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Resveratrol (Res) has been widely used in personal-care and pharmaceutical products due to its anti-oxidant, anti-inflammatory, cardioprotective and anti-cancer properties. However, the direct application of Res as functional additives have been limited due to its insolubility and instability. In this work, Res is loaded on the biodegradable ethyl cellulose (EC) carrier to prepare highly porous EC/Res microspheres via a foam-transfer method to understand the sustained release and the degradation property. The experimental results show that when the PVA concentration = 2.5 wt%, the stirring rate = 600 rpm, the oil/water (O/W) ratio = 1:2, the polymer concentration = 1:25, the heating rate = 0.33 °C/min, and the viscosity of EC = 90 ~ 110 mpa.s, the EC/Res microspheres are obtained with a yield up to 80.70 wt% and an encapsulation efficiency of 73.89% via the batch-process; the EC/Res microspheres are further produced continuously with a higher encapsulation efficiency of 90.63% with a yield of 85.58 wt%. The EC/Res microsphere reaches a cumulative release rate up to 91.97 wt% in the phosphate buffer solution (PBS) at pH = 5.0 under 45 °C for 120 h. After the EC/Res microspheres degraded in PBS (pH = 7.4) for 45 days, the hardness decreased from 12.01 ± 0.56 MPa to 4.02 ± 0.45 MPa. This work thus provides a practical solution for Res's application as sustained release functional additives. [ABSTRACT FROM AUTHOR]

Compared to Ti, yttrium (Y) has lower work function and higher effective mass, and Y silicide appears as one of the best candidates for NMOS source/drain contacts. In this work, ultrathin (≤ 5 nm) Y films are employed as interlayer between Ti and n+-Si to form ultrathin YSix/n+-Si contacts, while ultrathin TiSix/n+-Si contact is also fabricated as reference. The YSix/n+-Si and TiSix/n+-Si contacts are investigated in terms of specific contact resistivity (ρc). Also, as-formed YSix/n+-Si contacts with various Y thicknesses as well as TiSix/n+-Si contact were characterized by means of cross-sectional transmission electron microscopy (XTEM), energy dispersive X-ray spectroscopy (EDX), as well as secondary ion mass spectroscopy (SIMS). Compared to TiSix/n+-Si contact, YSix/n+-Si contacts show higher ρc, owing to the incorporation of oxygen into YSix/n+-Si contacts, reduction of P concentration at YSix/n+-Si interface and grooving of poly-YSix. As for TiN(3 nm)/Ti(5 nm)/Y/n+-Si contacts with different Y thicknesses, formation of YSixOy films is observed, which indicates that O contamination deteriorates seriously ρc of YSix/n+-Si contacts. Furthermore, one effective way to reduce O contamination is provided, i.e., thickening Ti layer, leading to an appreciable reduction of ρc for YSix/n+-Si contact by about one order of magnitude. Although ρc of YSix/n+-Si contact is higher than control TiSix/n+-Si contact, this work provides experimental evaluation of ultrathin YSix to help establish ρc reduction strategies for advanced CMOS technology. [ABSTRACT FROM AUTHOR]

Rhyzopertha dominica is one of the most important stored grain pests that seriously damage rice and wheat. At present, the method of controlling stored grain pests mainly relies on insecticide fumigation. However, the excessive use of pesticides not only leaves pesticide residues, with harmful effects on human health and the environment, but also induces insect resistance. Ozone is a strong oxidant with the characteristics of easy decomposition and without residue. Although ozone has been widely used in the food industry in recent years, research on the control of stored grain pests is limited. In this research, we used ozone treatment to control R. dominica adults and explore the molecular mechanisms that affect them. Here, we found that ozone treatment on R. dominica adults could decrease life span and increase malondialdehyde (MDA) content, as well as reduce activity of total superoxide dismutase (SOD) and catalase (CAT). Using RNA-seq technology, we identified 641 genes that were differentially expressed between ozone-treated and control R. dominica adults [fold-change of ≥ 2 (q-value < 5%)]. When comparing ozone treatment with control R. dominica adults, 330 genes were significantly upregulated and 311 were downregulated. RT-qPCR confirmed that 11 genes were differentially expressed in ozone-treated and control R. dominica adults. These genes were involved in insect cuticle protein and antioxidant system. This research showed that ozone treatment could reduce the lifespan of R. dominica through antioxidant system. It is an environmentally benign method for the control of stored grain pests and has great development potential. [ABSTRACT FROM AUTHOR]

