Optimal System Research Articles

Blood Pump Surface Roughness: Hemocompatibility and Machining Optimization.

Blood compatibility, defined as a material's ability to maintain blood flow without inducing coagulation or hemolysis, was investigated through surface roughness optimization in blood pump flow channels. This study examines how machining parameters (depth of cut, cutting speed, feed per tooth, and cutting width) affect surface roughness using orthogonal experiments, revealing their descending order of influence. Blood compatibility tests comparing cellular damage and adhesion across varying surface roughness levels demonstrated that rougher titanium alloy surfaces significantly increased hemolysis rates and promoted platelet adhesion, accelerating thrombus formation. Genetic algorithm optimization identified optimal parameters: 80 m/min cutting speed, 0.2 mm depth of cut, 1.25 mm cutting width, and 0.02 mm/tooth feed. These parameters minimize surface roughness while maintaining machining efficiency, crucially enhancing blood pump performance by reducing thrombogenic risks. The established evaluation system and parameter optimization methodology provide practical guidance for manufacturing blood-contacting medical devices with improved hemocompatibility.

CFD, energy, and exergy analysis and sustainability indicators of tilapia fish strips drying using an evacuated tubes indirect solar dryer.

This study evaluates the performance of an evacuated tube indirect solar dryer (ETISD) for drying tilapia strips at three thicknesses (4, 8, and 12mm) using computational fluid dynamics (CFD), energy-exergy analysis, and sustainability indicators. CFD simulations were employed to analyze airflow patterns, temperature distribution, and velocity profiles inside the drying room (DR) across five air velocities (0.02-0.06m/s). The optimal air flow rate of 0.03 m3/s provided a uniform drying temperature of 74.82°C, at solar noon. Simulations over two consecutive drying days (8 a.m.-5 p.m.) further assessed thermal and aerodynamic behavior, enhancing system optimization. Energy analysis revealed that the evacuated tube solar collector (ETSC) achieved a maximum input energy of 1311.8W and useful energy of 682.5W, with energy efficiencies of 44.5-51.2% (ETSC) and 16.18-21.57% (ETISD). Exergy efficiencies ranged from 8.51 to 21.99% (ETSC) and 29.23-84.76% (ETISD), highlighting thermodynamic performance. Sustainability indicators, including improvement potential (IP) (2.71-6.69W), waste exergy ratio (WER) (1.15-1.36), and sustainability index (SI) (1.09-1.28), demonstrated the system's environmental and operational viability. These findings underscore the ETISD's effectiveness for sustainable tilapia drying, balancing energy efficiency, thermal performance, and ecological impact.

Layout optimization of a dual-robot system for mirror milling of thin-walled parts

Layout optimization of a dual-robot system for mirror milling of thin-walled parts

Design and Electromagnetic Performance Optimization of a MEMS Miniature Outer-Rotor Permanent Magnet Motor

In this study, we present the design and electromagnetic performance optimization of a micro-electromechanical system (MEMS) miniature outer-rotor permanent magnet motor. With increased attention towards higher torque density and lower torque pulsations in MEMS micromotor designs, an adaptation of an external rotor can be highly attractive. However, with the design complexity involved in such high-performance MEMS outer-rotor motor designs, the ultra-miniature 3D coil structures and the thin-film topology surrounding the air gap have been one of the main challenges. In this study, an ultra-thin outer-rotor motor with 3D MEMS silicon-based coils and a MEMS-compatible manufacturing method for the 3D coils is presented. Additionally, finite element simulations are conducted for the thin-film topology around the air gap to optimize performance characteristics such as torque developed, torque pulsations, and back electromotive force amplitude. Ultimately, the average magnetic flux density increased by 37.1%, from 0.361 T to 0.495 T. The root mean square (RMS) value of the back EMF per phase rises by 14.4%. Notably, the average torque is improved by 11.3%, while the torque ripple is significantly reduced from 1.281 mNm to 0.74 mNm, corresponding to a reduction of 49.9% in torque ripple percentage.

Bi-level optimization of composite floor systems integrating fire resistance and vibration serviceability using multi-method computational intelligence process

Bi-level optimization of composite floor systems integrating fire resistance and vibration serviceability using multi-method computational intelligence process

Temperature Extensible Deep Potential Model for Molten NaF-BeF2-ZrF4: Predicting Transport Properties and Local Structure.

