Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcom...Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.展开更多
In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use th...In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.展开更多
Many data that exhibit asymmetrical behavior can be well modeled with skew-normal random errors.Moreover,data that arise from a heterogeneous population can be efficiently analyzed by a finite mixture of regression mo...Many data that exhibit asymmetrical behavior can be well modeled with skew-normal random errors.Moreover,data that arise from a heterogeneous population can be efficiently analyzed by a finite mixture of regression models.These observations motivate us to propose a novel finite mixture ofmode regression model based on amixture of the skew-normal distributions to explore asymmetrical data from several subpopulations.Thanks to the stochastic representation of the skew-normal distribution,we construct a Bayesian hierarchical modeling framework and then develop an efficient Markov chain Monte Carlo sampling algorithm to generate posterior samples for obtaining the Bayesian estimates of the unknown parameters and their corresponding standard errors.Simulation studies and a real-data example are presented to illustrate the performance of the proposed Bayesian methodology.展开更多
In this paper,we introduce a communication-efficient distributed estimation method tailored for massive datasets exhibiting skewness.The data are stored across multiple machines.We construct a surrogate likelihood whi...In this paper,we introduce a communication-efficient distributed estimation method tailored for massive datasets exhibiting skewness.The data are stored across multiple machines.We construct a surrogate likelihood which only need to transfer subgradient from local machines to approximate higher-order derivatives of the global likelihood.An enhanced EM algorithm is developed for computations.The proposed method not only addresses the non-normality of data by utilizing first-order gradient information in each transmission,ensuring low communication overhead,but also ensures privacy protection.Simulation studies illustrate the superior performance of the proposed methods.展开更多
Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and(or) the variance model, and cannot be used to select the joint mean, va...Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and(or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes,and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.展开更多
A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes.Moreover,data that arise from a heterogeneous population can be...A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes.Moreover,data that arise from a heterogeneous population can be efficiently analysed by a finite mixture of regression models.These observations motivate us to propose a novel finite mixture of median regression model based on a mixture of the skew-normal distributions to explore asymmetrical data from several subpopulations.With the appropriate choice of the tuning parameters,we establish the theoretical properties of the proposed procedure,including consistency for variable selection method and the oracle property in estimation.A productive nonparametric clustering method is applied to select the number of components,and an efficient EM algorithm for numerical computations is developed.Simulation studies and a real data set are used to illustrate the performance of the proposed methodologies.展开更多
We establish an identity for E f(Y)-E f(X),when X and Y both have matrix variate skew-normal distributions and the function f satisfies some weak conditions.The characteristic function of matrix variate skew normal dis...We establish an identity for E f(Y)-E f(X),when X and Y both have matrix variate skew-normal distributions and the function f satisfies some weak conditions.The characteristic function of matrix variate skew normal distribution is then derived.We then make use of it to derive some necessary and sufficient conditions for the comparison of matrix variate skew-normal distributions under six different orders,such as usual stochastic order,convex order,increasing convex order,upper orthant order,directionally convex order and supermodular order.展开更多
To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produce...To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.展开更多
We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the ...We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the distribution of the lengths of these intervals is relatively light compared to the distribution of the increments of the random walks,we derive the asymptotic tail distribution of the partial maximum sum over the random time interval.展开更多
Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energ...Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.展开更多
Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of soci...Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of social concern.Based on observation data concerning low-visibility phenomena derived from 105 national meteorological stations in Xinjiang,China over the past 20 years,we systematically analyzed the differences between manual and instrument observations for six types of low-visibility phenomena,with a focus on exploring their spatiotemporal distribution characteristics using instrument data.The results revealed that low-visibility phenomena were dominated by fog-and haze-related events(mist,fog,and haze)in northern Xinjiang and dust-related events(dust storms,blowing sand,and floating dust)in southern Xinjiang,with transitional characteristics observed in eastern Xinjiang.Compared with manual observations,the instrument measurements significantly improved the fine-scale low-visibility phenomenon identification process.On the basis of the instrument observation data,spatial-dimension analysis results indicated