Comprehensive Performance Assessment Research Articles

Thriving through innovation: Boosting green tech performance in China's new energy sector

In response to the challenges of sustainable development, green technological innovation (GTI) is considered as a key driver of high-quality growth. However, current research often lacks a comprehensive assessment of GTI's multidimensional performance and rarely explores improvement pathways from a structural perspective. This study evaluates the GTI performance of China's new energy sector using data from 62 listed companies between 2015 and 2021, employing a Back Propagation (BP) neural network model optimized by a genetic algorithm. Additionally, it explores performance enhancement paths through fuzzy set Qualitative Comparative Analysis (fsQCA). The findings indicate that: Industrial structure and human resource levels significantly boost GTI performance in the new energy sector; Optimizing and upgrading industrial structures plays a crucial role in GTI improvement; High Research and development (R&D) investment and human capital strengthen market competitiveness, leading to higher GTI levels; The development of GTI in the new energy sector exhibited a general upward trend, with notable differences across sub-industries and regions from 2015 to 2021. Based on these results, this study recommends differentiated industry support, targeted regional policies, and enhanced talent cultivation to improve GTI performance in China's emerging energy industry.

The informational relevance of social accounting for the management of financial institutions. A case study: Banca Transilvania Financial Group

This paper analyzes the relevance of social accounting information for managing financial institutions, using Banca Transilvania Financial Group (BTFG) as a case study. It explores how social accounting data can enhance decision-making processes within these institutions. Social information from BTFG’s annual integrated reports was used to construct a social balance sheet, and financial data was collected to calculate economic value added (EVA) and social value added (SVA). Research question include: Does social accounting represent a lever for substantiating the managerial decision in financial institutions? Results show that SVA is a valuable indicator for financial institution managers, reflecting the institution’s contributions to social well-being, environmental impact, and community support. Policy implications suggest regulatory bodies should mandate the inclusion of social accounting metrics in financial reporting standards to encourage socially responsible practices, enhance transparency, and incentivize institutions achieving high SVA. This paper contributes to the literature by demonstrating the practical application of social accounting in financial institutions and highlighting the importance of SVA as a managerial tool. It aligns with existing research on integrating corporate social responsibility (CSR) metrics into financial decision-making, enhancing the understanding of combining social and economic indicators for comprehensive performance assessment The abstract covers motivation, methodology, results, policy implications, and contributions to the literature.

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity

Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.

Enhanced Photocatalytic Activity in Photocatalytic Concrete: Synthesis, Characterization, and Comprehensive Performance Assessment of Nano-TiO2-Modified Recycled Aggregates

This study presents an exhaustive exploration into the development and rigorous evaluation of nano-TiO2-modified recycled aggregates (NT@RAs) as an environmentally sustainable substitute for natural aggregates in concrete applications. A methodical framework was devised for the synthesis and thorough characterization of NT@RAs, emphasizing the optimization of nano-TiO2 loading onto the RA surface and within its intricate porous structure. The investigation encompassed three distinct types of recycled aggregates: recycled glass sands (RGSs), recycled clay brick sands (RCBSs), and recycled concrete sands (RCSs). Of particular interest, NT@RGS, with its properties of an inherently smooth surface texture and low water absorption, was found to exert a favorable influence on the rheological behavior of concrete, manifested in reduced yield stress, thereby underscoring the potential for fine-tuning mix designs to enhance workability. As the substitution levels of NT@RGS and NT@RCBS escalated, an initial decrement in compressive strength was discernible, which subsequently reversed to strength restoration at optimized substitution ratios. This phenomenon is attributed to the synergistic interplay among NT@RA components. Remarkably, NT@RA-incorporated concrete demonstrated unparalleled self-cleaning abilities, surpassing the performance of concrete with direct nano-TiO2 powder incorporation. This comprehensive research contributes significantly to the advancement in sustainable, high-performance photocatalytic construction materials within the realm of concrete technology. It underscores the potential for enhancing not only the rheological and mechanical properties but also the environmental responsiveness of concrete through the innovative utilization of NT@RAs.

Optimal results through enhanced recovery: Achieving textbook outcomes with high compliance in elective liver surgery.

