Conventional Time Research Articles

Scalable Prediction of Northern Corn Leaf Blight and Gray Leaf Spot Diseases to Predict Fungicide Spray Timing in Corn

Managing foliar corn diseases like northern leaf blight (NLB) and gray leaf spot (GLS), which can occur rapidly and impact yield, requires proactive measures including early scouting and fungicides to mitigate these effects. Decision support tools, which use data from in-field monitors and predicted leaf wetness duration (LWD) intervals based on meteorological conditions, can help growers to anticipate and manage crop diseases effectively. Effective crop disease management programs integrate crop rotation, tillage practices, hybrid selection, and fungicides. However, growers often struggle with correctly timing fungicide applications, achieving only a 30–55% positive return on investment (ROI). This paper describes the development of a disease-warning and fungicide timing system, equally effective at predicting NLB and GLS with ~70% accuracy, that utilizes historical and forecast hourly weather data. This scalable recommendation system represents a valuable tool for proactive, practicable crop disease management, leveraging in-season weather data and advanced modeling techniques to guide fungicide applications, thereby improving profitability and reducing environmental impact. Extensive on-farm trials (>150) conducted between 2020 and 2023 have shown that the predicted fungicide timing out-yielded conventional grower timing by 5 bushels per acre (336 kg/ha) and the untreated check by 9 bushels per acre (605 kg/ha), providing a significantly improved ROI.

Deep Reinforcement Learning‐Based Control for Real‐Time Hybrid Simulation of Civil Structures

ABSTRACTReal‐time Hybrid Simulation (RTHS) is a cyber‐physical technique that studies the dynamic behavior of a system by combining physical and numerical components that are coupled through a boundary condition enforcer. In structural engineering, the numerical components are subjected to environmental loads that become dynamic displacements of the physical substructure applied through an actuator. However, the dynamics of the coupling between components and the complexities of the system lead to synchronization challenges that affect the accuracy of the simulation. Thus, requiring tracking controllers to ensure the fidelity of the simulation. This paper studies deep reinforcement learning (DRL) as a novel alternative to designing the tracking controller. Three controllers are designed: a DRL agent combined with a conventional time delay compensation, a conventional feedback controller combined with a DRL agent, and a DRL agent with a complex neural network architecture. The proposed approaches are tested using a virtual RTHS benchmark problem, and the results are compared with an optimized controller that has a proportional‐integral‐derivative controller and phase‐lead compensation. The results show that DRL can address the synchronization challenges of RTHS with a model‐free approach and simple neural network architectures. The work shown in this study is a critical step toward model‐free control methodologies that can transform and further develop the RTHS method. The proposed methodology can be used to address important challenges related to RTHS, including nonlinearities and uncertainties of the physical substructure, complex boundary conditions, and the computational efficiency when physical structures with complex dynamics are present.

Software Complex for Documenting Graphical Applications Based on Topological Decomposition

Contemporary software products and services necessitate extensive accompanying information, including user manuals and documentation. The task becomes more complex in the case of graphical applications, as it requires capturing screenshots and highlighting the described elements. Attaining high-quality visual information using conventional means demands considerable effort and time, since, unlike text, raster images do not provide an interface for retrieving metadata about individual elements, necessitating programmatic extraction of such information. This study presents a software complex designed to automate and expedite this process. The primary focus is on a topo­logical decomposition method that segments images based on topological features such as pixel brightness and proximity, enabling the segmentation of screenshots into control elements. This approach demonstrates high accuracy (96-99 %) in identifying graphical components across most tested images. The software product encompasses functions for the automatic capture of application windows, an algorithm for identifying control elements, and the capability to export the entire image or a specified region. Additionally, an algorithm for generating output templates for image insertion has been implemented. The decomposition method has exhibited effectiveness across the majority of test data. In instances with shadow effects, noise levels were recorded at a maximum of 34 %; however, in most cases, this figure did not exceed 3 %. Further research may contribute to enhancing these results. The findings of this study could prove valuable for the continued exploration of graphical user interface decomposition and the assessment of the method's effectiveness on diverse images. The developed software complex significantly simplifies and accelerates the documentation process, thereby opening new avenues in the automation of software development.

A Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features.

Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China's agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE705) and plant senescence reflectance index (PSRI). Moving beyond conventional time series analysis, we innovatively amplified key temporal characteristics through newly designed spatial feature parameters (SFPs) and phenological feature parameters (PFPs). This strategic enhancement of critical temporal points significantly improved classification performance by capturing subtle spatial patterns and phenological transitions that are often overlooked in traditional approaches. The study yielded three significant findings: (1) The synergistic application of NDRE705 and PSRI significantly outperformed single-index approaches, demonstrating the effectiveness of our dual-index strategy; (2) The integration of SFPs and PFPs with time series REVI markedly enhanced feature discrimination at crucial growth stages, with PFPs showing superior capability in distinguishing agricultural land types through amplified phenological signatures; (3) Our optimal classification scheme (FC6), leveraging both enhanced spatial and phenological features, achieved remarkable accuracy (93.21%) with a Kappa coefficient of 0.9159, representing improvements of 4.83% and 0.0538, respectively, over the baseline approach. This comprehensive framework successfully mapped 120,996 km2 of agricultural land, differentiating winter wheat-summer maize rotation areas (39.44%), single-season crop fields (36.16%), orchards (14.49%), and facility vegetable fields (9.91%). Our approach advances the field by introducing a robust, scalable methodology that not only utilizes the full potential of time series data but also strategically enhances critical temporal features for improved classification accuracy, particularly valuable for regions with complex farming systems and diverse crop patterns.

Sleep and circadian rhythms in delayed sleep-wake phase disorder: Phenotypic differences between patients with and without comorbid depression.

Delayed sleep-wake phase disorder involves chronic difficulty going to bed and waking up at conventional times and often co-occurs with depression. This study compared sleep and circadian rhythms between patients with delayed sleep-wake phase disorder with depression(DSWPD-D) and without (DSWPD-ND) comorbid depression. Clinical records of 162 patients with delayed sleep-wake phase disorder (70 DSWPD-D, 92 DSWPD-ND) were analysed, including a subset of 76 patients with circadian phase determined by the dim light melatonin onset. Variables assessed included sleep behaviour on work and free days, weekly sleep duration, social jet lag, chronotype, and phase relationships between dim light melatonin onset and sleep/wake times. Mean (SD) or median [Q1-Q3] values were compared using t-tests or Mann-Whitney. Patients with DSWPD-D showed longer sleep on workdays (DSWPD-D = 7.63 hr [1.70] versus DSWPD-ND = 6.20 hr [1.59]; p < 0.001), but not on free days. DSWPD-D also showed later sleep onset (DSWPD-D = 03:30 14;hours [02:49 hours-04:23 hours], DSWPD-ND = 02:53 hours [02:00 hours-03:41 hours]; p = 0.02) and wake times (DSWPD-D = 11:30 hours [09:30 hours-13:00 hours], DSWPD-ND = 08:45 hours [07:20 hours-11:00 hours]; p < 0.01) on workdays. Furthermore, DSWPD-D showed less social jet lag (DSWPD-D = 0.38 [0.00-1.75] versus DSWPD-ND = 2.17 [1.25-3.03]; p < 0.01), and reported higher anxiety symptoms (DSWPD-D = 71.4% versus DSWPD-ND = 45.8%; p = 0.03) and medication use (DSWPD-D = 75.0% versus DSWPD-ND = 43.8%; p = 0.01). DSWPD-D also showed wider dim light melatonin onset phase relationships with dim light melatonin onset-mid-sleep (DSWPD-D = -5.77 [1.32] versus DSWPD-ND = -4.86 [1.53]; p = 0.01) and dim light melatonin onset-waketime (DSWPD-D = -9.46 [1.82]; DSWPD-ND = -8.13 [2.08]; p = 0.01). Multivariable Poisson regression, adjusted for age and sex, showed more medication use, less social jet lag, and longer weekly sleep duration as significantly associated with DSWPD-D. These findings suggest potential biopsychosocial protective factors linked to depression in delayed sleep-wake phase disorder. Further research is required to confirm these phenotypic differences and their relevance to delayed sleep-wake phase disorder aetiology and treatment.