that low-visibility phenomena in Xinjiang were significantly influenced by terrain factors.Constrained by the Tianshan Mountains,haze-like phenomena formed a core agglomeration area in northern Xinjiang,whereas dust-and sand-related phenomena radiated outward,with the Taklimakan Desert at the center.Moreover,the gripping effect of the terrain promoted dust transmission along low-altitude channels.Temporally,fog-and haze-related phenomena occurred mainly during autumn and winter,and the proportion of these events decreased from 76.7%to 55.1%.The fog-and haze-related phenomena demonstrated a U-shaped rebound trend,while the proportion of mist phenomena decreased by 34.2%.Dust storms occurred during spring,accounting for 23.3%to 44.9%of all storms.Instrument measurement technology has the advantages of high spatial and temporal resolutions and multiparameter coordination but provides a limited dust-haze mixed-pollution identification capacity.This study provides crucial reference data for enhancing the understanding of low-visibility events in Xinjiang and the potential responses while improving the accuracy of pollution source tracking and meteorological process diagnosis tasks.展开更多
As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpi...As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpine plant distribution and their profound impact on fragile ecosystems have become a focus of ecological research and conservation efforts,with increasing urgency.Meconopsis,a typical representative of Chinese alpine plants,exhibits diverse adaptability,making it an ideal model for studying how alpine species respond to extreme environmental changes.A lack of comprehensive genus-level analyses may hinder the development of long-term conservation and management strategies.Given the genus's ecological importance,vulnerability,and the risk of trait homogenization in genus-level modeling,there is an urgent need to assess its future distribution patterns,migration trends,and adaptive mechanisms based on habitat classification.In this study,we employed the Maxent model,integrating multidimensional environmental variables,to develop genus-level models and representative habitat-based models(forest,meadows,and periglacial).Results indicate a northwestward expansion and southeastward contraction of suitable habitats under future climate scenarios,with migration patterns in latitude and elevation showing stage-specific characteristics.Key environmental factors varied across models but were mostly associated with seasonal growth traits and microhabitat conditions,highlighting both the universal ecological requirements and niche differentiation within Meconopsis.Based on these findings,we propose a dynamic conservation strategy framework informed by stage-specific responses and habitat differences.Future efforts should focus on incorporating alpine-specific environmental variables and optimizing specimen collection strategies to enhance model performance and support landscape planning and biodiversity conservation.展开更多
Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges...Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges.There has been a lack of a comprehensive and multidimensional assessment to inform strategic conservation planning.Therefore,this study integrated 4 key biodiversity indices including species richness(SR),phylogenetic diversity(PD),threatened species richness(TSR),and endemic species richness(ESR)to map species diversity distribution patterns,identify conservation gaps,and elucidate their effects of climatic factors.This study revealed that species diversity shows a clear trend of decreasing from the western region to the eastern region of Tajikistan.The central–western mountains(specifically the Gissar-Darvasian and Zeravshanian regions)emerge as irreplaceable biodiversity hotspots.However,we found a severe spatial mismatch between these priority areas and the existing protected areas(PAs).Protection coverage for all hotspots was alarmingly low,ranging from 31.00%to 38.00%.Consequently,a critical 64.80%of integrated priority areas fall outside of the current PAs,representing a major conservation gap.This study identified precipitation seasonality and isothermality as the principal drivers,collectively explaining over 50.00%of the diversity variation and suggesting high vulnerability to hydrological shifts.Furthermore,we detected significant geographic sampling bias in the public biodiversity databases,with the most critical hotspot being systematically under-sampled.This study provides a robust scientific basis for conservation action,highlighting the urgent need to strategically expand PAs in the under-protected southwestern region and to mitigate critical sampling gaps through targeted data digitization and field surveys.These measures are indispensable for securing Tajikistan’s unique biodiversity and achieving the Kunming-Montreal Global Biodiversity Framework Target 3(“30×30 Protection”).展开更多
Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most species...Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most speciesrich genus in the Bambusoideae subfamily.Based on the distribution data of 46 species and 20 environmental variables,we used the MaxEnt model combined with ArcGIS calculations to simulate current and future potential richness distributions under three distinct CO_(2) emission scenarios.The results showed that the MaxEnt model had a good predictive ability,with a mean area under the working characteristic curve(AUC value)of 0.91 for all species.The main environmental variables that impacted the future distribution of most Phyllostachys species were elevation,variations of seasonal precipitation,and mean diurnal range.Phyllostachys species are currently concentrated in southeastern China.Under future climate projections,18 species exhibited significant habitat contraction across three or more future climate scenarios,but suitable habitats for other species will expand.This enhancement is most pronounced under the extreme climate scenario(2090s-SSP585),primarily driven by high species gains contributing to elevated turnover values across scenarios.The center of maximum richness will progressively shift southwestward over time.Predictive modeling of Phyllostachys richness distribution dynamics under climate change enhances our understanding of its biogeography and informs strategic introduction programs to bamboo management and augments China’s carbon sequestration capacity.展开更多
Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly im...Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly impact evaluation results.To address this issue,this paper proposes a possibilistic approach to evaluate PV hosting capacity(PVHC).First,possibility distribution is used to model load demand in order to reflect uncertainties associated with human factor,whereas the interval model is applied to deal with uncertainties of PV outputs.Second,a voltage deterioration index is proposed considering overvoltage risk of entire system on time scale.After that,possibilistic PVHC evaluation method based on this index is proposed.A 6-bus system is used to illustrate advantages of the proposed method,followed by a discussion of role of PVHC possibility distribution in actual decision-making of utilities.Moreover,sensitivity of simulation parameters is analyzed to reduce computational burden.Finally,the proposed method is tested on the IEEE 123-bus DN to validate adaptability to a larger system and to analyze impact of PVHC results against different acceptable values set by utilities.展开更多
We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensi...We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.展开更多
Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in a...Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.展开更多
In this study,the effects of low-dose sodium hypochlorite disinfection on water quality and biofilm growth in drinking water distribution systems(DWDS)after ultrafiltration pretreatment was investigated.The influence ...In this study,the effects of low-dose sodium hypochlorite disinfection on water quality and biofilm growth in drinking water distribution systems(DWDS)after ultrafiltration pretreatment was investigated.The influence of pipeline hydraulic residence time(HRT)on disinfection efficiency,by-product formation,microbial activity,and biofilm growth were considered.The results show that both microbial activities and metabolite secretion were stimulated by increasing HRT,aggravating the potential risk of microbial pollution in DWDS.The enhanced microbial metabolism could further weaken disinfection efficiency by consuming extra residual Chlorine,after which the formation of disinfection by-products was facilitated.Residual Chlorine was found negatively correlated with HRT.With prolonging HRT from 5 to 40 h,the concentration of disinfection by-products(Chlorate,Chlorite,and Trichloromethane)was on a continuously increasing trend by 37%,140%,and 75%,respectively.But the water kept in pipeline still reliably satisfied the Standards for drinking water quality in China(GB5749–2022).Besides,more biofilm with denser morphologies developed on rubber pipeline gaskets rather than the iron/plastic ones.Rubber material was inappropriate for DWDS due to its potential risk of secondary biological pollution.Prolonging HRT also enhanced the accumulation of dominant bacteria(e.g.Bradyrhizobium and Obscuribacter)and decreased microbial diversity.展开更多
To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s...To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.展开更多
Air conditioning is a major energy-consuming component in buildings,and accurate air conditioning load forecasting is of great significance for maximizing energy utilization efficiency.However,the deep learning models...Air conditioning is a major energy-consuming component in buildings,and accurate air conditioning load forecasting is of great significance for maximizing energy utilization efficiency.However,the deep learning models currently used in the field of air conditioning load forecasting often suffer from issues such as distribution bias in load data and insufficient expression ability of nonlinear features in the model,which affect the accuracy of load forecasting.To address this,this paper proposes a novel load forecasting model.Firstly,the model employs the Dish-TS(DS)module to standardize the input window data through self-learning standardized parameters,thereby addressing the spatial intra-bias problem existing between data.Secondly,DS-Kansformer introduces Kolmogorov-Arnold Networks(KANs)to enhance the expression ability of nonlinear features.Finally,the output window is denormalized through the self-learning parameter of the DS module to restore the original distribution of the predicted data.In this paper,experiments were carried out based on the air-conditioning load dataset collected from a multi-functional comprehensive building,and the experimental results show that after adding the DS module,the Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and R-squared(R^(2))of the model are 20.46%,34.44%,and 92.61%,respectively;after introducing KAN,the MAE,RMSE,and R^(2) are 22.81%,35.72%,and 92.05%,respectively;the model also exhibits high prediction accuracy after integrating the two modules(with RMSE,MAE,and R^(2) being 19.75%,34.05%,and 92.78%,respectively),outperforming common time series prediction models,confirming the reliability and efficiency of the model,which can provide reliable support for intelligent energy management in buildings.展开更多
基金Supported by the National Natural Science Foundation of China(11261025,11201412)the Natural Science Foundation of Yunnan Province(2011FB016)the Program for Middle-aged Backbone Teacher,Yunnan University
文摘Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.