Existing studies suggest a positive correlation between high compliance with enhanced recovery programs (ERP) and improved outcomes. While individual outcome measures have advantages, composite benchmarks, such as textbook outcome (TO), offer a more comprehensive assessment of healthcare performance. Given the link between ERP and postoperative outcomes, this study aims to investigate the impact of ERP on TO attainment after liver surgery (LS). A prospective multicenter cohort of patients undergoing LS and exposed to ERP from 2016 to 2022 in France was analyzed. The primary outcome was to compare the rates of TO achieved between patients with high ERP compliance (>70%) and those with low ERP compliance (<70%) after LS. A total of 706 patients were included in the study, and 217 (30.7%) achieved TO: 170 patients with high ERP compliance (24%) versus 47 patients (6.6%) with low ERP compliance attained TO (p<0.001). High ERP compliance was associated to an increased likelihood of achieving TO [odds ratio (OR)=1.49 (95% CI: 1.01, 2.24); p=0.049], while cholangiocarcinoma [OR=0.11 (95% CI: 0.02, 0.39); p=0.003], high complexity LS [OR=0.22 (95% CI: 0.13, 0.36); p<0.001], intraoperative hypotension requiring vasopressors [OR=0.29 (95% CI: 0.10, 0.68); p=0.010], and post-operative ileus [OR=0.08 (95% CI: 0.00, 0.37); p=0.013] were negatively associated to the likelihood of achieving TO. Patients with high ERP compliance after LS experience elevated rates of TO, compared to those with low ERP compliance.

Comprehensive performance assessment of photovoltaic/thermal system using MWCNT/water nanofluid and novel finned multi-block nano-enhanced phase change material-based thermal collector: Energy, exergy, economic, and environmental (4E) perspectives

Comprehensive performance assessment of photovoltaic/thermal system using MWCNT/water nanofluid and novel finned multi-block nano-enhanced phase change material-based thermal collector: Energy, exergy, economic, and environmental (4E) perspectives

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study’s findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making.

Assessing the physiological properties of baker's yeast based on single-cell Raman spectrum technology

With rapid progress in the yeast fermentation industry, a comprehensive commercial yeast quality assessment approach integrating efficiency, accuracy, sensitivity, and cost-effectiveness is required. In this study, a new yeast quality assessment method based on single-cell Raman technology was developed and contrasted with traditional methods. The findings demonstrated significant associations (Pearson correlation coefficient of 0.933 on average) between the two methods in measuring physiological indicators, including cell viability and intracellular trehalose content, demonstrating the credibility of the Raman method compared to the traditional method. Furthermore, the sensitivity of the Raman method in viable but non-culturable cells was higher in measuring yeast cell viability (17.9 % more sensitive). According to the accurate quantitative analysis of metabolic activity level (MAL) of yeast cells, the cell vitality was accurately quantified at population and single-cell levels, offering a more comprehensive assessment of yeast fermentation performance. Overall, the single-cell Raman method integrates credibility, feasibility, accuracy, and sensitivity in yeast quality assessment, offering a new technological framework for quality assessments of live-cell yeast products.

Cost-benefit analysis in UK hotels: A hybrid SOCP-MCDM approach

Performance evaluation has been an important topic of concern for tourism industry practitioners as well as academic researchers, and its investigation in the UK hotel sector is paramount because this industry has been experiencing a higher level of competition. The present study contributes to the previous literature on hotel performance evaluation in general by proposing an innovative hybrid method combining the second-order cone programming (SOCP) method and multi-criteria decision-making (MCDM) method to estimate the performance of the UK hotel sector. The innovation lies in the synergistic combination of SOCP and MCDM methodologies, enabling a comprehensive assessment of hotel performance by managing a non-linear optimisation. Overall, this hybrid method benefits from the ability to be more flexible in addressing complex operational issues and provide more accurate results. This research provides a cost-benefit analysis within the proposed method, suggesting important policy implications in the tourism industry.

Research of real-time corn yield monitoring system with DNN-based prediction model.

