Bulk Property Research Articles

Predicting Landslide Deposit Zones: Insights from Advanced Sampling Strategies in the Ilopango Caldera, El Salvador

In landslide susceptibility modeling, research has predominantly focused on predicting landslides by identifying predisposing factors, often using inventories primarily based on the highest points of landslide crowns. However, a significant challenge arises when the transported mass impacts human activities directly, typically occurring in the deposition areas of these phenomena. Therefore, identifying the terrain characteristics that facilitate the transport and deposition of displaced material in affected areas is equally crucial. This study aimed to evaluate the predictive capability of identifying where displaced material might be deposited by using different inventories of specific parts of a landslide, including the source area, intermediate area, and deposition area. A sample segmentation was conducted that included inventories of these distinct parts of the landslide in the hydrographic basin of Lake Ilopango, which experienced debris flows and debris floods triggered by heavy rainfall from Hurricane Ida in November 2009. Given the extensive variables extracted for this evaluation (20 variables), the Induced Smoothed (IS) version of the Least Absolute Shrinkage and Selection Operator (LASSO) methodology was employed to determine the significance of each variable within the datasets. Additionally, the Multivariate Adaptive Regression Splines (MARS) algorithm was used for modeling. Our findings revealed that models developed using the deposition area dataset were more effective compared with those based on the source area dataset. Furthermore, the accuracy of models using deposition area data surpassed that of that using data from both the source and intermediate areas.

Exceptionally large fluctuations in orientational order: The lessons of large-deviation theory for liquid crystalline systems.

How condensed-matter simulations depend on the number of molecules being simulated (N) is sometimes itself a valuable piece of information. Liquid crystals provide a case in point. Light scattering and 2d-IR experiments on isotropic-phase samples display increasingly large orientational fluctuations ("pseudo-nematic domains") as the samples approach their nematic phase. The growing length scale of those locally ordered domains is readily seen in simulation as an ever-slower convergence of the distribution of orientational order parameters with N. But the rare-event character and exceptionally slow time scales of the largest fluctuations make them difficult to sample accurately. We show in this paper how taking a large-deviation-theory perspective enables us to leverage simulation-derived information more effectively. A key insight of the theory is that finding quantities such as orientational order parameters (extensive variables) is completely equivalent to deducing the conjugate (intensive) thermodynamic field required to equilibrate that amount of order-and that knowing the relationship between the two (the "equation of state") can easily be turned into knowing the relative free energy of that degree of order. A variety of well-known thermodynamic integration strategies are already founded on this idea, but instead of applying an artificially imposed external field, we use a priori statistical mechanical insights into the small and large-field limits to construct a simulation-guided, interpolated, equation of state. The free energies that result mostly need information from the most probable configurations, making the simulation process far more efficient than waiting for (or artificially generating) large fluctuations.

Unsupervised surface water mapping with airborne LiDAR data by leveraging physical properties of water

ABSTRACT Comprehensive mapping of surface water, especially smaller bodies of water (<1 ha), remains challenging due to the lack of robust and scalable extraction methods. Traditional methods require the use of either training procedures or the repetitive tuning of site-specific parameters, which present hurdles to automated mapping and introduce biases tied to training data and parameters. The dependence on water’s reflectance properties, including LiDAR intensity, further complicates the issue, as higher-resolution images inherently introduce increased noise. In response to these challenges, we propose a unique, unsupervised method that focuses on the geometric characteristics of water instead of its variable reflectance properties. Unlike existing approaches, our method relies exclusively on 3D coordinate observations from airborne LiDAR data, taking advantage of the presumption that connected surface water remains flat due to surface tension. Leveraging this physical constraint and spatial connectivity, our method precisely extracts water bodies of diverse sizes and reflectance without the need for training procedures or intensive parameter tuning. Notably, by relying solely on 3D coordinate observations, our approach significantly facilitates the fully automated generation of comprehensive 3D topographical maps of both water and terrain, eliminating the need for human intervention or supplementary optical imagery. We validated the robustness and scalability of this method across diverse terrains, including urban, coastal, and mountainous areas. Overall, the proposed method achieved a 11% higher accuracy, measured by intersection over union, compared to the highly competitive NDWI-based method. Moreover, it proved its effectiveness in both accuracy and scalability compared to supervised machine learning and deep learning methods.

