Robust Integration Research Articles

Big Data Analytics for Financial Decision Making in Malaysia

Purpose: To aim of the study was to analyze the big data analytics for financial decision making in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Big Data Analytics (BDA) significantly enhances financial decision-making in Malaysia by extracting valuable insights from large datasets. It helps improve risk management, customer segmentation, and operational efficiency for financial institutions. BDA enables predictive analysis of market behavior, supports personalized financial services, and enhances fraud detection capabilities. Despite its benefits, challenges like data privacy issues and the need for skilled data professionals persist. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & network externalities theory may be used to anchor future studies on big data analytics for financial decision making in Malaysia. Prioritize investments in scalable and secure data infrastructure to support the deployment and integration of BDA technologies across financial institutions. This includes upgrading data storage systems, enhancing data processing capabilities, and implementing robust data integration frameworks. Develop and enforce clear regulatory frameworks that govern the ethical use of BDA in financial decision-making. Regulatory guidelines should address data privacy, security standards, transparency in algorithmic processes, and consumer rights protection.

Stable germline transgenesis using the Minos Tc1/mariner element in the sea urchin Lytechinus pictus.

Stable transgenesis is a transformative tool in model organism biology. Although the sea urchin is one of the oldest animal models in cell and developmental biology, studies in this animal have largely relied on transient manipulation of wild animals, without a strategy for stable transgenesis. Here, we build on recent progress to develop a more genetically tractable sea urchin species, Lytechinus pictus, and establish a robust transgene integration method. Three commonly used transposons (Minos, Tol2 and piggyBac) were tested for non-autonomous transposition, using plasmids containing a polyubiquitin promoter upstream of a H2B-mCerulean nuclear marker. Minos was the only transposable element that resulted in significant expression beyond metamorphosis. F0 animals were raised to sexual maturity, and spawned to determine germline integration and transgene inheritance frequency, and to characterize expression patterns of the transgene in F1 progeny. The results demonstrate transgene transmission through the germline, the first example of a germline transgenic sea urchin and, indeed, of any echinoderm. This milestone paves the way for the generation of diverse transgenic resources that will dramatically enhance the utility, reproducibility and efficiency of sea urchin research.

High-efficiency C3 electrosynthesis on a lattice-strain-stabilized nitrogen-doped Cu surface

The synthesis of multi-carbon (C2+) fuels via electrocatalytic reduction of CO, H2O using renewable electricity, represents a significant stride in sustainable energy storage and carbon recycling. The foremost challenge in this field is the production of extended-chain carbon compounds (Cn, n ≥ 3), wherein elevated *CO coverage (θco) and its subsequent multiple-step coupling are both critical. Notwithstanding, there exists a “seesaw” dynamic between intensifying *CO adsorption to augment θco and surmounting the C-C coupling barrier, which have not been simultaneously realized within a singular catalyst yet. Here, we introduce a facilely synthesized lattice-strain-stabilized nitrogen-doped Cu (LSN-Cu) with abundant defect sites and robust nitrogen integration. The low-coordination sites enhance θco and concurrently, the compressive strain substantially fortifies nitrogen dopants on the catalyst surface, promoting C-C coupling activity. The n-propanol formation on the LSN-Cu electrode exhibits a 54% faradaic efficiency and a 29% half-cell energy efficiency. Moreover, within a membrane electrode assembly setup, a stable n-propanol electrosynthesis over 180 h at a total current density of 300 mA cm−2 is obtained.

A Review on Electric Vehicles for Holistic Robust Integration in Cities: History, Legislation, Meta-Analysis of Technology and Grid Impact

Electric vehicle technology is transitioning from mobility based on fossil fuel combustion to one based on vehicle electrification, in which the primary energy is increasingly renewable, and the generation of pollutants and CO2 emissions is being reduced. This paper provides a tour of the key aspects of these systems, reviewing their most important historical, legislative, and grid impact topics. For this purpose, a literature review of publications up to 2022 is conducted. The last decade is the subject of a deeper analysis, shedding light on the essential characteristics of this technology and fundamentally focusing on its integration into electrical distribution networks. This work is carried out based on a review of a selection of articles written by authors worldwide who have researched these topics. We ordered and analyzed the temporal evolution of the defined categories, obtaining their research line direction. A meta-analysis of grid impact was also carried out, prompting clear conclusions about the state of the art and potential future works.

Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights.