Chronotherapy with Cinacalcet has a striking effect on inhibition of parathyroid gland proliferation in rats with secondary hyperparathyroidism

Secondary hyperparathyroidism (sHPT) is a significant clinical complication of CKD leading to bone abnormalities and cardiovascular disease. Current treatment based on activating the parathyroid calcium-sensing receptor (CaSR) using calcimimetics such as Cinacalcet, aims to decrease plasma PTH levels and inhibit the progression of parathyroid hyperplasia. In the present study, we found significant diurnal rhythmicity of Casr, encoding the Cinacalcet drug target in hyperplastic parathyroid glands (p = 0.006). In rats with sHPT, Cinacalcet treatment timed prior to the acrophase of Casr expression (chronotherapy: Cina1) was compared with the usual timing of treatment early in the active phase (conventional: Cina2). Without Cinacalcet treatment, induction of sHPT resulted in a significant increase in parathyroid proliferation in terms of Ki-67+ cells compared to that of control rats (p = 0.001). Conventional Cinacalcet treatment (Cina2) did not significantly reduce Ki-67 index compared to untreated rats with sHPT (p = 0.09). In contrast, chronotherapy treatment (Cina1) resulted in a marked inhibition of parathyroid proliferation by Ki-67+ cells compared to untreated rats with sHPT (p = 0.0001). We found significantly reduced parathyroid Ki-67 index using chronotherapy compared to conventional timing of Cinacalcet (Cina1 vs. Cina2: 0.92±0.14% vs. 2.46±0.37%, p = 0.006). Transcriptomic analysis showed that the reduced proliferation of Cina1 was associated with downregulation of genes involved in mitotic activity, together with an increased adaptive response of energy metabolism, as evident from upregulated pathways of Oxidative phosphorylation and TCA cycle compared to the untreated uremic group. Conclusively, it is shown that the inhibitory effect of Cinacalcet on parathyroid cell proliferation is markedly impacted by the timing of administration, suggesting a possible benefit of using chronotherapy in Cinacalcet treatment of sHPT.

Contours of attitudes toward time: A study of tribes and subcultures within the Elba Mebel company

This article explores the expanding scope of social sciences. One of the new fields of social anthropology is corporate anthropology. The request for research in this field comes primarily from organizations called “living”. The article presents insights from a research project conducted at Elba Mebel, aimed at transforming the company’s organizational culture. Special attention is given to the relationship with time, which serves as a crucial social resource in the workplace. This resource becomes increasingly valuable within the modern innovative economy, where social, psychological, and organizational risks are ever-present. Understanding time relationships provides immediate benefits, such as improved labor organization and interpersonal interactions, while also enabling more comprehensive long-term planning. The results of this field study demonstrate that the use of corporate anthropology methods enables to significantly expand conventional time management approaches, helping organizations better utilize time as a resource to enhance both employee productivity and overall enterprise performance.

Conventional Radiotherapy Timing and Wound Complication Avoidance After Surgery for Metastatic Spine Disease. A LatAm Modified Delphi Study.

