Optimization Technology Research Articles

Optimizing talent management in creative industries: Theoretical insights into effective database utilization

This review paper explores the optimization of talent management in creative industries through the utilization of advanced database systems. Traditional talent management methods often lack the structure and objectivity provided by modern database-driven approaches, which leverage comprehensive talent profiles, advanced search algorithms, and robust analytics to enhance decision-making processes. Case studies from industry leaders like Pixar, Wieden+Kennedy, and Ubisoft illustrate the practical benefits of these technologies in recruitment, development, and overall talent optimization. The paper also discusses challenges such as data quality, privacy concerns, and resistance to change, providing practical recommendations for overcoming these obstacles. Creative industries can significantly improve their talent management practices by fostering a culture of data-driven decision-making and investing in comprehensive talent databases. Future research directions include the impact of AI and machine learning, integration of emerging technologies, and longitudinal studies on the effectiveness of database-driven approaches.

Open Innovation and Regulatory Challenges in New Modality Development: The Pivotal Role of Contract Development and Manufacturing Organisations in Advancing Antibody Drugs.

Ensuring regulatory-compliant manufacturing capability is an essential challenge for new treatment modalities, but its internalisation is not easy for pharmaceutical companies, especially start-ups. This study examines the functions and requirements of contracted development and manufacturing organisations (CDMOs) using the development process of antibody medicines as a case study. Utilizing PubMed, Cortellis and Patent Integration databases, this study delves into publication and contractual trends in monoclonal antibody drugs (mAbs) development, alongside an analysis of patent filings by CDMOs, offering a comprehensive overview of the evolving landscape in mAbs innovation. In the early stages of mAbs development, dedicated bio firms (DBFs) led R&D with superior drug discovery technology but lacked manufacturing capability, which was complemented by CDMOs. This collaboration was an opportunity for CDMOs to expand their capabilities beyond manufacturing technology into antibody drug candidate discovery and structural optimisation technology. From mid-development onwards, it established a technology platform based on these capabilities and developed and established partnerships with existing pharmaceutical companies, including mega pharma. The impact of institutions and regulations on the innovation process was assessed during this development process. These findings are expected to provide valuable insights into the innovation system for new modalities.

Special Issue on Robotics and Mechatronics Technology for Aerial Robots

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new applications. This special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

Promoting coordinated spatial governance of mega-cities in China via spatial organization of metropolitan areas

Super large-sized cities and very large-sized cities (“mega-cities” for short) have become the backbone of China's socio-economic development, but they also face outstanding challenges of "urban diseases" due to their high concentration. In comparison to their foreign counterparts, China's mega-cities possess unique characteristics such as rapid growth, high population density, and large numbers of residents. They naturally have the attributes of metropolitan areas due to an administrative management system of an urban system within city territory. Promoting coordinated spatial governance of mega-cities via spatial organization of metropolitan areas not only helps mega-cities better allocate resources at a larger scope and helps expedite the transformation of development patterns, but is also crucial for better implementing new-type urbanization strategies. In response to the issues in China’s previous research and practices related to mega-cities and metropolitan areas, leveraging the “Critical Technology for Spatial Optimization of Metropolitan Areas of City Clusters” of the national key R&D plan in the “14th Five-year Plan” period, we have employed a scientific approach that combines data and models to propose spatial governance strategies. These strategies encompass both the overall metropolitan area level and the cross-border regional level. Our primary focus lies in optimizing the spatial layout of the “three domains” – ecological space, production space, and living space, facilitating functional dispersal, enhancing spatial patterns, adjusting land use structures and spatial resource allocation, coordinating rail transit and the organization of functions along transit lines, and innovating service management for improvement of administrative efficiency. Through the implementation of these diverse measures, we aim to establish a comprehensive composite collaborative system.

A review on model predictive control strategies for AC motor drives

AbstractIn the past decade, model predictive control (MPC) has been widely used in AC motor drives. Although a series of improved MPC strategies have been proposed, researchers are still seeking more effective solutions for different application backgrounds and application requirements to achieve the goals such as algorithm simplification, weighting factor design, parameter robustness enhancement, current/torque ripple suppression, and so on. In this paper, aiming at the above problems, the MPC strategy and its optimisation technology for AC motor drive system are comprehensively reviewed. Firstly, this paper introduces the classical MPC technology with the permanent magnet synchronous motor drive system fed by two‐level voltage source inverter as the control object. Secondly, this paper introduces and discusses the existing MPC optimisation methods from the aspects of current/torque ripple suppression, algorithm simplification, weight factor design and parameter robustness enhancement. Finally, based on the current industrial development requirements for AC motor drive systems and the research status of MPC optimisation methods in the existing literature, the current challenges and future trends of MPC technology are discussed.

