Systematic Optimization Research Articles

Novel biaryloxazolidinone derivatives with broad-spectrum antibacterial activity, favorable drug-like profiles and in vivo efficacy against linezolid-resistant Staphylococcusaureus

The emergence of multidrug-resistant bacteria along with a declining pipeline of clinically useful antibiotics has led to the urgent need for the development of more effective antibacterial agents to treat drug-resistant bacteria. We previously discovered compound OB-158 with potent antibacterial activity but exhibited poor oral bioavailability. Herein, a systematic structural optimization of OB-158 to improve pharmacokinetic profiles yielded 26 novel biaryloxazolidinone analogues, and their activities against Gram-positive S. aureus, multidrug resistant S. aureus and Enterococcus faecalis were evaluated. Remarkably, compound 8b was identified with potent antibacterial activity against S. aureus (MIC=0.06μg/mL), MSSA (MIC=0.125μg/mL), MRSA (MIC=0.06μg/mL), LRSA (MIC=0.125μg/mL) and LREFa (MIC=0.5μg/mL). Compound 8b was demonstrated as a promising candidate through druglikeness evaluation including metabolism in microsomes and plasma, Caco-2cell permeability, plasma protein binding, cytotoxicity, and inhibition of CYP450 and human monoamine oxidase. Notably, compound 8b displayed excellent PK profile with appropriate T1/2 of 1.49h, high peak plasma concentration (Cmax=2320ng/mL), high plasma exposure (AUC0-t=8310hng/mL), and superior oral bioavailability (F=68.1%) in Sprague-Dawley rats. Ultimately, in vivo efficacy of compound 8b in a mouse model of LRSA systemic infection was also demonstrated. Taken together, compound 8b represents a promising drug candidate for the treatment of linezolid-resistant Gram-positive bacterial strains infection.

Novel 4-triazole phenyl amide (4-TPA) molecules: Potent promoters of α-synuclein fibril disassembly

Parkinson's disease profoundly compromises patients' daily lives, and the disassembly of α-synuclein aggregates, a primary pathological factor, represents a promising therapeutic approach. In this study, we conducted a systematic screening and optimization process to identify the novel scaffold B37, a 4-triazolyl-phenylamine derivative, exhibiting a potent disassembly activity of 1.1 μM against α-synuclein preformed fibrils. Notably, B37 demonstrated significant neuroprotective effects, ameliorated autophagic dysfunction induced by preformed fibrils, mitigated oxidative stress, and restored the co-localization of preformed fibrils with lysosomes. Transmission electron microscopy corroborated its in vitro disassembly function. Pharmacokinetic profiling revealed favorable parameters with a receptible blood-brain barrier permeability. B37 emerges as a promising lead compound for further optimization, aiming to develop a highly effective agent targeting the disassembly of α-synuclein aggregates to treat neurodegenerative diseases like Parkinson's disease.

Comparative Analysis of the Hydrazine Interaction with Arylene Diimide Derivatives: Complementary Approach Using First Principles Calculation and Experimental Confirmation.

Suitable functional group-engineered π-conjugated aromatic dimides based on perylene (PDI) and naphthyl scaffolds (NDI) demonstrated excellent sensitivity toward different gaseous analytes. However, to date, no methodical analysis has been performed to rationalize molecular-level interactions in the context of optical transduction, which is essential for systematic performance optimization of NDI/PDI-based molecular sensors. Therefore, in this present work, NDI/PDI scaffolds have been designed with amino acid functional groups (alanine, ALA and glutamic acid, GLU) at the terminal positions, and we subsequently compared the efficacy of four different imide derivatives as model hosts for hydrazine adsorption. Specifically, the adsorption of hydrazine at different interaction sites has been thoroughly investigated using ab initio calculations, where the adsorption energy, charge transfer, and recovery time have been emphasized. Theoretical results exhibit that irrespective of host specification the COOH groups offer a primary interaction site for hydrazine through the hydrogen bonding interaction. The presence of more COOH groups and relatively stronger interaction with secondary edge oxygen ensure that GLU functional moieties are a superior choice over ALU for efficient hydrazine binding. The molecular energy spectrum analysis exhibits more favorable HOMO/LUMO gap variations after hydrazine interaction in the case of PDI derivatives irrespective to the nature of the amino acid residues. Therefore, by a combination of both factors, PDI-GLU has been identified as the most suitable host molecule for hydrazine among four derivatives. Finally, the key theoretical predictions has been later experimentally validated by analyzing UV-visible spectroscopy and NMR studies, wherein the mechanism of interaction has also been experimentally verified by EPR analysis and FT-IR studies.

