Distinct Scenarios Research Articles

A study on contaminant leakage from Airborne Infection Isolation room during medical staff entry; Implementation of walking motion on hypothetical human model in CFD simulation

The recent outbreak of respiratory infectious diseases such as Middle East Respiratory Syndrome (MERS) and Coronavirus Disease-19 (COVID-19) highlights the critical role of Air Infection Isolation (AII) rooms in preventing the spread of disease. However, the risk of contaminant leakage from the AII room during medical staff interactions remains a significant challenge. This study advances our understanding by employing Computational Fluid Dynamics (CFD) simulations with a novel approach. While CFD simulations mitigate some limitations of field experiments, such as measurement resolution and variable changes, their accuracy is often limited by the oversimplification of models, particularly in representing human models and movement. Unlike prior studies, we incorporate minimal simplification, featuring a human model with Personal Protective Equipment (PPE) and realistic walking motions. This study explores four distinct scenarios with varied human movements, offering a comprehensive analysis of airflow and contaminant dispersion in AII rooms. The results indicated significant differences in airflow patterns and contaminant distribution when realistic walking motion was implemented in the human model, as opposed to simplified movement models. The contaminant leakage from the AII room ranged from 20.6 % to 28.6 %, suggesting that prior studies may underestimate infection risks due to oversimplified human models and motions. These findings underscore the importance of implementing realistic human models and motions in CFD simulations for accurate infection risk assessment in AII rooms. Additionally, our study opens pathways for future research to explore more complex human interactions in clinical settings, enhancing infection control strategies.

Gaining Consensus Around Patient Risk Groups and Prognostic Profiles to Guide CMV Management Among Patients With Allogeneic Hematopoietic Stem Cell Transplant: Insights From a Delphi Panel With Hematopoietic Stem Cell Transplant Experts

This study aimed to characterize patient risk groups and prognostic profiles to optimize clinical decision-making and guide appropriate medical cytomegalovirus (CMV) management among patients with allogeneic hematopoietic stem cell transplant (HSCT). Between 8/2021 and 2/2022, a 3-round modified Delphi study was conducted to generate consensus among 10 international experts in HSCT and infectious diseases. Experts were asked about treatment and prognoses for patients in 7 distinct clinical scenarios. Furthermore, experts were asked to risk-stratify patients by pre-/post-transplant characteristics. Consensus around opting for/against a treatment was observed if ≥75% or <25% of experts reported ≥50% likelihood to recommend or if treatments were ranked inside/outside the top 2 options and ≥75% of experts were within 1 SD of mean ranks. Experts agreed on several unmet needs in CMV disease management post-HSCT, particularly avoidance of treatment-limiting toxicities with conventional CMV therapy and the emergence of both refractory and drug-resistant treatment failures. Experts considered CMV viral load, resistance profile, and route of administration as critical to treatment selection. For newer CMV therapeutic options, experts listed a lack of long-term use data, concerns over potential resistance, high cost, and limited availability as challenges restricting adoption and successful patient management. Experts achieved consensus around patient risk stratifications and factors influencing therapeutic options. Recommendations emerging from this Delphi study may support practicing physicians when confronted with challenging CMV scenarios in patients with HSCT.

Enviro-economic investigation of Novel heat-integrated configurations for pressure swing distillation

Pressure Swing Distillation is a widely adopted solution for the separation of pressure-sensitive azeotropic mixtures in various chemical industries. While this method yields high-purity products, it comes with inherent challenges such as elevated operating costs and energy consumption. However, the temperature differential between the two columns involved presents a compelling opportunity for heat integration. In this study, 18 distinct heat integration scenarios were systematically evaluated using a process simulation software to identify the optimal column configuration for minimizing the Total Annualized Cost. These scenarios span five categories: the Conventional Method, Partial Heat Integration, Full Heat Integration, Internal Heat Integration Distillation Column, and External Heat Integration Distillation Column. The Vapor Recompression Column and Divided Wall Column schemes were applied to each method. Genetic Algorithm served as the optimization tool to determine the best arrangement with TAC as the objective function. Additionally, the environmental impact, assessed through CO2 emissions, was considered. Results indicate that the most economically and environmentally friendly solution is the I-HIDiC with divided wall columns, incorporating VRC systems in both columns. This approach offers a remarkable 66.5% cost savings and a substantial 96.3% reduction in CO2 emissions.

