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This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

The last decade created tremendous advances in new and unique thermoelectric generation materials, devices, fabrication techniques, and technologies via various global research and development. This article seeks to elucidate and highlight some of these advances to lay foundations for future research work and advances. New advanced methods and demonstrations in TE device and material measurement, materials fabrication and composition advances, and device design and fabrication will be discussed. Other articles in this Special Issue present additional new research into materials fabrication and composition advances, including multi-dimensional additive manufacturing and advanced silicon germanium technologies. This article will discuss the most recent results and findings in thermoelectric system economics, including highlighting and quantifying the interrelationships between thermoelectric (TE) material costs, TE manufacturing costs and most importantly, often times dominating, the heat exchanger costs in overall TE system costs. We now have a methodology for quantifying the competing TE system cost-performance effects and impacts. Recent findings show that heat exchanger costs usually dominate overall TE system cost-performance tradeoffs, and it is extremely difficult to escape this condition in TE system design. In regard to material performance, novel or improved enhancement principles are being effectively implemented. Furthermore, in addition to further advancements in properties and module developments of relatively established champion materials such as skutterudites, several high performance ZT ≈≥ 2 new material systems such as GeTe, Mg3(Sb,Bi)2 have also been relatively recently unearthed and module applications also being considered. These recent advancements will also be covered in this review.

In the era of ubiquitous high‐throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user‐friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error‐tolerant parsing of sequence records. In addition, three subcommands designed for real‐time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real‐time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a “Swiss army knife” of sequence and alignment processing—equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines. SeqKit2 has significantly expanded its capabilities, doubling the number of subcommands from 19 to 38, and adding support for three more compression file formats. This enhances its versatility and efficiency in sequence and alignment processing. Benchmarking results demonstrate that SeqKit2 consistently outperforms its predecessor and remains competitive with other similar tools. In addition, SeqKit2 has notably improved user‐friendliness with the introduction of new features, such as autocompletion for subcommands and command‐line flags and enhanced error‐handling mechanisms. Highlights SeqKit2 expands its capabilities, doubling the number of subcommands from 19 to 38, and adding support for three more compression file formats. SeqKit2 outperforms its predecessor and maintains competitive with other tools. SeqKit2 improves user‐friendliness with new features, like, autocompletion, progress bars, and enhanced error handling.

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

Solid oxide fuel cell (SOFC) performance degradation analysis and optimization studies are important prerequisites for its commercialization. Reviewing and summarizing SOFC performance degradation studies can help researchers identify research gaps and increase investment in weak areas. In this study, to help researchers purposely improve system performance, degradation mechanism analysis, degradation performance prediction, and degradation performance optimization studies are sorted out. In the review, it is found that the degradation mechanism analysis studies can help to improve the system structure. Degradation mechanism analysis studies can be performed at the stack level and system level, respectively. Degradation performance prediction can help to take measures to mitigate degradation in advance. The main tools of prediction study can be divided into model-based, data-based, electrochemical impedance spectroscopy-based, and image-based approaches. Degradation performance optimization can improve the system performance based on degradation mechanism analysis and performance prediction results. The optimization study focuses on two aspects of constitutive improvement and health controller design. However, the existing research is not yet complete. In-depth studies on performance degradation are still needed to achieve further SOFC commercialization. This paper summarizes mainstream research methods, as well as deficiencies that can provide partial theoretical guidance for SOFC performance enhancement.

Emissions from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been a major cause of climate change. Hybrid electric vehicles (HEVs) are a cleaner solution to reduce the emissions caused by transportation, and well-designed HEVs can also outperform conventional vehicles. This study examines various powertrain configurations and components to design a hybrid powertrain that can satisfy the performance criteria given by the EcoCAR Mobility Challenge competition. These criteria include acceleration, braking, driving range, fuel economy, and emissions. A total of five different designs were investigated using MATLAB/Simulink simulations to obtain the necessary performance metrics. Only one powertrain design was found to satisfy all the performance targets. This design is a P4 hybrid powertrain consisting of a 2.5 L engine from General Motors, a 150 kW electric motor with an electronic drive unit (EDU) from American Axle Manufacturing, and a 133 kW battery pack from Hybrid Design Services.

