Scaling Trends Research Articles

Palliative Care Knowledge and Attitudes among Patients with Neurodemyelinating Diseases (P8-6.005)

<h3>Objective:</h3> Evaluate PC knowledge and attitudes of patients with neurodemyelinating diseases. <h3>Background:</h3> Neurodemyelinating diseases lead to functional loss, requiring demanding care with great distress to patients and their caregivers. Palliative care (PC) would benefit these individuals. However, PC inpatient neuro services have only increased from 0.2% to 6% (2005–2014, respectively) without increases in ambulatory settings. <h3>Design/Methods:</h3> A cross-sectional, descriptive design with a convenience sample of 84 patients from 3 neuroimmunology clinics in a Northeastern healthsystem was performed. Participants completed a confidential survey composed of PC Knowledge Scale (PaCKS) and PC Health Information National Trends Survey (HINTS). Murray’s transition theory guided this study, which contextualizes the chronic condition integrating PC services, patient decision-making, communication, and care coordination. Descriptive statistics characterized the sample and the survey responses. <h3>Results:</h3> Most participants were white race (79%), non-Hispanic ethnicity (77%), female gender (69%) and 57% had formal higher education. 82% had multiple sclerosis (MS). A small proportion had advance directives (21%). Approximately 48% to 62% correctly answered PaCKS knowledge questions about PC eligibility criteria (3-items), goals (7-items), and operations (3-items). HINTS focused on PC attitudes and 42% indicating that they never heard of PC. Of the remaining participants, 77% to 87% agreed/strongly agreed with 3-items reflecting PC goal (coping, support, symptom management), whereas, 30% disagreed/strongly disagreed that PC gave patients more time at end-of-life. More than 50% accurately disagreed/strongly disagreed with 4-items that were more aligned with hospice care not PC. Most (67%) felt doctor’s had an obligation to inform patients about PC. <h3>Conclusions:</h3> Many participants were unaware of PC, had knowledge gaps, or inaccurate attitudes about its services and benefits. Results provide a call-to-action for nurses and providers to counsel patients and refer them to PC. Interventional studies are needed to test strategies to improve PC access and related outcomes. <b>Disclosure:</b> Ms. Ben-Zacharia has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Biogen. Ms. Ben-Zacharia has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Genentech. Ms. Ben-Zacharia has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Greenwich Biosciences. Ms. Ben-Zacharia has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for TG Theraputics. The institution of Ms. Ben-Zacharia has received research support from Biogen. The institution of Ms. Ben-Zacharia has received research support from Greenwich Biosciences. Ms. Bartels has nothing to disclose. Helen Brugger has nothing to disclose. Jena Malchiodi has nothing to disclose. Mrs. Carbone has nothing to disclose. Dr. Volandes has nothing to disclose. Francois Bethoux, MD has nothing to disclose.

Water scaling predication for typical sandstone geothermal reservoirs in the Xi'an Depression

The Xi'an Depression in the Guanzhong Basin of western China has been suggested to contain geothermal resources that could aid China in achieving carbon neutrality and optimizing energy structure. However, the high concentration of total dissolved solids (TDS) and scale-forming ions in geothermal water from the depression causes severe scaling problems in harvesting geothermal energy. To reduce scale-related problems, accurate identification of scale types and prediction of scaling during geothermal energy utilization are crucial. This study starts with identifying the types and trends of scaling in the study area, using index-based discriminant methods and hydrogeochemical simulation to calculate and analyze the mineral saturation index of water samples from some wellheads and of reconstructed fluid samples of geothermal reservoirs. The results indicate that the scales are mostly calcium carbonate scales rather than sulfate scales as a result of temperature changes. Several portions of the geothermal water systems are found to have distinct mineral scaling components. Quartz and chalcedony are formed in low temperature areas, while carbonate minerals are in high temperature areas. Despite the low iron content of geothermal water samples from the study area, scaling is very common due to scaling-prone iron minerals. The findings can be used to evaluate geothermal drainage systems and guide anti-scaling during geothermal energy utilization in similar settings.

