Modeling Strategy Research Articles

Mastering Real-Time Data Processing Applications : Optimization Strategies for Peak Performance

This comprehensive article explores strategies for optimizing real-time data processing applications in the era of big data and distributed systems. It examines the growing demand for real-time analytics across industries and organizations' challenges in maintaining low latency and scalability. The article discusses key optimization techniques, including leveraging the Akka framework's actor model and supervision strategies, implementing efficient data ingestion through stream processing and partitioning, utilizing caching strategies, employing robust monitoring and auto-scaling mechanisms, ensuring fault tolerance through checkpointing and graceful degradation, and adopting asynchronous communication patterns. Throughout the article, real-world case studies and performance metrics demonstrate the significant improvements these strategies can bring to system throughput, latency, and resilience in various sectors such as finance, e-commerce, telecommunications, and manufacturing.

A novel bi-level optimal strategy-assisted design boosting discovery of specific surface area of perovskite oxide for effective photocatalysis

Due to the inherent property of materials, photocatalysis plays a crucial role in diverse applications, such as the degradation of organic pollutants, fuel production, and nitrogen fixation. Perovskites, in particular, are remarkable for their high light absorption rate and energy conversion efficiency within the visible light range, thereby positioning them as promising photocatalysts. Given that specific surface area (SSA) is a critical metric for evaluating photocatalytic performance, accurately and quickly predicting the SSA of perovskites has emerged as a key area of technical research. Therefore, it is essential to explore the relationship between potential materials descriptors and SSA, which can significantly expedite the discovery of photocatalyzed perovskite materials with high SSA. In this study, we proposed a bi-level optimization method that combines descriptors identified strategy and model optimization strategy for predicting the SSA of unknown perovskite candidates, thereby elucidating the relationship between material features and SSA. Initially, at the upper level, the Gradient Boosting Regression combined with Recursive Feature Elimination (RFE-GBR) method was employed to identify crucial features that are closely correlated with the SSA of photocatalyzed perovskites. Subsequently, the Sure Independence Screening.Sparsifying Operator (SISSO) was utilized to develop new descriptors that exhibited strong correlations with SSA. Based on refined descriptors identifiied at the upper level, at the lower level, the optimal model strategy was obtained by Improved Osprey Optimization Algorithm (IOOA) using golden sine strategy and Gradient Boosting Regression (GBR) regression to tune the key parameters of GBR for optimizing SSA. The optimal designing method achieved remarkable predictive performance with R2 (R-squared) score of 0.90 and MAE (Mean Absolute Error) of 3.97 m2/g, along with 5-fold cross-validation. Then this method was applied to predict the SSA of 300 unidentified ABO3 perovskites. Comparison with experimental data revealed that our approach significantly accelerated the development of perovskite materials with superior photocatalytic properties, showing the potential of our model in advancing the field of photocatalysis.

Empowering Pregnant Women with Health Promotion Strategies to Prevent Chronic Energy Deficiency (CED): Systematic Review

Background: Pregnant women's nutrition is very important for the growth and development of the fetus. Nutrition for pregnant women is very important for the development and growth of the fetus. The application of the concept of a health promotion model strategy as the right strategy to improve and prevent CED is urgently needed. Purpose: To find out changes in the behavior of empowering pregnant women with a strategic model of a health promotion approach to prevent CED. Method: The method used systematic review. Articles were searched using keywords that had been determined by the researcher then the articles were extracted based on inclusion and exclusion criteria. Thus, found 7 articles suitable for analysis. Results: Changing behavior by empowering pregnant women using health promotion strategies in preventing CED has proven to be very effective. But in its application it becomes a complex thing because of the diversity of cultures in Indonesia. Conclusion: The diversity of community characteristics can affect the application of the model of empowering pregnant women in preventing CED. Several other factors such asbeliefs and environment that are understood also affect the understanding of each individual.