Understanding Color Interaction Effects in Accommodation Listing Images

Listing images are part of a property’s “shop window” to the world, impacting performance. Recent neuroscience and psychology research suggests that when combined in an interaction, color and scene in an image may not have a significant effect except for achromatic colors, which require fewer processing resources. As a first test of the achromatic feature integration theory of attention in tourism research, we use generalized linear mixed modeling on 0.26 million properties in 30 US locations with extensive control variables. The results find significantly stronger color interaction effects on demand for different scenes only for achromatic colors (white, gray, and black). Images that emphasize achromatic colors in scenes provide the most positive effect on consumers since they minimize processing resources. The research suggests that accommodation providers should take a more analytical approach to creating and selecting listing images, considering the potential interaction effects of image features on overall visitor behavior.

Lattice strain model for rare earth element partitioning between apatite and silicate melt: effect of apatite/melt composition and temperature with implications for lunar basalts

AbstractWe present new parameterized lattice strain models to predict the apatite/silicate melt partition coefficients of the rare earth elements (REE) in natural magmatic systems as a function of temperature and melt composition with high accuracy and precision. We collected published experimental REE partition coefficients for apatite coexisting with melt ranging from picrobasaltic to rhyolitic and phonolitic composition. Resulting dataset was analysed using the lattice strain model to assess the data quality. The three lattice strain parameters (D0, r0, and E) were subjected to a multivariate nonlinear least-squares analysis as a function of intensive variables, and we attempted to develop two independent models, on the basis of melt and apatite composition. In melt composition-based model, it was found that the D0 parameter increases with increasing melt polymerization, which can be expressed by the newly proposed simplified melt polymerization index P.I. = ($$\:{X}_{{\text{S}\text{i}\text{O}}_{2}}$$+2$$\:{X}_{{\text{A}\text{l}}_{2}{\text{O}}_{3}}$$+$$\:{X}_{{\text{T}\text{i}\text{O}}_{2}}$$+2$$\:{X}_{{\text{P}}_{2}{\text{O}}_{5}}$$)/($$\:{X}_{\text{M}\text{g}\text{O}}$$+$$\:{X}_{\text{F}\text{e}\text{O}}$$+$$\:{X}_{\text{C}\text{a}\text{O}}$$+2$$\:{X}_{\text{a}\text{l}\text{k}}$$), where individual $$\:{X}_{i}$$ variables represent the molar fractions of the oxides in the melt. By disentangling the effect of each component of the P.I., it was found that the CaO content of the melt is the oxide that affects more the D0 parameter. Thus, the D0 parameter is expressed as a power law function of melt CaO content. Through extensive search of the parameter space, the E and r0 variables were found to correlate strongly with linear combination of melt CaO, P2O5 and of reciprocal temperature, 1/T. Based on the apatite composition, we could not find any dependence of the partitioning parameters on compositional variables that would outperform solely a reciprocal temperature-based fit. The new parameterization was applied to predict REE partition coefficients in lunar basalts and suggests that lunar apatite could only equilibrate with evolved melt at late stages of fractional crystallisation.

The Impact of Imported Timber Price Fluctuations on the Margins of Wood Forest Product Exports: Empirical Evidence from Provincial-Level Data in China