Combining surgery and radiotherapy is the gold standard in treating spinal metastasis when spinal stabilization or surgical decompression is required. Determining the optimal timing for radiotherapy postsurgery is crucial to balance treatment efficacy minimizing wound complications. The study aimed to identify consensus and nonconsensus areas among Latin American spinal surgeons regarding the use, timing, risks, and surgical approach toconventional external beam radiotherapy(cEBRT) following spinal surgery for metastases, specifically focusing on the risk of radiotherapy-related wound complications. A modified Delphi survey was conducted. The expert panel included active members of AOSpine Latin America with extensive experience in vertebral metastasis surgery. The surveys include 37 statements covering areas of interest. Inter-expert consensus was considered weak (70-79.9%), moderate (80-89.9%), and strong (≥90%). At least 70% consensus was reached on 32 of the 37 statements (86.5%). This included strong consensus on 10 statements, moderate on 13, and weaker on nine. There was high consensus on sutures and lower consensus on risk factors for cEBRT delay. Experts reached strong agreement on the importance of poor nutrition as a risk factor for cEBRT-related wound complications. Perception of wound risk was greater with a posterior midline approach compared to other approaches, and the highest perceived complication risks were for junctional locations and sacral spine. We report strong agreements among the experts on important issues such as waiting times and risk factors for cEBRT. The findings underscore the significance of considering factors such as, spinal levels, surgical approaches, and sutures when making clinical decisions.

Visual outcomes of central retinal artery occlusion: Exploring treatment strategies beyond the conventional time window.

Central retinal artery occlusion (CRAO) is a vision-devastating emergency. However, widely-acknowledged treatment consensus is lacking and prehospital delays commonly occur. Hence, we aimed to investigate the visual outcomes of conservative treatments (CT), local intra-arterial fibrinolysis (LIF) and hyperbaric oxygen (HBO) therapy for non-arteritic CRAO (NA-CRAO) patients beyond the conventional time window. This retrospective comparative study included 99 NA-CRAO patients followed up for over 6 months. The subjects were divided into three groups: the CT (50 patients), LIF (10 patients) and HBO group (39 patients). The primary endpoint was the best-corrected visual acuity (BCVA) change at 6 months compared to baseline. The secondary endpoint was the improvement in BCVA at 1 year and final visits. No heterogeneity regarding demographics was identified. However, the HBO group had a more extended time-to-treatment period (median 6.0 days) and more advanced-stage CRAO cases (41 % stage III) than the CT (median 4.0 days, 14 % stage III) and LIF (median 0.6 days, 20 % stage III) groups. Despite this, the HBO group exhibited a significantly greater BCVA and a higher proportion of patients achieving significant vision improvement than those in the CT group at 6-, 12-month and final exams (51.3 % vs. 24.0 %, P < 0.05). The LIF appeared to improve outcomes more than CT over time without significance. Our study provided a concurrent comparison across 3 approaches and demonstrated that HBO therapy beyond the time window remained more effective in improving vision than CT alone for NA-CRAO patients, which had not been proposed by prior studies.

Multi-Global Navigation Satellite System for Earth Observation: Recent Developments and New Progress

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather.

Future forecast of global mean surface temperature using machine learning and conventional time series methods

Future forecast of global mean surface temperature using machine learning and conventional time series methods

Modeling and Forecasting the Prevalence of Human Immuno-Deficiency Virus (HIV) in Birnin Gwari Local Government Area of Kaduna State

Human immunodeficiency virus (HIV) has been prevalent in millions of people worldwide for more than 50 years. Therefore, its prevalence must be monitored to make well-informed decisions and create efficient health plans. This paper dwelt on the modeling and prediction of the prevalence of HIV among inpatients and outpatients visiting Jibril Mai-Gwari I Memorial Hospital in Birnin Gwari Local Government Area of Kaduna State. An expo-facto research methodology was adopted for this study through the collection of twelve (12) years of monthly data spanning the period 2010-2021 as extracted from the hospital’s inpatient and outpatient department records. Box and Jenkins analytical approach was used to model the data due to its presence of unit root, as a non-stationarity data cannot be modeled using the conventional time series models. Iterative methods were applied in the identification of several ARIMA models as ARIMA (2, 1, 5) model was found to be the best fit among the 25 iterated models based on its higher loglikelihood (-435.57), lower variance (24.95) and lower AIC (887.13). More so, the model was found to scale through the portmanteau tests of residual independence (Ljung-Box Chi-square 5.9261, p-value &gt; 0.05) and normality (Wilk 0.9830, p-value &gt; 0.05), a confirmatory test of model reliability and accuracy. Although the prognosis suggests that HIV prevalence may not change significantly in the upcoming years, the expanding uncertainty band suggests that it will be challenging to estimate HIV prevalence in the future with high confidence. To manage prevalence and lower the chance of an unanticipated increase, public health measures must be consistent and effective.