Investigation of an optimal control strategy for a cholera disease transmission model with programs

Cholera is a disease of poverty affecting people with inadequate access to safe water and basic sanitation. Conflict, unplanned urbanization and climate change all increase the risk of cholera. In this article, an optimal control deterministic mathematical model of cholera disease with cost-effectiveness analysis is developed and analyzed considering both direct and indirect contact transmission pathways. The model qualitative behaviors, such as the invariant region, the existence of a positive invariant solution, the two equilibrium points (disease-free and endemic equilibrium), and their stabilities (local as well as global stability) of the model are studied. Moreover, the basic reproduction number of the model is obtained. We also performed sensitivity analysis of the basic parameters of the model. Then an optimal control problem is designed with a control functional having five controls: vaccination, treatment, environment sanitation and personal hygiene, and water quality improvement program. We examined the existence and uniqueness of the optimal controls of the system. Through the implementation of Pontryagin's maximum principle, the characterization of the optimal controls optimality system is established. The numerical simulation results the integrated control strategies demonstrated that strategy 2, 7, and 12 are effective programs to combat cholera disease from the community. Based on the local circumstances, available funds, and resources, it is recommended to the government stakeholders and policymakers to execute any one of the three integrated intervention programs.

PASCL: supervised contrastive learning with perturbative augmentation for particle decay reconstruction

Abstract In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

Artificial intelligence-guided screening of under-recognized cardiomyopathies adapted for point-of-care echocardiography

Abstract Background Point-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols. Purpose To develop and validate deep learning models for detecting underdiagnosed cardiomyopathies on cardiac POCUS, leveraging a novel acquisition quality-adapted modeling strategy. Methods To develop the models, we identified transthoracic echocardiograms (TTEs) of patients across five hospitals in a large U.S. health system with transthyretin amyloid cardiomyopathy (ATTR-CM, confirmed by Tc99m-pyrophosphate imaging), hypertrophic cardiomyopathy (HCM, confirmed by cardiac magnetic resonance), and random controls from the same period enriched for the presence of severe AS. In a sample of 290,245 TTE videos, we used novel augmentation approaches and a customized loss function to weigh image and view quality to train a multi-label, view agnostic video-based convolutional neural network (CNN) to discriminate the presence of ATTR-CM, HCM, and/or AS+ and AS- controls. Models were tested across 3,758 real-world POCUS videos from 1,879 studies in 1,330 independent emergency department (ED) patients from 2011 through 2023 (A). Results Our multi-label, view-agnostic classifier demonstrated state-of-the-art performance in discriminating ATTR-CM (AUROC 0.98 [95%CI: 0.96-0.99]) and HCM (AUROC 0.95 [95% CI: 0.94-0.96]) on standard TTE studies. Automated metrics of anatomical view correctness confirmed significantly lower quality in POCUS vs TTE videos (median view classifier confidence of 0.63 [IQR: 0.44-0.88] vs 0.93 [IQR: 0.69-1.00], p<0.001). When deployed to POCUS videos, our algorithm effectively discriminated ATTR-CM and HCM with AUROC of up to 0.94 (parasternal long-axis (PLAX)), and 0.85 (apical 4 chamber (A4C)) (B), corresponding to positive diagnostic odds ratios of 46.7, and 25.5, respectively. In total, 18/35 (51.4%) of ATTR-CM and 32/57 (41.1%) of HCM patients in the POCUS cohort had a positive AI screen in the year before their eventual confirmatory imaging (C). Conclusions We define and validate an AI framework that enables scalable, opportunistic screening of under-diagnosed cardiomyopathies using POCUS.

An Improved Velocity-Aided Method for Smartphone Single-Frequency Code Positioning in Real-World Driving Scenarios