Accelerated high-cycle phase field fatigue predictions

Phase field fracture models have seen widespread application in the last decade. Among these applications, its use to model the evolution of fatigue cracks has attracted particular interest, as fatigue damage behaviour can be predicted for arbitrary loading histories, dimensions and complexity of the cracking phenomena at play. However, while cycle-by-cycle calculations are remarkably flexible, they are also computationally expensive, hindering the applicability of phase field fatigue models for technologically-relevant problems. In this work, a computational framework for accelerating phase field fatigue calculations is presented. Two novel acceleration strategies are proposed, which can be used in tandem and together with other existing acceleration schemes from the literature. The computational performance of the proposed methods is documented through a series of 2D and 3D boundary value problems, highlighting the robustness and efficiency of the framework even in complex fatigue problems. The observed reduction in computation time using both of the proposed methods in tandem is shown to reach a speed-up factor of 32, with a scaling trend enabling even greater reductions in problems with more load cycles.

Finding Trends in Software Research

This paper explores the structure of research papers in software engineering. Using text mining, we study 35,391 software engineering (SE) papers from 34 leading SE venues over the last 25 years. These venues were divided, nearly evenly, between conferences and journals. An important aspect of this analysis is that it is fully automated and repeatable. To achieve that automation, we used a stable topic modeling technique called LDADE that fully automates parameter tuning in LDA. Using LDADE, we mine 11 topics that represent much of the structure of contemporary SE. The 11 topics presented here should not be "set in stone" as the only topics worthy of study in SE. Rather our goal is to report that (a) text mining methods can detect large scale trends within our community; (b) those topic change with time; so (c) it is important to have automatic agents that can update our understanding of our community whenever new data arrives.

Evidence of biological recovery from gross pollution in English and Welsh rivers over three decades

Uncertainty around the changing ecological status of European rivers reflects an evolving array of anthropogenic stressors, including climate change. Although previous studies have revealed some recovery from historical pollution in the 1990s and early-2000s, there are contrasting trends among pollutants across Europe and recovery may have stalled or been reversed. To provide more contemporary evidence on trends and status, here we investigate changes in English and Welsh river macroinvertebrate communities over almost 30 years (1991–2019) using a network of nearly 4000 survey locations. Analysis comprised: i) trends in taxonomic and functional richness, community composition and ecological traits, ii) gains, losses and turnover of taxa, and the overall homogeneity of macroinvertebrate communities nationally, and iii) an exploration of how temporal trends varied with catchment characteristics. Taxonomic richness increased, primarily in the 1990s, whilst a shift towards pollution-sensitive taxa continued throughout the study period, accompanied by a growing prevalence in traits such as preferences for fast-flowing conditions, coarser substrata, and ‘shredding’ or ‘scraping’ feeding strategies. Changes consistent with improvement occurred in both urbanised and agricultural catchments, but were more pronounced in urban rivers as they gained pollution sensitive taxa that were otherwise more prevalent in rural rivers. Overall, these results indicate continuing biological recovery from organic pollution, consistent with national scale trends in water quality. Results reemphasise the importance of looking at multiple facets of diversity, with periods of near-constant richness disguising changes in taxonomic and functional composition. Whilst this national-scale picture is broadly positive, we highlight the need to investigate more local variations or pollutants that depart from this aggregate picture.

The selective drivers of allometry in sharks (Chondrichthyes: Elasmobranchii)

AbstractIn addition to the selective importance of interspecific morphological variation, ontogenetic morphological variation may reflect different selective regimes to which successive developmental stages are subjected. The typical body form of carcharhiniform sharks is considered relatively conserved, yet sharks exhibit a wide range of body sizes and shapes, representing adaptations to distinct ecological niches. Previous investigations of ontogenetic shifts in shark body form have provided evidence for both isometric and allometric changes, depending on the morphological characters and species investigated. These findings have led to suggestions of a relationship between body size and allometric growth in sharks. In this study we present evidence of ontogenetic allometric shifts in two species of carcharhiniform sharks (Sphyrna lewini and Rhizoprionodon longurio) from novel measurements. Our results are generally consistent with previous suggestions of body form conservatism across shark phylogeny, yet also suggest potential selective factors underlying observed instances of ontogenetic allometric shifts, and highlight where additional studies are required. We propose the ‘allometric niche shift’ hypothesis for interspecific differences in scaling trends, suggesting that long-distance movements and ontogenetic trophic niche shifts represent key drivers of allometry in sharks.

