Robust operability is one of the key properties of industrial processes with uncertain parameters, which helps the designers assess the operating performance and improve production reliability quite early. The objecti...
Robust operability is one of the key properties of industrial processes with uncertain parameters, which helps the designers assess the operating performance and improve production reliability quite early. The objective of this work is to develop a framework of operability analysis to depict the robust operational space and quantificationally assess the robust operability of the plant. First, a derivative-free optimization-based flexibility analysis model is designed to identify the largest flexibility space. Then, a line projection method is adopted to locate the boundary points of the operational space. Finally, the Delaunay triangulation and convex hull theory are combined to identify the robust operational space;meanwhile, the optimal operating condition is found, which provides operators with straightforward guidance to ensure reliable operations. After calculating the space volume, the ratio to the physical operational space can assess robust operability. Three case studies are considered to show the efficiency of the proposed method.
What is already known about this topic?Infertility represents a significant global public health concern,impacting approximately 15%of couples of reproductive age *** this,data on infertility prevalence in the demogra...
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What is already known about this topic?Infertility represents a significant global public health concern,impacting approximately 15%of couples of reproductive age *** this,data on infertility prevalence in the demographically diverse Asia-Pacific region remains *** is added by this report?This study examines the trends and distribution of infertility in the Asia-Pacific region from 1990 to 2021,revealing a significant increase in female *** growth rate of secondary infertility has exceeded that of primary ***,an increase in the prevalence of polycystic ovary syndrome(PCOS)may be a significant *** research also highlights geographical variations in the prevalence and trends of infertility across different *** are the implications for public health practice?The findings emphasize the significance of sexual and reproductive health services and rights in safeguarding fertility.
Abstract:Tuberculosis(TB)remains a significant global health challenge,ranking second only to COVID-19 as the leading cause of death from a single infectious agent,with 1.3 million TB-related deaths reported in *** ef...
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Abstract:Tuberculosis(TB)remains a significant global health challenge,ranking second only to COVID-19 as the leading cause of death from a single infectious agent,with 1.3 million TB-related deaths reported in *** efficacy has been compromised by the emergence of drug-resistant strains,including rifampin-resistant TB(RR-TB),multidrug-resistant TB(MDR-TB),and extensively drug-resistant TB(XDR-TB).Although first-line drugs like isoniazid,rifampicin,pyrazinamide,and ethambutol form the cornerstone of TB therapy,the rise of resistant strains necessitates the use of second-line drugs,which often come with increased toxicity and limited *** advances have focused on repurposing existing compounds and developing new drugs with novel mechanisms of *** agents such as second-generation bedaquiline analogs(TBAJ-587,TBAJ-876),sudapyridine(WX-081),delamanid,pretomanid,and TBI-166(pyrifazimine)have shown efficacy against resistant Mtb *** treatment regimens like the BPaLM protocol-combining bedaquiline,pretomanid,linezolid,and moxifloxacin-offer shorter,all-oral therapies with higher cure rates.
The concept of synthetic lethality(SL)has been successfully used for targeted *** further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are ***,graph neural network-based de...
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The concept of synthetic lethality(SL)has been successfully used for targeted *** further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are ***,graph neural network-based deep learning methods have been proposed for SL prediction,which reduce the SL search space of wet-lab based ***,these methods ignore that most SL interactions depend strongly on genetic context,which limits the application of the predicted *** this study,we proposed a graph recurrent network-based model for specific context-dependent SL prediction(SLGRN).In particular,we introduced a Graph Recurrent Network-based encoder to acquire a context-specific,low-dimensional feature representation for each node,facilitating the prediction of novel *** leveraged gate recurrent unit(GRU)and it incorporated a context-dependent-level state to effectively integrate information from all *** a result,SLGRN outperforms the state-of-the-arts models for SL *** subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival *** in vitro experiments and retrospective clinical analysis,we emphasize the potential clinical significance of this context-specific SL prediction model.
In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introd...
