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Abstract Faced with the increasing number of flights, efficient and orderly operations have become a critical issue for busy airports. The closely spaced parallel runway paired approach technology has been proven to be an effective solution for increasing runway capacity and operational efficiency. This research focuses on determining the safety separation between paired aircraft, particularly the collision safety limit. This study refines the kinematic modeling process by constructing a three-dimensional kinematic model and advancing the starting point of paired approach, making the simulation process more aligned with reality. Subsequently, the Monte Carlo simulation method is applied, integrating the kinematic model and considering collision risks to simulate the entire paired approach process. Multiple scenarios are created, and simulation experiments are conducted to determine the safety separation between the two paired aircraft. Finally, by adjusting parameter settings and taking the example of the two closely spaced parallel runways at Chongqing Jiangbei International Airport, the correlation between various influencing factors and the safety separation is analyzed. Different scenarios are simulated 100,000 times each, and the results show that the speed difference between the two paired aircraft has the most significant impact on the safety separation, followed by the paired approach mode and runway centerline spacing. The wake turbulence category matching has a minimal effect on the safety separation. The results provide a theoretical basis for airports with closely spaced parallel runways to further reduce the separation between paired aircraft, thereby enhancing airport capacity and runway operational efficiency.

Abstract To evaluate the influence distance and evolution law of the jet blast under the full thrust state of the aircraft engine, we constructed a refined single-engine nozzle model in this study. Using a specific B737-800 engine as an illustrative example, this model takes into account both the internal combustion of the engine and the shearing effect of the tail cone on the airflow; based on a structured grid, the grid and far-field independence of the numerical solution were verified. The numerical simulation results obtained via the Fluent software were compared with the data outlined in the Boeing aircraft characteristics manual for a thorough analysis. Our results indicated that the calculated jet influence distance from the refined nozzle model aligns more closely with the data in the aircraft characteristics manual, albeit with superior accuracy compared to the simplified nozzle model employed in previous studies. The findings of this study can serve as a valuable reference for computing jet influence distances across various aircraft types and engine models, providing data support for the study of safety intervals for aircraft crossing the runway behind the takeoff point.

Abstract Background Lumbar revision surgery can be performed by simple lumbar nerve decompression or lumbar interbody fusion, including percutaneous endoscopic lumbar discectomy, transforaminal lumbar interbody fusion (TLIF), etc. However, lumbar revision surgery is very difficult in surgical operation. We sought to explore the technique safety and efficacy of microscope-assisted minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) in lumbar revision surgery. Methods Cases of postoperative recurrence following lumbar spine surgery (n = 63) treated from December 2016 to July 2021 were retrospectively analyzed, including 24 cases of microscope-assisted MI-TLIF (microscopic group) and 39 cases of naked-eye MI-TLIF (naked-eye group). The operation time, intraoperative blood loss, incision length, postoperative drainage, length of hospital stay, initial operation, and visual analog score (VAS) of low back and leg pain before and at 7 days and 3 months after the operation and the last follow-up were compared between the two groups. The Oswestry Dysfunction Index (ODI) and the Japanese Orthopaedic Association (JOA) scores before and after the operation and the Bridwell interbody fusion grades at 1 year were compared. The independent t tests, Mann–Whitney U tests, and Chi-square tests were used for analysis. Results All 63 patients were successfully treated by operation and were followed up for an average of 31.5 ± 8.6 months (range 12–48 months). The two groups had no significant difference in sex, age, incision length, initial operation, or operative segment (P > 0.05). There was no significance in operation time, VAS score, ODI score, and JOA score of low back pain or Bridwell interbody fusion grade between the two groups (P > 0.05). Significant differences in intraoperative blood loss, postoperative drainage, and the lengths of hospital stay were observed between the two groups (P