Bridging Qatar's food demand and self-sufficiency: A system dynamics simulation of the energy–water–food nexus

The food sector in Qatar is confronted with formidable challenges due to its harsh environmental conditions. Striving for total food self-sufficiency in such an environment would inevitably exert pressure on the energy and water sectors. This heightened demand for energy and water translates into increased costs and escalates environmental impacts. Consequently, this study embarks on an in-depth analysis of food production within the context of Qatar's energy-water-food nexus, aiming to demonstrate how varying degrees of food self-sufficiency may impact the demand on Qatar's water and energy sectors, as well as on greenhouse gas (GHG) emissions. Moreover, this study demonstrates to what extent specific subsystems within the nexus can be modified to enhance sustainability. An energy-water-food nexus is meticulously crafted within the proposed framework to elucidate the intricate interdependencies among these sectors, incorporating pertinent external variables. These interconnections are then transmuted into a system dynamics model (SDM), facilitating a nuanced exploration of potential transformations and their ripple effects. Furthermore, a life-cycle thinking approach explicitly tailored to Qatar was implemented to estimate GHG emissions accurately. Four distinct scenarios are rigorously examined using the SDM, spanning from a status quo perspective to ambitious transitions toward full food self-sufficiency. The findings of the scenarios indicate that scenario 4, which partially provides the country with its food demands locally using desalinated water, treated wastewater, and groundwater and satisfies 20 % of its energy demand from solar energy, is the most ideal with an annual 5.36 × 1010 kWh/year energy consumption, 1.73 × 1012 l/year water demand, and 3.26 × 1010 kg CO2 eq./year emissions. The outcomes underscore the imperative for prioritizing less energy-intensive resources to mitigate overall energy consumption. Additionally, achieving an optimal national scenario necessitates a judicious equilibrium between food imports and domestic production.

Future eco-hydrological dynamics: Urbanization and climate change effects in a changing landscape: A case study of Birmingham's river basin

The global challenges of water availability and quality are driven by land use and climate changes, further exacerbated by global warming. Altered Land Use and Land Cover (LULC) due to urbanization, deforestation, and agriculture triggers many environmental issues to eco-hydrological systems, which are already profoundly impacted by shifting climate patterns. The Upper Black Warrior River Basin in Birmingham, Alabama, is a prime example of how converting forests into urban areas impairs water quality and increases surface runoff due to impermeable surfaces. This study delved into the temporal and spatial alterations in eco-hydrologically relevant flows, considering individual and combined climate change scenarios and LULC transformation. The Soil and Water Assessment Tool (SWAT) served as the modeling platform, calibrated (2016–2022) and validated (2011–2015) at a daily time scale. Five distinct scenarios were crafted to portray eco-hydrology's past, present, and future responses (runoff and river nutrients) to climate and LULC variations. The model demonstrated strong performance for discharge and predicted increased runoff in future periods, which is attributed to the anticipated warmer, wetter winters and hotter, drier summers characterized by more intense rainfall events. Notably, the study unveiled the adverse impact of climate change on monthly nitrates leaching into surface runoff, with the lowest levels predicted during May to September coinciding with reduced precipitation. Conversely, human activity positively impacts runoff, especially in FMC, which has a higher deforestation rate (42.8%) and increased runoff compared to VC. The study also emphasized the significant link between flow and nutrient concentration, highlighting the dominant role of nonpoint sources in contributing to these inputs. These findings have significant implications for local stakeholders, necessitating considering the potential consequences of integrated climate change and urbanization on eco-hydrology. Effective management strategies, such as improved land use practices and reduced nutrient leaching, must be implemented to minimize these impacts.

