This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independ...This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.展开更多
Mitochondria play a key role in lipid metabolism,and mitochondrial DNA(mtDNA)mutations are thus considered to affect obesity susceptibility by altering oxidative phosphorylation and mitochondrial function.In this stud...Mitochondria play a key role in lipid metabolism,and mitochondrial DNA(mtDNA)mutations are thus considered to affect obesity susceptibility by altering oxidative phosphorylation and mitochondrial function.In this study,we investigate mtDNA variants that may affect obesity risk in 2877 Han Chinese individuals from 3 independent populations.The association analysis of 16 basal mtDNA haplogroups with body mass index,waist circumference,and waist-to-hip ratio reveals that only haplogroup M7 is significantly negatively correlated with all three adiposity-related anthropometric traits in the overall cohort,verified by the analysis of a single population,i.e.,the Zhengzhou population.Furthermore,subhaplogroup analysis suggests that M7b1a1 is the most likely haplogroup associated with a decreased obesity risk,and the variation T12811C(causing Y159H in ND5)harbored in M7b1a1 may be the most likely candidate for altering the mitochondrial function.Specifically,we find that proportionally more nonsynonymous mutations accumulate in M7b1a1 carriers,indicating that M7b1a1 is either under positive selection or subject to a relaxation of selective constraints.We also find that nuclear variants,especially in DACT2 and PIEZO1,may functionally interact with M7b1a1.展开更多
Developing a high-efficiency catalyst with both superior low-temperature activity and good N_(2)selectivity is still challenging for the NH_(3)selective catalytic reduction(SCR)of NO_(x)from mobile sources.Herein,we d...Developing a high-efficiency catalyst with both superior low-temperature activity and good N_(2)selectivity is still challenging for the NH_(3)selective catalytic reduction(SCR)of NO_(x)from mobile sources.Herein,we demonstrate the improved low-temperature activity and N_(2)selectivity by regulating the redox and acidic properties of MnCe oxides supported on etched ZSM-5 supports.The etched ZSM-5 enables the highly dispersed state of MnCeOx species and strong interaction between Mn and Ce species,which promotes the reduction of CeO2,facilitates electron transfer from Mn to Ce,and generates more Mn^(4+)and Ce^(3+)species.The strong redox capacity contributes to forming the reactive nitrate species and-NH_(2)species from oxidative dehydrogenation of NH_(3).Moreover,the adsorbed NH_(3)and-NH_(2)species are the reactive intermediates that promote the formation of N_(2).This work demonstrates an effective strategy to enhance the low-temperature activity and N_(2)selectivity of SCR catalysts,contributing to the NO_(x)control for the low-temperature exhaust gas during the cold-start of diesel vehicles.展开更多
The rapidly evolving environment of assisted reproductive technology(ART)requires consideration of how new innovations are reshaping clinical practice as much as research.In particular,there are three key areas that,w...The rapidly evolving environment of assisted reproductive technology(ART)requires consideration of how new innovations are reshaping clinical practice as much as research.In particular,there are three key areas that,while full of promise,also present significant challenges:the use of artificial intelligence(AI)in embryo selection,the impact of personalized medicine on ART success rates,and the ethical considerations of genetic screening of embryos[1].This letter is meant to provoke further discussion and highlight the need for harmonized global guidelines as these advances continue to reshape the reproductive medicine environment.展开更多
Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these i...Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.展开更多
This research aims to study the bio-adsorption process of two dyes,Cibacron Green H3G(CG-H3G)and Terasil Red(TR),in a single system and to bring them closer to the industrial textile discharge by a binary mixture of t...This research aims to study the bio-adsorption process of two dyes,Cibacron Green H3G(CG-H3G)and Terasil Red(TR),in a single system and to bring them closer to the industrial textile discharge by a binary mixture of two dyes(TR+CG-H3G).The Cockle Shell(CS)was used as a natural bio-adsorbent.The characterizations of CS were investigated by Fourier transform infrared(FTIR),X-ray diffraction(XRD),scanning electron microscopy(SEM),energy-dispersive X-ray spectroscopy(EDX)and Brunauer–Emmett–Teller(BET).The adsorption potential of Cockle Shells was tested in two cases(single and binary system)and determined by:contact time(0–60 min),bio-adsorption dose(3–15 g/L),initial concentration(10–300 mg/L),temperature(22–61°C)and pH solution(2–12).The study of bio-adsorption(equilibrium and kinetics)was conducted at 22°C.The kinetic studies demon-strated that a pseudo-second-order adsorption mechanism had a good correlation coefficient(R2≥0.999).The Langmuir isotherm modeling provided a well-defined description of TR and CG-H3G