Molten NaF-BeF2-ZrF4 (FNaBeZr) has garnered significant interest as one of the potential nuclear fuel carrier salts for molten salt reactors. Here, deep potential molecular dynamic (DPMD) simulations by integrating first-principles calculation, machine learning, and molecular dynamic methods are employed to systematically investigate the transport properties and local structures of molten FNaBeZr across a broad temperature range. Such computational approaches will bypass resource-intensive trial-and-error experimentation and massive high-fidelity density functional theory (DFT) calculations, which represents that the DPMD framework achieves dual optimization by substantially reducing computational costs while enabling enhanced system scalability─a critical advancement for modeling complex coordination chemistry environments and deciphering coupled ionic transport phenomena at extended spatiotemporal scales. The trained deep potential model reproduces the densities, ionic self-diffusion coefficients, and pair/cluster structures from 773 to 973 K and successfully predicts these properties as well as the ionic conductivity and shear viscosity at extended temperatures (beyond the training range). Furthermore, the quantifiable correlations between structural features (e.g., first-peak height/position and coordination number) and temperature are established, and their latent connections to transport properties are also analyzed. The robust DPMD results demonstrate the temperature extensibility of the deep potential for molten FNaBeZr and provide critical technical parameters and engineering data for future simulations of molten fluorides and for the design optimization of pump systems and reprocessing infrastructure in high-temperature environments.

Integrated optimization of energy storage and green hydrogen systems for resilient and sustainable future power grids

Abstract This study presents a novel multi-objective optimization framework supporting nations sustainability 2030–2040 visions by enhancing renewable energy integration, green hydrogen production, and emission reduction. The framework evaluates a range of energy storage technologies, including battery, pumped hydro, compressed air energy storage, and hybrid configurations, under realistic system constraints using the IEEE 9-bus test system. Results show that without storage, renewable penetration is limited to 28.65% with 1538 tCO2/day emissions, whereas integrating pumped hydro with battery (PHB) enables 40% penetration, cuts emissions by 40.5%, and reduces total system cost to 570 k$/day (84% of the baseline cost). The framework’s scalability is confirmed via simulations on IEEE 30-, 39-, 57-, and 118-bus systems, with execution times ranging from 118.8 to 561.5 s using the HiGHS solver on a constrained Google Colab environment. These findings highlight PHB as the most cost-effective and sustainable storage solution for large-scale renewable integration.

Neighborhood evidence-driven artificial bee colony algorithm with application for optimal operation of wet flue gas desulfurization system

Artificial bee colony (ABC) algorithm is a widely used swarm intelligence algorithm due to its simple structure, good robustness and strong exploration ability. However, the unbalanced exploration and exploitation capabilities restrict its performance. To tackle this problem, a Neighborhood Evidence-Driven Artificial Bee Colony (NEDABC) algorithm is proposed in the framework of theory of belief functions, integrating information fusion with ABC algorithm. In the onlooker bee phase, instead of being guided by the current optimal solution or specific elite solutions, onlooker bees select their search directions by fusing the comprehensive evidence provided by food sources within their neighborhood. Both short-range solutions and high-quality solutions can have a great impact on the selection of search direction. With this constructive search strategy, onlooker bees will enhance the exploration and exploitation of their nearby potential areas. Under the joint action of the population, the potential optimal region will soon be found and exploited. The proposed algorithm is tested on a set of benchmark functions and compared with several variants of ABC. Experimental results reveal its powerful abilities in achieving higher accuracy and faster convergency. In addition, the NEDABC algorithm is applied to operation optimization of a wet flue gas desulfurization system, demonstrating the practicability and efficiency of the proposed algorithm in engineering applications.

Enhancing the acid rain resistance of recycled aggregate pervious concrete with reactive powder cementitious system

The pervious concrete prepared with recycled aggregate can be easily deteriorated by the erosion medium. This study aims to enhance the durability performance of recycled aggregate pervious concrete by using the reactive powder cementitious matrix. The cementitious matrix is obtained through simplex centroid optimization of Portland cement, fly ash and silica fume system. The accelerated aging test results indicate that utilized reactive powder cementitious matrix can enhance the acid rain resistance of pervious concrete. Meanwhile the deterioration on flexural strength of pervious concrete exposed in acid rain is severer than that of compressive strength. The permeability of pervious concrete first decreases and then increases with the exposure age in acid rain. The phase analysis and microstructure characterization results indicate that the deterioration of the pervious concrete in acid rain is mainly caused by the formation of gypsum erosion product. This study can contribute to the durability design of pervious concrete.