Model-based spectral coherence analysis

Recent data-driven efforts have utilized spectral decomposition techniques to uncover the geometric self-similarity of dominant motions in the logarithmic layer, and thereby validate the attached eddy model. In this paper, we evaluate the predictive capability of the stochastically forced linearized Navier–Stokes equations in capturing such structural features in turbulent channel flow at$Re_\tau =2003$. We use the linear coherence spectrum to quantify the wall-normal coherence within the velocity field generated by the linearized dynamics. In addition to the linearized Navier–Stokes equations around the turbulent mean velocity profile, we consider an enhanced variant in which molecular viscosity is augmented with turbulent eddy-viscosity. We use judiciously shaped white- and coloured-in-time stochastic forcing to generate a statistical response with energetic attributes that are consistent with the results of direct numerical simulation (DNS). Specifically, white-in-time forcing is scaled to ensure that the two-dimensional energy spectrum is reproduced and coloured-in-time forcing is shaped to match normal and shear stress profiles. We show that the addition of eddy-viscosity significantly strengthens the self-similar attributes of the resulting stochastic velocity field within the logarithmic layer and leads to an inner-scaled coherence spectrum. We use this coherence spectrum to extract the energetic signature of self-similar motions that actively contribute to momentum transfer and are responsible for producing Reynolds shear stress. Our findings support the use of coloured-in-time forcing in conjunction with the dynamic damping afforded by turbulent eddy-viscosity in improving predictions of the scaling trends associated with such active motions in accordance with DNS-based spectral decomposition.

Decadal scale trends in temperature, salinity, and sound speed in the Beaufort Sea since 1976

Profiles of conductivity and temperature as a function of depth (CTD) were collected in the Beaufort Sea during two U.S. Navy Ice Exercises in 2016, 2018, and 2020 (ICEXYY) in the month of March. The data show a significant departure from climatological values and significant variance at the interface between the Arctic Surface Layer (ASL) and the underlying Pacific Summer Water (PSW). Comparisons to older ICEX data collected over a 45 year period and to ice-tethered profiler (ITP) data since 2007 confirm both the subsurface warming trend and the variance at the ASL-PSW interface. Statistical analysis reveals a 0.4 degreeCelsius per decade increase in the near-surface temperature maximum (NSTM) near 75 m, which establishes two persistent acoustic propagation features beginning in the year 2000—a near-surface acoustic duct above 50 m and a subsurface acoustic duct, known as the Beaufort Lens, centered on 150 m water depth.

An Optimized Fractional Nonlinear Grey System Model and Its Application in the Prediction of the Development Scale of Junior Secondary Schools in China

As part of China’s compulsory nine-year education system, junior secondary education (JSSE) plays a vital role in supporting students’ physical and mental development. The accurate prediction of the development scale trend of JSSE is helpful for the government to estimate the scale of educational development within a chosen time frame so as to aid decision making.Nevertheless, China’s education system is complex, highly dimensional, and largely influenced by policy and other factors, which results in difficulty in modeling the education sample. Based on gray system theory, this paper proposes an improved fractional-order grey prediction model, OCFNGBM(1,1), to predict the development scale of JSSE. We describe the basic expressions of the model, the parameter estimation method, and the optimization method for hyperparameters and construct a scheme for optimizing the background value coefficients. Data collected from official websites from 2011 to 2021 are used to build the forecasting model, and data from 2011 to 2017 are used to evaluate the model’s accuracy. Our experimental results indicate that the OCFNGBM(1,1) model has higher accuracy than the classical nonlinear gray prediction model. The OCFNGBM(1,1) model was employed to forecast the development scale of JSSE in China from 2022 to 2024, which provided useful information. This research provides a resource to help the national education department to develop a comprehensive and long-term plan for the development goals, scale, speed, steps, and measures of relevant education.

New coupling mechanism and plasmonic scaling trend in transversely shifted cubic homodimers

New coupling mechanism and plasmonic scaling trend in transversely shifted cubic homodimers

NS3K: A 3-nm Nanosheet FET Standard Cell Library Development and its Impact

Nanosheet FETs (NSFETs) are attracting attention as promising devices that can replace FinFETs beyond the 5-nm node. Despite the importance of the devices, few studies analyze the impact of NSFETs at the block-level. In this article, we introduce NS3K, the first 3-nm NSFET standard cell library, and examine the results on a block-level scale. In addition to the overall process of designing a full library, we extended the scope of the buried power rail (BPR) to better layout designs. We showed that BPR, originally proposed to overcome power delivery problems, is also an effective solution for standard cell hegith reductions. Using BPR, we highlight that 4-track height standard cell designs have a negligible impact on power delivery and signal routing. Overall chip results show that the 3-nm NSFET outperforms the 5-nm FinFET by −27.4% in power, −25.8% in total wirelength, −8.5% in the number of cells, −47.6% in area, and 34.7% performance, respectively, owing to better device performance and interconnect scaling. However, careful device/layout designs and new interconnect structures must be applied to continue the scaling trend and maximize the advantages of 3-nm technology.

