Standard Software Research Articles

A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses

Electric power material warehouses are critical to optimizing power grid supply chains and reducing carbon emissions, aiding the power sector’s decarbonization and climate goals. Nevertheless, to our knowledge, there are no comprehensive assessments of the life cycle carbon emissions associated with storage warehouses, so the emission reduction potential of the ever-increasing number of automated technologies is still unknown. This study presents an extensive life cycle carbon footprint assessment model tailored for electric power material warehouses, and it encompasses both traditional and automated frameworks. Utilizing a process-based life cycle assessment (LCA) methodology, carbon emissions across five distinct stages are examined: storage buildings and facilities, loading and unloading, transportation, packaging, and information management systems. For this purpose, warehouses in Jiangsu Province, China, are employed as a case study. The results show that automating warehouses can achieve a reduction in total carbon emissions of 42.85% compared with traditional warehouses, with total life cycle emissions of 39,531.26 tCO2, and the transportation stage is identified as the predominant contributor. This research not only offers actionable recommendations for strategies, including renewable energy integration, intelligent control systems, and standardized packaging protocols, but also establishes a framework for future investigations of refining carbon accounting methodologies—particularly in underexplored domains such as packaging.

Compact Si-SiN photonic fiber optic gyroscope transceiver for large volume manufacturing

Miniaturized interferometric fiber optic gyroscopes (IFOGs) providing high-precision angular measurement are highly desired in various smart applications. In this work, we present a high-performance Si-SiN photonic FOG transceiver composed of an optical source, polarizer, splitter, and on-chip germanium (Ge) photodetector (PD). The transceiver is assembled in a standard butterfly package with a thermo-electric cooler (TEC). The optical loss (including two edge couplers, as well as one 3 dB splitter) and polarization extinction ratio (PER) are less than 7 dB and greater than 20 dB at room temperature, respectively. Built with the polarization maintaining (PM) fiber coil with 70 mm average diameter and 580 m length, the transceiver-based IFOG exhibits record-low bias stability of 0.022 deg/h at an integration time of 10 s, the angular random walk (ARW) of 0.0012 deg/h, and the bias instability of 0.003 deg/h, to the best of our knowledge. The preliminary reliability test agrees well with the practical requirements. Our work verifies that the on-chip Ge PD is eligible for high-performance FOG applications. Leveraged with the typical CMOS compatible 8-inch (200 mm diameter wafers) silicon photonics platform and decreased fiber splicing points, the presented transceiver provides a promising solution toward a low-loss and miniaturized FOG system with large volume manufacturing capability.

Расчет плотности гидросети равнинных арк­тических и субарктических территорий России по цифровой модели рельефа с использованием ГИС-технологий (на примере Архангельской области)

The authors have developed a program for calculating the density of the channel network of the current relief using SAGA GIS modules to identify low-amplitude tectonic disturbances in the sedimentary cover of the platform arctic and subarctic territories of the Russian Federation. The program allows automating the calculation of the drainage network density using the sequential execution of standard GIS package modules. The calculation takes into account not only the drainage network, but also linear depressions of the relief, which makes it possible to identify structures with increased activity of current tectonics. The authors have calculated the density of the channel network of the representative territory (Arkhangelsk Region) with different “windows”. Comparison of the results with tectonic disturbances have shown a high degree of coincidence. The proposed methodology is applicable to the plain arctic and subarctic territories of the Russian Federation.

Systems Neuroscience Computing in Python (SyNCoPy): a python package for large-scale analysis of electrophysiological data.

We introduce an open-source Python package for the analysis of large-scale electrophysiological data, named SyNCoPy, which stands for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g., time-lock analysis), frequency (e.g., power spectrum), and connectivity (e.g., coherence) domains. It enables user-friendly data analysis on both laptop-based and high-performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials), making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source MATLAB toolbox for advanced analysis of electrophysiological data.

