Optimal Size Research Articles

An optimal standalone wind-photovoltaic power plant system for green hydrogen generation: Case study for hydrogen refueling station

Sustainability goals include the utilization of renewable energy resources to supply the energy needs in addition to wastewater treatment to satisfy the water demand. Moreover, hydrogen has become a promising energy carrier and green fuel to decarbonize the industrial and transportation sectors. In this context, this research investigates a wind-photovoltaic power plant to produce green hydrogen for hydrogen refueling station and to operate an electrocoagulation water treatment unit in Ostrava, Czech Republic's northeast region. The study conducts a techno-economic analysis through HOMER Pro® software for optimal sizing of the power station components and to investigate the economic indices of the plant. The power station employs photovoltaic panels and wind turbines to supply the required electricity for electrolyzers and electrocoagulation reactors. As an off-grid system, lead acid batteries are utilized to store the surplus electricity. Wind speed and solar irradiation are the key role site dependent parameters that determine the cost of hydrogen, electricity, and wastewater treatment. The simulated model considers the capital, operating, and replacement costs for system components. In the proposed system, 240 kg of hydrogen as well as 720 kWh electrical energy are daily required for the hydrogen refueling station and the electrocoagulation unit, respectively. Accordingly, the power station annually generates 6,997,990 kWh of electrical energy in addition to 85595 kg of green hydrogen. Based on the economic analysis, the project's NPC is determined to be €5.49 M and the levelized cost of Hydrogen (LCH) is 2.89 €/kg excluding compressor unit costs. This value proves the effectiveness of this power system, which encourages the utilization of green hydrogen for fuel-cell electric vehicles (FCVs). Furthermore, emerging electrocoagulation studies produce hydrogen through wastewater treatment, increasing hydrogen production and lowering LCH. Therefore, this study is able to provide practicable methodology support for optimal sizing of the power station components, which is beneficial for industrialization and economic development as well as transition toward sustainability and autonomous energy systems.

Multi-objective optimisation of a thermocline thermal energy storage integrated in a concentrated solar power plant

Most of CSP plants built today are all equipped with storage devices. Most are sensitive heat storages, in particular 2-tank systems. The aim of this study is to optimise a thermocline storage, designed for the Andasol CSP plant in Spain, on the basis of exergy, environmental and economic criteria. The optimum tank geometry, particle size and heat transfer fluid/filler material pair must be determined. To achieve this, six different fillers operating with Dowtherm A synthetic oil are being investigated: machined ceramics, ceramics obtained from waste materials and natural rock. The NSGA-II multi-objective genetic algorithm is used to solve the problem. This optimisation selects recycled ceramics as best filler materials. The exergy efficiency is not affected by the filler, with more than 99.8%. The application of TOPSIS method combined with weighting derived from Shannon entropy, selects recycled ceramic obtained from electric furnaces as the best filler. This tank has a tapered shape (diameter of 27.4 m, height of 22.2 m) and a very small particle diameter (6 mm). Compared with an earlier design available in literature, the environmental impact is reduced by 19%. The LCOE reaches 10.8 c€/kWhe, a 48% reduction compared with the selling price of heat in Spain (in 2021).

Optimizing Educational Efficiency: Exploring the Relationship Between Bulk and Clinical Rotation Group Size in Medical Education Students’ Perspective in Busitema University Uganda