Personalized nutrition (PN) represents a transformative approach in dietary science, where individual genetic profiles guide tailored dietary recommendations, thereby optimizing health outcomes and managing chronic diseases more effectively. This review synthesizes key aspects of PN, emphasizing the genetic basis of dietary responses, contemporary research, and practical applications. We explore how individual genetic differences influence dietary metabolisms, thus underscoring the importance of nutrigenomics in developing personalized dietary guidelines. Current research in PN highlights significant gene-diet interactions that affect various conditions, including obesity and diabetes, suggesting that dietary interventions could be more precise and beneficial if they are customized to genetic profiles. Moreover, we discuss practical implementations of PN, including technological advancements in genetic testing that enable real-time dietary customization. Looking forward, this review identifies the robust integration of bioinformatics and genomics as critical for advancing PN. We advocate for multidisciplinary research to overcome current challenges, such as data privacy and ethical concerns associated with genetic testing. The future of PN lies in broader adoption across health and wellness sectors, promising significant advancements in public health and personalized medicine.

Integrasi Nilai-Nilai Keislaman Pada Peserta Didik Melalui Pembelajaran Pendidikan Agama Islam

This study focuses on integrating Islamic Values among Students through Islamic Education Learning. The research method employed is literature review (library research), involving the analysis of conceptual information as well as qualitative and quantitative data from previously published scholarly articles. The role of the researcher is crucial in analyzing the discussed subject matter. Based on the results and discussions concerning the integration of Islamic values in Islamic Education learning, several main objectives are sought. These include strengthening students' understanding of Islamic teachings and their application in daily life and fostering character development based on Islamic values such as honesty, discipline, responsibility, and social care. Additionally, the integration aims to enhance students' spiritual and moral capabilities to practice Islamic teachings effectively. It encourages them to apply Islamic values in various aspects of life including school, family, and society. Educational institutions need effective strategies to ensure the robust integration of Islamic values among students and address challenges that may arise during this process.

Nonlinear dynamic analysis of shear- and torsion-free rods using isogeometric discretization and outlier removal

AbstractIn this paper, we present a discrete formulation of nonlinear shear- and torsion-free rods introduced by Gebhardt and Romero (Acta Mechanica 232(10):3825–3847, 2021) that uses isogeometric discretization and robust time integration. Omitting the director as an independent variable field, we reduce the number of degrees of freedom and obtain discrete solutions in multiple copies of the Euclidean space $$\left( \mathbb {R}^3\right) $$ R 3 , which is larger than the corresponding multiple copies of the manifold $$\left( \mathbb {R}^3 \varvec{\times } S^2\right) $$ R 3 × S 2 obtained with standard Hermite finite elements. For implicit time integration, we choose the same integration scheme as Gebhardt and Romero in (2021) that is a hybrid form of the midpoint and the trapezoidal rules. In addition, we apply a recently introduced approach for outlier removal by Hiemstra et al. (Comput Methods Appl Mech Eng 387:114115, 2021) that reduces high-frequency content in the response without affecting the accuracy, ensuring robustness of our nonlinear discrete formulation. We illustrate the efficiency of our nonlinear discrete formulation for static and transient rods under different loading conditions, demonstrating good accuracy in space, time and the frequency domain. Our numerical example coincides with a relevant application case, the simulation of mooring lines.

Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.

This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables. We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period. The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors. The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness. This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.

Decarbonization in the Oil and Gas Sector: The Role of Power Purchase Agreements and Renewable Energy Certificates

This study examines the adoption of Power Purchase Agreements (PPAs) and Renewable Energy Certificates (RECs) as strategic tools for decarbonization in the oil and gas sector. Focusing on the 21 largest oil and gas companies across Europe, North America, and South America, the analysis reveals varied adoption rates and strategic emphases between regions. European companies exhibit robust integration of PPAs and RECs to expand renewable energy capacities and reduce emissions, aligning closely with aggressive EU climate policies. In contrast, American companies show a cautious approach, focusing more on emission reduction from existing operations than on renewable expansions. The study’s findings indicate that, while both regions are advancing in their decarbonization efforts, European companies are leading with more defined renewable energy targets and comprehensive low-carbon strategies. This research contributes to understanding how different regulatory environments and market conditions influence corporate strategies towards sustainable energy transitions in traditionally hard-to-abate industries.