This paper is devoted to the research of full implication triple I method under Pythagorean fuzzy environment. We first propose the concepts of Pythagorean t-norm, Pythagorean t-conorm, residual Pythagorean fuzzy implication operator (RPFIO) and Pythagorean fuzzy biresiduum. The full implication triple I method for Pythagorean fuzzy modus ponens (PFMP) and Pythagorean fuzzy modus tollens (PFMT) are also established. In addition, the properties of full implication triple I method of PFMP and PFMT models including the robustness, continuity and reversibility are analyzed. Finally, a practical problem is discussed to demonstrate the effectiveness of the Pythagorean fuzzy full implication multiple I method in medical diagnosis. The advantages of the new method are also explained. Overall, compared with the existing methods, the proposed methods are based on logical reasoning rather than using aggregation operators, so they can more accurately and completely express decision information. [ABSTRACT FROM AUTHOR]

In this paper, by integrating the q-rung orthopair fuzzy set (q-ROFS) with the N-soft set, we first propose a q-rung orthopair fuzzy N-soft set (q-ROFNSS). Based on the q-ROFNSS, we explore the q-rung orthopair fuzzy N-soft weighted average (q-ROFNSWA) operator and q-rung orthopair fuzzy N-soft weighted geometric (q-ROFNSWG) operator, and investigate some properties of the q-ROFNSWG operator and q-ROFNSWG operator including idempotency, monotonicity and boundedness. Finally, two kinds of multiple-attribute group decision-making (MAGDM) methods based on q-rung orthopair fuzzy N-soft aggregation operators are established. In addition, a practical example is provided to illustrate the effectiveness and correctness of the new decision-making approaches. Through comparison with existing methods, the advantages of our method are also elaborated. [ABSTRACT FROM AUTHOR]

Objectives: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong’s test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774–0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720–0.932) and clinical (0.814; 95% CI, 0.682–0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy.• Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy.• The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.Methods: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong’s test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774–0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720–0.932) and clinical (0.814; 95% CI, 0.682–0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy.• Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy.• The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.Results: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong’s test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774–0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720–0.932) and clinical (0.814; 95% CI, 0.682–0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy.• Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy.• The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.Conclusions: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong’s test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774–0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720–0.932) and clinical (0.814; 95% CI, 0.682–0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy.• Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy.• The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.Clinical relevance statement: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong’s test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774–0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720–0.932) and clinical (0.814; 95% CI, 0.682–0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy.• Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy.• The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.Key Points: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong’s test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774–0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720–0.932) and clinical (0.814; 95% CI, 0.682–0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy.• Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy.• The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings. [ABSTRACT FROM AUTHOR]

Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH) method are effective methods to study the interaction between marine structures and sea ice. However, both FEM and SPH methods have their own shortcomings in numerical simulations. There are mesh distortions and disappearance in FEM, and tensile instability, difficulty in applying boundary conditions, and low computational efficiency in SPH. Thus, to make up those problems, it is essential to develop a new numerical method. In the present study, the FEM–SPH adaptive method was applied in the numerical simulation of icebreaking, which could convert finite elements into SPH particles based on given conditions. Numerical models of cone and icebreaker interactions with level ice were established. Then, numerical simulation results were compared with empirical formula and model test results. In addition, the effects of cone angle and ice strength were analyzed. It was demonstrated that this algorithm could accurately predict the icebreaking resistance, which is the most concerned parameter in practice. Simultaneously, it could effectively simulate the accumulation process of ice rubbles. Thus, the FEM–SPH adaptive method is considered to be an effective way to simulate the interaction between marine structures and sea ice, and has great potential in the numerical simulation of icebreaking. [ABSTRACT FROM AUTHOR]