Dynamic Control of Cardboard-Blank Picking by Using Reinforcement Learning

Abstract Standard packaging lines with high output rates often struggle when dealing with uncertainties in the conditions of the handled materials. This paper focuses on a piece of machinery in an automatic packaging line, namely, an automated apparatus that extracts cardboard blanks from a buffer and transfers them to the next section through suction cups. In this context, the success of the operation depends on various controllable parameters, disturbances, and time-dependent variables, whose mutual relationships are not easily identifiable and whose understanding has so far been entrusted to the experiential knowledge of human operators. Currently, drops in picking success rates require the machine to be stopped and operators to intervene on-site, making use of their expertise to identify the issue and recalibrate the machine. To address the problem, this paper presents an artificial-intelligence-enabled controller, capable of continuously and autonomously recalibrating the apparatus and compensating for disturbances, in order to avoid missed or incorrectly picked cardboard blanks. In particular, this work exploits experimental data to build a model of the system, on which a reinforcement-learning algorithm is trained. The controller is tasked with regulating the controllable parameters while monitoring process variables. The developed agent is tested on the real apparatus to assess its performance.

Random Effects Meta-Analysis of Contingency Tables with Complete and Partially Complete Data, with Application to COVID-19 Research

We present a random effects meta-analytic approach for analyzing exchangeable 2 × 2 × 2 tables of COVID-19 deaths classified by two comorbidities, for example, diabetes and hypertension. We take the marginal tables of comorbidities as multinomial with table-specific cell probabilities drawn from a Dirichlet distribution. Conditionally hereon, we model the death counts for cell in the 2 × 2 table of possible comorbidity combinations as independent binomial random variables. We allow for a randomly drawn normally distributed frailty to model the correlation of the death counts within the same study. For complete tables, this model can be fitted by standard statistical software. We propose an approximate maximum likelihood (ML) imputation procedure by which tables with missing entries can be completed enabling the use of the same standard procedures and giving a better estimate of model parameters than one would get by leaving partial tables out. The properties of the method are illustrated by simulations.

A cross-sectional study to characterise the topical steroid dependent face by dermoscopy at a tertiary care centre

The occurrence of topical steroid dependent face (TSDF) in India has become widespread because of the abuse of creams containing steroids. Of late, many studies have been undertaken about topical steroid dependent face. Only few studies have demonstrated the dermoscopic features of this condition. Hence, we undertake a research to study the characteristic features of topical steroid dependent face by dermoscopy.A total of 398 patients who have applied topical steroid creams for more than one month and with symptoms indicative of topical steroid dependent face like redness, itching, burning, increased hair growth, acne were included in the study. The demographic, clinical and dermoscopic features were noted in such patients. These dermoscopic features were correlated with the clinical features and potency of steroid creams using Chi- square test. Statistical analysis was done using standard statistical software, ‘p’ value less than 0.05 was considered statistically significant.398 cases were included in the study. The mean age of the patients was 30.22 ± 5.7 years. The duration of topical corticosteroid application was 1-3 months in 30.15% patients, 3-6 months in 32.66% patients. Melasma and other hyperpigmentary causes was the most common cause of steroid usage in 32.16% patients. The most prevalent steroid used was 0.1% Betamethasone valerate. The most common presenting complaints were with itching (70.35%) and redness (69.84%). The dermoscopic features included mainly diffuse red areas (93.96%), terminal hair (75.87%), telangiectasias (74.87%) and brown globules (73.86%). Terminal hair, telangiectasias, focal white areas, white hair were seen on dermoscopy in statistically higher proportion of cases.Atrophy of skin, telangiectasias, red skin, hypertrichosis, easy bruisability and acneiform eruptions are the common clinical features of TSDF in our cohort. Dermoscopic findings along with these clinical features aid in meticulous diagnosis, differentiating it from other dermatological disorders. Hence, dermoscopic evaluation of TSDF provides an easy, diagnostic and time saving procedure that aids in quick detection and helps in management of such cases.

Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study

BackgroundBias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values. A Bayesian bias analysis combines the prior distribution with the data’s likelihood function whilst a Monte Carlo bias analysis samples the bias parameters directly from the prior distribution. No study has compared a Monte Carlo bias analysis to a Bayesian bias analysis in the context of MNAR missingness.MethodsWe illustrate an accessible probabilistic bias analysis using the Monte Carlo bias analysis approach and a well-known imputation method. We designed a simulation study based on a motivating example from the UK Biobank study, where a large proportion of the outcome was missing and missingness was suspected to be MNAR. We compared the performance of our Monte Carlo bias analysis to a principled Bayesian bias analysis, complete case analysis (CCA) and multiple imputation (MI) assuming missing at random.ResultsAs expected, given the simulation study design, CCA and MI estimates were substantially biased, with 95% confidence interval coverages of 7–48%. Including auxiliary variables (i.e., variables not included in the substantive analysis that are predictive of missingness and the missing data) in MI’s imputation model amplified the bias due to assuming missing at random. With reasonably accurate and precise information about the bias parameter, the Monte Carlo bias analysis performed as well as the Bayesian bias analysis. However, when very limited information was provided about the bias parameter, only the Bayesian bias analysis was able to eliminate most of the bias due to MNAR whilst the Monte Carlo bias analysis performed no better than the CCA and MI.ConclusionThe Monte Carlo bias analysis we describe is easy to implement in standard software and, in the setting we explored, is a viable alternative to a Bayesian bias analysis. We caution careful consideration of choice of auxiliary variables when applying imputation where data may be MNAR.

Multisegment Overlap–Save Method for Coherent Dedispersion

Dispersion occurs due to the interstellar medium, which functions as a prism and causes different time delays in radio waves of varying frequencies. The coherent dedispersion technique is often used in pulsar and fast radio burst observations to mitigate this phenomenon. The widely recognized Overlap–Save approach enables this dedispersion algorithm to efficiently perform long linear convolution. However, with the present implementation, the necessary filter length for dedispersion can reach 100 million points or more. The corresponding fast Fourier transform (FFT) points should be larger than this value and a GPU cluster can be used to tackle this demanding process. This study presents the Multisegment Overlap–Save Method (MS-OSM) to effectively address this problem. Our algorithm divides signal bands into separate short segments based on frequency component delays during dedispersion. By using the short segments shuffling technique with the Overlap-Save structure, MS-OSM can greatly reduce the FFT and inverse FFT points required down to fewer than 65,536 points. To evaluate the performance of MS-OSM, a synthetic pulsar signal is created and verified using standard software tools like DSPSR and PRESTO. The results show that MS-OSM maintains the same resolution while reducing execution time and resource usage. The validation of MS-OSM is taken by processing real pulsar observation data.

Abstract 4136204: Clearing Senescent Cells Improves Mouse Survival Rate Post Myocardial Infarction through Alteration of Cardiomyocyte and Immune Cell Subpopulations

Introduction: Cellular senescence often involves a p16-pathway, and p16 overexpression is a hallmark of senescent cells. The role of cellular senescence in myocardial infarction (MI) and any mediating mechanisms remain unclear. Aims: To investigate the effect of p16 + cell clearance on survival post MI and elucidate underlying mechanisms. Methods: We utilized INK-ATTAC transgenic mice, in which p16 + cells undergo targeted apoptosis upon exposure to AP20187 (AP). Sham and MI mice were treated with AP or vehicle (V) twice-weekly for one month, starting 3-4 hours post-MI. Survival rate improvement post MI in the AP group (Fig A, P<0.001) prompted us to evaluate effects of senescent cell clearance at the cardiac cellular level and its involvement in MI-associated pathways at 3 days post-MI. The infarct and peri-infarct areas were collected and snap-frozen. Nuclei were extracted with modified gentleMACS methods; libraries were constructed using 10X Genomics. Standard software (CellRanger, Seurat) were employed for single-nuclei-RNA-seq data analysis. Results: Cardiomyocytes (CMs) showed 5 distinct subsets. CM subclusters 1 and 2 showed enrichments of genes characteristic of infarct boarder zones, while most sham CMs were found in subcluster 0, reflecting healthy-CMs. AP-treated mice exhibited a higher proportion of healthy CMs (Fig B). Differentially expressed genes (DEGs) between V and AP group were enriched in the healthy CMs, suggesting that clearance of p16 + cells improves the status of CMs post MI (Fig C). Immune-cell profiling showed that by far the largest number of DEG for AP vs vehicle were expressed in the (arginase-1) Arg1 macrophage subgroup (Fig D). The percentage of Arg1 macrophages decreased in the AP group (FDR=0.059) (Fig E). We noted significant enrichment in the hypoxia and cardiac rupture pathways in Arg1 macrophages (Fig F), correlating with the high death rate from myocardial rupture in V-mice. Conclusion: These results suggest a significant role of p16 + senescent cells in post-MI mortality, potentially through effects on cardiomyocytes and a specific macrophage subpopulation, Arg1 macrophages.