Investigation on Effects of the Laser‐Enhanced Contact Optimization Process With Ag Paste in a Boron Emitter for n‐TOPCon Solar Cell

ABSTRACTTOPCon solar cell with boron (B)‐doped emitters plays an important role in photovoltaic cell technology. However, a major challenge to further improving the metallization‐induced recombination and electrical contact of B‐doped emitters. Laser‐enhanced contact optimization (LECO) technology is one of ideal candidates for reducing the metallization recombination and contact resistivity. In this study, we investigate the influence of LECO technology using special Ag paste with a decreased Pb content on the performance of the metallization‐induced recombination (J0,metal), contact resistivity (ρc), microtopography of the contact, the I–V parameters, and possible conductive mechanisms. The results showed that the linear resistivity is reduced from 3.56 to 2.60 μΩ·cm owing to special Ag paste, and after LECO treatment, it also has lower ρc about 0.91 mohm·cm2. Both of them have a large contribution to the FF enhancement. Meanwhile, the J0,metal drops from 500 to 200 fA/cm2, which provides a great contribution to the improvement in open‐circuit voltage. The efficiency improved by 0.26% absolute to 25.94%, mainly because of the increased open‐circuit voltage (Voc) of 4 mV and a fill factor (FF) of 0.26%. Simulated by COMSOL, the electron concentration rises to 4 × 1019 cm−3 after LECO treatment, which can generate a larger reverse current to provide a melting temperature for the glass frit, increasing the interface glass phase conductivity. The possible current transport mechanism of LECO is current tunneling effect, resulting in the decrease in the metallization recombination. After the optimization of the LECO process with low‐corrosion paste, we manufactured industrial‐grade TOPCon cells with Eff, Voc, Jsc, and FF values as high as 26.5%, 736 mV, 42.1 mA/cm2, and 85.5%, respectively.

Cold Coastal City Neighborhood Morphology Design Method Based on Multi-Objective Optimization Simulation Analysis

In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities.

An artificial intelligence based self-adaptive dynamic process control system for enhancing in-situ bioremediation of benzene-contaminated groundwater – Part I methods

This study develops a new AI-based self-adaptive dynamic process control (SADPC) system based on stepwise inference combined with genetic algorithm optimization technologies, including a filtered-clustering inference prediction model (FCI simulator), a stepwise inference controller (SI emulator), a model predictive control controller, and two optimizers. This system effectively reflects the dynamics and complexity of the bioremediation process and controls the remediation system based on the feedback information. To achieve this goal, a statistical model for simulating the enhanced bioremediation process through the FCI simulator is proposed, which can predict the resulting contamination situation based on the previous contamination situation and pumping/injecting flow rate. Then a bridge is established through the SI emulator, which can generate a control action based on a given contamination situation. This suggested decision makers that guidelines and policies on remediation-oriented SADPC systems could be tentatively investigated, developed, and applied in the future effort.

Study on pressure fluctuation characteristics of the mixed-flow pump with the tandem misaligned blade structure

Abstract As supporting equipment for deep-sea oil and gas resource exploitation, how to improve the transmission performance and stability of the impeller of the mixed-flow pump (MFP) has become a key technical problem. This paper focuses on studying the impeller blade of the MFP. Based on the blade optimization technology, four different tandem misaligned blade (TMB) angle schemes of α=0°,15°,45°,75° are proposed. The SST k-ω turbulence model is used to simulate the different blade angles under the pure liquid condition. The pressure pulsation of the MFP and the blade load distribution are studied. This result show that the TMB structure increases the pressure fluctuation inside the MFP. The pressure coefficient inside the impeller exhibits a more consistent pattern at an angle of α=45° compared to other angles, and the internal flow field of the MFP exhibits most stable. From this analysis of blade load distribution, TMBs have a greater effect on the pressure surface of the front blade of the MFP. When α=15°, the best pressurization performance of the MFP. This study may serve as a benchmark for subsequent iterations of optimizing the MFP’s model and hydraulic design.

The correlative factors of enterprise culture and enterprise human resource based on data analysis and federated learning

Under the current big topic of enterprise management, how to cultivate, use, and maintain talents is the critical problem of modern human resource management. Based on big data mining, cloud computing, and other related model optimization technologies, this study explores the crucial significance of enterprise culture construction and enterprise human resource management in achieving the business objectives of the entire enterprise. The establishment of this model also means that the enterprise’s profit margin and business indicators have been improved and developed under a certain model. The case results demonstrate that the optimized competency model is significant to studying corporate culture management system behavior, policy analysis, and improving corporate human resource management policies.