基金Project supported in part by Foundation for Science and Technology(FCT) (No.SFRD/BD/5987/2001)the Operational ProgramScience,Technology,and Innovation of the FCT,co-financed by theEuropean Regional Development Fund (ERDF)
文摘In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.
基金supported by the Academics and Technology Foundation of Kunming University of Science and Technology(Grants 2020YB208)supported by the Natural Science Research Foundation of Kunming University of Science and Technology(Grants KKSY201907003)supported by the National Natural Science Foundation of China(Grants 11861041).
文摘Many data that exhibit asymmetrical behavior can be well modeled with skew-normal random errors.Moreover,data that arise from a heterogeneous population can be efficiently analyzed by a finite mixture of regression models.These observations motivate us to propose a novel finite mixture ofmode regression model based on amixture of the skew-normal distributions to explore asymmetrical data from several subpopulations.Thanks to the stochastic representation of the skew-normal distribution,we construct a Bayesian hierarchical modeling framework and then develop an efficient Markov chain Monte Carlo sampling algorithm to generate posterior samples for obtaining the Bayesian estimates of the unknown parameters and their corresponding standard errors.Simulation studies and a real-data example are presented to illustrate the performance of the proposed Bayesian methodology.
基金Supported by the National Nature Science Foundation of China (Grant Nos. 12371274 and 11971362)the Science and Technology Development Fund,Macao SAR (Grant No. 0019/2021/A1)
文摘In this paper,we introduce a communication-efficient distributed estimation method tailored for massive datasets exhibiting skewness.The data are stored across multiple machines.We construct a surrogate likelihood which only need to transfer subgradient from local machines to approximate higher-order derivatives of the global likelihood.An enhanced EM algorithm is developed for computations.The proposed method not only addresses the non-normality of data by utilizing first-order gradient information in each transmission,ensuring low communication overhead,but also ensures privacy protection.Simulation studies illustrate the superior performance of the proposed methods.
基金supported by the National Natural Science Foundation of China under Grant Nos.11261025,11561075the Natural Science Foundation of Yunnan Province under Grant No.2016FB005the Program for Middle-aged Backbone Teacher,Yunnan University
文摘Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and(or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes,and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.
基金the National Natural Science Foundation of China[grant number 11861041]the Natural Science Research Foundation of Kunming University of Science and Technology[grant number KKSY201907003].
文摘A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes.Moreover,data that arise from a heterogeneous population can be efficiently analysed by a finite mixture of regression models.These observations motivate us to propose a novel finite mixture of median regression model based on a mixture of the skew-normal distributions to explore asymmetrical data from several subpopulations.With the appropriate choice of the tuning parameters,we establish the theoretical properties of the proposed procedure,including consistency for variable selection method and the oracle property in estimation.A productive nonparametric clustering method is applied to select the number of components,and an efficient EM algorithm for numerical computations is developed.Simulation studies and a real data set are used to illustrate the performance of the proposed methodologies.
基金supported by the National Natural Science Foundation of China(No.12071251,11571198,11701319).