Clinical rotation group size plays a pivotal role in shaping the quality, efficiency, and overall student experience in medical education. Despite its significance, determining the ideal group size remains a daunting task. This research endeavors to delve into the correlation between group size and what is termed as "bulk," representing the comprehensive efficiency and productivity of clinical rotation groups. By examining this relationship, the study aims to offer valuable insights into potential strategies for optimizing group size, thereby enhancing the educational experience for medical students. The study employed a mixed-methods approach, combining quantitative analysis of productivity metrics with qualitative assessments of student and faculty perceptions. Surveys, interviews, and observational data were utilized to capture the nuances of group dynamics, learning outcomes, and resource utilization across different group sizes. Statistical techniques such as regression analysis and ANOVA were employed to identify significant correlations and differences. Data were collected from medical students undergoing clinical rotations across multiple specialties. Group sizes varied from small (2-3) and large (4 and more). The study measured bulk using indicators such as patient encounters per student, learning outcomes, and faculty-student interaction. Results indicated a nonlinear relationship between bulk and group size. Smaller groups showed higher individual student engagement, personalized learning experiences, and increased faculty-student interaction. Conversely, larger groups exhibited greater overall productivity due to economies of scale and enhanced peer learning. However, beyond a certain threshold, further increases in group size led to diminishing returns, compromising individualized attention and educational quality. The findings underscore the importance of balancing group size to optimize educational outcomes in clinical rotations. While larger groups may offer efficiency gains, they must be carefully managed to mitigate potential drawbacks such as reduced individualized attention and faculty-student interaction. Smaller groups foster a more personalized learning environment but may lack the resource utilization efficiency of larger cohorts. Institutions should consider contextual factors such as specialty, learning objectives, and available resources when determining optimal group sizes. In conclusion, the relationship between bulk and clinical rotation group size is complex, with both benefits and limitations associated with different group sizes. Institutions should strive to strike a balance between efficiency and educational quality by tailoring group sizes to specific contexts and objectives. Further research is needed to explore additional factors influencing bulk and to develop targeted interventions for optimizing clinical rotation experiences.

The effects of solar insolation and cloud opacity on the optimum array size for a direct-coupled solar pumping system

The widespread adoption of solar photovoltaic pumping technology requires pertinent information on the sizing and design of these systems. This research investigated how the ratio of photovoltaic array peak power to motor power consumption, solar insolation and cloud opacity affects the water yield of a direct-coupled solar PV pumping system. The specific aim was to estimate the optimum photovoltaic power to motor power consumption (OPVM) ratio for a 3 W DC pump under Jamaican weather conditions. The PVM ratio is the ratio of peak array power to nominal motor power consumption. Four experimental direct coupled solar pumping systems were set up and the water yield for each measured daily for thirty days. Each system was identical in every respect except each having a different solar array peak power. The findings show that there is a strong correlation (Pearson coefficient of −0.903) between solar insolation and the optimum photovoltaic power to motor power consumption ratio. Additionally, there was found to be a strong correlation between average cloud opacity and optimum photovoltaic power to motor power consumption ratio (Pearson coefficient of 0.899). Both correlations were shown to be statistically significant, with both yielding p-values <0.001. The findings of the correlation analysis indicate that a relatively large PVM ratio would be economical when used on a site that regularly receives relatively low insolation and heavy cloud cover. Finally, the daily optimum photovoltaic power to motor power consumption ratio ranged from approximately 2.3 to 5.0. Generally, it was observed that the OPVM ratio shifted below 2.4 on clear sunny days and above 4.0 on very cloudy days. While these conclusions may not exactly extrapolate to scales of practical application, performance metrics of larger systems are expected to bear some similarity. Being guided by these results, additional studies at these larger scales are recommended.

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

As sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company’s sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes.

Lite RGB-based measurement method for ballast fouling index prediction through subsampling

Ballasted track is the major form of freight railroads. Despite various requirements for material selection, ballast will degrade due to wear and the infiltration of fine materials. These fine particles will cause ballast fouling and degraded track performance. The fouling condition is directly related to both ballast performance and track maintenance planning. It can be quantitatively assessed using the Fouling Index (FI), calculated as the sum of the percentages of materials passing through No. 4 (4.75 mm) and No. 200 (75 µm) sieves. Traditional fouling assessment methods rely on labor-intensive sieve analysis. While non-destructive techniques such as Ground Penetration Radar, Impulse Response, Surface Wave, and SmartRock offer alternatives, they require specialized equipment and skilled technicians for accurate interpretation. Deep learning models have also been employed, trying to assess ballast fouling conditions by segmenting particles. However, these models struggle to account for fine materials. Recently, the FI was found to be linearly correlated with the variance of RGB (Red, Green, Blue) intensities in fouled ballast images, and this correlation could be used to assess ballast fouling conditions. Improving upon the previously developed FI prediction model and making it more practical, this study proposes an enhanced and lite RGB-based model that predicts the FI through a simple random subsampling approach. The optimal subsample size is determined both experimentally and analytically. Based on the results obtained from this study, the correlation between FI and the variance of RGB intensities in fouled ballast images is further confirmed. The proposed lite model can achieve the same accuracy in FI prediction but requires fewer than 1 % of the data points needed by previous methods, which demonstrates its promising potential for ballast fouling condition assessment practice.