This paper adopts a comprehensive approach to integrating the wood forest product trade of China's provinces with the global market, utilizing over four million records of trade data from the Chinese Customs database and the United Nations Trade and Commerce database, creating a three-dimensional panel dataset structured by year—province—importing country. The paper decomposes the export trade share into extensive margin, quantity margin, and price margin, collectively referred to as the ternary margins of export, to analyze the impact of imported timber price fluctuations on these export margins of wood forest products. The findings indicate that fluctuations in imported timber prices increase uncertainty, thereby suppressing the growth of China's wood forest product export share, extensive margin, and quantity margin while increasing the price margin. Heterogeneity tests further reveal that the impact of imported timber price fluctuations is more pronounced for southern forest regions, labor-intensive wood products, and countries without a free trade agreement. Mechanism tests suggest that increasing forestry fixed asset investment intensity and enhancing timber supply capacity can help mitigate the uncertainty effects associated with imported timber price fluctuations. The paper proposes policy implications, including enhancing local timber supply resilience, strengthening government support for forestry, and reinforcing the supply chain.

On Collaboration and Automation in the Context of Threat Detection and Response with Privacy-Preserving Features

Organizations and their security operation centers often struggle to detect and respond effectively to an extensive quantity of ever-evolving cyberattacks. While collaboration, such as threat intelligence sharing between security teams, and response automation are often discussed in the cybersecurity community, issues like data sensitivity and confidence in detection may hinder their adoption. This work investigates the potentials and challenges of collaboration and automation to enhance incident response processes. We propose a reference architecture for data sharing in threat detection and response, aiming to boost collaborative and automated efforts across organizations while also considering privacy-preserving features. To address these challenges and potentials, we discuss how such a framework could enhance current response processes within and between organizations, validated with results in local attack detection, incident response, and data sharing.

Bedside Assessment of the Respiratory System During Invasive Mechanical Ventilation.

Assessing the respiratory system of a patient receiving mechanical ventilation is complex. We provide an overview of an approach at the bedside underpinned by physiology. We discuss the importance of distinguishing between extensive and intensive ventilatory variables. We outline methods to evaluate both passive patients and those making spontaneous respiratory efforts during assisted ventilation. We believe a comprehensive assessment can influence setting mechanical ventilatory support to achieve lung and diaphragm protective ventilation.

Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method

Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)—to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R2 of 0.75 and an RMSE of 16.48 Mg/ha. The Lasso-GA method results in an ETR model with an R2 of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R2 of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R2 by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R2 by 0.04 and reduces RMSE by 1.93 Mg/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 × 7 and 5 × 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data.

Design Rules for Optimizing Microporous Layer Properties and Back-Layer Effect Using Decal Transfer Method in PEMWE

Polymer electrolyte membrane water electrolysis (PEMWE) has the fatal drawback of using expensive noble metal catalysts, especially the rare iridium substances in the anode. Therefore, improvements in catalyst utilization are required to achieve high performance and durability even at a minimized use of catalyst. One option is introducing a titanium-based microporous layer (MPL) at the interface of the catalyst layer and porous transport layer (PTL) to create an increased contact area and form a better electrical connection. However, how the MPL should be designed and manufactured is not fully understand yet. Herein, we present a decal-transfer method, which is a convenient process to manufacture a uniform and thin MPL on a porous substrate layer. Based on this method, we investigated the two-phase transport phenomena of the MPL by varying the particle size and thickness of the MPL and the porosity of the substrate layer. It is concluded that a smaller micrometer-scale titanium particle size is advantageous in both catalyst utilization and water transport, resulting in lower kinetic and mass transport overpotential. Furthermore, the interface property acted as the determinant factor for the overall performance, while the bulk property of the PTL was not a critical factor unless it did not hinder the mass transport of oxygen gas in-plane direction.

Hybrid Mass Spectrometry Applied across the Production of Antibody Biotherapeutics.