Application of Fractional Order PI/PID Voltage Controllers for Three-Phase Voltage Source Inverter with Dynamic Load

Purpose: With the depletion of fossil fuels, the landscape of electrical energy generation has witnessed a transformative shift towards alternative sources such as fuel cells and photovoltaic (PV) systems that produce direct current (DC) electricity. In the realm of distributed power generation, three-phase voltage source inverters (VSIs) are extensively utilized for converting energy from DC sources to alternating current (AC) for the grid or loads. The pivotal objective of this study is to investigate and compare the performance of fractional-order Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) control structures against their integer-order counterparts in the voltage control loop of a VSI connected to a dynamic load system.&#x0D; Materials and Methods: The research employs frequency response analysis to design and implement both fractional-order PI/PID and integer-order PI/PID control structures for the voltage control loop. The simulation of the controlled system is conducted using MATLAB/Simulink, considering two distinct test scenarios – unstable and stable dynamic load conditions. The primary focus is on analyzing the inverter output voltage in the d-q axis under these scenarios.&#x0D; Findings: The analysis of the simulation results reveals noteworthy distinctions between the fractional-order and integer-order PI/PID controllers in the context of controlling the inverter system with dynamic loads. These findings shed light on the advantages of employing fractional order controllers, particularly in dynamic load scenarios, showcasing superior performance in comparison to their integer-order counterparts.&#x0D; Implications to Theory, Practice and Policy: The study's outcomes hold significant implications for the theory and practice of voltage control in distributed power generation systems. The superiority of fractional-order PI controllers underscores their potential for enhancing power quality, especially in systems with unstable and time-varying dynamic loads. These insights can inform the development of more effective control strategies for voltage source inverters, influencing both theoretical frameworks and practical applications. Policymakers may consider these findings when formulating regulations and incentives to promote the adoption of advanced control strategies in the evolving landscape of electrical energy generation.

Annihilation mechanisms for interacting skyrmions in magnetic nanowire

Abstract Magnetic skyrmions are considered potential candidates for spintronics-based memory and logic devices. For achieving high-density and high-speed devices, it is essential to study their interactions. In this paper, the interaction, dynamics, and annihilation mechanisms of Néel skyrmions in nanowire confinement under the influence of spin-transfer torque (STT) and edge forces have been studied. Initially isolated, two Néel skyrmions are brought into proximity, leading to distinct interaction scenarios characterized by varying current densities. We explore the impact of these interactions on skyrmion trajectories, size evolution, and annihilation phenomena. Our findings reveal the interplay of skyrmion-skyrmion repulsive forces, edge effects, and the influence of STT, shedding light on the rich dynamics of these topological magnetic textures. Furthermore, we unveil the distinct annihilation mechanisms of the leading and trailing skyrmions under different forces, providing valuable insights into the fundamental physics of skyrmion behavior in confined geometries.&amp;#xD;

Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance.

Assessment and characteristics of S-NPP VIIRS Deep Blue and Dark Target aerosol properties under clean, polluted and fire scenarios over the Amazon

The present study carries out the systematic performance evaluation of aerosol optical depth (AOD) products retrieved using Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue (DB) and Dark Target (DT) onboard Suomi National Polar-orbiting Partnership (S-NPP) satellite over the Amazon Basin. Characteristics and uncertainty were evaluated under distinct air pollution scenarios such as a clean background in the wet season, polluted conditions in the dry season with biomass burning emissions and peak burning season with higher fire activity. VIIRS retrievals were also analyzed under aerosol loading, particle size and surface vegetation coverage against the Aerosol Robotic Network (AERONET) measurements at 9 sites in 2012–2022. VIIRS DB showed good accuracy, with 78% of AOD matchups falling within the expected error, 84% in the wet season, and 71% in the dry and burning seasons. In contrast, VIIRS DT indicated poor accuracy (64%) and trends overestimate the AOD in all air pollution scenarios. Both algorithms were sensitive to AERONET sites with high elevation and dark vegetated coverage characteristics. VIIRS DB and DT systematically overestimated AERONET AOD as increased aerosol loading. DB trends underestimate aerosol under background conditions and overestimate with coarse and fine particle predominance. Additionally, both algorithms indicated poor accuracy under forest type with a large positive bias. VIIRS DB demonstrated the highest accuracy in the presence of aerosol loading in sites characterized by mixed land cover type. This was observed in both coarse and fine mode scenarios for grassland. VIIRS DT demonstrated satisfactory accuracy under background conditions and dominance of coarse particles within grassland land cover type. For mixed land cover, satisfactory accuracy was found under intermediate aerosol loading conditions. DB algorithm showed greater uncertainty associated with the coarse particle aerosol for the full period and all polluted scenarios. Overall, VIIRS DT accuracy was more sensitive to varying air pollution scenarios.