The availability of Global Navigation Satellite System (GNSS) raw observations in smartphones has driven research into low-cost GNSS solutions, especially in challenging urban environments, which have garnered significant attention from scholars in recent years. This study proposes an improved smartphone-based velocity-aided positioning method and conducts vehicle-mounted experiments in urban roads representing typical scenarios. The results show that when transitioning from low- to high-multipath environments, the number of visible satellites and carrier phase observations are highly sensitive to environmental factors, with frequent multipath effects. The introduction of robust pre-fit and post-fit residual algorithms has proven to be an effective quality control method. Additionally, using more refined observation models and appropriate parameter estimation algorithms led to a slight 6% improvement in velocity performance. The improved Kalman filter position estimation model (KFSPP-P) strategy, by incorporating velocity uncertainty into the state estimation process, overcomes the limitations of conventional velocity-aided smartphone positioning methods (KFSPP-V) in complex urban environments. In low-multipath environments, the accuracy of the KFSPP-P strategy is comparable to that of KFSPP-V, with an approximate 8% improvement in horizontal accuracy. However, in more challenging environments, such as tree-lined roads and urban environments, the KFSPP-P strategy shows significant improvements, particularly enhancing horizontal positioning accuracy by approximately 50%. These advancements demonstrate the potential of using smartphones to provide reliable positioning services in complex urban environments.

Equivalent spring model and efficient numerical strategy for simulating mechanical performance of seam-clip connections in aluminum alloy high-vertical standing seam metal cladding systems

The equivalent spring model has been validated as effective in simulating the mechanical performance of seam-clip connections in typical steel high-vertical standing seam metal cladding systems (SSMCSs). To enhance its applicability, this study further refines the equivalent spring model by developing thickness-related stiffness calibration models and an efficient numerical strategy for seam-clip connections in the aluminum alloy SSMCSs. Tensile tests are initially conducted to analyze the material properties of specimens with three commonly used thicknesses. Subsequently, the mechanical performance of the aluminum alloy seam-clip connections with different thicknesses is systematically studied, followed by the development of thickness-related stiffness calibration models. The tensile test results reveal notable nonlinearities and variability in the mechanical performance. To simulate this mechanical performance equivalently in the finite element (FE) model using data from tensile tests, an efficient numerical strategy is developed. This strategy involves methods for implementing simplified connections to establish the equivalent spring model and techniques for efficiently creating these simplified connections. It is found that both connectors and spring connections with calibrated parameters from tensile tests can effectively replicate the mechanical performance of seam-clip connections. To further validate the effectiveness of the equivalent spring model and the efficient numerical simulation strategy, a comparison of structural responses of the double-span sheet module (DSSM) obtained from tests and FE analysis is performed. The results reveal that all simulated structural responses on the DSSM with three common sheet widths align well with the corresponding experimental results. This highlights the feasibility and efficacy of the equivalent spring model and efficient numerical strategy in simulating the mechanical performance of seam-clip connections and delving deeper into the structural responses of the aluminum alloy high-vertical SSMCSs.

Exploration of Entrepreneurship Education Model and Practice in Five-Year Integrated Vocational Education Stage

Against the backdrop of the “20 Articles of Vocational Education,” how to promote high-quality vocational development, build a comprehensive, adaptive, and targeted entrepreneurship education model, promote student employment and entrepreneurship, and improve the quality of talent cultivation has become an important issue in the talent cultivation of five-year vocational colleges. This article takes students in the five-year integrated vocational education stage as the research object, starting from the development of the industrial economy, talent employment competitiveness, and students’ innovation ability, to expound on the practical significance of entrepreneurship education. Combining top-level design, curriculum setting, faculty strength, and practical bases, it analyzes the problems existing in and explores the entrepreneurship education model and practical strategies in the five-year integrated vocational education stage from four aspects: transforming vocational education concepts, classifying and constructing curriculum systems, attaching importance to the construction of entrepreneurial and entrepreneurial faculty, and relying on multi-party collaborative platforms.