The real-time monitoring of corn yield by a combine harvester is a critical data source for constructing the yield histogram, which significantly benefits precision management and decision-making in modern precision agriculture. While widely used, the current photoelectric sensor-based yield monitoring method has limitations. It detects the corn height on each scraper and calculates the yield through a geometric formula. However, it neglects the noticeable difference in the corn stacking patterns affected by factors such as feeding volume, terrain, and driving speed. This oversight often results in low accuracy and poor stability in the prediction of corn yield, highlighting the need for a more advanced approach. To resolve this, we employ EDEM discrete element simulation to demonstrate the large difference of corn stacking patterns on the scraper of the elevator corresponding to feeding volume. Then, we develop a real-time monitoring system on our self-developed double elevator testing rig for carrying out a composite dataset for training three machine learning algorithm-based models, namely Deep Neural Networks (DNN), Gradient Boosting Machine (GBM), and Random Forest (RF). Importantly, these models have undergone rigorous validation under various feeding volumes, ensuring their robustness and reliability. The auxiliary elevator speed is meticulously set at 150r/min, 225r/min, and 450r/min, providing a comprehensive performance assessment. The results denote that the DNN model performs best and is stable, with a coefficient of determination (R2) of 0.998, root mean square error (RMSE) of 0.526, and mean absolute error (MAE) of 0.425. The paper also performs field experiments to test the proposed three prediction models and the system. The results also denote the DNN-based prediction model's best performance for the lowest relative error of 2.29% and the highest average accuracy of 97.85%. Consequently, the proposed real-time corn yield monitoring system achieves high accuracy and reliability for the combine harvester applications.

Enhancing solar thermoelectric power generation with supercritical CO2 cooling: Hydraulic, thermal, and exergy analysis

This research investigates the dynamic behavior and impact of various factors on the hydraulic, thermal, and exergetic characteristics of a solar-based thermoelectric device using a pin–fin heatsink cooled by supercritical CO2. A comprehensive numerical model analyzes the heat dissipation and performance of the power generator, integrating a thermoelectric generator and a pin–fin heatsink with various pin shapes. Key geometric and operational parameters, such as the height of PN (P-type and N-type semiconductor) legs of the TEG, the number of thermocouples, operating pressure, Reynolds number, and CO2 temperature, are examined for a comprehensive performance assessment. The study highlights the superior performance of CO2 coolant over traditional water-cooling system. Near the critical temperature of CO2, enhanced heat transfer significantly boosts power output, conversion efficiency, and exergetic efficiency. For example, at 8 MPa, the TEG’s (thermoelectric generator) power output increases from 1.31 mW at 295 K to 2.35 mW at 310 K. Comparisons reveal that while water coolant lowers the cold side temperature more effectively, it results in reduced power output due to decreased temperature differentials and increased pressure loss. Conversely, CO2 coolant maintains higher cold side temperatures while having advantages in power output. At 315 K, the cold side temperature with water is 315.7 K compared to 346.7 K with CO2. Increasing the number of thermocouples from 18 to 32 for a leg height of 1 mm leads to an approximate 102.3 % increase in voltage. Raising the PN leg height from 1 mm to 2 mm for an NTC of 50 results in a nearly 99.8 % increase in voltage. Lozenge-shaped fin produces a peak power output of 2.73 mW, while square fin generates 2.62 mW. This research underscores CO2’s potential as a high-performance coolant in solar thermoelectric applications, offering insights into optimizing system design for maximum efficiency.

Development of an emulator of the sustainable energy harvesting pad system on a bike lane for charging lithium batteries

In response to the urgent imperative of combating global warming and advancing sustainable energy solutions, an innovative approach has emerged, capitalizing on bicycles and road bike lane infrastructure. This solution integrates a Smart Lithium Battery Charging System with a Sustainable Energy Harvesting Pad (SEHP) designed for cyclists. The SEHP harnesses piezoelectric energy from mechanical vibrations and kinetic energy from lightweight vehicles. It produces clean, renewable electricity as an alternative to traditional power sources. Comprehensive assessments of the SEHP's energy generation performance at various proficiency levels have revealed impressive capabilities. An electronic emulator system is developed to support academic and research communities, simulating scenarios on bike lanes to efficiently charge 36.36 Wh lithium batteries at various cycling proficiency levels. The study involved specific circuit design, seamless integration with the custom Smart Lithium Battery Charging System, and optimization using Microcontroller hardware and software solutions. Practical prototypes verified the emulator's functionality and real-world applicability, making it an authentic replica of the SEHP's outcomes. This innovative technology enhances our understanding of SEHP and enables comparative analysis against other energy sources, contributing to a more sustainable future.