Inventory model for green products with payment strategy, selling price and green level dependent demand using teaching learning based optimization algorithm

There has been a lot of research on pricing and lot-sizing practices for different payment methods; however, the majority has focused on the buyer’s perspective. While accepting buyers’ credit conditions positively impacts sales, requesting advance payments from purchasers tends to have a negative effect. Additionally, requiring a down payment has been found to generate interest revenue for the supplier without introducing default risk. However, extending the credit period, along with offering delayed payment options, has the potential to increase sales volume, albeit with an elevated risk of defaults. Taking these payment schemes into account, this study investigates and compares the per-unit profit for sellers across three distinct payment methods: advance payment, cash payment, and credit payment. The consumption rate of the product varies non-linearly not only with the time duration of different payment options but also with the price and the level of greenness of the product. The utmost objective of this work is to determine the optimal duration associated with payment schemes, selling price, green level, and replenishment period to maximize the seller’s profit. The Teaching Learning Based Optimization Algorithm (TLBOA) is applied to address and solve three numerical examples, each corresponding to a distinct scenario of the considered payment schemes. Sensitivity analyses confirm that the seller’s profit is markedly influenced by the environmental sustainability level of the product. Furthermore, the seller’s profitability is more significantly affected by the selling price index compared to the indices of the payment scheme duration and the green level in the demand structure.

Performance comparison of dynamically controlled particle swarm optimization and grey wolf optimizer for dynamic power reserve scheduling in multi areas problems

The primary goal of Multi-Area Economic Dispatch (MAED) is to effectively meet the dynamic power demands of multiple interconnected regions while optimizing the economical generation dispatch strategy. This paper introduces a comprehensive investigation into the application of two advanced optimization techniques, namely Dynamically Controlled Particle Swarm Optimization (DCPSO) and thereafter Grey Wolf Optimizer (GWO), and their variant adaptations to address the complex challenges posed by MAED issue. The performance and efficacy of these strategies are rigorously assessed in three distinct scenarios: (a) Single-Area Power System with Three Generation Units: This scenario represents a fundamental single-area power network comprising three generation units. (b) Two-Area System with Four Generating Units: In this case, a two-area system with four generating units is analyzed. (c) Four-Area System with Forty Generation Units and Six Tie Lines. In the most intricate scenario, a four-area interconnected power system is scrutinized, featuring a total of forty generation units. GWO technique produces the cost saving of $ 0.1 for single area, 2 area cost saving is $ 0.11 likewise for 4 area the cost saving is $ 474.10 over the PSO. The execution time taken by GWO is 12.7 sec. less compared to DPSO.

Greenhouse Gas Payback Time of Different HVAC Systems in the Renovation of Nordic District-Heated Multifamily Buildings Considering Future Energy Production Scenarios

The European Union (EU) has implemented several policies to enhance energy efficiency. Among these policies is the objective of achieving energy-efficient renovations in at least 3% of EU buildings annually. The primary aim of this study was to offer a precise environmental comparison among four similar district-heated multifamily buildings that have undergone identical energy efficiency measures. The key distinguishing factor among them lies in the HVAC systems installed. The chosen systems were as follows: (1) exhaust ventilation with air pressure control; (2) mechanical ventilation with heat recovery; (3) exhaust ventilation with an exhaust air heat pump; and (4) exhaust ventilation with an exhaust air heat pump with a Photovoltaic (PV) panel. This study involved a life cycle assessment that relied on actual material data from the housing company and energy consumption measurements. This study covered a period of 50 years for thorough analysis. A sensitivity analysis was also conducted to account for various future scenarios of energy production. The findings revealed that the building with an exhaust air heat pump exhibited the lowest greenhouse gas emissions and the shortest carbon payback period (GBPT), needing only around 7 years. In contrast, the building with exhaust ventilation without heat recovery showed the highest emissions and the longest carbon payback period (GBPT), requiring approximately 11 years. Notably, the results were significantly influenced by future scenarios of energy production, emphasizing the crucial role of emission factors in determining the environmental performance of distinct renovation scenarios.