Purity benchmarking study of error coherence in a superconducting qubit

In this study, we employ purity benchmarking (PB) to explore the dynamics of gate noise in a superconducting qubit system. Over 1110 h of observations on an Xmon qubit, we simultaneously measure the coherence noise budget across two different operational frequencies. We find that incoherent errors, which predominate in overall error rates, exhibit minimal frequency dependence, suggesting that they are primarily due to wideband, diffusive incoherent error sources. In contrast, coherent errors, although less prevalent, show significant sensitivity to operational frequency variations and telegraphic noise. We hypothesize that this sensitivity is due to interactions with a single strongly coupled environmental defect—modeled as a two-level system—which influences qubit control parameters and causes coherent calibration errors. Our results also demonstrate that PB offers improved sensitivity, capturing additional dynamics that conventional relaxation time measurements cannot detect, thus presenting a more comprehensive method for capturing dynamic interactions within quantum systems. The intricate nature of these coherence dynamics underscores the need for further research.

Validating ex210Pb sediment dating methods applied to a large anthropogenically-impacted river basin

Sediment dynamics in five sites within the Mobile River Basin, Alabama, impacted by (i) dam-and-lock use, (ii) urbanization, (iii) industrial/mining practices, (iv) flooding, and (v) storm surge events were evaluated to understand better anthropogenic impacts on regional sediment budgets. Three widely used sediment dating models based on excess 210Pb (ex210Pb) (i.e., Constant Rate of Supply, CRS, Constant Initial Concentration, CIC, and Advective Dispersion Equation, ADE) and two recently developed ones (i.e., Porosity Variation, PV and Porosity Variation Without Diffusion, PVWD) were tested to determine their applicability to these anthropogenically altered lacustrine and coastal areas. To verify results from ex210Pb models, we used conventional time markers, including regional hydrograph records and historical aerial images. We found that the traditional time-marker 137Cs is no longer helpful due to its current low inventories in the sediments. Drainage-to-lake area ratios were used to determine relative runoff and atmospheric radionuclide contributions. Statistical analysis of physical properties such as porosity, bulk and dry density, water content, and sediment geochemical compositions were utilized to support sediment transport and site development hypotheses. When constructing the sediment history at each site using these proxies, we found that the conventional CIC and ADE models produced unreliable ages because of violation of requirements for exponentially decreasing porosity and ex210Pb activity with depth. We found that two new models, PV and PVWD, that account for heterogeneous porosity, produced more reliable sediment ages. These two models produced ages with lower uncertainties than the CRS model, outperforming the other conventional models tested. We conclude that the PV and PVWD models are more appropriate for environments experiencing erosional and abrupt depositional events, which in our study resulted from dam construction and storm-surge events. Model sensitivity analysis showed that decreasing average particle density produces younger sediment ages by the PV and PVWD models. Higher ex210Pb activity analytical uncertainty resulted in lower sedimentation rates and higher estimated ages by all five models.

Block-based inverse lithography technology with adaptive level-set algorithm

Inverse lithography technology (ILT) is a key computational lithography approach aimed at inversely optimizing the photomask pattern to compensate for the image distortion in advanced optical lithography process. Traditional ILT algorithms, despite their capacity of significantly enhancing the image quality, bring challenges to the computational efficiency and mask manufacturability. To overcome those problems, this paper proposes a novel block-based ILT method driven by the level-set algorithm. This method leverages overlapped basis blocks with a level-set support area for mask representation, thus reducing the mask complexity. To circumvent the slow convergence rate dictated by the conventional Euler time step of the Courant-Friedrichs-Lewy condition, this research adopts the Barzilai-Borwein algorithm to update level set function using adaptive time step, which accelerates the optimization process. In addition, a testbed of digital lithography system is established to verify the proposed ILT method with a calibrated imaging model. It shows that the proposed method is superior over the widely-used and state-of-the-art ILT methods in terms of convergence speed and mask manufacturability.

