A New Method of Antenna Array Thinning Using Mathematical Sequences for Green Wireless Communication

In this paper, we analyze properties of prime number sequences produced by the alternating sum of higher-order subsequences of the primes. We also introduce a new sieve which will generate these prime number sequences via the systematic selection and elimination of prime number indices on the real number line.

Sequences are fundamental mathematical objects with a long history in mathematics. Sequences are also tools for the development of other concepts (e. g. the limit concept), as well as tools for the mathematization of real-life situations (e. g. growth processes). But, sequences are also interesting objects in themselves, with lots of surprising properties (e. g. Fibonacci sequence, sequence of prime numbers, sequences of polygonal numbers). Nowadays, new technologies provide the possibility to generate sequences, to create symbolic, numerical and graphical representations, to change between these different representations. Examples of some empirical investigation are given, how students worked with sequences in a computer-supported environment.

Architectural lighting has traditionally addressed visual performance and horizontal illuminance on the work plane, later focussing on energy efficiency, while only recently paying particular regard to human health outcomes. The present study evaluated the effectiveness of several light-emitting diode lighting strategies for delivering circadian stimulus to occupants of a typical office space while minimizing energy use. The study employed photometric simulations in a typical open-office space, delivering a criterion circadian stimulus of 0.3 to calculation points modelled at the simulated occupants’ eye level. Six luminaire types, two luminous intensity distributions, six spectral power distributions and two horizontal illuminances were evaluated, resulting in 144 unique lighting conditions. Additionally, the study calculated the discomfort glare for selected luminaires with the highest total lumen output, smallest aperture and direct-only luminous intensity distributions at the higher of the two horizontal illuminances (500 lx). The most impactful strategy involved supplementing common overhead lighting with a desktop luminaire delivering light directly to the simulated office occupants’ eyes, which provided greater circadian stimulus and used less energy than overhead luminaires that were capable of delivering the criterion circadian stimulus of 0.3.

Effect of winter feeding systems on farm greenhouse gas emissions

As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum computers, which operate at temperatures near absolute zero, are determined by computing system parameters, including the number and type of physical qubits, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. When combined with thermal system parameters such as cooling efficiency and cryostat heat transfer, the total energy use can be determined using a first-principles energy model. These models show that cooling of quantum computers differs in two fundamental ways from conventional data centers: (1) the energy required for cooling is much greater than the energy required for computation, and (2) the cooling loads are sensitive to the computational architecture. The temperature requirements for different qubit types can change energy requirements by orders of magnitude. Power use and computational power, as quantified by quantum volume, are analytically correlated. Approaches are identified for minimizing energy use in integrated quantum systems relative to computational power. Designing a sustainable quantum computer will require both efficient cooling and system design that minimizes cooling requirements.

Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

This paper investigates the feasibility of hydrogen-powered hybrid electric vehicles as a solution to transportation-related pollution. It focuses on optimizing energy use to improve efficiency and reduce emissions. The study details the creation and real-time performance assessment of a hydrogen hybrid electric vehicle (HHEV)system using an STM32F407VG board. This system includes a fuel cell (FC) as the main energy source, a battery (Bat) to provide energy during hydrogen supply disruptions and a supercapacitor (SC) to handle power fluctuations. A multi-agent-based artificial intelligence tool is used to model the system components, and an energy management algorithm (EMA) is applied to optimize energy use and support decision-making. Real Global Positioning System (GPS) data are analyzed to estimate energy consumption based on trip and speed parameters. The EMA, developed and implemented in real-time using Matlab/Simulink(2016), identifies the most energy-efficient routes. The results show that the proposed vehicle architecture and management strategy effectively select optimal routes with minimal energy use.

Environmental pollution by wastewater is a prominent crisis in the modern world, and currently, available treatments consume high amounts of electricity, leading to another global crisis of resources. Present system designs in Malaysia focus mainly on treating municipal effluents. This leaves a wide scope for treating other substrates such as landfill leachate effluents. Landfill leachate is one of the alarming pollutants of water reservoirs due to the presence of high concentrations of contaminants such as chemical oxygen demand, ammonia, phosphate, nitrogen, and harmful heavy metals. This raises an immediate focus to modernize its’ treatment system in Malaysia by using sustainable biological treatment with minimal use of energy. Thus, this research targets treating leachate landfill wastewater using an anaerobic digestion process by bacteria that will not only remove the high chemical oxygen demand contents of the wastewater but also utilize them to generate electricity. The two anaerobic electrogenic bacteria, Shewanella oneidensis and Klebsiella pneumoniae were used in this study at 10%, 30%, and 50% concentration, presenting a substantial removal of chemical oxygen demand (COD) from landfill leachate wastewater, in turn, producing a power output of 0.09μA to 0.36μA on a microliter scale, measured using a compact paper-based electro-fluidic array that mimics a microbial fuel cell. However, the bacterial digestion of landfill leachate was found to be inhibited due to the presence of high concentrations of ammoniacal nitrogen and phosphate.