Machine Learning-Driven GCC Loop Unrolling Optimization: Compiler Performance Enhancement Strategy Based on XGBoost

In contemporary compilers, the determination of the loop unrolling factor is traditionally based on manually crafted heuristic rules. This approach heavily relies on human intuition, which limits its ability to achieve optimized performance across diverse architectures and can sometimes even lead to performance declines. Additionally, developers face challenges in achieving cross-platform compatibility, often necessitating extensive redesign efforts. In response, this study introduces a method leveraging the XGBoost algorithm to predict the optimal loop unrolling factor for compiler optimization, thereby aiming to replace human thinking with machine learning methods and standardize development processes. Initially, the study gathers data on the loop unrolling factors as determined by profile guided optimization technology, analyzes program-specific loop feature vectors and employs cross-validation, including the Pearson correlation coefficient and feature importance ranking, to construct a dataset. Subsequent use of XGBoost to train this dataset models the decision-making process for selecting the most effective loop unrolling factor. The final step involves integrating XGBoost’s trained decision tree model into GCC to calculate the optimal loop unrolling factor during actual compilation. Empirical results on the RISC-V platform indicate that this new method, when tested against the SPEC CPU 2006 benchmark suite, offers up to 6.18% improvement in performance over the existing heuristic approach. It provides a new method for loop unrolling in compilers, and provides an innovative guide for the application of machine learning in compilers.

Aluminum demand and low carbon development scenarios for major countries by 2050

Aluminum is a widely used energy-intensive material and the second largest source of carbon emissions from metal after steel. Global demand for aluminum has grown rapidly since 1970 and has shown a high correlation with population and economic growth. These factors have posed challenges to carbon reduction of aluminum industry. Therefore, it is crucial to investigate global aluminum demand and emissions to develop low carbon pathways. However, few studies have conducted a comprehensive analysis of the global aluminum industry. We develop different scenarios of joint abatement measures to explore pathways for the aluminum industry to significantly reduce carbon emissions by 2050. Three scenarios are considered, namely, moderate mitigation scenario (MMS), enhanced mitigation scenario (EMS), and deep mitigation scenario (DMS). The results show that: 1) Aluminum industry has a high potential for carbon emission reduction. The emissions in the three scenarios will be reduced by 33%, 77% and 85% by 2050 compared to 2020, respectively. 2) The carbon emission reduction effect of energy structure optimization and low carbon technology application is significant, with the use of clean energy playing a decisive role. 3) There is an inverse U-shaped curve relationship between per capita primary aluminum demand and per capita GDP. The aluminum demand in China and India is still growing at a high rate, and carbon emissions are always at the top. This study will effectively support the development of a low-carbon transition path for the global aluminum industry.

High performance rapid single-flux-quantum bit-slice arithmetic logic unit

Two optimization technologies, namely, bypass and carry-control optimization, were demonstrated for enhancing the performance of a bit-slice Arithmetic Logic Unit (ALU) in 2n-bit Rapid Single-Flux-Quantum (RSFQ) microprocessors. These technologies can not only shorten the calculation time but also solve data hazards. Among them, the proposed bypass technology is applicable to any 2n-bit ALU, whether it is bit-serial, bit-slice or bit-parallel. The high performance bit-slice ALU was implemented using the 6 kA/cm2 Nb/AlOx/Nb junction fabrication process from Superconducting Electronics Facility of Shanghai Institute of Microsystem and Information Technology. It consists of 1693 Josephson junctions with an area of 2.46 × 0.81 mm2. All ALU operations of the MIPS32 instruction set are implemented, including two extended instructions, i.e., addition with carry (ADDC) and subtraction with borrow (SUBB). All the ALU operations were successfully obtained in SFQ testing based on OCTOPUX and the measured DC bias current margin can reach 86% - 104%. The ALU achieves a 100% utilization rate, regardless of carry/borrow read-after-write correlations between instructions.