bio-adsorption on cockle shells,exhibiting maximum capacities of 29.41 and 3.69 mg/g respectively at 22°C.The thermodynamic study shows that the reaction between the TR,CG-H3G dyes molecules and the bio-adsorbent is exothermic,spontaneous in the range of 22–31°C with the aleatory character decrease at the solid-liquid interface.The study of selectivity in single and binary systems has been performed under optimal operating conditions using the industrial textile rejection pH(pH=6.04).CG-H3G dye is found to have a higher selectivity than TR in single(0–60 min)and binary systems with a range of 6–45 min,as shown by the selectivity measurement.It was discovered that CS has the capability to remove both CG-H3G and TR dyes in both simple and binary systems,making it a superior bio-adsorbent.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly re...The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
Base-catalyzed nucleophilic substitution reactions ofβ-ketonitrile with azodicarboxylates have been developed.A series of disubstituted C—N coupling products were obtained in good to excellent yields under Et_(3)N c...Base-catalyzed nucleophilic substitution reactions ofβ-ketonitrile with azodicarboxylates have been developed.A series of disubstituted C—N coupling products were obtained in good to excellent yields under Et_(3)N catalysis.Monosubstitu-tion C—N bond formation reaction catalyzed by K_(2)CO_(3) also gave novel enol-based target products.This method is simple and mild,with good chemoselectivity,excellent substrate compatibility and tolerance for various functional groups,and achieves gram-scale synthesis.The reaction is a nucleophilic substitution process without the involvement of free radicals.展开更多
The highly selective catalytic hydrogenation of halogenated nitroaromatics was achieved by employing Pd‑based catalysts that were co‑modified with organic and inorganic ligands.It was demonstrated that the catalysts c...The highly selective catalytic hydrogenation of halogenated nitroaromatics was achieved by employing Pd‑based catalysts that were co‑modified with organic and inorganic ligands.It was demonstrated that the catalysts contained Pd species in mixed valence states,with high valence Pd at the metal‑support interface and zero valence Pd at the metal surface.While the strong coordination of triphenylphosphine(PPh3)to Pd0 on the Pd surface prevents the adsorption of halogenated nitroaromatics and thus dehalogenation,the coordination of sodium metavanadate(NaVO3)to high‑valence Pd sites at the interface helps to activate H2 in a heterolytic pathway for the selective hydrogenation of nitro‑groups.The excellent catalytic performance of the interfacial active sites enables the selective hydrogenation of a wide range of halogenated nitroaromatics.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
ZGH401 alloy was prepared under varying laser power levels and scanning speeds by the orthogonal test method using selective laser melting(SLM).The effect of different energy densities on microstructure and mechanical...ZGH401 alloy was prepared under varying laser power levels and scanning speeds by the orthogonal test method using selective laser melting(SLM).The effect of different energy densities on microstructure and mechanical properties of the formed alloy was investigated.The microstructure of ZGH401 was analyzed by scanning electron microscope,electron back-scattered diffraction,and electron probe microanalysis.The results show that the defects of the as-built ZGH401 are gradually reduced,the relative density is correspondingly enhanced with increasing the energy density,and the ultimate density can reach 99.6%.An increase in laser power leads to a corresponding rise in hardness of ZGH401,while a faster scanning speed reduces the residual stress in asbuilt ZGH401 samples.In addition,better tensile properties are achieved at room temperature due to more grain boundaries perpendicular to the build direction than parallel to the build direction.The precipitated phases are identified as carbides and Laves phases via chemical composition analysis,with fewer carbides observed at the molten pool boundaries than within the molten pools.展开更多
The selective hydrogenation ofα,β-unsaturated aldehydes/ketones enables precise control over product structures and properties by regulating hydrogen transport pathways and bond cleavage sequences to selectively red...The selective hydrogenation ofα,β-unsaturated aldehydes/ketones enables precise control over product structures and properties by regulating hydrogen transport pathways and bond cleavage sequences to selectively reduce C=C or C=O bonds while preserving other functional groups within the molecule.This approach serves as a critical strategy for the directional synthesis of high-value molecules.However,achieving such selectivity remains challenging due to the thermodynamic equilibrium and kinetic competition between C=O and C=C bonds inα,β-unsaturated systems.Consequently,constructing precisely targeted catalytic systems is essential to overcome these limitations,offering both fundamental scientific significance and industrial application potential.Metal-organic frameworks(MOFs)and their derivatives have emerged as innovative platforms for designing such systems,owing to their programmable topology,tunable pore microenvironments,spatially controllable active sites,and modifiable electronic structures.This review systematically summarizes the research progress of MOF-based catalysts for selec-tive hydrogenation ofα,β-unsaturated aldehydes/ketones in the last decade,with emphasis on the design strategy,conformational relationship,and catalytic mechanism,aiming to provide new ideas for the design of targeted catalyt-ic systems for the selective hydrogenation ofα,β-unsaturated aldehydes/ketones.展开更多