A new integrated modelling and control of ternary pumped storage hydro power generation for small signal stability studies

Pumped storage hydro power generation (PSHPG), a reliable renewable energy source with an energy storage feature, can impart efficient damping torque to power system oscillations to improve small signal stability with appropriate modelling and additional compensation through the governor. But ternary PSHPG (T-PSHPG) has the unique feature of hydraulic short circuit (HSC) mode, where both pumping and generating modes operate simultaneously. This study presents a novel fourth-order modified Heffron Phillips (MHP) model of T-PSHPG in coordination with a multi-band Power system stabilizer (MB-PSS) employing 2-way penstock, where primary penstock circulates water from the upper reservoir to the lower reservoir through the turbine and secondary penstock divides primary penstock, thereby creating short-circuit path through the pump. Also, this model includes additional reactive power droop control to maintain the voltage profile. A new state space matrix has been developed for coordinating T-PSHPG in HSC mode with MB-PSS. The control parameters of the proposed coordinated model are set by a new multi-objective differential evolution-improved particle swam optimization (DE-IPSO) algorithm. With variable load, random solar and wind source penetrations, it has been found that the proposed model can provide adequate damping actions. The sensitivity of the proposed model has been observed by varying critical model parameters by ±50 %. For all conditions, the settling time of speed variation is within 2.672 s to 5.951 s and the minimum damping ratio lies within 0.105 to 0.455. The minimum damping ratio is 0.32 and the settling time is found to be 3.1 s for system response subject to sudden solar power variation, which has been observed by shifting system eigenvalues toward the left half of the complex plane. Time and frequency domain parameters with sensitivity analysis predict system oscillations being damped effectively with consistency for the proposed modelling and control strategy. The results are verified in real-time with OPAL-RT real-time digital simulator. It has been justified by the present study that appropriate modelling of T-PSHPG along with coordinated control action provided by MB-PSS can handle critical power system oscillations efficiently and effective damping torque can be imparted in HSC mode.

Intra-cisterna-magna bevacizumab injection (ICM-BI) reproduces pathological alterations of cerebral small vessel diseases.

General modeling strategies for sporadic cerebral small blood vessel diseases (CSVDs) include limiting blood stream in large blood vessels and inducing systemic hypertension, in which small blood vessel deficit is either a secondary or concomitant pathology. In the current study, we introduce that intra-cisterna-magna Bevacizumab injection (ICM-BI) directly causes cerebral small blood vessel injury by neutralizing VEGF-A, the indispensable growth factor for angiogenesis. ICM-BI reproduces neuro-functional impairment, tight junction loss, cerebral micro-bleeds (CMBs), amyloid peptide accumulation, neuronal injury, white matter loss, and glial cell activation, which are common manifestations of sporadic CSVDs. Compared with existing CSVD models, small blood vessel injury is more prominent in the ICM-BI brain. Moreover, no significant alteration in large blood vessels or peripheral organs after ICM-BI is recorded. We thus propose that ICM-BI is a neat, economic and applicable methodology to establish murine sporadic CSVD model.

Determination of the REV size for heterogeneous rocks with different grain sizes: Deep learning and numerical approaches

Accurate determination of the representative elementary volume (REV) size plays a pivotal role in analysing the mechanical properties and failure processes of heterogeneous rocks in complex engineering environments. In this study, a novel microstructure modelling strategy (NMMS) for determining the REV size is proposed by combining deep learning and an improved phase-field method (PFM). Micro- and macroscale experiments are systematically conducted to determine the real microstructural characteristics and mechanical properties of heterogeneous rocks with different grain sizes. On the basis of this experimental evidence, geometric models of different sizes were reconstructed through deep learning to avoid the limitations of human-based methods, and an improved PFM was used for numerical calculations. These models were then employed to perform numerical tests under uniaxial loading conditions, and the coefficient of variation was introduced to determine the REV size of heterogeneous rocks with different grain sizes. The research findings indicate that the final REV size is the maximum value of the REVs defined by the evaluation properties within an acceptable coefficient of variation. At a criterion of 5% for the coefficient of variation, the REV sizes are 60 mm×60 mm, 70 mm×70 mm, and 90 mm×90 mm for fine-medium-grained (FMG), medium-grained (MG), and coarse-grained (CG) rocks, respectively. Furthermore, the REV determined by the NMMS was applied to investigate the effects of microstructure on macromechanical properties and damage evolution under triaxial loading conditions. The numerical results show that the NMMS can accurately predict the macromechanical properties and microcracking patterns of heterogeneous rocks, especially the intracrystalline cracks in feldspar, the interfacial cracks in gravel, and the “voids” of cracks in biotite. This research can provide some basic references for the optimal choice of the REV size of heterogeneous rocks.