Integration of induction, system optimization and genetic transformation in Veratrum californicum var. vitro cultures to enhance the production of cyclopamine and veratramine

Cyclopamine, a compound found in wild Veratrum has shown promising potential as a lead anti-cancer drug by effectively blocking cancer signaling pathways. However, its complex chemical structure poses challenges for artificial synthesis, thus limiting its supply and downstream drug production. This study comprehensively utilizes induction, system optimization, and transgenic technologies to establish an efficient suspension culture system for the high-yield production of cyclopamine and its precursor, veratramine. Experimental results demonstrate that methyl jasmonate (MeJA) effectively promotes the content of veratramine and cyclopamine in Veratrum californicum var. callus tissue, while yeast extract (YE) addition significantly increases cell biomass. The total content of veratramine and cyclopamine reached 0.0638 mg after synergistic treatment of suspension system with these two elicitors. And the content of the two substances was further increased to 0.0827 mg after the optimization by response surface methodology. Subsequently, a genetic transformation system for V. californicum callus was established and a crucial enzyme gene VnOSC1, involved in the steroidal alkaloid biosynthesis pathway, was screened and identified for genetic transformation. Combined suspension culture and synergistic induction system, the total content of the two substances in transgenic suspension system was further increased to 0.1228 mg, representing a 276.69% improvement compared to the initial culture system. This study proposes a complete and effective genetic transformation and cultivation scheme for V. californicum tissue cells, achieving milligram-level production of the anticancer agent cyclopamine and its direct precursor veratramine for the first time. It provides a theoretical basis for the industrial-scale production of these substances.

From Sequence to Solution: Intelligent Learning Engine Optimization in Drug Discovery and Protein Analysis.

This study introduces the intelligent learning engine (ILE) optimization technology, a novel approach designed to revolutionize screening processes in bioinformatics, cheminformatics, and a range of other scientific fields. By focusing on the efficient and precise identification of candidates with desirable characteristics, the ILE technology marks a significant leap forward in addressing the complexities of candidate selection in drug discovery, protein classification, and beyond. The study's primary objective is to address the challenges associated with optimizing screening processes to efficiently select candidates across various fields, including drug discovery and protein classification. The methodology employed involves a detailed algorithmic process that includes dataset preparation, encoding of protein sequences, sensor nucleation, and optimization, culminating in the empirical evaluation of molecular activity indexing, homology-based modeling, and classification of proteins such as G-protein-coupled receptors. This process showcases the method's success in multiple sequence alignment, protein identification, and classification. Key results demonstrate the ILE's superior accuracy in protein classification and virtual high-throughput screening, with a notable breakthrough in drug development for assessing drug-induced long QT syndrome risks through hERG potassium channel interaction analysis. The technology showcased exceptional results in the formulation and evaluation of novel cancer drug candidates, highlighting its potential for significant advancements in pharmaceutical innovations. The findings underline the ILE optimization technology as a transformative tool in screening processes due to its proven effectiveness and broad applicability across various domains. This breakthrough contributes substantially to the fields of systems optimization and holds promise for diverse applications, enhancing the process of selecting candidate molecules with target properties and advancing drug discovery, protein classification, and modeling.

Connected vehicles ecological driving based on deep reinforce learning: Application of Web 3.0 technologies in traffic optimization

With the fast development of Web3.0 technology, connected vehicles can now handle and communicate data more safely and effectively. When combined with 5G/6G communication technology, these vehicles can optimize emissions in transportation networks to a greater extent. This study proposed an ecologically car-following model from natural driving data by using deep reinforcement learning under the context of the rapid development of Web 3.0 technologies. Firstly, by utilizing naturalistic driving data, an environment for connected vehicle car-following is created. Secondly, this paper uses SAC (Soft Actor-Critic) deep reinforcement learning algorithm and designs novel reward function based on ecological driving principles and car-following characteristics to reduce fuel consumption and emissions while maintaining safe distance with leading vehicle. Subsequently, the established model is tested, and results indicate that model not only performs well in terms of collision occurrences, Time-to-Collision (TTC), and driving comfort on test set but also achieves reduction of 5.50% in fuel consumption and reductions of 15.04%, 5.63%, and 9.60% in pollutant emissions (NOx, CO, and HC) compared to naturalistic manually driven vehicles.

Power quality optimization technology for photovoltaic nanoscale electronic grid-connected systems based on FNN algorithm

With the continuous maturity and widespread application of photovoltaic power generation technology, the power quality of photovoltaic grid-connected systems has become a significant factor affecting the power grid’s stability and the lifespan of equipment. This research aims to improve the power quality in photovoltaic nanoscale electronic grid-connected systems. Through in-depth analysis of harmonic problems in grid-connected systems, a harmonic prediction and governance means using a fuzzy neural network algorithm was put forward. To improve the accuracy and adaptability of predictions, the long short-term memory network was combined with a fuzzy neural network to form an improved deep learning model. Comparative experiments confirmed that the research prediction model exhibited advantages in training convergence, prediction accuracy, and various error evaluation indicators, with an accuracy of up to 96.24% and an R2 value as high as 0.9998. It effectively reduced harmonic content by 2.4% and significantly improved the harmonic compensation effect. Based on the above-mentioned model, dynamic compensation governance for harmonics was implemented. By predicting the harmonic content in grid-connected electricity in real-time and taking corresponding measures for compensation, the adverse effects of harmonics on the entire grid-connected system were effectively reduced. The deep learning-based power quality optimization technology developed in this research can accurately predict harmonic problems in photovoltaic nanoscale electronic grid-connected systems. Moreover, it can provide efficient compensation governance solutions and help improve the entire power grid’s energy efficiency and operational stability.