A systematic understanding of the spatial-temporal evolution patterns of cultivated land fragmentation (CLF), its driving factors, and its relationship with the expansion of urban and rural construction land is essential for identifying strategies to mitigate CLF in rapidly urbanizing regions. This study combined landscape fragmentation with ownership fragmentation, analyzing CLF through three dimensions: resource endowment, spatial concentration, and convenience of utilization, with eight selected indicators. By comparing village-level data from 2013 to 2022, we explored the key drivers of CLF and its conflicts with urban and rural construction land expansion. The findings indicate a clear spatial variation in village-level CLF in Ezhou, characterized by low fragmentation in the northwest and northeast, and high fragmentation in the southwest and central regions. This pattern is in contrast to Ezhou’s economic development, which decreased progressively from east to north and south. Over the study period, village-level CLF in Ezhou evolved from being primarily moderately and relatively severely fragmented to predominantly severely and relatively severely fragmented, with an overall declining trend and more pronounced polarization. At the same time, the CLF within the village region demonstrated notable spatial clustering features, with a rapid increase observed between 2013 and 2022. It was also discovered that CLF is driven by various factors, with the main influences being the proportion of construction land, land use intensity, and population density. Cultivated land is the main source of both urban construction land (UCL) and rural construction land (RCL), with average contribution rates of 46.47% and 62.62%, respectively. This research offers empirical evidence for rapid urbanization and serves as a critical reference for rural revitalization and coordinated urban–rural development, with potential guidance for future policy formulation and implementation.

Against the background of the integrated development of urban and rural areas in China, land disputes are gradually increasing and becoming a prominent focus of interest in current Chinese society. In the process of dispute settlement, it is challenging for land disputes to reconstruct the evidence chain with the help of systematic land evidence, and the information asymmetry between parties intensifies social contradictions. Currently, in the context of several research practices concerning evidence, China lacks a comprehensive and traditional system of integrated land evidence theory. Specific applicable rules for different types of land evidence can only be summarized by judicial referees. The challenge in the field of land evidence lies in establishing specific rules and addressing the issue of scattered individual rules. To overcome the challenges posed by various land evidence types and the difficulties in applying traditional evidence rules, we employ the land case analysis method to examine judicial judgment documents related to land disputes. This approach allows us to summarize the facts of land dispute cases, compare the judgments made in these cases and further highlight the similarities and differences in the land evidence considered by the referees. Through this process, this paper aims to establish a legally significant practical framework for the classification of land evidence, filling the theoretical gap in the operational mechanism of land evidence and unlocking the potential for applying evidence methods in land dispute resolution.

Abstract Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure.

IntroductionGluten allergy is a major public health problem that is growing at an alarming rate. Specific mechanisms underlying sensitization to gluten remain incompletely understood. Currently, it is unclear whether chronic exposure to alcohol-soluble gluten extract via undamaged skin has the capacity to clinically sensitize mice for life-threatening anaphylaxis. Using an adjuvant-free mouse model, here we tested the hypothesis that chronic application of alcohol-soluble durum gluten (ASDG) extract will clinically sensitize mice for life-threatening anaphylaxis.MethodsThis study was conducted in a gluten-free Balb/c mouse colony that was established and maintained on a plant protein-free diet. Groups of adult female mice were exposed dermally to ASDG extract or vehicle once a week for 9-weeks. Specific (s) and total (t) IgE levels were quantified. Mice were challenged systemically with ASDG to measure symptoms of systemic anaphylaxis. Hypothermic shock response (HSR) and mucosal mast cell degranulation response (MMCR) were determined upon challenge. Spleen Th1, Th2, and other immune markers were quantified.ResultsWe found that chronic exposure to ASDG elicited robust elevation of sIgE and tIgE. Systemic challenge with ASDG, but not vehicle, elicited life-threatening anaphylaxis associated with dramatic HSR and MMCR. Correlation analysis demonstrated direct positive inter-relationships among IgE, HSR, and MMCR. Anaphylaxis was associated with significant elevation of prototypic Th2 but not Th1 immune markers in the spleen.Discussion/ConclusionOur study collectively demonstrates that ASDG is intrinsically allergenic; and chronic exposure to ASDG via undamaged skin can clinically sensitize mice for life-threatening anaphylaxis via activating the systemic Th2 immune responses.