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In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-supervised learning (SSL) framework that combines contrastive learning and graph neural networks (CL-GNN) for predicting protein-ligand binding affinities, which is a critical aspect of drug discovery. Traditional methods for affinity prediction are expensive and time-consuming, prompting the development of more efficient computational approaches. CL-GNN utilizes a contrastive learning strategy, a form of SSL, to learn from a large data set of 371 458 unique unlabeled protein-ligand complexes. By employing graph neural networks and molecular graph enhancement techniques, the model effectively captures protein-ligand interactions in a self-supervised manner. The fine-tuned model demonstrates competitive performance, achieving high Pearson's correlation coefficients and low root-mean-square errors on benchmark data sets. The proposed method outperforms existing machine learning models, showcasing its potential for accelerating the drug development process. The method effectively quantifies the similarity between protein-ligand complex representations learned in the pretraining and downstream testing phases through cosine similarity assessment. This approach not only revealed potential connections between complexes in their binding properties but also provided new insights into the understanding of drug mechanisms of action. In addition, the transparency of the model is significantly improved by visualizing the importance of key protein residues and ligand atoms. This visualization tool provides insight into the model's predictive decision-making process, providing key biological insights for drug design and optimization.
In this study,we investigate the optical properties of a quantum-corrected black hole(BH)in loop quantum gravity surrounded by a plasma ***,we determine the photon and shadow radii resulting from quantum corrections a...
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In this study,we investigate the optical properties of a quantum-corrected black hole(BH)in loop quantum gravity surrounded by a plasma ***,we determine the photon and shadow radii resulting from quantum corrections and the plasma medium in the environment surrounding a quantum-corrected *** findings indicate that the photon sphere and BH shadow radii decrease owing to the quantum correction parameterα,which acts as a repulsive gravitational ***,we investigate the gravitational weak lensing by applying the general formalism used to model the deflection angle of the light traveling around the quantum-corrected BH within the plasma *** show,in conjunction with the fact that the combined effects of the quantum correction and non-uniform plasma frequency parameter can decrease the deflection angle,that the light traveling through the uniform plasma can be strongly deflected than the non-uniform plasma environment surrounding the quantum-corrected ***,we examine the magnification of the lensed image brightness under the effect of the quantum correction parameterα,including the uniform and non-uniform plasma effects.
In this paper,the isothermal oxidation experiments were used to study the effect of Ag on the high-temperature oxidation behavior of Mg-6.5Gd-5.6Y-0.1Nd-0.01Ce-0.4Zr(wt%)alloy oxidized at 350℃,400℃ and 450℃ for 120...
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In this paper,the isothermal oxidation experiments were used to study the effect of Ag on the high-temperature oxidation behavior of Mg-6.5Gd-5.6Y-0.1Nd-0.01Ce-0.4Zr(wt%)alloy oxidized at 350℃,400℃ and 450℃ for 120 *** results show that the oxidation weight gain of the alloy mainly occurs in the early oxidation stage(0-20 h).This reason attributes to the lack of protective oxide film and the rapid inward diffusion of oxygen through the macroscopic defects of the incomplete oxide *** dense oxide films such as Y_(2)O_(3),Gd_(2)O_(3),and ZrO2 form,they hinder the inward transport of oxygen ions and improve the high-temperature oxidation resistance of the *** addition,the role of the Ag element at three temperatures is *** addition of Ag mainly promotes the formation of eutectic phases such as Mg3Gd,Mg24Y5,and Ag2Gd,which reduces the content of Gd and Y elements in the alloy matrix,resulting in a decrease in the diffusion rate of Gd and Y elements during the oxidation process at 350℃ and 400℃,and weakens the oxidation resistance of Ag-containing ***,in the oxidation experiment at 450℃,a large amount of eutectic phase is solid dissolved into the matrix,reducing the difference in element *** this time,it is detected that the Ag element promoted the outward diffusion of Gd and Y elements,accelerating the formation of the oxide *** oxidation resistance of Ag-containing alloys is improved.
The development of low-orbit satellite communication networks marks the beginning of a new era in global communication. However, in the context of large-scale LEO satellite communication scenarios, the traditional adj...
