Domain Model Research Articles

Review of Gen AI Models for Financial Risk Management

In this paper, we propose and demonstrate a prototype for leveraging Generative AI (GenAI) in financial risk analysis, specifically focusing on fine-tuning GPT models with proprietary data. Financial risk modeling, development, validation, and approval require not only advanced AI techniques but also careful implementation, given the vast and complex datasets involved in such tasks. The research underscores the critical importance of human oversight in mitigating potential failures that can arise from fully automated mathematical models. The study explores the application of Large Language Models (LLMs) in various financial risk domains, such as credit risk assessment, market risk forecasting, and anomaly detection. While synthetic data generation is excluded from this work, the research highlights the use of zero-shot classification leveraging Hugging Face models and OpenAI tools. ChatGPT achieved over 70% accuracy in generating relevant questions and demonstrated 60% correctness in classification tasks. Additionally, we present a prototype pipeline that integrates GenAI capabilities into financial workflows, which is implementable on small-scale computing systems. This includes backend testing via Flask and rapid prototyping using cURL commands, offering a practical approach to testing and deploying models. By fine-tuning GenAI with domain-specific data and optimizing decision-making processes, this research highlights the transformative potential of integrating generative AI into financial risk management. The study provides insights into enhancing model efficiency, regulatory compliance, and scalability. Moreover, it addresses critical challenges such as handling large datasets and ensuring ethical AI use in decision-making systems. This work contributes to advancing the adoption of GenAI in financial analytics, paving the way for innovative, robust, and efficient methodologies to support the evolving demands of the financial sector.

Mechanisms in thyroid eye disease - the TSH receptor interacts directly with the IGF-1 receptor.

The pathogenesis of Thyroid Eye Disease (TED) has been suggested as due to signal enhancement in orbital fibroblasts as a result of autoantibody-induced, synergistic, interaction between the TSH receptor (TSHR) and the IGF-1 receptor (IGF-1R). This interaction has been explained by a "receptor cross talk", mediated via β-arrestin binding. Here, we have examined if this interaction can be mediated via direct receptor contact using modeling and experimental approaches. First, we docked a model of the leucine rich domain (LRD) of the TSHR ectodomain (ECD) to an available cryo-EM based structure of the active-state IGF-1R dimer and demonstrated the stability of the complex using molecular dynamics (MD) simulations. We then extended the complex with the full-length TSHR and the transmembrane helices of the IGF1R and a 3000 ns simulation also showed stability of this complex. We then performed co-immunoprecipitation studies with anti-TSHR and anti-IGF-1R antibodies using cells expressing the IGF-1R and the full-length TSHR and also cells that expressed the IGF-1R and only the TSHR-ECD and, therefore, unable to bind β-arrestin. These studies showed a 360 kD complex protein in the immunoprecipitation which was present in both the full-length TSHR and the TSHR-ECD-only expressing cells evidencing a direct interaction of receptors via their ectodomains in the absence of arrestin. Co-localized staining of TSHR and IGF1R in the TSHR-ECD cells further supported this direct interaction. These data showed that the TSHR and IGF-1R can interact directly and in the absence of β-arrestin binding. Understanding these interactions is important in the pathogenesis of TED and its therapeutic intervention.

Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)