Spiking neural networks (SNNs) have garnered significant attention due to their computational patterns resembling biological neural networks. However, when it comes to deep SNNs, how to focus on critical information effectively and achieve a balanced feature transformation both temporally and spatially becomes a critical challenge. To address these challenges, our research is centered around two aspects: structure and strategy. Structurally, we optimize the leaky integrate-and-fire (LIF) neuron to enable the leakage coefficient to be learnable, thus making it better suited for contemporary applications. Furthermore, the self-attention mechanism is introduced at the initial time step to ensure improved focus and processing. Strategically, we propose a new normalization method anchored on the learnable leakage coefficient (LLC) and introduce a local loss signal strategy to enhance the SNN’s training efficiency and adaptability. The effectiveness and performance of our proposed methods are validated on the MNIST, FashionMNIST, and CIFAR-10 datasets. Experimental results show that our model presents a superior, high-accuracy performance in just eight time steps. In summary, our research provides fresh insights into the structure and strategy of SNNs, paving the way for their efficient and robust application in practical scenarios.

Spiking neural networks (SNNs) have attracted intensive attention due to the efficient event-driven computing paradigm. Among SNN training methods, the ANN-to-SNN conversion is usually regarded to achieve state-of-the-art recognition accuracies. However, many existing ANN-to-SNN techniques impose lengthy post-conversion steps like threshold balancing and weight renormalization, to compensate for the inherent behavioral discrepancy between artificial and spiking neurons. In addition, they require a long temporal window to encode and process as many spikes as possible to better approximate the real-valued ANN neurons, leading to a high inference latency. To overcome these challenges, we propose a calcium-gated bipolar leaky integrate and fire (Ca-LIF) spiking neuron model to better approximate the functions of the ReLU neurons widely adopted in ANNs. We also propose a quantization-aware training (QAT)-based framework leveraging an off-the-shelf QAT toolkit for easy ANN-to-SNN conversion, which directly exports the learned ANN weights to SNNs requiring no post-conversion processing. We benchmarked our method on typical deep network structures with varying time-step lengths from 8 to 128. Compared to other research, our converted SNNs reported competitively high-accuracy performance, while enjoying relatively short inference time steps.

In the 70 years since it has been founded, China’s cultivated land protection work has made remarkable achievements: less than 10% of the world’s cultivated land has fed 22% of the world’s population and 900 million peasants have the foundation on which to survive and develop. However, under the strictest protection system such as “Grow Teeth” (GT), there is still a deviation of “norm-value” and “demand-efficacy” in the Administrative Protection of Cultivated Land (APCL) at this stage. This paper uses normative analysis method, similar case research method as well as value analysis method to find that the legitimacy of the current APCL system is insufficient: on the one hand, under the perspective of functionalist “needs and efficacy”, the existing Cultivated Land Protection Law (Draft) (CLPL) and other normative documents cannot meet the needs of APCL penalties, relief, public welfare, etc.; On the other hand, from the perspective of normative “value legitimacy”, APCL legitimacy value foundation is lacking due to the limitations of overall value fragmentation, insufficient compatibility value and fragile defensive value. Therefore, the value base of APCL should be dismantled under the guidance of “function for use” to disassemble the functions of punishment, relief and public welfare, so as to specifically realize the construction of CLPL subjects, the inheritance of regulations, the transformation of responsibility subdivision, and the Land Administration Law and other regulatory continuations, to carry out protective measures such as clarifying the scope of punishment and giving compulsory force after coordination to cultivated land protection inspection recommendations, so as to give full play to the efficacy of APCL.

Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role in smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current recommendation methods are based on learning the users’ check-in history and the users’ relationship network in the social network to make recommendations.Therefore, urban crowds’ regular travel patterns cannot be effectively utilized. In this paper, we propose a POI recommendation algorithm (HMRec) based on prior knowledge of human mobility patterns to solve this problem. Specifically, we propose the Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors for human mobility patterns. The framework incorporates attention mechanisms to capture spatio-temporal information in urban traffic patterns. HMPE employs downstream tasks and design upsampling modules to reconstruct representation vectors for task objectives, enabling end-to-end training of the framework and obtaining pre-trained parameters for the human mobility pattern extractor. Furthermore, we introduce the Human Mobility Recommendation (HMRec) algorithm, which improves feature cross-interactions in the breadth model and incorporates prior knowledge of human patterns. This ensures that the recommendation results align more closely with human travel patterns in urban environments. Comparative experiments conducted on the Foursquare dataset demonstrate that HMRec outperforms baseline models with an average performance improvement of approximately 3%. Finally, we discuss existing challenges and future research directions, including approaches to address the issue of data sparsity.

Wheat is a major food allergen per the regulatory bodies of various nations. Hypersensitivity reactions to wheat have been steadily increasing for reasons that are not completely understood. Wheat-allergy models typically use adjuvants to induce sensitization to wheat proteins followed by an intraperitoneal challenge to elicit anaphylaxis. Although these models are very useful, they lack the ability to reveal the intrinsic allergenicity potential of wheat. To improve the mouse model of wheat allergy, we tested the hypothesis that repeated skin application of salt-soluble protein extract (SSPE) from durum wheat will clinically sensitize the mice to oral anaphylaxis to SSPE. Balb/c mice were bred and maintained on a plant-protein-free diet and used in the experiments. Adult female mice were exposed to SSPE once a week for 9 weeks via a solution on intact skin. Sensitization was measured by SSPE-specific IgE (sIgE) antibody and total IgE (tIgE) levels. Oral anaphylaxis was quantified by hypothermic shock response (HSR), and mucosal mast cell response (MMCR) was quantified by measuring MMCP-1 after oral challenge. Using single mouse data, correlation analyses were performed to determine the relationship among the allergenicity readouts. Spleen cytokines were quantified using a protein microarray method. Our results show that (i) repeated skin exposures to SSPE elicited robust increases in the sIgE and tIgE levels; (ii) skin exposure to SSPE was sufficient to sensitize mice for oral anaphylaxis and MMCR; (iii) both HSR and MMCR showed a strong correlation with each other, as well as with sIgE, and a modest correlation with tIgE levels; (iv) selected Th2/Th17/Th1 cytokines were elevated in skin-sensitized mice; and (v) oral allergen-challenged mice showed selective elevation of IL-6 and a panel of chemokines compared to saline-challenged mice. Together, we report the development and characterization of a novel adjuvant-free wheat-allergy mouse model that uses skin sensitization without tape-stripping followed by oral elicitation of anaphylaxis. Furthermore, validation of quantifiable wheat allergenicity readouts makes this model particularly suitable as a pre-clinical testing tool to assess the intrinsic sensitization/oral-anaphylaxis elicitation potential of novel wheat proteins (e.g., processed wheat) and to develop hypo/non-allergenic wheat products.

We theoretically and numerically investigate the stability and transient attraction dynamics of flat-top solitons (or platicons) in normal group velocity dispersion (GVD) microcavity via modulation-assisted four wave mixing instead of mode interaction. The spatial bifurcation structure of the stationary states depending on modulation power, pump power and cavity detuning is mapped, indicating a significantly extended existence range by the modulation-assisted approach. We explore the transformation between discrete energy spectrum of dark solitons and a quasi-continuous spectrum of platicons with and without a third-order dispersion and identify the connections between these two states. Robust trapping and manipulation of temporal platicons is also verified by implementation of phase modulation, and explained by transient attraction dynamics. One may manipulate the duration of the pulse by varying modulation power, pump power, or cavity detuning. Specifically, the effect of high-order dispersion on Kerr frequency comb generation is studied, and the results show that different orders of GVD term affect the 3dB comb bandwidth, repetition rate, spectral position of dispersive waves, as well as temporal profile and pulse peak power.