Challenges and Opportunities in International Trade Policy Implementation: Insights from the Cameroonian Ministry of Trade

This study meticulously explores the intricate dynamics of Cameroon's trade policies, aiming to elucidate their current state, identify significant challenges, and propose strategic enhancements for more robust economic integration and growth. Against the backdrop of a globalized economy, Cameroon's trade policies play a crucial role in shaping its economic landscape. The research employed qualitative methodologies, including an extensive literature review of works published by various authors on the topic, to garner a comprehensive understanding of the policy environment. The findings reveal critical barriers such as infrastructural deficits, regulatory inefficiencies, and policy implementation challenges that impede effective trade operations. These obstacles not only hinder trade efficiency but also limit Cameroon's potential to capitalize on global market opportunities. The study concludes with several strategic recommendations: prioritizing infrastructure investment to alleviate logistical bottlenecks, streamlining regulatory frameworks to enhance transparency and reduce bureaucratic hurdles, and improving policy-making processes. These measures are designed to fortify Cameroon's trade policy framework, thereby enhancing its economic prospects and global trade integration. The research underscores the importance of a holistic, strategic approach to trade policy development, advocating for sustained, coordinated efforts among all relevant stakeholders. By addressing the identified challenges and leveraging highlighted opportunities, Cameroon can significantly improve its trade policy outcomes, fostering sustainable economic growth. Keywords: Trade Policy, Cameroon, Economic Integration, Regulatory Framework, Stakeholder Engagement, Infrastructure Investment.

Ice-Induced Vibration Analysis of Fixed-Bottom Wind Turbine Towers

This research tackles the challenge of ice-induced vibrations in fixed-bottom offshore wind turbines, particularly the risk of frequency lock-in (FLI), a critical loading condition arising from slow ice movement against structures. We introduce an innovative FLI analysis process grounded in the ductile damage–collapse (DDC) mechanism, which offers a more accurate and significantly lower probability of FLI occurrence than conventional methods. Through dynamic evaluations employing a time-domain ice load model and FLI displacement analyses, we demonstrate that FLI can lead to higher structural vibrations than those caused by continuous brittle ice crushing. A case study of a 5 MW monopile wind turbine tower utilising Abaqus confirms the necessity for incorporating FLI considerations into structural design to ensure safety and performance in ice-prone environments. Comparing the DDC mechanism with the ISO method, our study reveals that the DDC approach predicts higher displacement and acceleration values during FLI, nearly an order of magnitude greater than those induced by ice loads with a 50-year return period. The research underscores the importance of robust ice-load integration in design strategies for offshore wind turbines, especially in regions susceptible to ice. It highlights the DDC mechanism as a novel strategy for enhancing structural resilience against ice-induced hazards.

All-RNA-mediated targeted gene integration in mammalian cells with rationally engineered R2 retrotransposons

All-RNA-mediated targeted gene integration methods, rendering reduced immunogenicity, effective deliverability with non-viral vehicles, and a low risk of random mutagenesis, are urgently needed for next-generation gene addition technologies. Naturally occurring R2 retrotransposons hold promise in this context due to their site-specific integration profile. Here, we systematically analyzed the biodiversity of R2 elements and screened several R2 orthologs capable of full-length gene insertion in mammalian cells. Robust R2 system gene integration efficiency was attained using combined donor RNA and protein engineering. Importantly, the all-RNA-delivered engineered R2 system showed effective integration activity, with efficiency over 60% in mouse embryos. Unbiased high-throughput sequencing demonstrated that the engineered R2 system exhibited high on-target integration specificity (99%). In conclusion, our study provides engineered R2 tools for applications based on hit-and-run targeted DNA integration and insights for further optimization of retrotransposon systems.

All-glycerogel stretchable supercapacitor with stable performance in a wide temperature window from −20 to 80 °C

Stretchable supercapacitors (s-SCs) are extensively used in portable and wearable smart electronic devices. However, existing s-SCs exhibit low electrical conductivity/stretchability, weak electrode/electrolyte interfaces, and limited operational temperatures, which severely limit their practical applications. Accordingly, we have developed a stretchable glycerogel supercapacitor (s-GGSC) addressing these limitations. Electrode is fabricated by glycerol-mediated robust integration of poly(vinyl alcohol) and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate through a simple strategy of partial drying–reswelling and thermal annealing. This eliminates the conventional trade-off between electrical conductivity and stretchability and provides adaptability to wide temperature ranges (−20 to 80 °C). A compatible electrolyte is also fabricated using poly(vinyl alcohol), lithium perchlorate, and glycerol. Heat-mediated facile exchange of inter- and intramolecular hydrogen bonding enables robust bonding between the electrode and electrolyte in the developed s-GGSC, thus solving interfacial problems under harsh mechanical conditions. The high electrical conductivity (1089 ± 49 S m−1) and fracture strain (232%) of the electrode preclude the use of an additional current collector. s-GGSC exhibits areal capacitance retentions of 90.92% and 93.27% after 7 d of incubation at −20 and 80 °C, respectively, and excellent cycling stability (81.48% capacitance retention) after 10,000 cycles. Furthermore, the areal capacitance of the device remains constant after intense mechanical deformations, such as bending, twisting, and stretching. s-GGSC outperforms existing flexible and/or stretchable supercapacitors under harsh conditions, confirming its suitability as a potential power source for wearable and stretchable smart electronic devices.