We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of ...We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.展开更多
Compared with natural enzymes, nanozymes have the advantages of high stability and low cost;however,selectivity and sensitivity are key issues that prevent their further development. In this study, we report a cascade...Compared with natural enzymes, nanozymes have the advantages of high stability and low cost;however,selectivity and sensitivity are key issues that prevent their further development. In this study, we report a cascade nanozymatic system with significantly improved selectivity and sensitivity that combines more substrate-specific reactions and sensitive fiuorescence detection. Taking detection of ascorbic acid(AA)as an example, a cascade catalytic reaction system consisting of oxidase-like N-doped carbon nanocages(NC) and peroxidase-like copper oxide(Cu O) improved the reaction selectivity in transforming the substrate into the target product by more than 1200 times against the interference of uric acid. The cascade catalytic reaction system was also applicable for transfer from open reactors into a spatially confined microfiuidic device, increasing the slope of the calibration curves by approximately 1000-fold with a linear detection range of 2.5 nmol/L to 100 nmol/L and a low limit of detection of 0.77 nmol/L. This work offers a new strategy that achieves significant improvements in selectivity and sensitivity.展开更多
This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—cove...This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.展开更多
The hydroformylation of olefins,known as the"oxo reaction",involves the use of syngas(CO/H_(2))to produce aldehyde with an additional carbon atom.However,side reactions such as the isomerization or hydrogena...The hydroformylation of olefins,known as the"oxo reaction",involves the use of syngas(CO/H_(2))to produce aldehyde with an additional carbon atom.However,side reactions such as the isomerization or hydrogenation of olefins often result in unexpected products and other by-products.Recent efforts in developing efficient ligands represent the most effective approach to addressing these challenges.In this study,we described a Bis-OPNN phosphorus ligand facilitated Rh-catalyzed hydroformylation with a high degree of linear selectivity across various olefins.Under mild conditions,a broad range of olefins were efficiently converted into linear aldehydes with high yields and excellent regioselectivity.The protocol also showed impressive functional group tolerance and was successfully applied to modify drugs and natural products,including the total synthesis of(±)-crispine A.Preliminary mechanistic studies revealed that this Bis-OPNN phosphorus ligand anchoring the rhodium catalyst is crucial for controlling the linear selectivity.展开更多
文摘This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.
基金supported by the National Natural Science Foundation of China(32270670,32288101,32271186,and 32200482)the National Basic Research Program of China(2015FY111700)the CAMS Innovation Fund for Medical Sciences(2019-I2M-5-066).
文摘Mitochondria play a key role in lipid metabolism,and mitochondrial DNA(mtDNA)mutations are thus considered to affect obesity susceptibility by altering oxidative phosphorylation and mitochondrial function.In this study,we investigate mtDNA variants that may affect obesity risk in 2877 Han Chinese individuals from 3 independent populations.The association analysis of 16 basal mtDNA haplogroups with body mass index,waist circumference,and waist-to-hip ratio reveals that only haplogroup M7 is significantly negatively correlated with all three adiposity-related anthropometric traits in the overall cohort,verified by the analysis of a single population,i.e.,the Zhengzhou population.Furthermore,subhaplogroup analysis suggests that M7b1a1 is the most likely haplogroup associated with a decreased obesity risk,and the variation T12811C(causing Y159H in ND5)harbored in M7b1a1 may be the most likely candidate for altering the mitochondrial function.Specifically,we find that proportionally more nonsynonymous mutations accumulate in M7b1a1 carriers,indicating that M7b1a1 is either under positive selection or subject to a relaxation of selective constraints.We also find that nuclear variants,especially in DACT2 and PIEZO1,may functionally interact with M7b1a1.
基金the National Natural Science Foundation of China(Nos.22125604,22106100,21976117,22276119)Shanghai Rising-Star Program(No.22QA1403700).