A generalized memristor is introduced in Liu-Chen chaotic system for amplitude control. Different from other chaotic systems, the oscillation behavior can be rescaled in amplitude and frequency by three independent controllers. One controls the amplitude of two related variables, and the other two control both amplitude and frequency of some variables. More distinctively, the proposed memristive chaotic system shows abundant coexisting pairs of attractors under the condition of broken symmetry. The circuit experiment based on Multisim agrees with the numerical exploration.

Due to the rapid development of communication and sensing technology, a large amount of mobile data is collected so that we can infer the complex movement laws of humans. For cities, some unusual events may endanger public safety. If the early warning of an abnormal event can be issued, it is of great application value to urban construction services. To detect urban anomalies, this paper proposes the Hierarchical Urban Anomaly Detection (HUAD) framework. The first step in this framework is to build rough anomaly characteristics that need to be calculated by some traffic flow consisted of subway and taxi data. In the second step, the alternative abnormal regions were obtained. Then, the long short-term memory (LSTM) network is used to predict the traffic to get the historical anomaly scores. Following that, the refined anomaly characteristics are generated from adjacent regions, adjacent periods and historical anomalies. The final abnormal regions were detected by One-Class Support Vector Machine (OC-SVM). At last, based on real data sets, we analyzes the traffic flow of the target region and adjacent regions from multiple perspectives in view of the large crowd gathering activities, and the effectiveness of the method is verified.

Zucchini (Cucurbita pepo L.) is one of the main vegetable crops grown under protected cultivation in northern China. Low-temperature (LT) stress severely inhibits the growth of zucchini seedlings, resulting in reductions in yield and quality. Here, using three kinds of different humic acids, including coal-based humic acid (CHA), fulvic acid (FA), and biochemical humic acid (BHA), we investigated the effects of humic acids against LT stress (5 °C) in zucchini seedlings. Treatment with all three kinds of humic acids improves LT stress tolerance by decreasing oxidative damage through increases in antioxidative enzyme activities and the contents of soluble sugar and proline in zucchini seedlings, especially after BHA application. Comparative transcriptomic analysis revealed that a total of 17 differentially expressed genes (DEGs) were commonly induced in the leaves of FA-, CHA-, and BHA-treated zucchini seedlings under LT stress, including calmodulin, ethylene-responsive transcription factors (TFs), peroxidases, and 10 TFs, including two NAC and seven WRKY genes. Altogether, these results indicated that supplementation with humic acids reprograms plant metabolism and modulates the expression of genes involved in ROS scavenging, phytohormone metabolism, or signaling pathways, finally improving LT stress tolerance in zucchini seedlings.

Shengma Biejia decoction (SMBJD), a traditional Chinese formula recorded in the Golden Chamber, has been widely used for the treatment of malignant tumors. However, its underlying molecular targets and mechanisms are still unclear. This study showed that SMBJD inhibited tumor growth and stimulated hemogram recovery significantly in a multiple myeloma xenograft model. Western blot and immunohistochemistry assays of tumor tissues showed that SMBJD reduced the ratio of autophagy-related proteins LC3-II/LC3-I, while P62 and apoptosis-related proteins cleaved caspase-3/caspase-3 and Bax/Bcl-2 were upregulated. In vitro experiments demonstrated the time-dependent and dose-dependent cytotoxicity of SMBJD on multiple myeloma cell lines H929 and U266 through MTT assays. The LC3-II/LC3-I ratio and number of GFP-LC3 puncta showed that SMBJD inhibited the autophagy process of H929 and U266 cells. Moreover, both SMBJD and 3-methyladenine (3-MA) caused a decrease in LC3-II/LC3-I, and SMBJD could not reverse the upregulation of LC3-II/LC3-I caused by bafilomycin A1 (Baf-A1). Furthermore, the results of annexin V-FITC and propidium iodide double staining demonstrated that SMBJD treatment induced the apoptosis of H929 and U266 cells. These data prove that SMBJD inhibits autophagy and promotes apoptosis in H929 and U266 cells. The results also show that rapamycin could reduce the rate of SMBJD-induced apoptosis in H929 and U266 cells, at a concentration which had no effect on apoptosis but activated autophagy. In addition, analysis of the mechanism indicated that levels of phosphorylated ERK and phosphorylated mTOR were increased by treatment with SMBJD in vivo and in vitro. These results indicate that SMBJD, an old and effective herbal compound, could inhibit the viability of H929 and U266 cells and induce autophagy-mediated apoptosis through the ERK/mTOR pathway. Thus, it represents a potential therapy strategy for multiple myeloma.