Tensor-Train Thermo-Field Memory Kernels for Generalized Quantum Master Equations.

The generalized quantum master equation (GQME) approach provides a rigorous framework for deriving the exact equation of motion for any subset of electronic reduced density matrix elements (e.g., the diagonal elements). In the context of electronic dynamics, the memory kernel and inhomogeneous term of the GQME introduce the implicit coupling to nuclear motion and dynamics of electronic density matrix elements that are projected out (e.g., the off-diagonal elements), allowing for efficient quantum dynamics simulations. Here, we focus on benchmark quantum simulations of electronic dynamics in a spin-boson model system described by various types of GQMEs. Exact memory kernels and inhomogeneous terms are obtained from short-time quantum-mechanically exact tensor-train thermo-field dynamics (TT-TFD) simulations and are compared with those obtained from an approximate linearized semiclassical method, allowing for assessment of the accuracy of these approximate memory kernels and inhomogeneous terms. Moreover, we have analyzed the computational cost of the full and reduced-dimensionality GQMEs. The scaling of the computational cost is dependent on several factors, sometimes with opposite scaling trends. The TT-TFD memory kernels can provide insights on the main sources of inaccuracies of GQME approaches when combined with approximate input methods and pave the road for the development of quantum circuits that implement GQMEs on digital quantum computers.

Scaling and statistics of bottom-up synthesized armchair graphene nanoribbon transistors

Bottom-up assembled nanomaterials and nanostructures allow for the studies of rich and unprecedented quantum-related and mesoscopic transport phenomena. However, it can be difficult to quantify the correlations between the geometrical or structural parameters obtained from advanced microscopy and measured electrical characteristics when they are made into macroscopic devices. Here, we propose a strategy to connect the nanomaterial morphologies and the device performance through a Monte Carlo device model and apply it to understand the scaling trends of bottom-up synthesized armchair graphene nanoribbon (GNR) transistors. A new nanofabrication process is developed for GNR transistors with channel length down to 7 nm. The impacts of the GNR spatial distributions and the device geometries on the device performance are investigated systematically through comparison of experimental data with the model. Through this study, challenges and opportunities of transistor technologies based on bottom-up synthesized GNRs are pinpointed, paving the way to the further improvement of the GNR device performance for future transistor technology nodes.

Comparison of the Modified Barthel Index (MBI) Score Trends Among Workers With Stroke Receiving Robotic and Conventional Rehabilitation Therapy.

Stroke is one of the top causes of adult-acquired disabilities and the fifth most prominent cause of death worldwide. Working-age populations contribute about 40% of the stroke cases which occur annually in Malaysia. The modified Barthel Index (MBI) score has been used for self-care assessment to determine if stroke patients can meet their fundamental needs. The study was designed to compare the trend of MBI scores of workers who had a stroke and underwent robotic rehabilitation therapy to those who had conventional therapy. A cohort study was conducted among workers who had a stroke in northeastern Malaysia. They were assigned either to undergo robotic or conventional rehabilitation therapy. The robotic therapy is performed three times per day for four weeks. Meanwhile, conventional therapy involved walking exercises five days per week for two weeks. Data were collected for both therapies on the admission, at week 2 and week 4. The MBI, modified Rankin Scale (mRS) and Hospital Anxiety and Depression Scale (HADS) trends were examined one month after the therapies. The R (version 4.2.1) (R Core Team, Vienna,Austria) and RStudio (R Studio PBC, Boston, USA) were applied to perform the descriptive analyses on the respective platforms. Repeated measures of analysis of variance were performed to evaluate the outcomes trend and the effectiveness of the two therapies was also compared. A total of 54 stroke patients participated in this study of which 30 (55.6%) of them received robotic therapy. The age of the subjects ranged from 24 to 59 years and the majority (74.1%) were male. Stroke outcomes were evaluated using mRS, HADS, and MBI scores. Except for their age, the individuals' characteristics did not significantly differ between those undergoing conventional therapy and those receiving robotic therapy. After four weeks, it was found that the good mRS had increased, whereas the poor mRS had decreased. Comparing the therapy groups, the MBI scores improved significantly with time, although there were no significant differences between the therapy groups. However, the interaction term between the treatment group (p=0.031) and improvements over time was significant (p=0.001), indicating that robotic was more effective than conventional therapy in improving the MBI scores. For HADS score, there was a significant difference between the therapy groups (p=0.001), with those receiving robotic therapy having higher HADS score. Functional recovery occurs in acute stroke patients when the mean Barthel Index score rises from the baseline (on admission) to week 2 (during therapy)and subsequently on discharge (week 4). Based on these findings, it appears that there was not one therapy superior to the other; nevertheless, robotic therapy may be better tolerated and more effective in certain individuals.