The Effect of Time Pressure on Motion Economy and Smoothness of Interventional Radiology Trainee Performance in Simulated Central Venous Line Placement.

To evaluate the effect of being under time pressure on procedural performance using hand motion analysis. Eight radiology trainees performed central venous access on a phantom while recording video and hand motion data using an electromagnetic motion tracker. Each trainee performed the procedure six times: the first three trials without any prompts (control), while for the next three, they were asked to perform the task as fast as possible (time pressure). Validated hand motion metrics were analyzed, and two blinded and independent evaluators rated procedural performance using a previously validated task-specific global rating scale (GRS). Motion/time ratios and linear mixed-effect methods were used to control for time, and constants for both strategies were compared. Hand motion analysis showed that trainees completed the simulated procedure faster under time pressure (46 ± 18s vs. 56 ± 27s, p = 0.008) than during the control strategy. However, when controlling for time, trainees moved their hands 79 more centimeters (p = 0.04), made 15 more translational movements (p = 0.003) and 18 more rotational movements (p = 0.01) when under time pressure compared to at their own pace. Although trainees could perform the procedure faster under time pressure, there was a deterioration in hand motion economy and smoothness. This suggests that hand motion metrics offer a more comprehensive assessment of technical performance than time alone.

Synthesis and Characterizations of Novel Copolymers with Zwitterionic Moiety-Based Solid Polymer Electrolytes for Solid-State Lithium Metal Battery

Electric vehicles (EVs) have become the dominant choice in the automotive industry, displacing internal combustion engine vehicles (ICEVs) to some extent. The development of high-efficiency batteries is essential to meet these market demands. Conventional lithium-ion batteries (LIBs), which consist of a cathode, anode, separator, and liquid electrolyte (such as 1M LiPF6 in carbonate solvents), have been widely utilized in portable electronic devices, hybrid electric vehicles (HEVs), and EVs. This popularity is attributed to their relatively high energy density and extended cycle life [1]. However, LIBs with energy density exceeding 240 Wh kg-1 face challenges in terms of thermal stability, potentially leading to fire and explosions by overcharging or accidental cell penetration. This drawback significantly hinders their application in automotive settings [2, 3]. In response to these safety concerns, there is a shift towards incorporating solid-state electrolytes (SSEs) as a replacement for liquid electrolytes. SSEs include solid polymer electrolyte (SPE), inorganic solid electrolyte (ISE), and composite polymer electrolyte (CPE).Solid-state lithium metal batteries (SSLMBs) incorporating SPE hold significant promise for advancing energy storage technologies due to their high energy density and improved safety features. However, the low ionic conductivity and ionic transference number of SPEs present challenges in their application for solid-state batteries. This work focuses on designing novel copolymers with polar soft unit and zwitterionic unit to address these challenges.In this study, poly(ethylene glycol) methyl ether acrylate (PEGMEA) and sulfobetaine methacrylate (SBMA) were selected as the polar soft segment and zwitterionic unit, respectively. Various ratios of PEGMEA/SBMA in copolymers were synthesized and characterized, and the resultant SPEs consisting of resultant copolymers and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) were prepared. Subsequently, their electrochemical, thermal, mechanical, and morphological properties were systematically investigated. The copolymers-based SPEs exhibited improved electrochemical properties. The optimized SPE comprising the copolymer with the molar ratio of PEGMEA/SBMA = 1/3 and 50 wt% LiTFSI exhibited the ionic conductivity of approx. 2×10–4 S cm–1, lithium-ion transference number of approx. 0.3, Li+ diffusion coefficient of 15×10-12 cm² s-1, and oxidation stability of 5.2 V (vs. Li/Li+) at 25 oC. Additionally, the mechanical properties of such SPEs were assessed, showing improved tensile strength (up to approx. 6 MPa) and Young’s modulus (up to approx. 83 MPa) with increasing SBMA content. The selected SPE with the best electrochemical properties was sandwiched between a Li metal electrode and a LiFePO4 electrode to assemble a SSLMB. As a result, the discharge capacity (DC) at 0.1 C-rate and room temperature was approx. 170 mA h g–1. Furthermore, the DC at a 0.5 C-rate was approx. 146 mA h g–1, and the capacity retention of 70% obtained after 470 cycles. In summary, this work demonstrates the potential of tailored copolymers with zwitterionic moiety-based SPEs for SSLMB. The comprehensive characterization and performance assessments provide valuable insights into the design and optimization of polymer electrolytes for next-generation energy storage systems.The copolymers and corresponding SPEs underwent characterization through various techniques such as DSC, SEM, XRD, and FTIR. The Ionic conductivity of the SPEs was determined by analyzing the results obtained from EIS measurement. The lithium-ion diffusion coefficient (DLi+) for SPE in a symmetric SSLMB cell, Li/SPE/Li, was also calculated based on the EIS results [4]. The oxidation stability window of the SPE was measured using the linear sweep voltammetry technique, and the ion transfer number was determined using the Evans-Vincent-Bruce method [5].