Post expression from the host cells, biotherapeutics undergo downstream processing steps before final formulation. Mass spectrometry and biophysical characterization methods are valuable for examining conformational and stoichiometric changes at these stages, although typically not used in biomanufacturing, where stability is assessed via bulk property studies. Here we apply hybrid MS methods to understand how solution condition changes impact the structural integrity of a biopharmaceutical across the processing pipeline. As an exemplar product, we use the model IgG1 antibody, mAb4. Flexibility, stability, aggregation propensity, and bulk properties are evaluated in relation to perfusion media, purification stages, and formulation solutions. Comparisons with Herceptin, an extensively studied IgG1 antibody, were conducted in a mass spectrometry-compatible solution. Despite presenting similar charge state distributions (CSD) in native MS, mAb4, and Herceptin show distinct unfolding patterns in activated ion mobility mass spectrometry (aIM-MS) and differential scanning fluorimetry (DSF). Herceptin's greater structural stability and aggregation onset temperature (Tagg) are attributed to heavier glycosylation and kappa-class light chains, unlike the lambda-class light chains in mAb4. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) revealed that mAb4 undergoes substantial structural changes during purification, marked by high flexibility, low melting temperature (Tm), and prevalent repulsive protein-protein interactions but transitions to a compact and stable structure in high-salt and formulated environments. Notably, in formulation, the third constant domain (CH3) of the heavy chain retains flexibility and is a region of interest for aggregation. Future work could translate features of interest from comprehensive studies like this to targeted approaches that could be utilized early in the development stage to aid in decision-making regarding targeted mutations or to guide the design space of bioprocesses and formulation choices.

Predictive Value of Preoperative Morphology Parameters in Patients Undergoing On-Pump and Off-Pump Coronary Artery Bypass Surgery.

Coronary heart disease is the most common cause of death worldwide. It is responsible for almost a third of deaths in patients over the age of 35. Various biomarkers are currently being studied in detail for their value in predicting postoperative mortality in patients undergoing CABG. The aim of this study is to analyze the predictive value of certain blood morphological parameters in CABG and off-pump coronary artery bypass grafting (OPCAB). A total of 520 patients who underwent surgery in two consecutive years and underwent CABG (404) or OPCAB (116) were included in this retrospective study. Gender, age, comorbidities, five-year survival rate, detailed information on hospitalization, surgery, intensive care unit parameters and preoperative blood samples from the cubital vein were recorded. Inverse propensity treatment weighting was applied to adjust for confounding factors at baseline. No differences were found between OPCAB and CABG as an isolated comparison. In the standardized population, patients with abnormal lymphocyte counts had an increased risk of death at one-year and five-year follow-up. In the standardized population, abnormal red blood cell distribution width (RDW-SD), neutrocyte-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were associated with increased mortality in each period analyzed. Abnormal PLR, RDW-SD and NLR are associated with increased early and late mortality in patients undergoing CABG and OPCAB. Abnormal lymphocytes are only associated with increased late mortality.

High-viscoelasticity alginate-based coatings for reversible zinc metal anodes

High-safety aqueous zinc (Zn) ion batteries are troubled by dendrite growth and hydrogen evolution reaction on Zn anode, which can be well solved via the construction of surface protective layer. The recent researches mainly focus on the bulk property of the protective layer, but its separation from Zn anode is ignored. In this work, high-viscoelasticity alginate-based (HVAA) layer was in-situ constructed on Zn anodes by the cross-linking of sodium alginate with formaldehyde and the plastifying of glycerin. HVAA layer can combine with H2O and coordinate with Zn2+ ions in aqueous electrolyte to achieve viscoelasticity on Zn anode, which can accommodate volume change of Zn anode during plating/stripping processes to continuously protect Zn anodes under the real battery system. Physical block effect of HVAA layer impedes hydrogen evolution corrosion reaction by avoiding the immediate contact of Zn anodes with aqueous electrolyte. Ionic conductive ability of HVAA layer by coordinating oxygen-containing groups with Zn2+ can uniform ion flux to induce the homogeneous deposition of Zn2+ on Zn anode. Zn anode with HVAA endows ZnZn symmetric battery with stably cycle of 4000 h and Zn-iodide full batteries with better electrochemical performance of 8000 cycles comparing with bare Zn anode. This work opens a novel route to continuously protect Zn anode through the high-viscoelasticity films to closely fit with Zn surface and implement the high-value application of renewable sources.

Hierarchical equations of motion for multiple baths (HEOM-MB) and their application to Carnot cycle.