文摘Developing a high-efficiency catalyst with both superior low-temperature activity and good N_(2)selectivity is still challenging for the NH_(3)selective catalytic reduction(SCR)of NO_(x)from mobile sources.Herein,we demonstrate the improved low-temperature activity and N_(2)selectivity by regulating the redox and acidic properties of MnCe oxides supported on etched ZSM-5 supports.The etched ZSM-5 enables the highly dispersed state of MnCeOx species and strong interaction between Mn and Ce species,which promotes the reduction of CeO2,facilitates electron transfer from Mn to Ce,and generates more Mn^(4+)and Ce^(3+)species.The strong redox capacity contributes to forming the reactive nitrate species and-NH_(2)species from oxidative dehydrogenation of NH_(3).Moreover,the adsorbed NH_(3)and-NH_(2)species are the reactive intermediates that promote the formation of N_(2).This work demonstrates an effective strategy to enhance the low-temperature activity and N_(2)selectivity of SCR catalysts,contributing to the NO_(x)control for the low-temperature exhaust gas during the cold-start of diesel vehicles.
文摘The rapidly evolving environment of assisted reproductive technology(ART)requires consideration of how new innovations are reshaping clinical practice as much as research.In particular,there are three key areas that,while full of promise,also present significant challenges:the use of artificial intelligence(AI)in embryo selection,the impact of personalized medicine on ART success rates,and the ethical considerations of genetic screening of embryos[1].This letter is meant to provoke further discussion and highlight the need for harmonized global guidelines as these advances continue to reshape the reproductive medicine environment.
基金supported by the National Natural Science Foundation of China(42250101)the Macao Foundation。
文摘Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.
基金supported by the University Salah Boubnider-Constantine 3 (Algeria).
文摘This research aims to study the bio-adsorption process of two dyes,Cibacron Green H3G(CG-H3G)and Terasil Red(TR),in a single system and to bring them closer to the industrial textile discharge by a binary mixture of two dyes(TR+CG-H3G).The Cockle Shell(CS)was used as a natural bio-adsorbent.The characterizations of CS were investigated by Fourier transform infrared(FTIR),X-ray diffraction(XRD),scanning electron microscopy(SEM),energy-dispersive X-ray spectroscopy(EDX)and Brunauer–Emmett–Teller(BET).The adsorption potential of Cockle Shells was tested in two cases(single and binary system)and determined by:contact time(0–60 min),bio-adsorption dose(3–15 g/L),initial concentration(10–300 mg/L),temperature(22–61°C)and pH solution(2–12).The study of bio-adsorption(equilibrium and kinetics)was conducted at 22°C.The kinetic studies demon-strated that a pseudo-second-order adsorption mechanism had a good correlation coefficient(R2≥0.999).The Langmuir isotherm modeling provided a well-defined description of TR and CG-H3G bio-adsorption on cockle shells,exhibiting maximum capacities of 29.41 and 3.69 mg/g respectively at 22°C.The thermodynamic study shows that the reaction between the TR,CG-H3G dyes molecules and the bio-adsorbent is exothermic,spontaneous in the range of 22–31°C with the aleatory character decrease at the solid-liquid interface.The study of selectivity in single and binary systems has been performed under optimal operating conditions using the industrial textile rejection pH(pH=6.04).CG-H3G dye is found to have a higher selectivity than TR in single(0–60 min)and binary systems with a range of 6–45 min,as shown by the selectivity measurement.It was discovered that CS has the capability to remove both CG-H3G and TR dyes in both simple and binary systems,making it a superior bio-adsorbent.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金supported by the National Natural Science Foundation of China(32160782 and 32060737).
文摘The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
文摘Base-catalyzed nucleophilic substitution reactions ofβ-ketonitrile with azodicarboxylates have been developed.A series of disubstituted C—N coupling products were obtained in good to excellent yields under Et_(3)N catalysis.Monosubstitu-tion C—N bond formation reaction catalyzed by K_(2)CO_(3) also gave novel enol-based target products.This method is simple and mild,with good chemoselectivity,excellent substrate compatibility and tolerance for various functional groups,and achieves gram-scale synthesis.The reaction is a nucleophilic substitution process without the involvement of free radicals.