Clean energy technologies and energy systems for industry and power generation: Current state, recent progress and way forward

This vision article accompanies a Special Issue of Applied Thermal Engineering dedicated to the Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series held during 2022, including the 5th SEE SDEWES Conference Vlore, 3rd LA SDEWES Conference Sao Paulo, and 17th SDEWES Conference Paphos. The article discusses a selection of papers presented at these conferences and selected for publication in Applied Thermal Engineering, all connected by a focus on clean energy technologies and systems for the advancement of sustainable thermal energy systems. This research area covers a wide range of technologies but is primarily focused on the power generation sector, energy storage and utilization, efficiency improvements, sustainable technical solutions, and the facilitation of the robust integration of renewable energy resources into wider energy systems. Additional work on previously established research topics is also present, but new directions have crystallized, especially research into thermal management for power devices and microelectronics. Based on the recent contributions to the research field presented in the included papers, this vision article discusses the development and application of cutting-edge technological solutions and identifies the challenges that require focused attention. Current shortcomings of developing and established technologies will be developed by the underlying fundamental research, reduction of prohibitively high costs, expansion of operating conditions, and the inclusion of advanced control approaches, sophisticated combinations of renewable energy sources, and modeling tools for system investigations. Scientists, researchers, and engineers can benefit from the summary of the topics at the forefront of the research field and find guidelines and ways forward for future research activities.

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

Eco-friendly synthesis and application of NH2-COOH functionalized Fe3O4@graphite carbon microballs in heavy metal removal

Heavy metal pollution in wastewater is quite challenging for the environment. This study pioneers the synthesis of a novel adsorbent, amine-acid functionalized Fe3O4 decorated carbon micro-balls (NH2-COOH/Fe3O4@CMBs) via hydrothermal process and controlled thermal treatment under nitrogen atmosphere, utilizing eco-friendly resource glucose as a carbon precursor. The formation of cubic Fe3O4 nanoparticles and crystalline graphite within a carbon core-shell structure is confirmed. NH2-COOH/Fe3O4@CMBs show a porosity conducive to ion exchange in the range of 100–300 nm via FE-SEM, exhibiting a significant surface area of 494 m²/g with optimal pore volume. Within the carbon matrix, H2-TPR affirms the robust integration of Fe3O4, essential for high adsorption rates. In practical applications, NH2-COOH/Fe3O4@CMBs demonstrate exceptional performance, removing ∼ 99 % of Pb2+ ions in 30 min., showcasing an impressive adsorption capacity of 360 mg/g. The rapid and potent adsorption is attributed to the electron-rich graphite layers and the presence of abundant chelating sites. This research introduces NH2-COOH/Fe3O4@CMBs as a commercially viable and environmentally responsible solution for the removal of hazardous heavy metals from wastewater. Scientific understanding of adsorptive material not only advances but also offers a tangible approach to mitigate a pressing public health concern, marking a significant stride towards sustainable water treatment technologies.

Integrasi Artificial Intelegent Berbasis Sistem Operasi Android pada Smart Home

This study explores the integration of Artificial Intelligence (AI) into smart home systems using the Android operating system to enhance security, privacy, efficiency, and user comfort. Key security measures include data encryption, robust authentication methods, sandboxing, and AI integration, specifically leveraging Google Assistant for improved privacy controls. Maintenance strategies for smart homes emphasize energy management, device condition monitoring, and enhanced safety features. AI adaptation to user habits enhances productivity and situational awareness, while Android's role in connecting various IoT devices facilitates remote control and energy-efficient recommendations. Methods such as Eco Android, Greensource, byte-code transformations, and automated energy diagnosis tools aid in optimizing energy use. The comparison between smart and non-smart homes highlights the efficiency and convenience of smart homes despite higher installation costs and potential network issues. The development and deployment of an Android-based application, SafeHause, exemplifies practical implementation, emphasizing end-to-end testing, security updates, and user education. The findings affirm that AI integration with Android significantly improves the smart home experience by enhancing energy optimization, data security, and personalized user interaction. Furthermore, the study discusses future trends in smart home technology, such as the potential for more advanced AI algorithms and machine learning techniques to provide even greater personalization and automation. The importance of regular software updates and the role of user feedback in refining smart home systems are also highlighted, ensuring that these technologies continue to evolve and meet user needs effectively.