Abstract. Many urban areas are characterized by both a growing population and an intensification of summer heat events in the context of climate change. Thus, more and more people are exposed to heat stress and corresponding health consequences. Measures for climate change adaptation, such as unsealing strategies, are needed in the existing urban fabric to reduce sensible heat flux by increasing latent heat flux to cool down the urban environment without requiring additional space or changing the basic function of the area. Unsealing measures like grass grid pavers (GGPs) can also help to reduce flooding risks due to increased infiltration and water storage capacities. Up to now, a parameterization of GGPs for microclimatic simulations is not available. To fill this research gap, we present a new GGP model parameterization developed for the fluid dynamics microclimate ENVI-met model based on field measurements with double-ring infiltrometers, etc., which can also be implemented in other microscale models in the field of urban climatology. To analyse the microclimatic effects and the cooling potential of this GGP parameterization, scenario analyses were performed using a validated ENVI-met model setup for an urban high-density study area in Cologne, Germany. An extreme scenario was designed to address the maximum cooling potential of the GGPs in comparison to the dominant sealed asphalt surfaces in the study area, along with a more realistic scenario with a usage-compatible installation of GGPs in the model domain only in side streets and inner courtyards while main streets remain sealed. We found a maximum cooling potential of up to −20.1 K for ground surface temperature and up to −7.1 K for air temperature at 1 m above ground level for the hottest hour of a simulated 3 d heat wave in summer 2022, which represents a 20-year heat event in Cologne. On spatial average, a decrease of up to −11.1 K for surface temperature and up to −2.9 K for air temperature was determined. On temporal average for the 3 d heat event, statistically significant mean temperature differences of −5.8 K for surface temperature and −1.1 K for air temperature were simulated. Cooling effects are more pronounced during the daytime for surface temperature, especially in unshaded areas, while cooling effects for air temperature are strongest during the nighttime. Model results also show that the entire air volume in the study area is cooled down due to this adaptation measure, even in areas of the domain where no surfaces have been unsealed in the scenario design. The more realistic GGP scenario shows cooling effects of a comparable magnitude to the extreme GGP scenario. Thus, even partial GGP unsealing is an effective adaptation measure for reducing extreme temperatures in cities if water availability is not limited.

Highly Robust Active Damping Approach for Grid-Connected Current Feedback Using Phase-Lead Compensation

The grid-current-feedback active damping (GCFAD) strategy has been widely utilized in LCL-type cascaded H-bridge static var generator (SVG) systems. Although digital control in GCFAD enhances sampling accuracy, it introduces delays in the subsequent calculation and execution processes, thereby reducing the effective damping region and weakening the robustness of the LCL-type cascaded H-bridge SVG system under a broad domain of grid impedance changes. This paper expands the effective damping area from (0, fR) (fR ∈ (fs/6, fs/3)) to (0, 0.45fs) by introducing a phase-lead compensator into the active damping feedback loop to reduce the delay. This reduces the negative effect of the digital control on the system’s effective damping region and significantly enhances its robustness under an extensive domain of grid impedance variations. The improved GCFAD is employed to establish the system’s discrete domain model and the controller parameters are adjusted in conjunction with the mentioned model. Simulation outcomes indicate the efficiency of the presented approach.

Enhancing the Life Cycle Management of Built Environment With Integrated Land Administration Functions Based on BIM/IFC and LADM

ABSTRACTThe maintenance of physical and legal data related to development in different formats necessitates the manual verification of construction projects' compliance with legal requirements and regulations. However, supporting the BIM/IFC standard, which models the design and construction process of buildings, with legal models has the potential to facilitate compliance with legal requirements and regulations, provide clarity in ownership and boundary management, oversee project approval and control, and ensure that legal areas are secured in 3D. This study focuses on the design of an integrated legal model based on the land administration domain model (LADM) standard, considering the functions of land administration in Türkiye, and explores the possibilities of mapping this model to the IFC data format. The study aims to represent land administration functions in BIM/IFC projects and integrate these processes with physical development. The findings from testing the proposed framework's applicability on an existing BIM project reveal opportunities for the integrated management of legal processes that create rights, restrictions, and responsibilities (RRRs) in spatial entities with physical processes.

Collision Avoidance for Maritime Autonomous Surface Ships Based on Model Predictive Control Using Intention Data and Quaternion Ship Domain

With the increasing proportion of ships in logistics and the growing prosperity of traffic in maritime, negotiation and cooperative collision avoidance between ships is becoming more and more essential for navigational safety. This paper proposes a Model Predictive Control method that utilizes intention data of the target ship and a quaternion ship domain model to achieve collision avoidance while considering COLREGs, named IQMPC. Firstly, by utilizing the intention data of other ships, trajectories of the own ship and the target ship are well predicted to detect potential collision risks and take optimal avoidance actions in advance while risks exist. Secondly, the quaternion ship domain with its adjacent area is divided into four different parts to reflect the urgency of ship encounters. Collision risk evaluation functions are designed to determine avoidance actions conforming to COLREGs. Thirdly, several different ship encounter scenarios were simulated based on IQMPC to verify its capability.