Seismic risk estimates for reinforced concrete structures with incorporation of corrosion and aftershock

Numerous reinforced concrete (RC) structures are exposed to aggressive environments, such as chloride, mainshock and aftershock. These environmental and extreme loads have the potential to increase the seismic risks during structure's service life. This study introduces a practical probabilistic methodology to estimate the seismic risk of corroded RC frame subjected to mainshock-aftershock sequences. In this methodology, a time-variant modeling strategy is used to simulate the geometrical and mechanical properties of structural degraded materials. The Bayesian updating theorem is employed to calibrate the demand models, which are then used to estimate the fragilities and confidence intervals considering the model uncertainties. A Copula-based approach is conducted to generate joint probability of mainshock and aftershock intensities and the seismic hazard of the mainshock-aftershock scenario. Finally, the seismic risk and associated confidence intervals are estimated by integrating over all mainshock and aftershock intensities levels. A typical corroded RC frame structure is used to illustrate the proposed methodology. The results show that the contribution of corrosion and aftershock would lead a 10 times higher seismic risks compared to scenarios considering only mainshock damage. It is necessary to account for the influence of both corrosion and aftershock in seismic design.

Sample Size Planning in the Design of Two-Level Randomized Cost-Effectiveness Trials

This study introduces recent advances in statistical power analysis methods and tools for designing and analyzing randomized cost-effectiveness trials (RCETs) to evaluate the causal effects and costs of social work interventions. The article focuses on two-level designs, where, for example, students are nested within schools, with interventions applied either at the school level (cluster design) or student level (multisite design). We explore three statistical modeling strategies—random-effects, constant-effects, and fixed-effects models—to assess the cost-effectiveness of interventions, and we develop corresponding power analysis methods and tools. Power is influenced by effect size, sample sizes, and design parameters. We developed a user-friendly tool, PowerUp!-CEA, to aid researchers in planning RCETs. When designing RCETs, it is crucial to consider cost variance, its nested effects, and the covariance between effectiveness and cost data, as neglecting these factors may lead to underestimated power.

Enhanced modelling and control strategy for grid-connected PV system utilizing high-gain Quasi-Z source converter and optimized ANN-MPPT algorithm

Enhanced modelling and control strategy for grid-connected PV system utilizing high-gain Quasi-Z source converter and optimized ANN-MPPT algorithm

A Sequential Population Pharmacokinetic Model of Zilovertamab Vedotin in Patients with Hematologic Malignancies Extrapolated to the Pediatric Population.

Recently a number of antibody-drug conjugate (ADC) pharmacometric models have been reported in the literature, describing one or two ADC-related analytes. The objective of this analysis was to build a population pharmacokinetic (popPK) three-analyte ADC model to describe efficacy and safety of zilovertamab vedotin, an ROR1-targeting ADC conjugated to monomethyl auristatin E (MMAE). Data from a phase 1 study of zilovertamab vedotin in subjects with hematologic malignancies was used in a stepwise ADC modeling strategy based on the simplified ADC popPK model proposed by Gibiansky. This choice provided opportunity to model three analytes: conjugated monomethyl auristatin E (acMMAE), total monoclonal antibody (total mAb), and free MMAE. The model was extrapolated to the pediatric population using a clearance maturation function and accounting for weight dependent pharmacokinetic (PK) changes. The simplified model provided a good structure to fit the adult acMMAE, total mAb, and free MMAE data. Analysis showed that MMAE was released through deconjugation of the payload and full proteolytic degradation of the acMMAE. Deconjugation was associated with an immediate release of MMAE, proteolytic clearance introduced a delay in the release of MMAE. Simulation of the model extrapolated to the pediatric population was the basis for pediatric dosing strategies forzilovertamab vedotinthat were approved in the United States and European Union. The total mAb, acMMAE, and free MMAE model showed a good fit to the data. The pediatric population can match the acMMAE adult exposure at the same weight-based dose regimen without concerns that the toxic MMAE concentration will reach higher levelsthan found in adults.