Life cycle environmental impact assessment of the “Sindhuja-I” wave energy converter

The life cycle assessment (LCA) is an inevitable part of ocean wave energy systems. This paper presents an LCA of the Sindhuja-I ocean wave energy converter (WEC). The WEC is a point absorber type WEC tested at the Tuticorin port in India. This study utilized the ISO 14044 standard and SimaPro LCA software to assess the environmental impact of the WEC in comparison to a coal power plant, offshore wind, and tidal energy devices. The functional unit considered is the generation of 1 kWh of electricity. The WEC has a global warming potential of 174 gCO2-eq/kWh, showcasing an impressive 81.9% reduction in emissions compared to coal power plants. Stainless steel contributes significantly to emissions, accounting for 93.7%, 94.6%, 99.4%, and 94.1% of total CO2, NOx, SO2, and PM2.5 emissions, respectively. The paper emphasizes the potential environmental benefits of recycling WEC materials at the end of their lifespan. Furthermore, the study suggests that the longevity of a point absorber device can positively impact its overall environmental footprint, given its minimal moving parts and low maintenance requirements. The WEC has a promising environmental footprint of ocean wave energy conversion, particularly compared to traditional fossil fuel-based power generation.

A Streamlined Workflow for Microscopy-Driven MALDI Imaging Mass Spectrometry Data Collection.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a rapidly advancing technology for biomedical research. As spatial resolution increases, however, so do acquisition time, file size, and experimental cost, which increases the need to perform precise sampling of targeted tissue regions to optimize the biological information gleaned from an experiment and minimize wasted resources. The ability to define instrument measurement regions based on key tissue features and automatically sample these specific regions of interest (ROIs) addresses this challenge. Herein, we demonstrate a workflow using standard software that allows for direct sampling of microscopy-defined regions by MALDI IMS. Three case studies are included, highlighting different methods for defining features from common sample types─manual annotation of vasculature in human brain tissue, automated segmentation of renal functional tissue units across whole slide images using custom segmentation algorithms, and automated segmentation of dispersed HeLa cells using open-source software. Each case minimizes data acquisition from unnecessary sample regions and dramatically increases throughput while uncovering molecular heterogeneity within targeted ROIs. This workflow provides an approachable method for spatially targeted MALDI IMS driven by microscopy as part of multimodal molecular imaging studies.

Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models

The STARTS model is a structural equation model that decomposes individual differences in a longitudinally assessed variable into a time-invariant stable component, a time-varying autoregressive component, and a time-point specific state component. Typically, the model is estimated with Maximum Likelihood in standard SEM software. Here, we show how different STARTS models (the single-indicator univariate and multivariate and the multiple-indicator univariate model) can be incorporated into the Dynamic Structural Equation Model framework. We use the data of an experience sampling study to illustrate the expositions and we provide codes to fit the models in Mplus. As the DSEM parameters are estimated with a Bayesian approach, we also report the results of three simulation studies in which we examined the statistical properties of the parameter estimates obtained in the DSEM framework. Finally, we discuss how to estimate variants of the STARTS model and we make suggestions for future applied and methodological research.

Comprehensive Review and Future Research Directions on ICT Standardisation

Standardisation has become imperative to retaining order and development in modern society. The simplest actions, such as train timings and the width of the railroad, would be very difficult to achieve without Standardisation. Standardisation also solves problems, such as the use of mobile devices, which requires travel abroad when out of range. We perform a large-scale quantitative analysis for papers dealing with (1) standards and (2) Information and communications technology (ICT) data in three important databases, namely Web of Science, IEEE Explore, and ACM digital library, in this paper. These three databases presented 216 articles that were divided into five categories: standard-related review and survey studies, information management across hardware and software standards, energy management standards, machine learning model classification performance, privacy-aware software system standards, and health information and communications technology standards. This paper discusses how Standardisation facilitates the planning of the entire research and innovation process by encouraging discussions regarding the specific outputs the research aims to achieve. The paper further illustrates that references related to standardisation within the call topics act as a crucial motivating factor in the decision to adopt standardisation. In conclusion, our contribution provides a better understanding of standards in peer-reviewed publications and an essential foundation for future research. In addition, we demonstrate that standards play an important role in innovation.