Unconventional resistivity scaling in topological semimetal CoSi

Nontrivial band topologies in semimetals lead to robust surface states that can contribute dominantly to the total conduction. This may result in reduced resistivity with decreasing feature size contrary to conventional metals, which may highly impact the semiconductor industry. Here we study the resistivity scaling of a representative topological semimetal CoSi using realistic band structures and Green’s function methods. We show that there exists a critical thickness dc dividing different scaling trends. Above dc, when the defect density is low such that surface conduction dominates, resistivity reduces with decreasing thickness; when the defect density is high such that bulk conduction dominates, resistivity increases as in conventional metals. Below dc where bulk states are depopulated, the persistent Fermi-arc remnant states give rise to decreasing resistivity down to the ultrathin limit, unlike topological insulators. The observed CoSi scaling can apply to broad classes of topological semimetals, providing guidelines for materials screening in back-end-of-line interconnect applications.

Correlation and differences of patient-reported outcomes vs. Likert-Rating of MS symptoms in a real-world cohort using a digital patient app.

Multiple Sclerosis (MS) is a chronic and progressive neurological autoimmune disease currently affecting 250,000 individuals in Germany. Patients suffering from the disease can be severely impaired in their day-to-day activities. BRISA is a digital app specifically designed to help MS patients monitor their disease by regularly tracking symptoms. Lengthy and time-consuming questionnaires for patient-reported outcomes (PRO) are the standard method to assess the patients' current condition. Here, we examine whether simplified versions of these questionnaires can provide comparable information regarding individual symptom presentations in BRISA users. 828 users were included in the analysis. Patients who provided onboarding information and answered at least one questionnaire and the corresponding simplified smiley symptoms assessment were included. Correlation of questionnaire and symptom scores was calculated using Pearson's correlation. Our analysis cohort predominantly consisted of female, 26-55-year-olds. Relapsing-remitting MS (RRMS) was the most common MS type recorded. Most patients were diagnosed 2-5 years ago. Questionnaires regarding fatigue and vision impairment were among the most answered, those regarding bowel movement and sexual satisfaction received fewest responses. Overall, the scores from questionnaires and symptoms correlated positively. Scoring correlation could also be shown across the subgroups divided by gender, age groups, type of MS, and time since diagnosis of the disease. Scores recorded from traditional PRO questionnaires can be reflected more easily as a trend in a simplified scale using smileys. Nevertheless, traditional questionnaires are needed to also maintain a more objective assessment. In conclusion, the patient will benefit most from an adaptive combination of regular traditional PRO questionnaire assessments and simplified symptom recording.

Spatiotemporal Variation Characteristics of Precipitation in the Huaihe River Basin, China, as a Result of Climate Change

Climate change is a global scientific problem, and its impact on the spatiotemporal variations of precipitation has been a crucial research topic. Although previous studies have used different methods to evaluate the precipitation characteristics of the Huaihe River Basin, the time series were short and the few stations could not fully and accurately represent the precipitation characteristics. In this study, daily temperature and precipitation data were collected from 233 meteorological stations in the Huaihe River Basin from 1960–2020. The Mann–Kendall test was used to analyze the trend and significance of interannual and interseasonal scale changes in temperature and precipitation in the Basin, respectively. The correlation between temperature and precipitation was analyzed using the Pearson correlation coefficient method. The spatial distribution of the significance of temperature and precipitation changes and the spatial distribution of the correlation between temperature and precipitation in the basin were plotted. The temperature in the basin tended to increase on interannual and interseasonal scales, with more noticeable changes in spring and winter. Precipitation showed an overall decreasing trend but an increasing trend in localized areas in the south. A decreasing trend in the interseasonal variation scale was observed in spring, an increasing trend in winter, a decreasing trend in the northeastern region in summer, an increasing trend in the southwestern region, and an increasing trend in the northern and southern parts in autumn were observed. The correlation between average temperature and precipitation on interannual and interseasonal scales was analyzed using Pearson’s correlation coefficient method, and the annual average temperature and annual average precipitation in the Huaihe River basin were found to be negatively correlated, except for sporadic areas that showed extremely weakly positive correlations or no correlations.