Artificial Intelligence Large Language Models Are Nearly Equivalent to Fourth-Year Orthopaedic Residents on the Orthopaedic In-Training Examination: A Cause for Concern or Excitement?

Background The rapid improvement of generative artificial intelligence (AI) models in medical domains including answering board-style questions warrants further investigation regarding their utility and accuracy in answering orthopaedic surgery written board questions. Previous studies have analyzed the performance of ChatGPT alone on board exams, but a head-to-head analysis of multiple current AI models has yet to be performed. Hence, the objective of this study was to compare the utility and accuracy of various large language models (LLMs) in answering Orthopaedic Surgery In-Training Exam (OITE) written board questions to each other as well as orthopaedic surgery residents. Methods A complete set of questions from the OITE 2022 exam was inputted into various LLMs and results were calculated and compared against orthopaedic surgery residents nationally. Results were analyzed by overall performance and question type. Type A questions related to knowledge and recall of facts, Type B questions involved diagnosis and analysis of information, and Type C questions focused on the evaluation and management of diseases, requiring knowledge and reasoning to develop treatment plans. Results Google Gemini was the most accurate tool answering 69.9% of questions correctly. Google Gemini also performed superiorly to ChatGPT and Claude on Type A (76.9%) and Type C questions (67.4%), with Claude performing superiorly on Type B questions (70.7%). Questions without images were answered with greater accuracy compared to those with images (65.9% vs. 34.1%). All LLMs performed above the average of a first-year orthopaedic surgery intern, with Google Gemini and Claude performance approaching that of fourth- and fifth-year orthopaedic surgery residents. Conclusion The study assessed LLMs like Google Gemini, ChatGPT, and Claude against orthopaedic surgery residents on the OITE. Results showed that these LLMs perform on par with orthopaedic surgery residents, with Google Gemini achieving best performance overall and in Type A and C questions while Claude performed best in Type B questions. LLMs have the potential to be used to generate formative feedback and interactive case studies for orthopaedic trainees.

A Review on Face Mask Recognition.

This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. Through a detailed comparison, their respective workflows, strengths, limitations, and applicability across different contexts are examined. The review underscores the paramount importance of accurate face mask detection, especially in response to global public health challenges such as pandemics. A central focus is placed on the role of datasets in driving algorithmic performance, addressing key factors, including dataset diversity, scale, annotation granularity, and modality. The integration of depth and infrared data is explored as a promising avenue for improving robustness in real-world conditions, highlighting the advantages of multimodal datasets in enhancing detection capabilities. Furthermore, the review discusses the synergistic use of real-world and synthetic datasets in overcoming challenges such as dataset bias, scalability, and resource scarcity. Emerging solutions, such as lightweight model optimization, domain adaptation, and privacy-preserving techniques, are also examined as means to improve both algorithmic efficiency and dataset quality. By synthesizing the current state of the field, identifying prevailing challenges, and outlining potential future research directions, this paper aims to contribute to the development of more effective, scalable, and robust face mask detection systems for diverse real-world applications.

A Novel Reciprocal Domain Adaptation Neural Network for Enhanced Diagnosis of Chronic Kidney Disease