Exploration of current situation of psychotropic drugs research and development in China based on drug clinical trials

ObjectiveTo understand the current status of research and development (R&D) of psychotropic drugs.MethodsRetrieved psychotropic drugs clinical trials (PDCTs) registered in China from 2019 to 2024 using the platform of chinadrugtrials.org.cn, and systematically analyzed the data.ResultsIncluded four perspectives: 1) for general information, we screened 1377 PDCTs, with phase bioequivalency (BE) accounting for the majority (78.5%), covering 411 pharmaceutical companies and 212 leading institutions, and the start-up time in 2024 was significantly shortened (P < 0.05); 2) for indications, 11 indications were involved, with the highest number of PDCTs for depression (30.9%); 3) for drugs, 222 drugs were involved, of which 52 were innovative drugs (33 with disclosed targets), and 13 were improved new drugs with six administration routes; 4) for trial design, four exploratory designs were retrieved, including population pharmacokinetics (9), pharmacogenomics (12), biomarker detection (3), and drug combination (3).ConclusionsIn recent years, clinical trials of psychotropic drugs in China have been developing. Innovative targets discovery, dosage forms/drug delivery systems optimization, and exploratory designs have the potential to break the current treatment dilemma. This study introduced the hotspots and potential development directions of psychotropic drugs R&D in China from the above aspects, providing new ideas for psychiatric treatment, drug development, and clinical trial methods.

Analysis of the feasibility of using Invar process pipes in multichannel cryogenic helium transfer lines based on the second law of thermodynamics

This work describes examples of the use of cryogenic lines and their designs, referring in detail to typical structural nodes found in cryogenic transfer lines. As a special case, multichannel cryogenic transfer lines are described, in which the process pipes are made of Invar. This has a significant impact on the number of internal supports and the method of thermal shrinkage compensation, which directly impact into reduced heat input during the transfer of cryogenic media. The second law of thermodynamics and the Gouy-Stodola theorem are discussed from the perspective of their application in optimizing and evaluating heat and mass transfer devices. The next part of the work presents the internal structure of the selected 250 m multichannel cryogenic transfer line. Several variants of the method of supporting process pipes have been presented and compared with the solution using Invar. For each solution, an entropy analysis was carried out in order to select the best design in terms of the entropy generated in the process pipes. From the examples presented, it is proven that entropy minimization method can be used for complex optimization of entire cryogenic distribution systems, as well as their indi-vidual components.

A Study on the Influence Mechanism of the Oil Injection Distance on the Oil Film Distribution Characteristics of the Gear Meshing Zone

Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational fluid dynamics (CFD) and dynamic mesh technology. By implementing the volume of fluid (VOF) multiphase flow model and the standard k-ω turbulence model, the study simulates the dynamic distribution of lubricant in gear meshing zones and analyzes critical parameters such as the oil volume fraction, eddy viscosity, and turbulent kinetic energy. The results show that reducing the oil injection distance significantly enhances lubricant coverage and continuity: as the injection distance increases from 4.8 mm to 24 mm, the lubricant shifts from discrete droplets to a dense wedge-shaped film, mitigating lubrication failure risks from secondary atomization and energy loss. The optimized injection distance also improves the spatial stability of eddy viscosity and suppresses excessive dissipation of turbulent kinetic energy, enhancing both the shear-load capacity and thermal management. Dynamic data from monitoring point P show that reducing the injection distance stabilizes lubricant velocity and promotes more consistent oil film formation and heat transfer. Through multiphysics simulations and parametric analysis, this study elucidates the interaction between geometric parameters and hydrodynamic behaviors in jet lubrication systems. The findings provide quantitative evaluation methods for structural optimization and energy control in gear lubrication systems, offering theoretical insights for thermal management and reliability enhancement in high-speed transmission. These results contribute to the lightweight design and sustainable development of industrial equipment.

Optimization of Management Information System for Improving Hospital Performance

In the current era of digitalization, the implementation of a Hospital Management Information System (HMIS) has become an urgent necessity to improve service quality, accelerate administrative processes, and minimize errors in patient data management. Through an integrated system, hospitals can better coordinate human resources, logistics, and medical services. The optimization of HMIS does not solely focus on technological aspects but also includes improving human resource competencies, maintaining digital infrastructure, and enforcing internal policies that support digital transformation. One of the major challenges is the resistance of healthcare workers to the shift from manual to digital systems. Therefore, an adaptive and participatory managerial approach is needed to ensure that all hospital units accept and fully utilize information technology. An optimally implemented HMIS can have a positive impact on hospital operational efficiency, information transparency, and data-driven decision-making. Additionally, increased accuracy of medical data contributes to patient safety and performance evaluation of healthcare personnel. Thus, hospital management supported by a reliable information system becomes a vital foundation in delivering high-quality and sustainable healthcare services.