Q.ANTUM NEO with LECO Exceeding 25.5 % cell Efficiency

In recent years, we have developed the Q.ANTUM NEO solar cell, a rear-passivated double-sided contact solar cell that achieves power conversion efficiencies exceeding 25.5 %. Our streamlined and cost-efficient process leverages the proprietary Laser Enhanced Contact Optimization (LECO) technology. The LECO process has undergone extensive optimization and has been successfully implemented in both our pilot line and mass production facilities, resulting in significant improvements in solar cell efficiency. LECO facilitates a novel formation of the contact interface between the silicon surface and metallization, leading to remarkably low j0,met values (as low as 160 fA cm−2). The contact resistivity of 20 μm wide contacts on a high sheet resistance emitter is below 1 mΩ cm2, with minimal impact on fill factor (FF) and overall cell efficiency. Rigorous reliability tests, including TC600 and DH3000, demonstrate module degradation well below 5%rel. Our champion cell conversion efficiency achieved in the pilot line using LECO technology reaches 25.6 %, accompanied by a notable open-circuit voltage exceeding 730 mV.

A Review on Application of Pin-Fins in Enhancing Heat Transfer

The pin-fin is one of the main technologies in enhancing heat transfer. The accelerated flow and vortex structures are produced, which can disrupt the development of the flow boundary layer. The configuration of the pin-fin is obvious for heat transfer and flow characteristics, including its shape, size, and arrangement in the cooling channel. This work provides a detailed introduction to the application of pin-fins in enhancing heat transfer and reducing flow resistance, including the conventional shapes, improved shapes based on circular pin-fins and irregular shapes. At the same time, the influence of the diameter, height and density of pin-fins on heat transfer and flow performance is studied, and the influence mechanism is analyzed from the perspective of boundary layers. In addition, some applications that combine pin-fins with other cooling methods to further improve performance are analyzed. In terms of the optimization technology, the structure optimization for pin-fin shape and the layout optimization for pin-fin array are summarized. Therefore, this review provides a wide range of literature for the design of internal cooling channel pin-fins.

Performance analysis and multi-objective optimization of a novel solid oxide fuel cell-based poly-generation and condensation dehumidification system

The application of energy supply systems based on fuel cells contributes to meeting the requirement of sustainable development, and developing high-efficiency, low-pollution, and multifunction poly-generation systems coupled with fuel cells is increasingly attractive. This study proposes a novel combined cooling, heating, power poly-generation and dehumidification system including a solid oxide fuel cell, a gas turbine, a double effect absorption refrigerator, a condensation dehumidification unit, an organic Rankine cycle, and a heat exchanger. Thermodynamic, economic, and environmental analysis models of the entire system are developed to evaluate system comprehensive performance. The effects of system parameters such as the inlet temperature of fuel cell, steam to carbon ratio, air flow rate, and high temperature generator temperature on system performance are analyzed. The analysis findings reveal that the proposed system can provide power, cooling, and heating of 551.904 kW, 20.307 kW, and 193.009 kW, and the dehumidification capacity per unit is 14.516 g/kg when 1.238 kg/s of humid air is treated. The system exergy efficiency is found to be 70.534 %, and the system cost rate is 15.578 $/h with carbon dioxide emission being 0.301 kg/kWh at the design condition. Two groups of multi-objective optimizations of the whole system are further conducted to acquire the optimal system performance and working conditions. The optimization results demonstrate that the optimal exergy efficiency of 72.912 % appears at point C1 in the first group of optimization with the lowest carbon dioxide emission being 0.293 kg/kWh, and the optimal qualities of humid air are found to be 1.606 kg/s at C2 and D2 in the second group of optimization. After optimization, the system carbon dioxide emission is decreased by 0.008 kg/kWh, the exergy efficiency is increased by 2.378 %, and the quality of humid air in dehumidification process is developed by 0.368 kg/s. In summary, the proposed poly-generation and dehumidification system can meet multifunction energy demands and achieve good performance, and the research work could also provide theoretical basis for developing evaluation and optimization technologies of fuel cell-based poly-generation systems.