文摘We establish an identity for E f(Y)-E f(X),when X and Y both have matrix variate skew-normal distributions and the function f satisfies some weak conditions.The characteristic function of matrix variate skew normal distribution is then derived.We then make use of it to derive some necessary and sufficient conditions for the comparison of matrix variate skew-normal distributions under six different orders,such as usual stochastic order,convex order,increasing convex order,upper orthant order,directionally convex order and supermodular order.
基金National Natural Science Foundation of China(12125509,11961141003,12275361,U2267205,12175152,12175121)National Key Research and Development Project(2022YFA1602301)Continuous-support Basic Scientific Research Project。
文摘To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.
基金supported by Xinjiang Normal University Outstanding Young Teacher Research Launch Fund Project(Grant No.XJNU202116)。
文摘We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the distribution of the lengths of these intervals is relatively light compared to the distribution of the increments of the random walks,we derive the asymptotic tail distribution of the partial maximum sum over the random time interval.
基金supported by Integrated Distribution Network Planning and Operational Enhancement Using Flexibility Domains Under Deep Human-Vehicle-Charger-Road-Grid Coupling(U22B20105).
文摘Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.
基金supported by the Central Government Guidance Funds for Local Science and Technology Development Program(grant no.ZYYD2025ZY21)the Science and Technology Plan Project of the Xinjiang Production and Construction Corps(2023AB036)+1 种基金the Xinjiang Meteorological Bureau High-Level Key Talent Programthe Natural Science Foundation of the Xinjiang Uygur Autonomous Region(2023D01A17 and 2025D01A109).
文摘Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of social concern.Based on observation data concerning low-visibility phenomena derived from 105 national meteorological stations in Xinjiang,China over the past 20 years,we systematically analyzed the differences between manual and instrument observations for six types of low-visibility phenomena,with a focus on exploring their spatiotemporal distribution characteristics using instrument data.The results revealed that low-visibility phenomena were dominated by fog-and haze-related events(mist,fog,and haze)in northern Xinjiang and dust-related events(dust storms,blowing sand,and floating dust)in southern Xinjiang,with transitional characteristics observed in eastern Xinjiang.Compared with manual observations,the instrument measurements significantly improved the fine-scale low-visibility phenomenon identification process.On the basis of the instrument observation data,spatial-dimension analysis results indicated that low-visibility phenomena in Xinjiang were significantly influenced by terrain factors.Constrained by the Tianshan Mountains,haze-like phenomena formed a core agglomeration area in northern Xinjiang,whereas dust-and sand-related phenomena radiated outward,with the Taklimakan Desert at the center.Moreover,the gripping effect of the terrain promoted dust transmission along low-altitude channels.Temporally,fog-and haze-related phenomena occurred mainly during autumn and winter,and the proportion of these events decreased from 76.7%to 55.1%.The fog-and haze-related phenomena demonstrated a U-shaped rebound trend,while the proportion of mist phenomena decreased by 34.2%.Dust storms occurred during spring,accounting for 23.3%to 44.9%of all storms.Instrument measurement technology has the advantages of high spatial and temporal resolutions and multiparameter coordination but provides a limited dust-haze mixed-pollution identification capacity.This study provides crucial reference data for enhancing the understanding of low-visibility events in Xinjiang and the potential responses while improving the accuracy of pollution source tracking and meteorological process diagnosis tasks.
文摘As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpine plant distribution and their profound impact on fragile ecosystems have become a focus of ecological research and conservation efforts,with increasing urgency.Meconopsis,a typical representative of Chinese alpine plants,exhibits diverse adaptability,making it an ideal model for studying how alpine species respond to extreme environmental changes.A lack of comprehensive genus-level analyses may hinder the development of long-term conservation and management strategies.Given the genus's ecological importance,vulnerability,and the risk of trait homogenization in genus-level modeling,there is an urgent need to assess its future distribution patterns,migration trends,and adaptive mechanisms based on habitat classification.In this study,we employed the Maxent model,integrating multidimensional environmental variables,to develop genus-level models and representative habitat-based models(forest,meadows,and periglacial).Results indicate a northwestward expansion and southeastward contraction of suitable habitats under future climate scenarios,with migration patterns in latitude and elevation showing stage-specific characteristics.Key environmental factors varied across models but were mostly associated with seasonal growth traits and microhabitat conditions,highlighting both the universal ecological requirements and niche differentiation within Meconopsis.Based on these findings,we propose a dynamic conservation strategy framework informed by stage-specific responses and habitat differences.Future efforts should focus on incorporating alpine-specific environmental variables and optimizing specimen collection strategies to enhance model performance and support landscape planning and biodiversity conservation.