Vector bosons in the rotating frame of negative curvature wormholes

In this study, we investigate the relativistic dynamics of vector bosons within the context of rotating frames of negative curvature wormholes. We seek exact solutions for the fully-covariant vector boson equation, derived as an excited state of zitterbewegung. This equation encompasses a symmetric rank-two spinor, enabling the derivation of a non-perturbative second-order wave equation for the system under consideration. Our findings present exact results in two distinct scenarios. Notably, we demonstrate the adaptability of our results to massless vector bosons without compromising generality. The evolution of this system is shown to correlate with the angular frequency of the uniformly rotating reference frame and the curvature radius of the wormholes. Moreover, our results highlight that the interplay between the spin of the vector boson and the angular frequency of the rotating frame can give rise to real oscillation modes, particularly evident in excited states for massless vector bosons. Intriguingly, we note that the energy spectra obtained remain the same whether the wormhole is of hyperbolic or elliptic nature.

Analysis of a nuclear power plant’s responses to the open-phase fault of preferred power supply

This paper explores the phenomenon of open-phase fault of nuclear power plants for preferred power supply, focusing on three distinct scenarios, normal operation, start, and overhaul. For each scenario, the paper discusses the responses of existing relay protection devices, which are crucial for detecting and mitigating open-phase faults. These responses vary depending on the impedance at the fault location and the load rate of the plant. This paper highlights that open-phase faults can lead to three-phase imbalances in the power system, but their impact on voltage levels is not always straightforward due to the transformer and motor compensation effects. Additionally, the paper identifies blind zones in open-phase fault detection, where existing relays may fail to respond effectively, leading the plant to enter a station blackout state. In conclusion, the research underscores the importance of open-phase fault detection and protection in nuclear power plants. It reveals potential vulnerabilities in existing relay configurations and emphasizes the need for enhanced monitoring and safety measures to prevent extended plant operation under open-phase fault conditions.

Water-exit dynamics of a ventilated underwater vehicle in wave environments with a combination of computational fluid dynamics and machine learning

A ventilated vehicle exiting water in a wave environment is a complex nonlinear process, and the mechanism by which the wave conditions influence this process remains poorly understood. This paper describes realistic simulations of a ventilated vehicle exiting a water body under various wave conditions. Comprehensive analysis is conducted for a range of distinct wave scenarios, and a machine learning-based method is developed for the rapid forecasting of vehicle-related parameters. A three-layer backpropagation neural network is constructed, and its prediction performance is verified. Subsequently, predictive and optimization procedures are employed to determine the optimal wave phase for the water exit of the vehicle. Different wave conditions are shown to significantly affect the evolution of the ventilated cavity as well as the kinematic and loading characteristics of the vehicle. The pitch angular velocity and angle at the moment when the head of the vehicle reaches the free surface exhibit a positive cosine trend under different wave conditions. No regularity of the pitch angular velocity at the moment when the tail reaches the free surface is evident. The neural network exhibits exceptional proficiency in predicting the motion parameters and load characteristics of the vehicle. The optimal point for the vehicle to exit the water is determined to be at a wave phase of 0.125π, while the most hazardous point occurs when the wave phase is 1.1875π.

Optimal design and orientation of cooling technology for photovoltaic Plants: A comparative simulation study

Continued development in the field of solar photovoltaics requires improvements in cooling technology. Therefore, the present comprehensive simulation study aims to reach the optimal design and orientation of cooling technologies that achieve the highest performance of photovoltaic (PV) panels. To achieve the optimal design and orientation of the cooling channel inlet of air-cooled photovoltaic thermal collectors, seven different designs of cooling techniques distinct for cooling the PV panels from the bottom side were studied, including conventional side cooling (S1), cooling from the bottom through one slot (S2), cooling from the bottom through two slots (S3), cooling from the bottom through three slots (S4), cooling from the side and through one hole (S5), cooling from the side and through two holes (S6), and cooling from the side and through three holes (S7). Outcomes presented that the second scenario S2 is the optimal one, where the air outlet temperature is 315.9 K and the thermal efficiency is 14.6 %. In the second part of the study, the influences of air flowing (with Re varying between 1000 and 5500) on the performance of PV panels for two distinct scenarios (S1 and S2) were studied. According to simulation studies, using situations S1 and S2, the outlet temperatures are 318.26 K and 319.14 K, respectively, with Re = 1072 and Gr = 8.74E5. Also, S2 has a considerable thermal efficiency advantage over S1. The highest thermal efficiency for S1 and S2 is 51 % and 59 %, respectively, for Gr = 8.74E5 and Re = 5432 with an improvement of 15.68 %.