文摘The highly selective catalytic hydrogenation of halogenated nitroaromatics was achieved by employing Pd‑based catalysts that were co‑modified with organic and inorganic ligands.It was demonstrated that the catalysts contained Pd species in mixed valence states,with high valence Pd at the metal‑support interface and zero valence Pd at the metal surface.While the strong coordination of triphenylphosphine(PPh3)to Pd0 on the Pd surface prevents the adsorption of halogenated nitroaromatics and thus dehalogenation,the coordination of sodium metavanadate(NaVO3)to high‑valence Pd sites at the interface helps to activate H2 in a heterolytic pathway for the selective hydrogenation of nitro‑groups.The excellent catalytic performance of the interfacial active sites enables the selective hydrogenation of a wide range of halogenated nitroaromatics.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金National Defense Science and Technology Project Management Center(2021-JCJQ-JJ-0092)。
文摘ZGH401 alloy was prepared under varying laser power levels and scanning speeds by the orthogonal test method using selective laser melting(SLM).The effect of different energy densities on microstructure and mechanical properties of the formed alloy was investigated.The microstructure of ZGH401 was analyzed by scanning electron microscope,electron back-scattered diffraction,and electron probe microanalysis.The results show that the defects of the as-built ZGH401 are gradually reduced,the relative density is correspondingly enhanced with increasing the energy density,and the ultimate density can reach 99.6%.An increase in laser power leads to a corresponding rise in hardness of ZGH401,while a faster scanning speed reduces the residual stress in asbuilt ZGH401 samples.In addition,better tensile properties are achieved at room temperature due to more grain boundaries perpendicular to the build direction than parallel to the build direction.The precipitated phases are identified as carbides and Laves phases via chemical composition analysis,with fewer carbides observed at the molten pool boundaries than within the molten pools.
文摘The selective hydrogenation ofα,β-unsaturated aldehydes/ketones enables precise control over product structures and properties by regulating hydrogen transport pathways and bond cleavage sequences to selectively reduce C=C or C=O bonds while preserving other functional groups within the molecule.This approach serves as a critical strategy for the directional synthesis of high-value molecules.However,achieving such selectivity remains challenging due to the thermodynamic equilibrium and kinetic competition between C=O and C=C bonds inα,β-unsaturated systems.Consequently,constructing precisely targeted catalytic systems is essential to overcome these limitations,offering both fundamental scientific significance and industrial application potential.Metal-organic frameworks(MOFs)and their derivatives have emerged as innovative platforms for designing such systems,owing to their programmable topology,tunable pore microenvironments,spatially controllable active sites,and modifiable electronic structures.This review systematically summarizes the research progress of MOF-based catalysts for selec-tive hydrogenation ofα,β-unsaturated aldehydes/ketones in the last decade,with emphasis on the design strategy,conformational relationship,and catalytic mechanism,aiming to provide new ideas for the design of targeted catalyt-ic systems for the selective hydrogenation ofα,β-unsaturated aldehydes/ketones.
文摘We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.
基金supported by the National Natural Science Foundation of China (Nos. 22174014 and 22074015)。
文摘Compared with natural enzymes, nanozymes have the advantages of high stability and low cost;however,selectivity and sensitivity are key issues that prevent their further development. In this study, we report a cascade nanozymatic system with significantly improved selectivity and sensitivity that combines more substrate-specific reactions and sensitive fiuorescence detection. Taking detection of ascorbic acid(AA)as an example, a cascade catalytic reaction system consisting of oxidase-like N-doped carbon nanocages(NC) and peroxidase-like copper oxide(Cu O) improved the reaction selectivity in transforming the substrate into the target product by more than 1200 times against the interference of uric acid. The cascade catalytic reaction system was also applicable for transfer from open reactors into a spatially confined microfiuidic device, increasing the slope of the calibration curves by approximately 1000-fold with a linear detection range of 2.5 nmol/L to 100 nmol/L and a low limit of detection of 0.77 nmol/L. This work offers a new strategy that achieves significant improvements in selectivity and sensitivity.
文摘This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.
基金financial support from the National Key Research and Development Program of China(No.2021YFF0600704).
文摘The hydroformylation of olefins,known as the"oxo reaction",involves the use of syngas(CO/H_(2))to produce aldehyde with an additional carbon atom.However,side reactions such as the isomerization or hydrogenation of olefins often result in unexpected products and other by-products.Recent efforts in developing efficient ligands represent the most effective approach to addressing these challenges.In this study,we described a Bis-OPNN phosphorus ligand facilitated Rh-catalyzed hydroformylation with a high degree of linear selectivity across various olefins.Under mild conditions,a broad range of olefins were efficiently converted into linear aldehydes with high yields and excellent regioselectivity.The protocol also showed impressive functional group tolerance and was successfully applied to modify drugs and natural products,including the total synthesis of(±)-crispine A.Preliminary mechanistic studies revealed that this Bis-OPNN phosphorus ligand anchoring the rhodium catalyst is crucial for controlling the linear selectivity.