基金the Chinese Academy of Sciences Research Center for Ecology and Environment of Central Asia(RCEECA),the construction and joint research for the China-Tajikistan“Belt and Road”Joint Laboratory on Biodiversity Conservation and Sustainable Use(2024YFE0214200)the Shanghai Cooperation Organization Partnership and International Technology Cooperation Plan of Science and Technology Projects(2023E01018,2025E01056)the Chinese Academy of Sciences President’s International Fellowship Initiative(PIFI)(2024VBC0006).
文摘Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges.There has been a lack of a comprehensive and multidimensional assessment to inform strategic conservation planning.Therefore,this study integrated 4 key biodiversity indices including species richness(SR),phylogenetic diversity(PD),threatened species richness(TSR),and endemic species richness(ESR)to map species diversity distribution patterns,identify conservation gaps,and elucidate their effects of climatic factors.This study revealed that species diversity shows a clear trend of decreasing from the western region to the eastern region of Tajikistan.The central–western mountains(specifically the Gissar-Darvasian and Zeravshanian regions)emerge as irreplaceable biodiversity hotspots.However,we found a severe spatial mismatch between these priority areas and the existing protected areas(PAs).Protection coverage for all hotspots was alarmingly low,ranging from 31.00%to 38.00%.Consequently,a critical 64.80%of integrated priority areas fall outside of the current PAs,representing a major conservation gap.This study identified precipitation seasonality and isothermality as the principal drivers,collectively explaining over 50.00%of the diversity variation and suggesting high vulnerability to hydrological shifts.Furthermore,we detected significant geographic sampling bias in the public biodiversity databases,with the most critical hotspot being systematically under-sampled.This study provides a robust scientific basis for conservation action,highlighting the urgent need to strategically expand PAs in the under-protected southwestern region and to mitigate critical sampling gaps through targeted data digitization and field surveys.These measures are indispensable for securing Tajikistan’s unique biodiversity and achieving the Kunming-Montreal Global Biodiversity Framework Target 3(“30×30 Protection”).
基金supported by the National Science Foundation of China(32201643)the Key Research Projects of Yibin,research and integrated demonstration and key technologies for smart bamboo industry(YBZD2024-1).
文摘Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most speciesrich genus in the Bambusoideae subfamily.Based on the distribution data of 46 species and 20 environmental variables,we used the MaxEnt model combined with ArcGIS calculations to simulate current and future potential richness distributions under three distinct CO_(2) emission scenarios.The results showed that the MaxEnt model had a good predictive ability,with a mean area under the working characteristic curve(AUC value)of 0.91 for all species.The main environmental variables that impacted the future distribution of most Phyllostachys species were elevation,variations of seasonal precipitation,and mean diurnal range.Phyllostachys species are currently concentrated in southeastern China.Under future climate projections,18 species exhibited significant habitat contraction across three or more future climate scenarios,but suitable habitats for other species will expand.This enhancement is most pronounced under the extreme climate scenario(2090s-SSP585),primarily driven by high species gains contributing to elevated turnover values across scenarios.The center of maximum richness will progressively shift southwestward over time.Predictive modeling of Phyllostachys richness distribution dynamics under climate change enhances our understanding of its biogeography and informs strategic introduction programs to bamboo management and augments China’s carbon sequestration capacity.
基金supported by the National Key R&D Program of China(2018YFB0904700).