Probing and deploying key wireless sensing technologies in the realm of 6G

In an effort to implement seamless, precise, and real-time wireless sensing within indoor environments, this paper delves into the principles and applications of sensing technology grounded in indoor wireless signals. The cornerstone of this study is the comprehensive understanding of wireless signal sensing which is intrinsically composed of four major stages. Initially, signal acquisition occurs, serving as the foundation for the entire process. Subsequently, data pre-processing refines these signals, ensuring the relevance and accuracy of the information for the upcoming stages. Thereafter, the methodical process of feature extraction takes place, which provides a nuanced understanding of the signal's key characteristics. This is vital in offering clear insights into the signal's inherent traits and potential implications. Finally, classification and identification occur. This stage sorts and labels the signals based on their unique attributes, allowing for an organized and efficient analysis. Furthermore, this paper extends to analyzing three distinct application scenarios that utilize this intelligent sensing technology. The first one entails behavior recognition, which could be instrumental in understanding and predicting human activity patterns within a specific space. Another application is in passive location identification, offering a non-intrusive means to track movements and locations within an indoor setting. By investigating these applications, we aim to provide a broad understanding of the potential of indoor wireless signal-based sensing technology, and to further its development and refinement for more diverse and efficient uses.

A novel intelligent control approach for wind energy conversion systems with synchronous reluctance generators

SummaryThis study addresses a significant research gap in the field of wind energy, focusing on the underutilized potential of synchronous reluctance generators (SynRG) in wind power conversion systems (WPCS). Despite notable advancements in wind energy controls, the capabilities of SynRG within these systems have not been fully explored. In response, we introduce an innovative model‐free control (MFC) approach, termed intelligent proportional (iP), specifically designed and optimized for WPCS equipped with SynRG. This research encompasses the modeling, control, and simulation of SynRG‐based WPCS, aiming to optimize system performance and enhance the quality of grid power supply. The proposed vector control (VC) method utilizes a MFC approach (VC‐iP) to address the limitations inherent in traditional VC methods with proportional‐integral (PI) controllers (VC‐PI). Although widely used for their simplicity, VC‐PI methods often lead to power fluctuations and decreased power and current quality, consequently reducing overall system efficiency. In contrast, VC‐iP markedly enhances power quality and system efficiency, especially in situations involving fluctuating power demand and variable wind speeds. The efficacy of VC‐iP is demonstrated through simulations in two distinct test scenarios that include variations in power demand and random wind speed fluctuations. The results affirm the superiority of VC‐iP in maintaining stable, high‐quality power output from WPCS. This method notably minimizes fluctuations, improves current quality, reduces harmonic distortion and steady‐state error, ensures reliable grid integration, and contributes to a more efficient and sustainable energy system.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Formal optimization techniques select hydrogen to decarbonize California

System planning across economic sectors is becoming increasingly necessary. Building upon existing solutions for deep decarbonization, the inclusion of renewable capacity to meet up to 8 MMT/year hydrogen demand is carried out. An hourly economic dispatch problem modeling the 2050 California electric grid given this hydrogen demand constraint is solved. Hydrogen demand outside of the power generation sector is fixed, but the demand for power generation is endogenously determined. The factor to consider offshore wind capacity, in addition to a conservative and an aggressive hydrogen technology adoption approach, creates four distinct scenarios to evaluate. The difference in results then provides a basis for discussing the costs and benefits associated with using hydrogen to further decarbonize across all sectors. The carbon reduction achieved outside of the power generation sector is 27 MMT despite a slight increase in carbon within the power generation sector. The seasonal storage requirement for hydrogen spans from 72 to 149 TBtu dependent upon the renewable capacity mix. This level of hydrogen demand results in 21% to 41% of total electric load being dedicated to hydrogen production. Battery energy storage has the lowest energy throughput in the middle of the year coinciding with peak electrolyzer operation.