Laser beam powder bed fusion (LB-PBF) is a widely-used metal additive manufacturing process due to its high potential for fabrication flexibility and quality. Its process and performance optimization are key to improving product quality and promote further adoption of LB-PBF. In this article, the state-of-the-art machine learning (ML) applications for process and performance optimization in LB-PBF are reviewed. In these applications, ML is used to model the process-structure–property relationships in a data-driven way and optimize process parameters for high-quality fabrication. We review these applications in terms of their modeled relationships by ML (e.g., process—structure, process—property, or structure—property) and categorize the ML algorithms into interpretable ML, conventional ML, and deep ML according to interpretability and accuracy. This way may be particularly useful for practitioners as a comprehensive reference for selecting the ML algorithms according to the particular needs. It is observed that of the three types of ML above, conventional ML has been applied in process and performance optimization the most due to its balanced performance in terms of model accuracy and interpretability. To explore the power of ML in discovering new knowledge and insights, interpretation with additional steps is often needed for complex models arising from conventional ML and deep ML, such as model-agnostic methods or sensitivity analysis. In the future, enhancing the interpretability of ML, standardizing a systemic procedure for ML, and developing a collaborative platform to share data and findings will be critical to promote the integration of ML in LB-PBF applications on a large scale.

With a rising emphasis on public safety and quality of life, there is an urgent need to ensure optimal air quality, both indoors and outdoors. Detecting toxic gaseous compounds plays a pivotal role in shaping our sustainable future. This review aims to elucidate the advancements in smart wearable (nano)sensors for monitoring harmful gaseous pollutants, such as ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), hydrogen sulfide (H2S), sulfur dioxide (SO2), ozone (O3), hydrocarbons (CxHy), and hydrogen fluoride (HF). Differentiating this review from its predecessors, we shed light on the challenges faced in enhancing sensor performance and offer a deep dive into the evolution of sensing materials, wearable substrates, electrodes, and types of sensors. Noteworthy materials for robust detection systems encompass 2D nanostructures, carbon nanomaterials, conducting polymers, nanohybrids, and metal oxide semiconductors. A dedicated section dissects the significance of circuit integration, miniaturization, real-time sensing, repeatability, reusability, power efficiency, gas-sensitive material deposition, selectivity, sensitivity, stability, and response/recovery time, pinpointing gaps in the current knowledge and offering avenues for further research. To conclude, we provide insights and suggestions for the prospective trajectory of smart wearable nanosensors in addressing the extant challenges.

A decentralized optimization policy for service placement in fog computing is presented. The optimization is addressed to place most popular services as closer to the users as possible. The experimental validation is done in the iFogSim simulator and by comparing our algorithm with the simulator’s built-in policy. The simulation is characterized by modeling a microservice-based application for different experiment sizes. Results showed that our decentralized algorithm places most popular services closer to users, improving network usage and service latency of the most requested applications, at the expense of a latency increment for the less requested services and a greater number of service migrations.

Purpose The purpose of this paper is to improve autonomous flight performance of an unmanned aerial vehicle (UAV) having actively sweep angle morphing wing using simultaneous UAV and flight control system (FCS) design. Design/methodology/approach An UAV is remanufactured in the ISTE Unmanned Aerial Vehicle Laboratory. Its wing sweep angle can vary actively during flight. FCS parameters and wing sweep angle are simultaneously designed to optimize autonomous flight performance index using a stochastic optimization method called as simultaneous perturbation stochastic approximation (SPSA). Results obtained are applied for flight simulations. Findings Using simultaneous design process of an UAV having actively sweep angle morphing wing and FCS design, autonomous flight performance index is maximized. Research limitations/implications Authorization of Directorate General of Civil Aviation in Turkey is crucial for real-time UAV flights. Practical implications Simultaneous UAV having actively sweep angle morphing wing and FCS design process is so beneficial for recovering UAV autonomous flight performance index. Social implications Simultaneous UAV having actively sweep angle morphing wing and FCS design process achieves confidence, high autonomous performance index and simple service demands of UAV operators. Originality/value Composing a novel approach to improve autonomous flight performance index (e.g. less settling and rise time, less overshoot meanwhile trajectory tracking) of an UAV and creating an original procedure carrying out simultaneous UAV having actively sweep angle morphing wing and FCS design idea.