Lithium sulfur (Li-S) batteries were considered the next generation of energy devices with the greatest development potential because of their large theoretical capacity and high energy density. However, many challenges exist in the research field of Li-S batteries, such as low utilization rate of active substances and inferior cycling performance. Here, a simple and feasible preparation strategy was applied to fabricate the composite cathode of polyamidoamine (PAMAM) dendritic macromolecule polymer modified the Ketjen Black mixed sublimated sulfur (PAMAM@KB@S). Benefiting from the improved polysulfides adsorbability due to the abundant functional groups which originate from PAMAM, PAMAM@KB@S composites with 3.94 mg sulfur-loading at a current density of 0.5 C exhibit a high initial specific discharge capacity of 894.3 mAh g−1 and maintain a specific discharge capacity of 455.7 mAh g−1 after 200 cycles. And the average Coulombic efficiency of PAMAM@KB@S electrode (96.7%) within 200 cycles is higher than that of KB@S electrode (88.5%). [ABSTRACT FROM AUTHOR]

This paper develops a single-granulation hesitant fuzzy rough set (SGHFRS) model from the perspective of granular computing. In the multi-granulation framework, we propose two types of multi-granulation rough sets model called the optimistic multi-granulation hesitant fuzzy rough sets (OMGHFRSs) and pessimistic multi-granulation hesitant fuzzy rough sets (PMGHFRSs). In the models, the multi-granulation hesitant fuzzy lower and upper approximations are defined based on multiple hesitant fuzzy tolerance relations. The relationships among the SGHFRSs, OMGHFRSs and PMGHFRSs are also established. In order to further measure the uncertainty of multi-granulation hesitant fuzzy rough sets (MGHFRSs), the concepts of rough measure and rough measure about the parameters α and β are presented and some of their interesting properties are examined. Finally, we give a decision-making method based on the MGHFRSs, and the validity of this approach is illustrated by two practical applications. Compared with the existing results, we also expound its advantages. [ABSTRACT FROM AUTHOR]

Abstract: Quantum mechanical and hybrid QM/QM′ ONIOM methodology have been applied to investigate the structure and molecular interactions between DB08446 and the binding site of HIV-1 RT. The binding energy calculated using the ONIOM2(M06-2X-D3/6-311 + G(2df,2p):PM6) method is − 369.43 kJ/mol, demonstrating that DB08446 can strongly bind to HIV-1 RT. The pairwise interaction energy analysis at the M06-2X-D3/6-311 + G(2df,2p) and RI-PWPB95-D3/def2-QZVPP levels of theory disclose that there are more attractive interactions than repulsive interactions in the binding site. In particular, residues Lys103, Val106, Tyr188, and Trp229 were major contributors for DB08446 binding to HIV-1 RT. Further detailed structure analysis and NCI visualization confirm that the H-bond, X-bond, π-π, CH•••π, and van der Waals interactions, etc. were the dominant stabilization factors of non-bonded interaction between DB08446 and the binding site of HIV-1 RT.Graphical abstract: [ABSTRACT FROM AUTHOR]

A 6MW spar-type floating offshore wind turbine (FOWT) model is put forward and a fully coupled aero-hydro-servo-elastic time domain model is established in the fatigue, aerodynamics, structures and turbulence (FAST) code. Influence rules of wind load and wave load on the characteristics of 6MW spar-type FOWT are investigated. Firstly, validation of the model is carried out and a satisfactory result is obtained. The maximal deviations of rotor thrust and power between simulation results and reference values are 4.54% and −2.74%, respectively. Then the characteristics, including rotor thrust, rotor power, out-of-plane blade deflection, tower base fore-aft bending moment, and mooring line tension, are researched. The results illustrate that the mean value of dynamic response characteristics is mainly controlled by the wind-induced action. For characteristics of tower base fore-aft bending moment and platform pitch motion, the oscillation is dominated by the wave-induced action during all conditions considered. For characteristics of out-of-plane blade tip deflection and mooring line tension, the oscillation is commanded by combination effect of wave and wind loads when the wind speed is lower than the rated wind speed (hereinafter referred to as below rated wind speed) and is controlled by the wave-induced action when the wind speed is higher than the rated wind speed (hereinafter referred to as above rated wind speed). As to the rotor thrust and power, the oscillation is dominated by the wind induced action at below rated wind speed and by the combination action of wind and wave loads at above rated wind speed. The results should be useful to the detailed design and model basin test of the 6MW spar-type FOWT. [ABSTRACT FROM AUTHOR]