Nursing students' perceptions and experiences in pressure injury risk assessment: A qualitative study

AimThis study aims to capture the perspectives of senior undergraduate nursing students concerning pressure injury risk assessment, shedding light on their practical experiences and insights. BackgroundPressure injuries pose a significant challenge in healthcare, demanding vigilant attention from healthcare professionals. Central to mitigating this challenge is the imperative role of risk assessment, placing nurses at the forefront of prevention strategies. Given the critical nature of this responsibility, the need for a robust and well-structured integration of risk assessment teaching within undergraduate nursing education becomes increasingly apparent. DesignThis study was conducted as a descriptive qualitative study. MethodThis research was conducted at the Nursing Faculty of a State University in Ankara/Turkey, involving 19 senior students enrolled in the nursing program. Data was collected online using in-depth interviews with semi-structured interview questions between November and December 2022. After data collection, transcripts were generated and subjected to thematic analysis. The study was reported following the COREQ Checklist. ResultsThe study revealed two overarching themes: Opinions and Experiences. Within the Opinions theme, subthemes emerged, including "Features of Teaching," "Pressure Injury Definition," and "Improving Teaching Methods." The Experiences theme encompassed sub-themes such as the "Limited Relationship between Theoretical Teaching and Clinical Practice" and "Assessing the Risk of Pressure Injury in Practice." ConclusionParticipants affirmed that theoretical instruction sufficiently addressed pressure injury risk assessment yet expressed concerns about the inadequacy of clinical teaching. Additionally, they underscored the need for innovative teaching methods in the theoretical instructional process.

YADE - An extensible framework for the interactive simulation of multiscale, multiphase, and multiphysics particulate systems

This contribution presents the key elements of YADE, an extensible open-source framework for dynamic simulations. During the past 19 years, YADE has evolved from “Yet Another Dynamic Engine” to a versatile multiscale and multiphysics solver, counting a large, active, and growing community of users and developers. The computationally intense parts of the source code are written in C++, using flexible object models that allow for easy implementation of new features. The source code is wrapped in Python, equipping the software with an interactive kernel used for rapid and concise scene construction, simulation control, post-processing, and debugging. The project, including documentation and examples, is hosted on https://yade-dem.org, while the source code is freely available on GitLab. Over the last decade, YADE has expanded in terms of capabilities thanks to the contribution of many developers from different fields of expertise, including soil and rock mechanics, chemical engineering, physics, bulk material handling, and mineral processing. The rapid growth of YADE can be attributed to (1) the careful and robust design of the framework core, (2) a continuous integration pipeline with fully embedded thorough tests which are executed upon each merge request, ensuring stable compilation for various operating systems, and (3) user-friendliness, facilitated by the Python interface, detailed documentation, and rigorous user support. In this paper, we review the main features of YADE, highlighting its versatility in terms of applications, its flexibility in terms of code development, as well as recent improvements in terms of computational efficiency. Program summaryProgram Title: YADE - Yet Another Dynamic EngineCPC Library link to program files:https://doi.org/10.17632/n4f5fw97rd.1Developer's repository link:https://gitlab.com/yade-dev/trunkLicensing provisions: GNU General Public License 2Programming language: C++, PythonNature of problem: Numerical simulation of many-particle systems requires accurate models for particle-to-particle interactions, efficient contact detection between objects of various shapes, and robust time integration. In addition, the flow of fluids, thermal effects, as well as other coupled problems in the presence of particles are found in many fundamental and practical applications and they need dedicated computational tools. YADE provides a computational framework to perform such simulations using the discrete element method and multiple extensions of it.Solution method:YADE simulates particulate systems using the Discrete Element Method (DEM) in a flexible platform combining C++ and Python. It provides a large variety of shape and interaction models, an explicit time-integration scheme, and many post-processing tools. YADE features dedicated solvers for both fluid and heat fluxes at the pore scale, and it supports couplings with third-party software such as Escript (finite element method) and OpenFOAM (computational fluid dynamics).

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.