SAR images change detection using proposed lamina attention-based noise-resistant UNet advance network (LANRUNet++)

In the past few years, change detection techniques using Convolutional Neural Networks (CNNs) and conventional methods have garnered considerable attention in satellite image analysis. However, both categories of existing approaches have their respective shortcomings. For instance, methods based on CNNs tend to overlook the interactions amongst multilayer convolutions, while traditional methods may contain errors in the initial classification stage that limit the optimization of network. To address these limitations, we propose a new attentionbased, noise-resistant advanced UNet model named as Lamina Attention-Based Noise-Resistant UNet Advance Network (LANRUNet++)for detecting changes in satellite imagery. UNet++ is a unique neural network structure composed of an encoder and decoder, that are linked by a sequence of dense convolutional blocks nested within each other. A core principle underlying the proposed LANRUNet++ is to effectively bridge the semantic disparity between the encoder and decoder feature maps prior to their fusion. We present a lamina attention mechanism within UNet++, which dynamically assigns weights to features extracted from various convolutional layers. This facilitates improved incorporation of spatial context information. Moreover, we added a loss function that is robust to noise and can effectively minimize the influence of inaccurate annotations. As a result, our model becomes less susceptible to errors in initial classification outcomes. Furthermore, we added a Pixel to Density Ratio Parameter that further reduces noisy labels, improving the robustness of the model.We conducted a comprehensive performance assessment of LANRUNet++ on SAR images and conducted a comparative analysis against a range of cutting-edge techniques currently available. The results of our experiments demonstrate that our proposed approach surpasses other existing methods in its ability to detect changes, achieving a higher level of accuracy with various metrics.

Pentahomoserine functionalized graphene oxide decorated polyamide thin film for enhanced separation of green tea polyphenols

The isolation of valuable compounds from the byproducts of food processing industries presents a significant contemporary challenge. Thin film nanocomposite (TFN) membranes have been identified as potential media for separating bioactive compounds. In this work, pentahomoserine functionalized graphene oxide (FGO) based TFN membranes were prepared for the effective separation of tea polyphenols epigallocatechin (EGC) and epigallocatechin gallate (EGCG). The nanocomposite selective polyamide (PA) layer was prepared through the dispersion of FGO in an aqueous monomer solution. The prepared membranes possess high average surface roughness with improved hydrophilicity and thermal stability. Additionally, FGO membranes contain more hydroxyl or carboxyl groups with cross-linked PA layer through interfacial polymerization (IP) process. Moreover, FGO membranes featured increased hydroxyl or carboxyl groups, facilitating cross-linking with the PA layer via intermolecular forces. The effects of amine functionality on the membrane characteristics such as surface morphology, roughness and hydrophilicity were also investigated. Remarkably, the membranes demonstrated rejection efficiency of 96.3% towards polyphenols. Furthermore, comprehensive assessments of reusability performance of the membrane highlighted their potential for industrial applications. This study provides valuable insights into the fabrication of highly efficient TFN membranes for biomolecule separation across diverse technological domains.