Lessons learned in wind-driven desalination systems in the Canary Islands: Useful knowledge for other world islands

This study addresses the imperative transition towards renewable energy for the powering of desalination technologies, focusing particularly on island contexts. The Canary Archipelago serves as a unique wind energy-desalination laboratory, with numerous experimental projects conducted since the late 1980s. The lessons learnt that are identified in this paper highlight the critical factors influencing project success or failure, including the modernization of desalination plants, the optimal selection of wind farm sites and turbine technology, adequate resources for operating and maintenance tasks, and optimal battery sizing to minimize system shutdowns. In this context, reverse osmosis emerges as the most advantageous technology for variable operating conditions. System interfaces between wind turbines and desalination units are also discussed and it is identified that further research is needed for mechanical or hydrostatic interface systems, while electrical interface systems suit low-medium water demand areas. Recommendations are given for system design and maintenance with the aim of providing valuable information for other island communities grappling with the water-energy nexus and climate change mitigation.

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

The power transmission system is in a stressed condition due to the increasing electricity demand. This stress, exacerbated by the deregulated power system environment, necessitates an urgent additional supply to maintain system adequacy. One important initiative to alleviate the transmission burden which meets the load demand can be rectified by composite compensation scheme. This paper showcases intelligent composite compensation under varying loads, emphasizing loss minimization. The approach integrates a loss control scheme involving Distributed Generation (DG) and Optimal Reactive Power Dispatch (ORPD) with multi-DG installation termed composite compensation, employing a novel optimization technique called Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP). The study identifies optimal locations and sizes for composite compensation in the power transmission system, demonstrating its superiority over traditional optimization techniques namely the evolutionary programming (EP) and artificial immune systems (AIS). Results are demonstrated for four cases involving single DG, 3 DGs and composite compensation validated on IEEE 30-Bus Reliability Test System (RTS). ICAMEP is superior over EP and AIS in achieving the highest loss reduction.

Thermal performance of packed-bed latent heat storage tank integrated with flat-plate collectors under intermittent loads of building heating

Packed-bed latent heat storage (PBLHS) system has been widely studied in the solar energy application area due to its properties of solving the time-space mismatch of energy supply and demand. The paper delves into the cyclic thermal performance of PBLHS tanks, seamlessly integrated with flat-plate collectors, to manage building heat loads effectively. A 1D transient mathematic model of PBLHS integrated with the solar collector for a building is established using MATLAB which is validated with the experimental results from the literature. Based on this model, the effect of different sizes, flow rates, inlet temperature thresholds and different heating months are discussed. The results show that: 1). Building heat load can have a significant influence on the thermal performance of the PBLHS system. 2). The optimal size of the tank for a 96 m2 house is 1.0*1.0 m and the optimal flow rate is 0.05 kg/s 3). The weather data can significantly affect the thermal performance of PBLHS, the storage tank heating durations for one week in different heating months are 108.4h, 73.6h,70.8h and 81.7h. These insights are pivotal for the optimal design of PBLHS tanks, paving the way for more efficient building heating solutions.

Metal-Free Atom Transfer Radical Polymerization to Prepare Recylable Micro-Adjuvants for Dendritic Cell Vaccine.