Plant-based chitosan for the development of biodegradable packaging materials

Plant-derived materials and edible films have developed as viable substitutes for standard packaging materials, enabling sustainable and ecologically acceptable alternatives. Chitosan, a cationic carbohydrate polymer derived from animal or marine sources, as well as from agricultural waste such as mushrooms or various fungi possesses excellent properties such as film formation, mechanical strength, non-toxicity, biodegradability, edibility, UV-blocking ability, antioxidant activity, and antibacterial functionality, justifying its potential as packaging/coating material for fresh agricultural products. Chitosan is obtained through the deacetylation of chitin. The quantity of waste generated in a mushroom farm varies from 5–20 % of the total yielding quantity. Filamentous fungi's cellular structure, which is rich in chitin, provides a convenient method for chitin extraction. Fungal-derived chitosan offers the advantage of controllable physicochemical characteristics, including degree of deacetylation and molecular weight, compared to chitosan obtained from crustaceans. This versatility makes fungal chitosan suitable for various utilizations in food, pharmaceutical, and biomedical management. It can be utilised for different purposes in these fields. This review primarily emphasizes the extraction of chitin from mushrooms and various fungal sources, comparing different extraction methods and chitosan-based materials fabrication techniques. Additionally, it discusses the crucial characteristics of chitosan that make it convenient for high value-added functions in the food industry. To sum up, plant-based chitosan films have the potential to completely transform the packaging sector by providing environmentally friendly substitutes for traditional materials. Accepting these advances will help build a more resilient and sustainable earth, encourage the circular economy, and reduce the amount of plastic trash produced.

Paper waste and carbon emissions from oral contraceptive leaflets.

Oral contraceptives (OC) are the most used form of contraception among women in the U.S. and Europe. Like other medications, their packaging must include patient information leaflets. This study quantifies the environmental impact of paper waste generated by these leaflets. We conducted an observational analysis, measuring the weight of leaflets, pills, and packaging components across various OC brands. Significant variations in leaflet weights were observed. On average, leaflets accounted for 55% of the package weight, while pills and blister dispensers represented only 32%. The mean weight of OC leaflets was 12.3 ± 5.5 grams (4.7-21.9 grams), leading to an estimated annual paper waste of 6,118.4 tons, 5,763.5 tons of carbon dioxide equivalent emissions, and the use of approximately 146,841 trees for production. Standardizing leaflet weight to the lightest reported can reduce annual waste by 3780.5 tons of paper. This study highlights the substantial environmental cost of the waste generated from OC leaflets and proposes practical strategies to mitigate waste, including electronic leaflets and standardized packaging. Targeting these materials presents a significant opportunity to enhance sustainability, aligning with global efforts to reduce greenhouse gas emissions from the healthcare sector.

New algorithm of three-phase equilibrium calculations for CO2-hydrocarbon-water systems

In this century, global warming caused by the accumulation of atmospheric carbon dioxide has become an essential concern. The industry has two main decarbonization options: CO2 capture for permanent storage in geological formations and CO2 utilization for enhancing oil recovery (EOR). Both methods require accurate reservoir simulation to correctly describe the fluids flowing process and design an optimal injection plan. However, existing reservoir simulation practices often neglect the interaction and dissolution of CO2 and hydrocarbon components in water, leading to inaccurate predictions. Neglecting CO2 dissolution in water undermines storage estimation for capture and results in unreliable EOR descriptions. Therefore, to address this, our study focuses on developing a reliable and accurate phase equilibrium calculation package for hydrocarbon-CO2-water three-phase systems, which meets the demanding efficiency and robustness criteria of reservoir simulation. In this paper, we first introduce the methodology of the three-phase equilibrium calculation algorithm, then illustrate the numerical techniques employed to improve computational efficiency and finally demonstrate superior robustness of this algorithm through comprehensive case studies. Our research contributes three key improvements. First, we have increased the reliability of phase behavior descriptions by considering the dissolution of CO2 and hydrocarbon components in water. Second, we have enhanced the robustness of the three-phase equilibrium calculations algorithm, enabling accurate determination of three-phase statuses and fluid behaviors where standard commercial software may fail. Third, our algorithm exhibits notable efficiency improvements, ensuring its suitability for three-phase compositional simulations. The findings of this research provide valuable insights into the accurate modeling of hydrocarbon-CO2-water three-phase systems and offer practical solutions for designing effective CO2 reduction strategies.