Unlocking enhanced new type of safe open system lithium-ion battery performance: Mn3O4 nano-cubes and their sulfur mixers cathode material

In the pursuit of advancing lithium-ion (Li-ion) battery technology towards enhanced safety and performance, we present research study on the synthesis and evaluation of manganese oxide-based cathode materials. Different phases of manganese oxides (Mn3O4 and MnO2), alongside their different ratio of two manganese oxides phases and Mn3O4 with sulfur (S) combination, we employed a simple hydrothermal and solid-state grinding techniques. Characterized by field emission transmission electron microscopy (FE-TEM), our synthesized materials revealed distinct phases of MnO2 and Mn3O4 accompanied by nanorods (NRs) and nanocubes (NCs) morphologies, respectively. We conducted a comprehensive assessment of the Li-ion battery performance of these materials, initially without S, within a new type of safe open system framework. Notably, Mn3O4 NCs (MNCs) developed as important, exhibiting a maximum discharge capacity of 346.03 mAh/g at a constant current discharging rate of 0.5 A/g. We introduced 30 % of S into 70 % of the MNCs matrix to form the MNCSs-73 composite, resulting in rather moderate enhancement in capacity performance. The MNCSs-73 displayed an exposed discharge capacity of approximately 394.8 mAh/g under identical testing conditions. Our findings underscore the exceptional potential of MNCs and MNCSs-73 composites cathode materials for highly suitable for open system novel safe Li-ion battery applications and other energy storage research field.

Performance assessment of VSC-based HVDC system in asynchronous grid interconnection: Offline and real-time validation of control design with symmetric optimum PI tuning

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer.High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system.The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation.The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.

A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction

This study proposes a hybrid model (Conformer-LSTM) based on Conformer and Long Short-Term Memory networks (LSTM) to overcome the limitations of existing techniques and enhance the accuracy and generalizability of wave height predictions. The model combines the advantages of self-attention mechanisms and convolutional neural networks. It captures global dependencies through multi-head self-attention and utilizes convolutional layers to extract local features, thereby enhancing the model’s adaptability to dynamic changes in time series. The LSTM component handles long-term dependencies, optimizing the coherence and stability of predictions. Additionally, an adaptive feature fusion weight network is introduced to further improve the model’s recognition and utilization efficiency of key features. Experimental data come from the National Oceanic and Atmospheric Administration buoy data, covering wave height, wind speed, and other data from key maritime areas. Evaluation metrics include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), ensuring a comprehensive assessment of model performance. The results show that the Conformer-LSTM model outperforms traditional LSTM, CNN, and CNN-LSTM models at multiple sites, confirming its potential in wave height prediction.

A Performance and Data-Driven Method for Optimization of Traditional Courtyards

As urbanization and rapid industrialization accelerate, rural areas face increasing pressure on resources and the environment, leading to challenges such as energy waste and reduced comfort. Traditional village planning and design methods are based on economic benefits and often lack consideration of climate adaptability. To address these issues, a comprehensive assessment of building and courtyard performance should be introduced early in the planning of traditional villages. This approach can better adapt the buildings to their climatic conditions. Introducing relevant performance indicators, such as outdoor comfort, indoor lighting, and building energy consumption, at the initial design stage is crucial. This article employs performance-based multi-objective optimization algorithms and machine learning techniques to investigate the design workflow of courtyards and their combinations. The goal is to enhance planners’ design efficiency in village planning by integrating data-driven and performance-driven methods. The research results show that during the performance-driven phase, by adjusting the spatial morphology and architectural parameters, the performance of the courtyard significantly improved compared to the baseline model. Energy efficiency increased by 32.3%, the physiological equivalent temperature (PET) comfort time ratio in winter was enhanced by 8.3%, and the ratio in summer increased by 3.8%. During the data-driven phase, the classification prediction accuracy of courtyard performance can reach 83%, and the F1 score is 0.81. In the project validation phase, it has also been proven that the performance of different plans can be quickly verified. Compared to the base’s original status, the design solutions’ performance score can be improved from 59.12 to 85.62. In summary, this workflow improves the efficiency of the interaction between design decisions and performance evaluation in the conceptual stage of village planning, providing a solid foundation for promoting subsequent solutions.