In the development of dendritic cell (DC) vaccines, the maturation of DCs is a critical stage. Adjuvants play a pivotal role in the maturation of DCs, with a major concern being to ensure both efficacy and safety. This study introduces an innovative approach that combines high efficacy with safety through the synthesis of micro-adjuvants grafted with copolymers of 2-(methacrylamido) glucopyranose (MAG) and methacryloxyethyl trimethyl ammonium chloride (DMC). The utilization of metal-free surface-initiated atom transfer radical polymerization enables the production of safe and recyclable adjuvants. These micrometer-sized adjuvants surpass the optimal size range for cellular endocytosis, enabling the retrieval and reuse of them during the ex vivo maturation process, mitigating potential toxicity concerns associated with the endocytosis of non-metabolized nanoparticles. Additionally, the adjuvants exhibit a "micro-ligand-mediated maturation enhancement" effect for DC maturation. This effect is influenced by the shape of the particle, as evidenced by the distinct promotion effects of rod-like and spherical micro-adjuvants with comparable sizes. Furthermore, the porous structure of the adjuvants enables them to function as cargo-carrying "micro-shuttles", releasing antigens upon binding to DCs to facilitate efficient antigen delivery.

PLGA nanocapsules as a delivery system for a recombinant LRP-based therapeutic.

Telomerase activity is directly affected by the laminin receptor precursor (LRP) protein, a highly conserved nonintegrin transmembrane receptor, which has been shown to have therapeutic effects in ageing, and age-related diseases. Recently, it has been found that overexpression of LRP-FLAG, by plasmid transfection, leads to a significant increase in telomerase activity in cell culture models. This may indicate that upregulation of LRP can be used to treat various age-related diseases. However, transfection is not a viable treatment strategy for patients. Therefore, we present a nanoencapsulated protein-based drug synthesised using poly(lactic-co-glycolic acid) (PLGA) nanocapsules for delivery of the 37 kDa LRP protein therapeutic. PLGA nanocapsules were synthesised using the double emulsification-solvent evaporation technique. Different purification methods, including filtration and centrifugation, were tested to ensure that the nanocapsules were within the optimal size range, and the BCA assay was used to determine encapsulation efficiency. The completed drug was tested in a HEK-293 cell culture model, to investigate the effect on cell viability, LRP protein levels and telomerase activity. A significant increase in total LRP protein levels with a concomitant increase in cell viability and telomerase activity was observed. Due to the observed increase in telomerase activity, this approach could represent a safer alternative to plasmid transfection for the treatment of age-related diseases.

Metal‐Free Atom Transfer Radical Polymerization to Prepare Recylable Micro‐Adjuvants for Dendritic Cell Vaccine

AbstractIn the development of dendritic cell (DC) vaccines, the maturation of DCs is a critical stage. Adjuvants play a pivotal role in the maturation of DCs, with a major concern being to ensure both efficacy and safety. This study introduces an innovative approach that combines high efficacy with safety through the synthesis of micro‐adjuvants grafted with copolymers of 2‐(methacrylamido) glucopyranose (MAG) and methacryloxyethyl trimethyl ammonium chloride (DMC). The utilization of metal‐free surface‐initiated atom transfer radical polymerization enables the production of safe and recyclable adjuvants. These micrometer‐sized adjuvants surpass the optimal size range for cellular endocytosis, enabling the retrieval and reuse of them during the ex vivo maturation process, mitigating potential toxicity concerns associated with the endocytosis of non‐metabolized nanoparticles. Additionally, the adjuvants exhibit a “micro‐ligand‐mediated maturation enhancement” effect for DC maturation. This effect is influenced by the shape of the particle, as evidenced by the distinct promotion effects of rod‐like and spherical micro‐adjuvants with comparable sizes. Furthermore, the porous structure of the adjuvants enables them to function as cargo‐carrying “micro‐shuttles”, releasing antigens upon binding to DCs to facilitate efficient antigen delivery.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Influence of grain size on the electrochemical performance of Li7‑3xLa3Zr2AlxO12 solid electrolyte