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The development of low-orbit satellite communication networks marks the beginning of a new era in global communication. However, in the context of large-scale LEO satellite communication scenarios, the traditional adjacent connection transmission method limits the advantages of low latency in optical communication. Multi-hop transmission increases the number of hops and propagation distance, thereby affecting time-sensitive business transmissions. Therefore, based on the design of optical interconnect parallel subnetworks, this paper proposes a scheduling strategy for time-sensitive business transmissions between LEO satellites. Firstly, this strategy integrates the gate control scheduling mechanism from Time-Sensitive Networking (TSN) transmission in the interconnect parallel subnetwork scenario. Secondly, considering issues like queuing after subnetwork division, excessive burden, and algorithm complexity, mathematical problem abstraction modeling is applied to subsequent route scheduling, with reinforcement learning used to solve the problem. Through simulation experiments, it has been observed that compared to SPF (Shortest Path First) and ELB (Equal Load Balance), this approach can effectively enhance the control capability of end-to-end latency for TSN services in long-distance transmissions within Low Earth Orbit mega-constellations. The integration of reinforcement learning decision algorithms also reduces the complexity compared to traditional constraint-solving algorithms, ensuring a certain level of practicality. Overall, this solution can enhance the communication efficiency and performance of time-sensitive services between satellite constellations. By integrating time-sensitive network transmission technologies into optically interconnected subnets, further exploration and realization of low-latency and controllable latency satellite communication networks can be pursued.
Melt-spun poly(trimethylene terephthalate) (PTT) fibers are generally prepared by mechanical drawing. Herein, PTT fibers and nonwovens were prepared using spunbonding technology by air drawing. The surfaces of PTT fib...
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Melt-spun poly(trimethylene terephthalate) (PTT) fibers are generally prepared by mechanical drawing. Herein, PTT fibers and nonwovens were prepared using spunbonding technology by air drawing. The surfaces of PTT fibers are smooth and their evenness is very uniform. When drawing air velocity increases from 0 to 16.5 m/s at mass throughput of 36.0 mL/min, crystallinity and tensile strength of fibers increase from 11.5 to 21.2% and from 53.9 +/- 5.3 to 177.3 +/- 18.7 MPa, respectively, while elongation at break of samples decreases from 675.2 +/- 52.0 to 374.8 +/- 37.2%. Comprehensive analysis indicates that PTT spunbond nonwoven achieves the best combination of different properties when drawing air velocity and bonding temperature are 14.0 m/s and 60 degrees C, respectively. Under this condition, pore size, tensile strength along machine direction, tensile strength cross machine direction, bursting strength, filtration efficiency for PM2.0, pressure drop, and porosity of nonwovens are 8.3 +/- 1.9 mu m, 5.88 +/- 0.54 MPa, 6.49 +/- 0.59 MPa, 0.110 +/- 0.006 MPa, 92.2 +/- 1.6%, 119 +/- 14 Pa, and 68.2%, respectively. The prepared PTT spunbond nonwovens are potentially competitive in the fields of packaging and air filtration due to their pleasing comprehensive properties.
In this study,the perovskite nanocomposite PrFe_(x)Co_(1-x)O_(3)(Pr(S))was successfully synthesized by the sol-gel method;PrFe_(x)Co_(1-x)O_(3)/Al-pillared montmorillonite(Pr(S)/Mt)catalysts were prepared by impregnat...
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In this study,the perovskite nanocomposite PrFe_(x)Co_(1-x)O_(3)(Pr(S))was successfully synthesized by the sol-gel method;PrFe_(x)Co_(1-x)O_(3)/Al-pillared montmorillonite(Pr(S)/Mt)catalysts were prepared by impregnation(D)method and solid-melting(G)method,respectively,with Pr(S)as the active component and Al-pillared montmorillonite as the *** catalysts were applied to treat the 2-hydroxybenzoic acid(2-HA)-simulated wastewater by catalytic wet peroxide oxidation(CWPO)technique,and the chemical oxygen demand(COD)removal rate and the 2-HA degradation rate were used as indicators to evaluate the catalytic *** results of the experiment indicated that the solid-melting method was more conducive to preparing the catalyst when the Co/Fe molar ratio of 7:3 and the optimal structural properties of the catalysts were *** influence of operating parameters,including reaction temperature,catalyst dosage,H_(2)O_(2)dosage,pH,and initial 2-HA concentration,were optimized for the degradation of 2-HA by *** results showed that 97.64%of 2-HA degradation and 75.23%of COD removal rate were achieved under more suitable experimental *** addition,after the catalyst was used five times,the degradation rate of 2-HA could still reach 76.93%,which implied the high stability and reusability of the *** high catalytic activity of the catalyst was due to the doping of Co into PrFeO_(3),which could promote the generation of HO·,and the high stability could be attributed to the loading of Pr(S)onto Al-Mt,which reduced the leaching of reactive *** study of reaction mechanism and kinetics showed that the whole degradation process conformed to the pseudo-firstorder kinetic equation,and the Langmuir-Hinshelwood method was applied to demonstrate that catalysis was dominant in the degradation process.
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