Towards AI-Driven Healthcare: Systematic Optimization, Linguistic Analysis, and Clinicians' Evaluation of Large Language Models for Smoking Cessation Interventions.

Creating intervention messages for smoking cessation is a labor-intensive process. Advances in Large Language Models (LLMs) offer a promising alternative for automated message generation. Two critical questions remain: 1) How to optimize LLMs to mimic human expert writing, and 2) Do LLM-generated messages meet clinical standards? We systematically examined the message generation and evaluation processes through three studies investigating prompt engineering (Study 1), decoding optimization (Study 2), and expert review (Study 3). We employed computational linguistic analysis in LLM assessment and established a comprehensive evaluation framework, incorporating automated metrics, linguistic attributes, and expert evaluations. Certified tobacco treatment specialists assessed the quality, accuracy, credibility, and persuasiveness of LLM-generated messages, using expert-written messages as the benchmark. Results indicate that larger LLMs, including ChatGPT, OPT-13B, and OPT-30B, can effectively emulate expert writing to generate well-written, accurate, and persuasive messages, thereby demonstrating the capability of LLMs in augmenting clinical practices of smoking cessation interventions.

Optimized Wide-Angle Metamaterial Edge Filters: Enhanced Performance with Multi-Layer Designs and Anti-Reflection Coatings

Optimized Wide-Angle Metamaterial Edge Filters: Enhanced Performance with Multi-Layer Designs and Anti-Reflection Coatings

Colorimetric and Photothermal Dual-Modal Switching Lateral Flow Immunoassay Based on a Forced Dispersion Prussian Blue Nanocomposite for the Sensitive Detection of Prostate-Specific Antigen.

Prostate-specific antigen (PSA) is a key marker for a prostate cancer diagnosis. The low sensitivity of traditional lateral flow immunoassay (LFIA) methods makes them unsuitable for point-of-care testing. Herein, we designed a nanozyme by in situ growth of Prussian blue (PB) within the pores of dendritic mesoporous silica (DMSN). The PB was forcibly dispersed into the pores of DMSN, leading to an increase in exposed active sites. Consequently, the atom utilization is enhanced, resulting in superior peroxidase (POD)-like activity compared to that of cubic PB. Antibody-modified DMSN@PB nanozymes serve as immunological probes in an enzymatic-enhanced colorimetric and photothermal dual-signal LFIA for PSA detection. After systematic optimization, the LFIA based on DMSN@PB successfully achieves a 4-fold amplification of the colorimetric signal within 7 min through catalytic oxidation of the chromogenic substrate by POD-like activity. Moreover, DMSN@PB exhibits an excellent photothermal conversion ability under 808 nm laser irradiation. Accordingly, photothermal signals are introduced to improve the anti-interference ability and sensitivity of LFIA, exhibiting a wide linear range (1-40 ng mL-1) and a low PSA detection limit (0.202 ng mL-1), which satisfies the early detection level of prostate cancer. This research provides a more accurate and reliable visualization analysis methodology for the early diagnosis of prostate cancer.

Controlling chaos using edge computing hardware

Machine learning provides a data-driven approach for creating a digital twin of a system – a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 ±7.0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 7.0$$\\end{document} nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing “edge.”

Electrical conductivity enhancement of rGO-MoS2 composite electrode for inverted PSC by parameter optimization

Carbon-based electrodes are widely used in photovoltaic, metal-ion batteries, supercapacitors, and sensors due to their low ohmic resistance and ability to integrate modifiers. Reduced graphene oxide (rGO) represents a promising alternative to graphite in carbon-based electrodes because of its high electrical conductivity, large surface area, and robust mechanical properties. This study aimed to optimize the fabrication parameters of rGO-MoS2 composite electrodes prepared via a one-pot hydrothermal method, focusing on maximizing their electrical conductivity for potential electronic applications. Taguchi analysis was employed to evaluate the impact of three key factors: rGO-MoS2 mixing ratio, annealing temperature, and annealing time. The novelty of this work lies in the systematic optimization approach using the Taguchi method, which allowed for the identification of the most significant factors and their optimal levels. The optimal combination was found to be a 1:1 ratio, 75 °C annealing temperature, and 15-min annealing time, resulting in an impressive electrical conductivity of 11 800 S m⁻1. This optimized rGO-MoS2 composite electrode exhibited an approximate 11 % reduction in sheet resistance compared to the unoptimized composite. Furthermore, all samples show an improvement in sheet resistance values compared to pure rGO and MoS2, which were 82.5 Ω/sq and 48.9 Ω/sq. Numerical simulations further revealed a 13.23 % improvement in device performance when the optimized rGO-MoS2 composite was implemented as the top electrode in an inverted perovskite solar cell, demonstrating the significant potential of this material for various electronic applications.

Optimization of all-polymer/Sb2Se3 tandem solar cells for enhanced efficiency: a comprehensive TCAD modeling approach

Abstract This paper introduces a novel tandem configuration, utilizing an all-thin film all-polymer solar cell (all-PSC) with a wide bandgap of 1.76 eV for the front cell and Sb2Se3 with a narrow bandgap of 1.2 eV for the bottom cell. The design of this tandem is performed by comprehensive optoelectronic TCAD tools, essential for optimizing parameters across multiple layers to reach maximum power conversion efficiency (PCE). Experimental validation of models is conducted through calibration and validation against fabricated reference all-polymer and Sb2Se3 solar cells, yielding calibrated PCEs of approximately 10.1% and 10.5%, respectively. Subsequently, validated simulation models for both top and rear cells are utilized to design a 2-T all-polymer/Sb2Se3 tandem cell, which initially achieves a PCE of 10.91%. Through systematic optimization steps, including interface engineering and homojunction structure design, a remarkable PCE of 24.24% is achieved at the current matching point, showcasing the potential of our proposed tandem solar cell design. This study represents a significant advancement in the field of thin-film tandem solar cells, offering promising avenues for efficient and cost-effective photovoltaic technologies, particularly in applications requiring flexibility.

A Systematic Optimization Method for Permanent Magnet Synchronous Motors Based on SMS-EMOA.

The efficient design of Permanent Magnet Synchronous Motors (PMSMs) is crucial for their operational performance. A key design parameter, cogging torque, is significantly influenced by various structural parameters of the motor, complicating the optimization of motor structures. This paper proposes an optimization method for PMSM structures based on heuristic optimization algorithms, named the Permanent Magnet Synchronous Motor Self-Optimization Lift Algorithm (PMSM-SLA). Initially, a dataset capturing the efficiency of motors under various structural parameter scenarios is created using finite element simulation methods. Building on this dataset, a batch optimization solution aimed at PMSM structure optimization was introduced to identify the set of structural parameters that maximize motor efficiency. The approach presented in this study enhances the efficiency of optimizing PMSM structures, overcoming the limitations of traditional trial-and-error methods and supporting the industrial application of PMSM structural design.

Thermal performance analysis of battery thermal management system utilizing bionic liquid cooling plates with differentiated velocity distribution strategy

Bionic channel liquid cooling plates (BC-LCPs) show high heat dissipation performance in battery thermal management system (BTMS), but suffer from the poor temperature-equalizing ability. Herein, a BTMS with novel BC-LCPs and differentiated velocity distribution strategy is proposed to overcome the low temperature uniformity, and balance the cooling performance and power consumption. The BC-LCPs are firstly designed by precisely tailoring the series cobweb channels inspired by the bionic structures in the nature. Through systematic optimization combining single factor analysis with orthogonal test and complementary test, differentiated velocities (vI and vII) of 0.20 and 0.46 m∙s−1 are adopted, respectively. Ultimately, the LC system demonstrates exceptional cooling performance while maintaining lower energy consumption. For example, with a discharge rate of 3C and an environment temperature of 50 °C, the temperature and temperature difference of the battery pack are controlled synergistically below 30.9 and 4.47 °C, respectively. At the same time, the Ptotal can reach as low as 0.202 W, which is reduced by 42.0 % compared to the case with uniform velocity strategy.

Gram-scale microwave-assisted synthesis of high-quality CsPbBr3 nanocrystals for optoelectronic applications