Contemporary Li‐ion batteries are facing substantial challenges like safety and limited energy density. The development of all‐solid‐state battery cells mitigates safety hazards and allows the use of Li‐metal anodes increasing energy density. Garnet‐type solid electrolytes can be vital to achieving an all‐solid‐state cell and an understanding of the influence of its microstructure on the electrochemical performance is crucial for material and cell design. In this work the influence of grain size on the Li‐ion conductivity of Li7‐3xLa3Zr2AlxO12 (x = 0.22) is presented. The synthesis and processing procedure allows changing the ceramic grain size, while maintaining the same synthesis parameters, eliminating influences of the synthesis on grain boundary composition. Field assisted sintering technology is a powerful method to obtain dense, fine‐grained ceramics with an optimal grain size of 2‐3 µm, where the conductivity is double that of the counterpart (0.7 µm). A total Li‐ion conductivity of 0.43 mS cm‑1 and an activation energy of 0.36 eV were achieved. The oxide‐based all‐solid‐state battery cell combining the garnet‐type electrolyte, a Li‐metal anode and a thin‐film LiCoO2 cathode was assembled and cycled at room temperature for 90 hours. This represents a proof of concept, for the application of oxide‐based electrolytes at ambient temperatures.

Macrofaunal diversity patterns in coastal marine sediments: re-examining common metrics and methods

Complex biodiversity patterns arise in marine systems due to overlapping ecological processes, including organism interactions, resource distribution, and environmental conditions. Despite the importance of documenting these patterns, describing diversity in natural ecosystems remains challenging. Here, we investigate 3 nearshore sub-Arctic sites to describe benthic macroinfaunal taxa and biological traits, with the ultimate aim of determining whether common diversity metrics and typical sampling efforts adequately capture community composition in these systems. First, we assess how diversity relates to sediment depth and examine relationships among commonly used taxonomic and functional diversity indices. Second, using a power analysis, we explore how sampling effort influences the interpretation of diversity patterns in coastal systems. We report significant variation in community composition among sites, even across small spatial scales of km, and find that taxonomically diverse communities do not necessarily correspond to high functional diversity. We further find that although environmental factors such as sediment depth consistently affect macroinfaunal diversity, the direction and magnitude of these relationships are site-dependent. Finally, we demonstrate that typical sampling effort for coastal benthic studies (for example, &lt;5 push cores of ~7 cm diameter) may not capture macroinfaunal community composition adequately, potentially obscuring hotspots in common diversity metrics such as taxonomic or functional richness. However, indices such as Simpson’s diversity may be well-suited to resource-limited studies with restricted sampling capacity. Our results highlight the importance of adopting multi-pronged approaches to biodiversity assessment and determining optimal sample sizes for marine benthic systems, particularly in the context of biodiversity monitoring for conservation purposes.

Optimal sizing and placement of battery energy storage system for maximum variable renewable energy penetration considering demand response flexibility: A case in Lombok power system, Indonesia

Indonesia is a tropical climate country with considerable renewable electrical energy source prospects, including photovoltaic (PV) and wind energies. Nevertheless, several variable renewable energy sources (VREs) have exhibited uncertain attributes and substantial reliance on natural conditions, leading to unstable load-related power supply risks. Hence, integrating battery energy storage systems (BESSs) with VRE generators is a dependable approach to bolster renewable energy generator applications on a large-scale grid while providing load demand flexibility. This study determined adequate sizing and placement of the BESS to achieve maximum VRE generator penetration while considering the demand response flexibility. Key indicators, including technical minimum load and system ramp capacity, were identified to achieve maximum penetration of thermal generators. This study also combined a unit commitment procedure with direct current optimal power flow (DC-OPF) as a novel approach to determine the maximum VRE penetration level. An optimization problem model concerning mixed integer linear programming (MILP) was subsequently employed in this study using the CPLEX solver in the general algebraic modeling system (GAMS). The study is based on the IEEE RTS-24 system modified and a real-life case study of the Lombok energy system in Indonesia. Results from the simulated Lombok power system highlighted that optimal sizing and placement of the BESS could lower system costs by 37.66%, 33.63%, and 22.26% compared to the current system conditions during the weekday, weekend, and the lowest day scenarios, respectively. The VRE penetrations of the generators were also higher than the current system conditions by 83%, 51%, and 39% during the weekday, weekend, and the lowest day scenarios, respectively.

Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties

As the demand for battery-powered and hydrogen-based electric vehicles continues to rise, the need for integrated charging and refueling systems with energy systems becomes more critical. Therefore, it is crucial to consider the stations serving diverse vehicle users during the system design process. This paper presents an optimal sizing strategy for a multi-energy system that covers clean energy sources and two energy storage technologies under the presence of a fast-charging station and a hydrogen refueling station for various types of car users. For the considered system, the optimal sizing problem aims to minimize the loss of power supply probability (LPSP) and annualized total life cycle cost (TLCC) through a two-stage stochastic programming-based multi-objective optimization approach. Using a scenario-based approach, the study considers the uncertainties associated with renewables, homeowner’s vehicle demands, and energy demands from homes and stations. Different cases are produced to evaluate the impacts of the different stations with the availability of various electric vehicle profiles under different levels of reliability. The TLCC decreases if LPSP increases in each case. Also, the cost of installing a fast charging station is significantly lower than that of a hydrogen refueling station for all LPSP limits. Moreover, the overall cost of the system design is at its lowest when homeowners’ vehicles provide vehicle-to-grid services. It is worth noting that when the station loads which come from the customers’ vehicle increase by 10%, the TLCC increases by 1.19%. Additionally, the revenue from hydrogen refueling station is approximately 70% less than the fast charging station.

Conventional sintering of nano-crystalline Yttria-Stabilized Zirconia enables high-strength, highly translucent and opalescent dental ceramics

Development of restorative materials capable of mimicking optical and mechanical performance of natural teeth is a quest in aesthetic density. Yttria-Stabilized Zirconia (YSZ) ceramics represent one of the most popular choices for dental restorations, owing to their biocompatibility, white colour, and the possibility to use CAD-CAM technologies. In particular, YSZ doped with 3 mol. % yttria (3YSZ) is popular because it presents high strength. Nonetheless, the limited light transmission of commercially available high strength 3YSZ does not meet the requirements of highly aesthetic cases. On the other side, YSZ presenting a larger portion of yttria are more translucent but exhibit modest strength. Here, we report on fabrication of dense zirconia nanostructures in bulk form via conventional pressure-less sintering at temperatures down to 1100–1200 °C, achieving highly translucent and strong 3YSZ with significant opalescent behaviour. Both Hall−Petch and inverse Hall-Petch relationship were observed in 3YSZ samples with average grain size in the range of 250 nm and 55 nm, demonstrating the importance of grain size control to enhance both optical and mechanical properties of zirconia ceramics, simultaneously. Maximum biaxial strength of 1980 ± 260 MPa, in-line light transmission of 38% in the visible spectrum and opalescence approaching that of enamel were obtained at optimum grain size of 80 ± 5 nm. The notable optical properties are linked to the miniaturization of the residual pores and refinement of grain size towards the nanoscale while the superior mechanical strength is justified by the activation of different energy dissipation processes at nano and macroscale.

Gas Engine CCHP System Optimization: An Energy, Exergy, Economic, and Environment Analysis and Optimization based on Developed Northern Goshawk Optimization Algorithm

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Scaling photonic integrated circuits with InP technology: A perspective

The number of photonic components integrated into the same circuit is approaching one million, but so far, this has been without the large-scale integration of active components: lasers, amplifiers, and high-speed modulators. Emerging applications in communication, sensing, and computing sectors will benefit from the functionality gained with high-density active–passive integration. Indium phosphide offers the richest possible combinations of active components, but in the past decade, their pace of integration scaling has not kept up with passive components realized in silicon. In this work, we offer a perspective for functional scaling of photonic integrated circuits with actives and passives on InP platforms, in the axes of component miniaturization, areal optimization, and wafer size scaling.