A comparative study between early and conventional intestinal stoma closure in a tertiary care institute

Background: Little is known about ideal time for stoma closure. Therefore, the aim of this study is to compare early versus conventional stoma closure following bowel surgery in terms of quality of life (QoL), length of hospital stay (LoH), and postoperative complications. Methods: This randomized controlled trial was conducted at a medical college affiliated teaching hospital from May 2023 to June 2024. The 116 patients undergoing bowel surgery with temporary stoma creation were randomized into early closure (3 weeks post-surgery) and conventional closure (8-12 weeks post-surgery) groups. Primary outcome was anastomotic leak rate. Secondary outcomes included other postoperative complications, LoH, and QoL assessed using the EORTC QLQ-C30 questionnaire. Results: There was no significant difference in anastomotic leak rates between early (6.9%) and conventional (12.1%) closure groups. Overall complication rates were similar (56.9% early vs 50% conventional). LoH did not differ significantly between groups. QoL scores were significantly higher in the early closure group, with 100% of patients achieving considerable improvement (EORTC score 26-36) compared to 79.3% in the conventional group (p&lt;0.05). Conclusions: Early stoma closure is safe and feasible in appropriately selected patients, with no increased risk of anastomotic leak or other complications. It significantly improves QoL compared to conventional closure timing, without prolonging hospital stay. These findings suggest that early stoma closure may be a beneficial option for suitable patients following bowel surgery.

Time-frequency reassignment of blade tip timing signal

The health condition of a rotor blade can be assessed by its dynamic frequency with respect the rotational speed, which can be derived from the time–frequency analysis of the blade vibration signal. Blade tip timing (BTT), as a non-contact measurement technique, provides an alternative to traditional contact strain measurement methods. However, the vibration signal measured by BTT is usually undersampled, which limits the application of conventional time–frequency analysis methods. BTT sparse time–frequency methods utilize the sparsity to reconstruct undersampled BTT signals. Nevertheless, BTT sparse time–frequency representation suffers from poor concentration and inaccurate estimation of the dynamic frequency, especially in the case of fast varying speed and limited BTT sensors. In this paper, a BTT time–frequency reassignment method is proposed to enhance the concentration of the BTT time–frequency spectrum and improve the accuracy of dynamic frequency estimation. Firstly, BTT time–frequency reassignment utilizes the blade natural frequency, obtained from the Campbell diagram, as prior information. The blade dynamic frequency with respect to the rotational speed is estimated by combining the prior frequency with the measured frequency in the Bayesian framework. Subsequently, weights are assigned to both the time–frequency coefficients and the posterior frequency. The time–frequency coefficients are relocated around the posterior frequency according to the weights. The advantages of BTT time–frequency reassignment are demonstrated through a simulation, a laboratory test and a full-scale aeroengine test. Both simulation and experimental results demonstrate that BTT time–frequency reassignment enhances the concentration of the BTT time–frequency spectrum and improves the accuracy of the estimated dynamic frequency when limited BTT sensors are available. The influence of the prior frequency is discussed to evaluate the robustness of BTT time–frequency reassignment. In the full-scale aeroengine test, the reliability of BTT time–frequency reassignment is verified under the fast varying speed.