We have developed a computer code for the thermodynamic hierarchical equations of motion derived from a spin subsystem coupled to multiple Drude baths at different temperatures, which are connected to or disconnected from the subsystem as a function of time. The code can simulate the reduced dynamics of the subsystem under isothermal, isentropic, thermostatic, and entropic conditions. The extensive and intensive thermodynamic variables are calculated as physical observables, and Gibbs and Helmholtz energies are evaluated as intensive and extensive work. The energy contribution of the system-bath interaction is evaluated separately from the subsystem using the hierarchical elements of the hierarchical equations of motion. The accuracy of the calculated results for the equilibrium distribution and the two-body correlation functions is assessed by contrasting the results with those obtained from the time-convolution-less Redfield equation. It is shown that the Lindblad master equation is inappropriate for the thermodynamic description of a spin-boson system. Non-Markovian effects in thermostatic processes are investigated by sequentially turning on and off the baths at different temperatures with different switching times and system-bath coupling. In addition, the Carnot cycle is simulated under quasi-static conditions. To analyze the work performed for the subsystem in the cycle, thermodynamic work diagrams are plotted as functions of intensive and extensive variables. The C++ source codes are provided as supplementary material.

Restricted Phase Space Thermodynamics of Nonlinear Electrodynamics-Anti-de Sitter Black Holes

Abstract We study the Restricted Phase Space Thermodynamics (RPST) of magnetically charged anti-de Sitter (AdS) black holes sourced by nonlinear electrodynamics (NED). The first law and the corresponding Euler relation are examined using the scaling properties. While the mass is homogeneous in the first order, the intensive variables are observed to follow zeroth-order homogeneity. We use numerical and graphical techniques to find the critical points of the various thermodynamic quantities. By utilizing the rescaling properties of the equation of states, we study the thermodynamic processes using different pairs of variables. From our analysis, we infer that although the RPST of the NED-AdS black hole resembles those of Reissner–Nordström-AdS, Kerr-AdS, and Kerr–Sen-AdS black holes in most of its aspects, hinting at a possible universality, there exists one particular $\mu -C$ process that differs in its behavior from its counterparts in earlier reported works.

Multi-stage reasoning on introspecting and revising bias for visual question answering

Visual Question Answering (VQA) is a task that involves predicting an answer to a question depending on the content of an image. However, recent VQA methods have relied more on language priors between the question and answer rather than the image content. To address this issue, many debiasing methods have been proposed to reduce language bias in model reasoning. However, the bias can be divided into two categories: good bias and bad bias. Good bias can benefit to the answer prediction, while the bad bias may associate the models with the unrelated information. Therefore, instead of excluding good and bad bias indiscriminately in existing debiasing methods, we proposed a bias discrimination module to distinguish them. Additionally, bad bias may reduce the model’s reliance on image content during answer reasoning and thus attend little on image features updating. To tackle this, we leverage Markov theory to construct a Markov field with image regions and question words as nodes. This helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question. To verify the effectiveness of our network, we evaluate our network on VQA v2 and VQA cp v2 datasets and conduct extensive quantity and quality studies to verify the effectiveness of our proposed network. Experimental resu- lts show that our network achieves significant performance against the previous state-of-the-art methods.

Back‐Propagating Rupture: Nature, Excitation, and Implications

AbstractRecent observations show that certain rupture phase can propagate backward relative to the earlier one during a single earthquake event. Such back‐propagating rupture (BPR) was not well considered by the conventional earthquake source studies and remains a mystery to the seismological community. Here we present a comprehensive analysis of BPR, by combining theoretical considerations, numerical simulations, and observational evidences. First, we argue that BPR in terms of back‐propagating stress wave is an intrinsic feature during dynamic ruptures; however, its signature can be easily masked by the destructive interference behind the primary rupture front. Then, we propose an idea that perturbation to an otherwise smooth rupture process may make some phases of BPR observable. We test and verify this idea by numerically simulating rupture propagation under a variety of perturbations, including a sudden change of stress, bulk or interfacial property and fault geometry along rupture propagation path. We further cross‐validate the numerical results by available observations from laboratory and natural earthquakes, and confirm that rupture “reflection” at free surface, rupture coalescence and breakage of prominent asperity are very efficient for exciting observable BPR. Based on the simulated and observed results, we classify BPR into two general types: interface wave and high‐order re‐rupture, depending on the stress recovery and drop before and after the arrival of BPR, respectively. Our work clarifies the nature and excitation of BPR, and can help improve the understanding of earthquake physics, the inference of fault property distribution and evolution, and the assessment of earthquake hazard.