Tikhonov Regularization of Second-order plus First-order Primal-dual Dynamical Systems for Separable Convex Optimization

Tikhonov Regularization of Second-order plus First-order Primal-dual Dynamical Systems for Separable Convex Optimization

Mobile genetic elements mediating antimicrobial resistance drive the evolutionary process of Clostridioides difficile ST37/RT017

BackgroundClostridioides difficile (C. difficile) ST37/RT017 is one of the most prevalent genotypes, exhibiting resistance to multiple antimicrobial agents and widespread dissemination, particularly in East Asia. However, its evolutionary history and genetic adaptation remains limited. Here, we aimed to systematically assess the genetic diversity, key evolutionary events, and potential driving forces of C. difficile ST37/RT017.ResultsTo explored dynamic trends in the genomic characterization, diversity and changes, both phylogenetic and Bayesian evolutionary analyses revealed that the C. difficile ST37/RT017 strains were clustered into three variant lineages as a directed bus-like topology, from VL I, to VL II, and VL III. An incremental increase in the median number of resistance genes was observed, with one in VL I, five in VL II, and six in VL III. Distinguishing features included variations in resistance genes or fluoroquinolone resistance mutation, such as erm(B), tet(M), aac(6’)-Ie-aph(2’’)-Ia, ant(6)-Ia and gyrA (T82I). Further analysis of evolutionary mechanisms revealed that Tn916, carrying tet(M), was present in 87.9% (160/182) of VL III and 92.6% (163/176) of VL II, but only 4.1% (5/122) of VL I. The Tn6194-like element, carrying erm(B), was found in 25.3% (46/182) of VL II and 84.7% (149/176) of VL III, with none detected in VL I. Furthermore, other functional genes, especially srtB, were notable in C. difficile ST37/RT017, which gradually acquired resistance genes from VL I to VL II and VL III.ConclusionsThe systematically analysis in this study suggests that the acquisition of antibiotic resistance genes was the primary driver of adaptive evolution in C. difficile ST37/RT017. Horizontal gene transfer, particularly through mobile genetic elements is a key genetic mechanism in the adaptive evolution of C. difficile ST37/RT017. Based on these genetic profiles, the active establishment and optimization of a rational system for antibiotic use will be crucial to prevent the emergence of a C. difficile ST37/RT017 variant.

Comparative study on the redox-responsive properties and antitumor efficacy of polyurethane nanocarriers with mono-, Di-, and trisulfide bonds.

Comparative study on the redox-responsive properties and antitumor efficacy of polyurethane nanocarriers with mono-, Di-, and trisulfide bonds.

Transformer-based latency prediction for stream processing task

Transformer-based latency prediction for stream processing task

Real-time monitoring and optimization methods for user-side energy management based on edge computing

This paper presents a comprehensive framework for real-time monitoring and optimization of user-side energy management systems leveraging edge computing technology. The proposed approach addresses key challenges in traditional centralized energy management by bringing computation and data processing closer to end devices. The framework encompasses three main components: an edge computing-based system architecture for data acquisition and processing, real-time monitoring methods for energy consumption and power quality, and optimization techniques for demand response and distributed energy resource coordination. Through case studies and experimental analysis, we demonstrate that the proposed framework achieves significant improvements in energy efficiency, response time, and cost reduction compared to conventional centralized approaches. The results show up to 30% increase in renewable energy utilization and 25% reduction in operating costs across various deployment scenarios. This work provides valuable insights into the application of edge computing for next-generation energy management systems while highlighting remaining challenges and future research directions.

Theoretical analysis of power control and energy efficiency in full-duplex massive MIMO systems

This paper presents a theoretical analysis of power control and gain optimization in full-duplex massive MIMO systems under varying levels of self-interference (SI). The study investigates the impact of SI on spectral efficiency (SE) and energy efficiency (EE), deriving analytical conditions for optimal uplink power and SI cancellation to ensure stable downlink performance. Unlike prior analyses that focus primarily on spectral efficiency, this study centers on energy-efficient operation, incorporating asymmetric traffic conditions and practical power constraints. An adaptive algorithm is also proposed to dynamically adjust SI cancellation for optimal EE–SE trade-off. Numerical results evaluate the system’s performance across different interference levels, antenna configurations, and distances, highlighting the role of adaptive SI mitigation. The findings demonstrate that effective power control and interference cancellation can significantly enhance EE, with improvements of up to 30% observed at shorter distances. This research provides valuable insights into the design of efficient full-duplex systems, contributing to the advancement of next-generation wireless networks.

Development of a magnetic field-assisted grinding system and multi-objective optimization of titanium alloy grinding processes

Development of a magnetic field-assisted grinding system and multi-objective optimization of titanium alloy grinding processes