Pre-stack wave-equation time migration

In conventional time migration, the Green’s function is commonly calculated using ray tracing which may produce inaccurate images for complex structures with strongly lateral velocity variations. To mitigate this issue, we develop an accurate prestack wave-equation time migration method, borrowing from reverse time migration (RTM). First, a two-way acoustic wave equation is derived in the image-ray coordinate system. Then, we directly solve this equation using the finite-difference method to extrapolate source- and receiver-side wavefields, which does not require any approximation as in conventional ray-based time migration. Finally, a zero-lag crosscorrelation imaging condition is applied to generate the time migration image. In addition, we derive the dispersion relation and stability condition for the finite-difference method in the image-ray coordinate system, which provides guidance for temporal and spatial grid discretization. The perfectly matched layer (PML) boundary condition is utilized in the image-ray coordinate system to attenuate boundary reflections. Numerical examples of synthetic and field data verify the feasibility and adaptability of the proposed approach for low signal-to-noise land data. In addition, after mapping the images from the time domain to the depth domain, the image results are even better than RTM results.

Heat transfer analysis of Cu-Water nanofluid in a square enclosure using Caputo fractional derivative and machine learning

Fractional-based fluid transport modeling provides a deeper and more comprehensive understanding of the thermophysical properties of small-scale fluids and polymer solutions. Moreover, the fractional-order fluid dynamics framework finds applications in various fields, including renewable energy systems, thermal energy storage, and oil storage tanks. Inspired by these developments, the current study addresses the computational challenges inherent in modeling nanofluid dynamics within a square enclosure by introducing a fractional-order approach to enhance the simulation of buoyant flow and heat transport. Our model integrates the linear interpolation (L1 approach) and finite difference approximation for temporal and spatial derivatives by utilizing the Caputo time-fractional derivative instead of the conventional time derivative. This integration facilitates the use of the Backward Difference Alternating Direction Implicit (BD-ADI) method, which was specifically chosen to reduce the computational burden associated with the non-local characteristics of fractional models. Our results show that by altering the fractional order, significant improvements in heat transfer efficiency are achieved compared to classical models. For instance, compared to the integer order, a lower fractional-order parameter of γ=0.80 with a 4% nanoparticle volume fraction at a Rayleigh number of 103 increases the heat transfer rate by 15.77%. However, at a Rayleigh number of 106, the enhancement reduces to 4.15%, indicating that fractional adjustments’ influence diminishes at higher thermal buoyancy levels. This behavior of the fractional-order nanofluid model, influenced by thermal buoyancy force, has been investigated by using a Multiple Linear Regression(MLR) analysis with 258 numerical samples. Besides, MLR analysis further identifies the nanoparticle volume fraction as a critical factor, influencing the heat transfer rate by 10.48% at lower fractional-order settings.

Comparative Study of Machine Learning (ML) and Conventional Time Series Methodologies in Modelling the Exports Trade of Pakistan

Export trade is a pivotal driver of economic growth and stability in any nation, including Pakistan. Accurate modelling of export trade holds immense significance as it allows for informed decision-making and strategic planning. By employing advanced techniques like machine learning alongside traditional time series models, we can gain deeper insights into the dynamics of exports, anticipate trends, and adapt policies and strategies accordingly. This study focuses on comparative study of different machine learning and time series models in forecasting the exports trade of Pakistan. The dataset used in this study spans from 1972 to 2021, containing yearly data on exports (% of GDP) for Pakistan. This study focuses on employing various machine learning models, including the MLP model, ELM model, and classical time series models like ARIMA and Exponential smoothing, to model and forecast the exports of Pakistan. The model that best met the KPI criteria was chosen as the optimal candidate for predicting the behaviour of the exports trade data. The outcomes of the study concluded that MLP model outperformed in performance metrics, with MSE = 0.49, RMSE = of 0.70, MAE = 0.53, and a MAPE = 4.27. among all alternative models, including the Extreme Learning Machine model, ARIMA model, and Simple Exponential Smoothing. Our study highlights the effectiveness of the Multi-layer Perceptron (MLP) model as a valuable tool for forecasting exports. Its accuracy empowers policymakers to make well-informed decisions, ensuring a more sustainable and prosperous economic future.