Fully inorganic halide perovskite nanocrystals (NCs) are promising candidates for optoelectronic applications due to their beneficial properties such as their amenability to solution processing, high absorption coefficients, and high quantum yields. Various methods have been developed to prepare perovskite CsPbX3 (X = Cl, Br, and I) NCs; however, large-scale synthesis with high-quality CsPbX3 NCs remains challenging. In the study, we report an advanced method for the gram-scale synthesis of all-inorganic perovskite NCs via specially designed microwave-assisted synthesis, which offers uniform reaction energy transfer, excellent reproducibility, and flexible parameter tuning. The composition, size and uniformity of the CsPbBr3 NCs have been optimized by controlling the surfactant ratio, precursor ratio, and reaction time. This systematic optimization leads to the production of gram-scale (> 0.165 g per a 100 ml reaction mixture for a single synthesis cycle) high-quality, phase-pure CsPbBr3 NCs with a uniform size of ∼ 10 nm. These optimized NCs exhibit a sharp photoluminescence peak centered at 515 nm with a photoluminescence quantum yield of 94 % and produce excellent radioluminescence under X-ray irradiation. To test the feasibility of these NCs for use in real optoelectronic devices, a photodetector consisting of CsPbBr3 NCs and monolayer MoS2 was fabricated, demonstrating enhanced photosensitivity and a faster response time compared with a pure MoS2-based photodetector. The proposed gram-scale microwave-assisted synthesis method thus has the potential to promote the commercialization of metal halide perovskite NCs through cost-effective mass production for use in many optoelectronic applications.

Assessment and validation of a numerical model for phase change materials heatsink on thermoelectric devices

The use of phase change materials (PCM) in heatsink designs are gaining attention due to its many favorable properties. Some PCM heatsinks designs have been suggested for use with thermoelectric cooling devices (TECs), however, the heatsink designs are often arbitrary where PCM is just added to voids within the heatsink to improve the thermal properties. There is a need to systematically understand the effects of PCM on TEC performance. While a numerical model is key to such systematic study and optimization, the application involves multiple physical phenomena and the resulting model can be prohibitively expensive to run. In this contribution, a numercical model using computational fluid dynamics (CFD) is built to predict the performance of PCM heatsink for use with TECs as cooling wearable. Experiments are calibrated and carried out to prescribe the boundary conditions and ensure that the multiphysics phenomena are accurately captured. The numerical methodology described here can be used for design optimizations of PCM heatsinks to be used in conjunction with TECs. Our numerical model shows that for maximum TEC efficiency, the PCM should be kept at a liquid fraction of less than 40%.

Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning

Achieving highly accurate predictions based on less data with multiple formulations has become a significant challenge. Unlike the traditional prediction model that ignores the similarities and differences between multi-formula batteries, the proposed multi-source transfer RUL prediction is an effective way to jointly transfer several samples and merge degradation information from multi-source domains, thereby taking full advantage of the information. Aiming at the problem of negative transfer and insufficient transfer when multi-source domains are available, dynamic time warping is employed to adaptively select transferable samples, ignoring the local individual differences. The least absolute shrinkage and selection operator are utilized to generate an optimal subset and allocate personalized weights. Then, an optimization strategy for the cycle life tests is proposed by automatically giving a reasonable threshold of stopping the test. The proposed method is verified based on an actual dataset with multiple formulations collected under different test conditions from a battery manufacturer. Experimental results show that the proposed method has high accuracy based on even a small amount of data. The systematic optimization strategy can significantly reduce the time and cost of the test.

One‐pot green approach for rapid and effective anionic dye remediation: encapsulation within alginate nanocapsules

AbstractBACKGROUNDThe encapsulation technique was applied to efficiently eliminate Congo red (CR) from aqueous solutions. During the ionotropic gelation between calcium (Ca2+) ions and alginate (AL), CR was effectively entrapped within the AL nanocapsules in a one‐step process. Suitable conditions for efficient CR removal via encapsulation were revealed by the systematic optimization of parameters including pH, time and stirring speed, etc.RESULTSAccording to the experimental observations, the stirring rate and temperature were found to have an insignificant effect on the encapsulation of CR molecules. When the pH value of the medium was 3, the highest level of encapsulation efficiency was achieved in a period of 15 min. At a preliminary CR concentration of 2000 mg L−1 and pH 3, the encapsulation efficiency was calculated at ≈98.9%, with an encapsulation capacity of 2800 mg dye g−1 AL. The zeta potential values of AL and CR/AL nanocapsules were determined to be +7.05 eV and −14.9 eV, respectively, and the results showed that the particles tended to agglomerate. TEM micrographs also showed that the nanocapsules were nanosized and agglomerated. Soil and UV degradation studies showed that the dye‐entrapped nanocapsules degraded remarkably. These results highlighted the great potential of encapsulation for dye removal in economical and practical applications.CONCLUSIONEncapsulation was confirmed to be an economical and practical technique for effectively eliminating CR from aqueous solutions. Under UV light irradiation, the dye molecules entrapped within alginate nanocapsules displayed photodegradation. © 2024 Society of Chemical Industry (SCI).