文摘Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly impact evaluation results.To address this issue,this paper proposes a possibilistic approach to evaluate PV hosting capacity(PVHC).First,possibility distribution is used to model load demand in order to reflect uncertainties associated with human factor,whereas the interval model is applied to deal with uncertainties of PV outputs.Second,a voltage deterioration index is proposed considering overvoltage risk of entire system on time scale.After that,possibilistic PVHC evaluation method based on this index is proposed.A 6-bus system is used to illustrate advantages of the proposed method,followed by a discussion of role of PVHC possibility distribution in actual decision-making of utilities.Moreover,sensitivity of simulation parameters is analyzed to reduce computational burden.Finally,the proposed method is tested on the IEEE 123-bus DN to validate adaptability to a larger system and to analyze impact of PVHC results against different acceptable values set by utilities.
文摘We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0208)the National Natural Science Foundation of China(Nos.42171148 and 42330512)the Key R&D Project from the Science and Technology Department of Tibet(No.XZ202501ZY0030).
文摘Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.
基金supported by the National Natural Science Foundation of China(Nos.52170070,52400022,and 52200088)the Youth S&T Talent Support Programme of Guangdong Provincial Association for Science and Technology(GDSTA)(No.SKXRC202406)+1 种基金the“One hundred Youth”Science and Technology Plan,Guangdong University of Technology,China(No.263113906)China Postdoctoral Science Foundation(No.2023M740754).
文摘In this study,the effects of low-dose sodium hypochlorite disinfection on water quality and biofilm growth in drinking water distribution systems(DWDS)after ultrafiltration pretreatment was investigated.The influence of pipeline hydraulic residence time(HRT)on disinfection efficiency,by-product formation,microbial activity,and biofilm growth were considered.The results show that both microbial activities and metabolite secretion were stimulated by increasing HRT,aggravating the potential risk of microbial pollution in DWDS.The enhanced microbial metabolism could further weaken disinfection efficiency by consuming extra residual Chlorine,after which the formation of disinfection by-products was facilitated.Residual Chlorine was found negatively correlated with HRT.With prolonging HRT from 5 to 40 h,the concentration of disinfection by-products(Chlorate,Chlorite,and Trichloromethane)was on a continuously increasing trend by 37%,140%,and 75%,respectively.But the water kept in pipeline still reliably satisfied the Standards for drinking water quality in China(GB5749–2022).Besides,more biofilm with denser morphologies developed on rubber pipeline gaskets rather than the iron/plastic ones.Rubber material was inappropriate for DWDS due to its potential risk of secondary biological pollution.Prolonging HRT also enhanced the accumulation of dominant bacteria(e.g.Bradyrhizobium and Obscuribacter)and decreased microbial diversity.
基金supported in part by the Research on Key Technologies for the Development of an Active Balancing Cooperative Control Systemfor Distribution Networks and the National Natural Science Foundation of China under Grant 521532240029,Grant 62303006.
文摘To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.
基金supported by the National Natural Science Foundation with grant No.12374408。
文摘Air conditioning is a major energy-consuming component in buildings,and accurate air conditioning load forecasting is of great significance for maximizing energy utilization efficiency.However,the deep learning models currently used in the field of air conditioning load forecasting often suffer from issues such as distribution bias in load data and insufficient expression ability of nonlinear features in the model,which affect the accuracy of load forecasting.To address this,this paper proposes a novel load forecasting model.Firstly,the model employs the Dish-TS(DS)module to standardize the input window data through self-learning standardized parameters,thereby addressing the spatial intra-bias problem existing between data.Secondly,DS-Kansformer introduces Kolmogorov-Arnold Networks(KANs)to enhance the expression ability of nonlinear features.Finally,the output window is denormalized through the self-learning parameter of the DS module to restore the original distribution of the predicted data.In this paper,experiments were carried out based on the air-conditioning load dataset collected from a multi-functional comprehensive building,and the experimental results show that after adding the DS module,the Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and R-squared(R^(2))of the model are 20.46%,34.44%,and 92.61%,respectively;after introducing KAN,the MAE,RMSE,and R^(2) are 22.81%,35.72%,and 92.05%,respectively;the model also exhibits high prediction accuracy after integrating the two modules(with RMSE,MAE,and R^(2) being 19.75%,34.05%,and 92.78%,respectively),outperforming common time series prediction models,confirming the reliability and efficiency of the model,which can provide reliable support for intelligent energy management in buildings.