International Society for Gynecologic Endoscopy (ISGE) guidelines and recommendations on gynecological endoscopy during the evolutionary phases of the SARS-CoV-2 pandemic

This work focuses on a microgrid supplied by wind and photovoltaic resources, considering the available climate in Romania. The paper aims at developing a management system for a microgrid composed of a hybrid power system and an ensemble of 8 households. The goal is the minimal use of energy from the national distribution network. The hybrid generation system has 8 photovoltaic panels and 4 wind turbines, with maximum power of 37,6 kW. A Matlab/Simulink model of the microgrid was used for simulation of the control system based on decision tables. The decision rules were developed in order to take better advantage of the available resources and to assure a certain independence from the main electrical grid. The tests were done over a one-day interval, using real experimental data. It showed that the energy consumed can be covered from microgrid's own sources for most of the day (80%). Also, the rest of the day, the consumption is partly provided by photovoltaic panels and wind turbines.

In recent years, research on biped walking robots has been focused on passive dynamic walking. The most important benefit of passive walking is low energy consumption; however, the walking itself is not robust and cannot be sustained without control. To minimize the energy that is used for control, we propose a simple switching control method based on phase trajectory. In this paper, we simulate a planar passive biped walker descending a moderate slope and apply the proposed walking control method. A significant improvement in sustainability of walking was achieved with minimal use of energy.

Performance of advanced control sequences in handling uncertainty in energy use and indoor environmental quality using uncertainty and sensitivity analysis for control components

The rapid expansion of multi-cloud infrastructures amplifies energy consumption and carbon footprint, challenging telecom operators to balance performance, cost, and sustainability. This paper introduces EcoSched, a green-cloud scheduler that dynamically places and migrates workloads across on-premises clusters and public clouds (Azure, AWS) to minimize total energy use while satisfying latency and availability SLAs. EcoSched combines workload forecasting, carbon-intensity APIs, and a multi-objective optimization engine. In a 30-day experiment with video-processing and signaling services under bursty loads, EcoSched reduced energy consumption by 24 % and carbon emissions by 18 % compared to baseline round-robin scheduling, with SLA violation rate under 1 %. We detail system design, prediction models, scheduler algorithm, architecture, quantitative evaluation, and discuss deployment considerations

We present a method for designing a walking controller for the walking robot Cornell Ranger. Our goal is a controller that can be designed using model-based optimization, and then transferred directly to the robot without the need for after-the-fact hand-tuning. The structure of the controller is hierarchical, with a high-level balance controller that plans step-to-step motions, and a lower-level joint controller that coordinates the individual joint motors to achieve the desired limb motions. The balance controller is designed through optimization, with the explicit goals of a) achieving a desired walking speed while b) minimizing energy use and c) avoiding falls due to disturbances. We demonstrate this walking controller on the Cornell Ranger, and find that the resulting gait is comparable to that of a previous (hand-tuned) controller, with regard to energy use, speed regulation, and fall prevention.

<p>This study researches the development of a multi-objective optimization environment to continue the investigation of the potential for housing in Toronto to meet Passive House certification. BEopt was utilized to develop various deep energy retrofits for century detached, century-semi, and wartime archetype homes in Toronto, ON. The optimization environment utilized various design retrofits to develop configurations of variables which minimize energy use and life cycle cost. The developed solutions were input into WUFI Passive to determine which combination of variables can achieve Passive House certification for the lowest cost. The optimization results demonstrated that for a life cycle cost of $65,053-$92,950 depending on geometry and housing type, an archetype home in Toronto can reduce energy use by 69-72% percent and can meet standards for Passive House. Although these numbers include average pricing data, assumptions, and generalizations, the findings of this research demonstrate the applicability of high performance building standards to be retrofit to existing homes to allow sustainability in the built environment to be achieved. </p>