A Million Person Study Innovation: Evaluating Cognitive Impairment and other Morbidity Outcomes from Chronic Radiation Exposure Through Linkages with the Centers for Medicaid and Medicare Services Assessment and Claims Data.

The study of One Million U.S. Radiation Workers and Veterans, the Million Person Study (MPS), examines the health consequences, both cancer and non-cancer, of exposure to ionizing radiation received gradually over time. Recently the MPS has focused on mortality patterns from neurological and behavioral conditions, e.g., Parkinson's disease, Alzheimer's disease, dementia, and motor neuron disease such as amyotrophic lateral sclerosis. A fuller picture of radiation-related late effects comes from studying both mortality and the occurrence (incidence) of conditions not leading to death. Accordingly, the MPS is identifying neurocognitive diagnoses from fee-for-service insurance claims from the Centers for Medicare and Medicaid Services (CMS), among Medicare beneficiaries beginning in 1999 (the earliest date claims data are available). Linkages to date have identified ∼540,000 workers with available health information. Such linkages provide individual information on important co-factor and confounding variables such as smoking, alcohol consumption, blood pressure, obesity, diabetes and many other health and demographic characteristics. The total person-level set of time-dependent variables, outcomes, organ-specific dose measures, co-factors, and demographics will be massive and much too large to be evaluated with standard software. Thus, development of specialized open-source software designed for large datasets (Colossus) is nearly complete. The wealth of information available from CMS claims data, coupled with individual dose reconstructions, will thus greatly enhance the quality and precision of health evaluations for this new field of low-dose radiation and neurocognitive effects.

HARNESSING GROUP THEORY FOR SIMPLIFIED ELECTRIC CIRCUIT ANALYSIS

Electric circuit analysis is fundamental to advancing modern technology, yet traditional methods often become complex and inefficient when applied to large-scale or intricate networks. Group theory, a branch of abstract algebra renowned for its capacity to exploit symmetry, has shown promise as a solution to this challenge. By applying group theoretical principles to circuit analysis, particularly through symmetry operations and transformation groups, engineers can simplify complex systems, reducing computational demands and revealing deeper insights into circuit behavior. Through systematic symmetry-based simplifications, group theory can decrease the number of equations required by up to 40% in certain configurations, enhancing both analytical efficiency and practical implementation. However, challenges include the mathematical complexity inherent to group theory, the limitations in analyzing highly irregular or nonlinear circuits, and the need for compatible computational tools. Recommendations include the integration of group theoretical approaches into standard circuit analysis software, focused training for electrical engineers, and further research to address the application of these methods in emerging technologies such as quantum and neuromorphic computing. Therefore, this review aims to elucidate the practical potential of group theory in transforming electric circuit analysis, presenting a streamlined approach that balances theoretical rigor with engineering feasibility.

A finite element based approach for nonlocal stress analysis for multi-phase materials and composites

AbstractThis study proposes a numerical method for calculating the stress fields in nano-scale multi-phase/composite materials, where the classical continuum theory is inadequate due to the small-scale effects, including intermolecular spaces. The method focuses on weakly nonlocal and inhomogeneous materials and involves post-processing the local stresses obtained using a conventional finite element approach, applying the classical continuum theory to calculate the nonlocal stresses. The capabilities of this method are demonstrated through some numerical examples, namely, a two-dimensional case with a circular inclusion and some commonly used scenarios to model nanocomposites. Representative volume elements of various nanocomposites, including epoxy-based materials reinforced with fumed silica, silica (Nanopox F700), and rubber (Albipox 1000) are subjected to uniaxial tensile deformation combined with periodic boundary conditions. The local and nonlocal stress fields are computed through numerical simulations and after post-processing are compared with each other. The results acquired through the nonlocal theory exhibit a softening effect, resulting in reduced stress concentration and less of a discontinuous behaviour. This research contributes to the literature by proposing an efficient and standardised numerical method for analysing the small-scale stress distribution in small-scale multi-phase materials, providing a method for more accurate design in the nano-scale regime. This proposed method is also easy to implement in standard finite element software that employs classical continuum theory.