MODERN MORPHOLOGICAL APPROACHES IN PROSTATE PATHOLOGY DETECTION

Prostate diseases are among the most common ones in men. Disease incidence is increasing both in the Russian Federation and all over the world. Objective. The purpose of the study is to examine the morphological aspects of benign hyperplasia and cancer using scanning electron microscopy. Materials and Methods. The authors analyzed morphological data of 30 patients with cancer (stage 1–3) and 10 patients with benign prostatic hyperplasia. The samples were examined using FE1 Quanta 200 3D and FE1 Quanta 600 FEG microscopes. Statistical processing was performed calculating intensive and extensive parameters of the mean values. Statistical significance of the mean and relative quantitative values was determined by Student's t-test. Results. Benign prostatic hyperplasia is characterized by stromal sclerosis, abnormal prostate glands with areas of glandular hyperplasia, including atypical conglomerates with loose cell-cell contacts. In addition, hemorrhages were observed in cancer. As the tumor progressed, cell polymorphism increased, and tumor emboli were formed. Conclusion. Scanning electron microscopy can detect atypical cells. The detection of atypical cells in patients with benign hyperplasia is a poor prognostic sign, as it indicates possible tumor progression. Tumor emboli formation in cancer indicates stage 3 and is associated with a high risk of metastasis.

Simulations evidencing two surface tensions for fluids confined in nanopores

As first recognized by Hill in 1960′s, one character distinguishing the thermodynamics of small systems from the macroscopic one is that some intensive variables are no longer defined uniquely for small systems. For example, the differential chemical potential, defined as the derivative of a thermodynamic potential with respect to particle number, is no longer equal to the integral one, given by the ratio of Gibbs free energy to the particle number. The concept of differential and integral surface tensions has been introduced recently to account for the increasing surface contribution to thermodynamic potentials when a system shrinks down in size. Simulations constitute a powerful tool for testing new concepts. The present work provides the simulation evidence for distinct differential and integral surface tensions. Our results point out some useful directions for future experimental investigations to check the general validity of the concept of differential and integral surface tensions.

Thermodynamic quantum Fokker–Planck equations and their application to thermostatic Stirling engine

We developed a computer code for the thermodynamic quantum Fokker–Planck equations (T-QFPE), derived from a thermodynamic system–bath model. This model consists of an anharmonic subsystem coupled to multiple Ohmic baths at different temperatures, which are connected to or disconnected from the subsystem as a function of time. The code numerically integrates the T-QFPE and their classical expression to simulate isothermal, isentropic, thermostatic, and entropic processes in both quantum and classical cases. The accuracy of the results was verified by comparing the analytical solutions of the Brownian oscillator. In addition, we illustrated a breakdown of the Markovian Lindblad-master equation in the pure quantum regime. As a demonstration, we simulated a thermostatic Stirling engine employed to develop non-equilibrium thermodynamics [S. Koyanagi and Y. Tanimura, J. Chem. Phys. 161, 114113 (2024)] under quasi-static conditions. The quasi-static thermodynamic potentials, described as intensive and extensive variables, were depicted as work diagrams. In the classical case, the work done by the external field is independent of the system–bath coupling strength. In contrast, in the quantum case, the work decreases as the coupling strength increases due to quantum entanglement between the subsystem and bath. The codes were developed for multicore processors using Open Multi-Processing (OpenMP) and for graphics processing units using the Compute Unified Device Architecture. These codes are provided in the supplementary material.