Pyrolytic Conversion of Waste High-Density Polyethylene to Wax: Temperature Optimization and Characterization of Wax

The formation of wax from waste high-density polyethylene by thermal decomposition through pyrolysis is examined in this work. To get wax from waste high-density polyethylene, the thermal cracking reaction is run in a semi-batch pyrolysis reactor at 550 °C, 600 °C, and 650 °C. The yield percentage, melting point, specific gravity, and penetration degree of wax varied depending on the temperature. At 600 °C, waste high-density polyethylene yielded the highest wax output (87.25%) with a 0.7768 specific gravity, a 59 °C melting point, and an 81.8 mm penetration degree. The fourier transform infrared spectroscopy (FTIR) analysis concluded the presence of aliphatic hydrocarbon compounds (alkane, alkene, alcohol, and cycloalkane), which is also confirmed by gas chromatography-mass spectrometry (GC-MS) analysis. The innovation in this study lies in the systematic exploration and optimization of temperature conditions for wax production from waste high-density polyethylene (HDPE) bottles.

Systematic optimization and evaluation of culture conditions for the construction of circulating tumor cell clusters using breast cancer cell lines

BackgroundCirculating tumor cell (CTC) clusters play a critical role in carcinoma metastasis. However, the rarity of CTC clusters and the limitations of capture techniques have retarded the research progress. In vitro CTC clusters model can help to further understand the biological properties of CTC clusters and their clinical significance. Therefore, it is necessary to establish reliable in vitro methodological models to form CTC clusters whose biological characteristics are very similar to clinical CTC clusters.MethodsThe assays of immunofluorescence, transmission electron microscopy, EdU incorporation, cell adhension and microfluidic chips were used. The experimental metastasis model in mice was used.ResultsWe systematically optimized the culture methods to form in vitro CTC clusters model, and more importantly, evaluated it with reference to the biological capabilities of reported clinical CTC clusters. In vitro CTC clusters exhibited a high degree of similarity to the reported pathological characteristics of CTC clusters isolated from patients at different stages of tumor metastasis, including the appearance morphology, size, adhesive and tight junctions-associated proteins, and other indicators of CTC clusters. Furthermore, in vivo experiments also demonstrated that the CTC clusters had an enhanced ability to grow and metastasize compared to single CTC.ConclusionsThe study provides a reliable model to help to obtain comparatively stable and qualified CTC clusters in vitro, propelling the studies on tumor metastasis.

Topology Optimization of a Compliant Constant-Force End Effector for Robotic Operations Over Uneven Surfaces

Abstract A compliant constant-force mechanism (CCFM) is a specific type of compliant mechanism that serves as a passive force regulation device. When subjected to a load, it undergoes deformation, resulting in an almost consistent output force regardless of changes in input displacement. Traditional methods used to design CCFMs typically rely on either stiffness combination or parametric optimization based on existing design configurations. To enable the direct synthesis of CCFMs according to desired boundary conditions, this study proposes a systematic topology optimization method. This method includes a new morphology-based scheme designed to ensure the connectivity of the topological results, thereby achieving this objective. Using this approach, a CCFM suitable for end effector applications is designed and manufactured through 3D printing. Four of these CCFMs are then utilized to create an innovative compliant constant-force end effector for robotic operations on uneven surfaces. The experimental results demonstrate that the presented design achieves output force modulation through elastic deformation, eliminating the need for additional sensors and controllers to regulate the output force. The presented design can be mounted on a robotic arm to provide overload protection and maintain a consistent force output during operation when encountering irregular and uneven surfaces.

Towards low-carbon power networks: Optimal location and sizing of renewable energy sources and hydrogen storage

This paper proposes a systematic optimization framework to jointly determine the optimal location and sizing decisions of renewables and hydrogen storage in a power network to achieve the transition to low-carbon networks efficiently. We obtain these strategic decisions based on the multi-period alternating current optimal power flow (AC MOPF) problem that jointly analyzes power network, renewable, and hydrogen storage interactions at the operational level by considering the uncertainty of renewable output, seasonality of electricity demand, and electricity prices. We develop a tailored solution approach based on second-order cone programming within a Benders decomposition framework to provide globally optimal solutions. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC MOPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment cost, emission tax price, and conversion efficiency.

Correction: Achievement of Green and Sustainable CVD Through Process, Equipment and Systematic Optimization in Semiconductor Fabrication

Correction: Achievement of Green and Sustainable CVD Through Process, Equipment and Systematic Optimization in Semiconductor Fabrication