Testing Neuroprotective Strategies in Prolonged Field Care Model of Traumatic Brain Injury and Hemorrhagic Shock.

Prolonged Field Care (PFC) is a military adaptation of Tactical Combat Casualty Care providing extended pre-hospital management during delayed extrication. Effects of addition of Valproic Acid (VPA) to Fresh Frozen Plasma (FFP) in a PFC model of hemorrhagic shock and traumatic brain injury (TBI) are not known. We hypothesized that VPA is associated with decreased neurological impairment, and its protective changes are detected at the transcriptomic level. Swine underwent TBI and 40%-blood volume hemorrhage. After 2-hours of shock, they were randomized to: 1)normal saline (NS), 2)NS+250 ml FFP (NS+FFP), or 3)NS+FFP+150 mg/kgVPA(NS+FFP+VPA). At 72-hours, they were transfused packed red blood cells before being euthanized. Intraoperative variables and neurological outcomes were compared. Brain lesion size was measured, and gene expression profiles were analyzed using RNA-sequencing. Pathway and network analyses were performed on differentially expressed genes. Real-time PCR was performed to validate key genes. NS+FFP and NS+FFP+VPA required significantly less crystalloid resuscitation(974 ml-NS+FFP; 1461ml-NS+FFP+VPA vs. 4540 ml-NS, p<0.001), had smaller brain lesion size(2477mm3-NS+FFP; 3018.0mm3-NS+FFP+VPA vs. 4517.0mm3-NS, p<0.01), and less functional neurologic impairment compared to NS. Per pathway analysis of differentially expressed genes, VPA was associated with enrichment of numerous metabolic changes in injured brains, which were not observed with FFP. Network analysis showed enrichment of various gene networks. MT-ATP8 gene was downregulated in VPA-treated animals. The addition of FFP to the resuscitation protocol resulted in a significant reduction in crystalloid requirements. Both, the FFP and FFP+VPA groups showed improved neurological recovery compared to NS alone and had distinctive transcriptomic profiles in injured brains at 72-hours. MT-ATP8, involved in worsening ischemia following brain injury, was down-regulated in VPA-treated animals.

Design and modelling strategy for cold-formed steel exoskeletons for the seismic-energy retrofit of reinforced concrete structures

Recent seismic events have highlighted the vulnerability of reinforced concrete (RC) buildings constructed before the enforcement of seismic design codes. This study is focused on a cold-formed steel exoskeletons, known as the Resisto 5.9 Tube system, used to enhance both seismic and thermal performance of existing structures. The main focus is the application of a simplified design and modelling strategy to elementary reinforced concrete structures with different heights. The design strategy is aimed to optimize the elements of Resisto 5.9 Tube according to the specific building under investigation. The modelling strategy is developed to reduce the computational effort when implementing the reinforcement system in structural software without negatively affecting the reliability of the results.

Comparing ‘fair’ machine learning models for detecting at-risk online gamblers

ABSTRACT Researchers have worked to develop machine learning models that detect at-risk online gamblers, enabling personalized harm prevention tools. However, existing research has not evaluated these models’ potential to reinforce or amplify sociodemographic biases leading to treatment disparity, a recognized issue in the machine learning field. We sought to develop and compare three examples of potentially fair models using online gambling data. In two large samples of transaction data from a provincially owned Canadian gambling website (N1 = 9,145, N2 = 10,716), we developed three machine learning models based on competing concepts of fairness: fairness via unawareness, classification parity, and outcome calibration. We hypothesized that significant relationships existed between reporting a high risk of past-year gambling problems (the dependent variable) and participants’ age and sex. Further, we hypothesized that the three ‘fair’ models would show differing levels of classification performance both in aggregate and within sociodemographic groups. Significant age and sex effects were found, refuting the fairness via unawareness modeling strategy. Superiority across all performance metrics was not present for either of the remaining models. For the fairest practices in any jurisdiction, classification parity and outcome calibration models should be tested in situ, and incorporate the perspectives and preferences of end users who will be affected.

Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies.