to practical linear antenna arrays under mutual coupling effects, which have shown very similar results to the radiation pattern of the isotropic antenna elements with very deep sidelobe levels and the same beamwidth.

Adopting Artificial Intelligence in Public Healthcare: The Effect of Social Power and Learning Algorithms

Although the use of artificial intelligence (AI) in healthcare is still in its early stages, it is important to understand the factors influencing its adoption. Using a qualitative multi-case study of three hospitals in China, we explored the research of factors affecting AI adoption from a social power perspective with consideration of the learning algorithm abilities of AI systems. Data were collected through semi-structured interviews, participative observations, and document analysis, and analyzed using NVivo 11. We classified six social powers into knowledge-based and non-knowledge-based power structures, revealing a social power pattern related to the learning algorithm ability of AI.

Analysis of fault diagnosis of DC motors by power consumption pattern recognition

Early detection of faults in DC motors extends their life and lowers their power usage. There are a variety of traditional and soft computing techniques for detecting faults in DC motors. Many diagnostic techniques have been developed in the past to detect such fault-related patterns. These methods for detecting the aforementioned potential failures of motors can be utilized in a variety of scientific and technological domains. Motor Power Pattern Analysis (MPPA) is a technology that analyzes the current and voltage provided to an electric motor using particular patterns and protocols to assess the operational status of the motors without disrupting production. Engineers and researchers, particularly in industries, face a difficult challenge in monitoring spinning types of equipment. In this work, we are going to explain how to use the motor power pattern/signature analysis (MPPA) of a power signal driving a servo to find mechanical defects in a gear train. A hardware setup is used to simplify the demonstration of obtaining spectral metrics from the power consumption signals. A DC motor, a set of metal or nylon drive gears, and a control circuit are employed. The speed control circuit was eliminated to allow direct monitoring of the DC motor's current profiles. Infrared (IR) photo-interrupters with a 35 mm diameter, eight-holed, standard servo wheel were employed to gather the tachometer signal at the servo's output. The mean value of the measurements was 318 V for the healthy profile, while it was 330 V for the faulty gears power data. The proposed power consumption profile analysis approach succeeds to recognize the mechanical faults in the gear-box of a DC servomotor via examining the mean level of the power consumption pattern as well as the extraction of the Power Spectral Density (PSD) through comparing faulty and healthy profiles

Radiation Power Pattern Distortion Analysis Using MATLAB for MST Radar System

Most often, in MST radar system, a few number of transmitters are non-operational due to various factors, making the liner sub-arrays corresponding to these transmitters in effective. This results in the thinning of the aperture and deviation of the excitation from the specified Taylor distribution. The array pattern will be distorted due to this deviation, when compared to the reference pattern. This chapter gives a complete analysis to quantify the distortion in the radiation pattern due to Aperture thinning. MATLAB was extensively used to analyze the results. The results of the radiation pattern in both principal planed and for different azimuth angles with and without thinning/tilt are presented. Radiation pattern is viewed in both polar and rectangular (2-D and 3-D) forms. Conclusions on the results obtained are presented.

The United Kingdom’s response to the 2014-2018 Islamic State Terrorist Group attacks

Advancements in technology, information, and communication have transformed warfare from a conventional method to psychological warfare (psywar). In the past, warfare was heavily associated with various weapons, such as rifles, bombs, or even nuclear power, to attack an area for specific purposes. In the modern era, warfare is more concerned about technology and information superiority to threaten the enemy faster and more robust. Modern warfare targets the psychology of society in order to win the war. Islamic State (IS) has attracted the world’s attention for its successful strategy in using Twitter in waging war on a country, in this case, the United Kingdom. The purpose of this research is to describe the United Kingdom government’s response in fighting Islamic State attacks on Twitter. This research uses descriptive methods by collecting data from books, the internet, journals, and scientific articles. This study indicates that the United Kingdom responds to Islamic State attacks through two patterns, hard and soft power. The hard power method is carried out through intelligence, police, and economic power to collaborate with international organizations such as the United Nations. The soft power pattern was implemented by creating official state Twitter accounts such as @UKAgainstDaesh, @coalition, @TerrorismPolice, and collaboration with the Global Internet Forum Counter-Terrorism (GIFCT). This strategy plays an essential role in stopping the spread of online terrorism-related content online by blocking related photos, videos, and texts of terrorism.

Regional development in Amazonas, Peru: science-society interactions for sustainability

Scientific-technological knowledge maintains the anthropocentric power-pattern and exploitive attitude with regard to nature, but sustainability science asks for an integration of territorial and decontextualized knowledge systems. Visual participatory methodologies involving diverse local stakeholder facilitate dialogue on environmental and sustainability issues. Inspired by visual ethnography and mediated discourse analysis, the present article uses semiological analysis to reconstruct the depicted narratives on the nature-society system in drawings representing “regional development”. The drawings were elaborated in a series of participatory workshops involving university faculty and students, regional government and non-governmental organizations and farmers from local communities in the northern Amazonian region of Peru. The analysis reveals a prevailing anthropo and technology centered, “colonial” conception of the nature-society system, and a marginalization of alternative narratives. Beyond confirming the potential for visual participatory methods to enhance multi-stakeholder dialogue, it demonstrates how semiological analysis can be used to deepen an understanding of the cultural, organizational and technological constraints facing critical, trans-disciplinary efforts to decolonize the technology-centered, anthropocentric mainstream worldview of nature and society.

Poetic Negotiations: Salad Bowl Feminism in Selected Poetry of Fehmida Riaz, Pat Mora and Joan Loveridge-Sanbonmatsu

The research attempts to evaluate the depiction of women's oppression in specific postcolonial contexts at the hands of the interlocked power pattern formed by manifold factors like patriarchy, class conflict, religion, ethnicity and imperialism in the selected poetry of the renowned Pakistani poetess Fehmida Riaz, the Latino American Poetess Pat Mora, and the Japanese poetess Sanbonmatsu. It applies the theory of Postcolonial Feminism to bring to the fore the oppression of postcolonial women at the intersection of gender, class, race, religion and culture, hence, offering a critique of Western Feminist discourse and its slogan of sisterhood, which tends to erase heterogeneity in women's situations across the globe. The theory of Third World Feminism as well as the portrayals in these poetic compositions from a variety of postcolonial social formations, highlight the fact that postcolonial women are not a monolithic and archetypal suffering category as presented in Western discourses; instead, their resistant agency and subversive subjectivity also stands at the center of their creative writings.

The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece

Within the context of Greece promising a success story in the fight against the disease, this paper proposes a novel method for studying the evolution of the Greek COVID-19 infection curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based on the ongoing spread of COVID-19 and the insufficient data for applying classic time-series approaches, the analysis builds on the visibility graph algorithm to study the Greek COVID-19 infection curve as a complex network. By using the modularity optimization algorithm, the generated visibility graph is divided into communities defining periods of different connectivity in the time-series body. These periods reveal a sequence of different typologies in the evolution of the disease, starting with a power pattern, where a second order polynomial (U-shaped) pattern intermediates, being followed by a couple of exponential patterns, and ending up with a current logarithmic pattern revealing that the evolution of the Greek COVID-19 infection curve tends towards saturation. In terms of Gaussian modeling, this successive compression of the COVID-19 infection curve into five parts implies that the pandemic in Greece is about to reach the second (decline) half of the bell-shaped distribution. The network analysis also illustrates stability of hubs and instability of medium and low-degree nodes, implying a low probability of meeting maximum (infection) values in the future and high uncertainty in the variability of other values below the average. The overall approach contributes to the scientific research by proposing a novel method for the structural decomposition of a time-series into periods, which allows removing from the series the disconnected past-data facilitating better forecasting, and provides insights of good policy and decision-making practices and management that may help other countries improve their performance in the war against COVID-19.