Special Topic on Spintronic Devices for Energy-Efficient Computing

The traditional scaling trend of semiconductor devices is approaching its limit with the node size in manufacturing already down to 2 nm, with a great guidance from Moore’s law. Heterogenous integration has recently been one of the major driving forces to push the semiconductor technologies further, with a great engineering effort to sum up the power of known and established technologies.

Hydrological and Meteorological Drought Monitoring and Trend Analysis in Abbay River Basin, Ethiopia

The definition of drought is very controversial due to its multi-dimensional impact and slow propagation in onset and end. Predicting the accurate occurrence of drought remains a challenging task for researchers. The study focused on hydrological and meteorological drought monitoring and trend analysis in the Abbay river basin, using the streamflow drought index (SDI), standardized precipitation index (SPI), and reconnaissance drought index (RDI), respectively, to fill this research gap. The study also looked into the interrelationships between the two drought indicators. The SDI, SPI, and RDI were calculated using long-term streamflow, precipitation, and temperature data collected from 1973 to 2014. The data were collected from eight streamflow stations and fifteen meteorological gauge stations. DrinC software (Drought Indices Calculator) was used to calculate the SDI, SPI, and RDI values. The result from meteorological drought using SPI12 and RDI12 shows that 1975, 1981, 1984, 1986, 1991, 1994, and 2010 were extreme drought years, whereas 1983, 1984, 2001, and 2010 were the most extreme hydrological drought years based on the SDI12 result. Except for Bahir Dar and Gondar, a severe drought occurs at least once a decade in all stations considered in this study. In general, the SPI, RDI, and SDI results indicated that the study area was exposed to the most prolonged severe and extreme drought from 1981 to 1991. The findings of this study also demonstrated that the occurrence of hydrometeorological droughts in the Abbay river basin has a positive correlation at long time scales of 6 and 12 months. The trend analysis using the Mann–Kendall test implied that there was a significant meteorological drought trend in two stations (Debre Berhan and Fiche) at SPI12 and RDI12 time scale, but for the remaining thirteen stations, there is no trend in all time scales. The hydrological drought trend analysis in the basin on a seasonal (SDI3) and yearly (SDI12) time scale also revealed that three streamflow stations have a positive trend (Kessie, Gummera, and Border). This implies that water resource management is still a vital tool for the sustainable development of the Abbay river basin in the future.

Analysis of Spatio-Temporal Evolution Characteristics of Drought and Its Driving Factors in Yangtze River Basin Based on SPEI

Using a dataset of 114 meteorological stations in the Yangtze River Basin from 1980–2019, the standardized precipitation evapotranspiration index (SPEI) was calculated based on the Penman-Monteith evapotranspiration model for multiple time scales, and the spatial and temporal evolution characteristics and driving factors of drought in the Yangtze River Basin were analyzed by combining spatial and temporal analysis methods as well as geodetector. The main results obtained are as follows: (1) The climate of the Yangtze River Basin is an overall wet trend, and the trend of summer drought is more similar to the annual scale trend. (2) Most areas in the Yangtze River Basin showed mild drought or no drought, and there is little difference in drought condition among the Yangtze River Basin regions. The areas with drought conditions are mainly distributed in the southwest and east of the Yangtze River Basin. (3) There are significant seasonal differences in drought conditions in all regions, and the drought condition is more different in autumn compared to spring, summer and winter. (4) The average annual precipitation and elevation factors are the dominant driving factors of drought in the Yangtze River Basin, and the double-factor interaction has a greater influence on the drought variation in the Yangtze River Basin than the single-factor effect, indicating that the difference of drought condition in the Yangtze River Basin is the result of the combination of multiple factors.