BackgroundTumor associated macrophages (TAMs), a kind of inflammatory cells in the tumor microenvironment, are crucial for the occurrence and development of various tumors which increased the expression of CD163. Nevertheless, not much has been established regarding soluble CD163 and its connection to tumor diagnosis. In this case, a meta-analysis was conducted to determine the tumor diagnostic importance of serum sCD163.MethodsIn order to assess the correlation between sCD163 and the overall survival (OS) or progression-free survival (PFS) among tumor patients, a systematic perusal of literature published until June 2020 was conducted. Relevant data were primarily obtained from papers that have the following qualifications: 1) a confidence interval (CI) of 95%; 2) a report of the hazard ratios; and, 3) pooled by means of the Mantel–Haenszel random-effect representation.ResultsFor the final meta-analysis, eight papers comprised of 1,236 cases were involved. Through pooled investigation, it was determined that a correlation exists between elevated serum sCD163 and worse OS (HR = 2.24, 95% CI: 1.50–3.35, P

The influences of transition metals (Sc, Ti, V, Cr and Mn) doping at different distances on the stability, electronic structure, conductivity and optical properties of CdS are studied by using VASP code. The results show that the Cr-doped CdS is easy to form, followed by V, Ti, Sc and Mn. The next nearest-neighbor doping is the most stable in each transition metal-doped system. The band-gaps gradually decrease with the order of Sc, Ti, V, Cr, Mn-doping, all doped systems become the degenerate semiconductor. Doping not only enhances conductivity, but also improves photocatalytic performance, especially Mn-doped CdS has obvious improvement. The absorption intensity in the visible light range of the single-doping is more than that of two atoms doped system in different distances, of which Mn and Cr single-doping are the most prominent.

NiFe _2 O _4 @CoFe _2 O _4 core–shell nanofibers were prepared by the co-electrospinning method. The prepared core–shell NiFe _2 O _4 @CoFe _2 O _4 nanofibers electrode has the specific capacitance of 480 Fg ^−1 at a current density of 1Ag ^−1 . After being cycled 2000 times at a current density of 5Ag ^−1 , the capacity of NiFe _2 O _4 @CoFe _2 O _4 core–shell nanofibers remained 87%. The electrochemical performance of the supercapacitor is improved by the synergistic action of the multi-metal and indicates that core/shell NiFe _2 O _4 @CoFe _2 O _4 nanofibers are promising materials.

The prevalence of wheat allergy has reached significant levels in many countries. Therefore, wheat is a major global food safety and public health issue. Animal models serve as critical tools to advance the understanding of the mechanisms of wheat allergenicity to develop preventive and control methods. A comprehensive review on the molecular mechanisms of wheat allergenicity using animal models is unavailable at present. There were two major objectives of this study: To identify the lessons that animal models have taught us regarding the molecular mechanisms of wheat allergenicity and to identify the strengths, challenges, and future prospects of animal models in basic and applied wheat allergy research. Using the PubMed and Google Scholar databases, we retrieved and critically analyzed the relevant articles and excluded celiac disease and non-celiac gluten sensitivity. Our analysis shows that animal models can provide insight into the IgE epitope structure of wheat allergens, effects of detergents and other chemicals on wheat allergenicity, and the role of genetics, microbiome, and food processing in wheat allergy. Although animal models have inherent limitations, they are critical to advance knowledge on the molecular mechanisms of wheat allergenicity. They can also serve as highly useful pre-clinical testing tools to develop safer genetically modified wheat, hypoallergenic wheat products, novel pharmaceuticals, and vaccines.

Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July-August) had the lowest PA while winter had the highest (January-February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice. [ABSTRACT FROM AUTHOR]