A novel series of dihydro-hydroxyl-phene-thylsulfanyl-ω-cyclohexyl/phenyl-oxopyrimidine derivatives have been synthesized and their in vitro anti-hepatitis C virus activities have been evaluated using Huh 7.5.1 cells. Some of the compounds showed moderate anti-hepatitis C virus activities, with EC range from 7.53 to 0.13 μM. Among all the compounds, 6-(cyclohexylmethyl)-5-ethyl-2-((2-hydroxy-2-phenylethyl)thio)-pyrimidin-4 (3H)-one ( 3a) had the most promising potential in inhibiting hepatitis C virus with an EC value of 0.13 μM and SI value of 121. It was noticed that some of these compounds are both active on hepatitis C virus and human immunodeficiency virus. In addition to experimental evaluation, structure-activity relationships and the molecular modeling analysis of these new congeners are also discussed. [ABSTRACT FROM AUTHOR]

Floating wind turbines (FWTs) are subjected to combined aerodynamic and hydrodynamic loads varying both in time and amplitude. In this study, a multi-column tension-leg-type FWT (i.e., WindStar TLP system) is investigated for its global performance under normal operating conditions and when parked. The selected variables are analysed using a fully coupled aero-hydro-servo-elastic time domain simulation tool FAST. Three different loading scenarios (wind only, wave only and both combined) are examined to identify the dominant load influencing each response. The key response variables are obtained and compared with those for an NREL 5MW baseline wind turbine installed on land. The results should aid the detailed design of the WindStar TLP system. [ABSTRACT FROM AUTHOR]

To obtain the optimal hydrogen-driven chemical reaction process for the preparation of MgSi as an anodic material of Li-ion batteries, the effects of the dehydrogenation temperatures on the structure and electrochemical properties are systematically investigated. The results indicate that MgSi prepared by isothermal dehydrogenation at 310 and 330 °C exhibit better cycling stability than the samples prepared at 290 and 350 °C due to the joint effects of the product purity and the particle size, especially for the 330 °C-prepared sample. In particular, the initial discharge capacity of the MgSi prepared at 330 °C is 1010 mAh/g with a coulombic efficiency of 92 %. After 50 cycles, the discharge capacity is approximately 416 mAh/g and the corresponding capacity retention is calculated to be approximately 41 %. [ABSTRACT FROM AUTHOR]

In this paper, the photophysical and photosensitive properties of Elsinochrome A (EA), another member of perylenequinone photosensitizers derived via artificially biological synthesis, were studied by taking hypocrellin A (HA) and hypocrellin B (HB) as the references. It was found that the quantum yield of singlet oxygen produced via the photosensitization of EA was 0.98 with HB (0.84) as a reference. Furthermore, the semiquinone anion could be produced via photosensitization of EA in oxygen-free solution. Besides, taking advantage of nanosecond time-resolved spectral technique, the transient triplet absorption spectra and the lifetime of EA in cyclohexane were determined by the use of the ground-state depletion method. [ABSTRACT FROM AUTHOR]

A series of 6-naphthylmethyl substituted HEPT analogues bearing a β-carbonyl and a terminal phenyl ring or ester groups on the N-1 side chain of uracil were synthesized, and the in vitro anti-HIV activity was evaluated. Most of these HEPTs were considerably less potent and selective or inactive, only a few compounds showed moderate or high activity against HIV-1. The results demonstrated that the anti-HIV-1 activity of 6-naphthylmethyl substituted HEPT analogues was diminished or eliminated when the β-oxygen of N-1 side chain was replaced by a carbonyl group. [ABSTRACT FROM AUTHOR]