ABSTRACTChronic kidney disease (CKD) is a major global health concern caused mostly by high blood pressure and glucose levels. Detecting CKD early is critical for reducing its negative consequences since it can lead to increased mortality rates. With CKD's rising incidence expected to make it the fifth biggest cause of death by 2040, rapid advances in diagnostic approaches are required. This study presents the Reciprocal Domain Adaptation Network (RDAN) as a potential approach to the various issues of CKD diagnosis. RDAN is a neural network model that will help to traverse the complexity of CKD diagnosis by smoothly combining diverse data sets. RDAN consists of two critical units at its foundation: Mutual Model Adaptation (MMA) and Domain Model Learning. The MMA unit uses a powerful Global and Local Pyramid Pooling technique to extract rich features from a variety of data domains. Meanwhile, the DML unit uses semi‐supervised domain‐independent features combined with MMA features to improve representation learning. RDAN includes a reciprocal regularizer to promote cross‐domain knowledge transfer, maximising feature representation for accurate CKD identification. An analysis of RDAN's performance on a variety of real‐world datasets showed remarkable results in terms of accuracy (96.94%), precision (98.81%), recall (98.73%), F1‐Score (98.88%), and area under the curve (AUC—99.35%). These results highlight the unmatched expertise of RDAN in managing data bias, domain changes, and privacy issues related to CKD diagnosis. Beyond statistical measures, RDAN's implications promise revolutionary breakthroughs in early CKD identification and subsequent therapeutic therapies. RDAN stands out as a groundbreaking method for diagnosing CKD. It delivers exceptional accuracy and can be seamlessly applied in various clinical environments.

From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption

ABSTRACT The Dark Energy Spectroscopic Instrument Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing–Arizona Sky Survey (BASS), and the Mayall z-band Legacy Survey (MzLS). The citizen science project Galaxy Zoo DECaLS 5 (GZD-5) has provided extensive and detailed morphology labels for a sample of 253 287 galaxies within the DECaLS survey. This data set has been foundational for numerous deep learning-based galaxy morphology classification studies. However, due to differences in signal-to-noise ratios and resolutions between the DECaLS images and those from BASS and MzLS (collectively referred to as BMz), a neural network trained on DECaLS images cannot be directly applied to BMz images due to distributional mismatch. In this study, we explore an unsupervised domain adaptation (UDA) method that fine-tunes a source domain model trained on DECaLS images with GZD-5 labels to BMz images, aiming to reduce bias in galaxy morphology classification within the BMz survey. Our source domain model, used as a starting point for UDA, achieves performance on the DECaLS galaxies’ validation set comparable to the results of related works. For BMz galaxies, the fine-tuned target domain model significantly improves performance compared to the direct application of the source domain model, reaching a level comparable to that of the source domain. We also release a catalogue of detailed morphology classifications for 248 088 galaxies within the BMz survey, accompanied by usage recommendations.

RNA-TorsionBERT: leveraging language models for RNA 3D torsion angles prediction.

Predicting the 3D structure of RNA is an ongoing challenge that has yet to be completely addressed despite continuous advancements. RNA 3D structures rely on distances between residues and base interactions but also backbone torsional angles. Knowing the torsional angles for each residue could help reconstruct its global folding, which is what we tackle in this work. This paper presents a novel approach for directly predicting RNA torsional angles from raw sequence data. Our method draws inspiration from the successful application of language models in various domains and adapts them to RNA. We have developed a language-based model, RNA-TorsionBERT, incorporating better sequential interactions for predicting RNA torsional and pseudo-torsional angles from the sequence only. Through extensive benchmarking, we demonstrate that our method improves the prediction of torsional angles compared to state-of-the-art methods. In addition, by using our predictive model, we have inferred a torsion angle-dependent scoring function, called TB-MCQ, that replaces the true reference angles by our model prediction. We show that it accurately evaluates the quality of near-native predicted structures, in terms of RNA backbone torsion angle values. Our work demonstrates promising results, suggesting the potential utility of language models in advancing RNA 3D structure prediction. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/RNA-TorsionBERT. Supplementary data are available from the website.

Identification of ALEKSIN as a novel multi-IRF inhibitor of IRF- and STAT-mediated transcription in vascular inflammation and atherosclerosis.

Cardiovascular diseases (CVDs) include atherosclerosis, which is an inflammatory disease of large and medium vessels that leads to atherosclerotic plaque formation. The key factors contributing to the onset and progression of atherosclerosis include the pro-inflammatory cytokines interferon (IFN)α and IFNγ and the pattern recognition receptor (PRR) Toll-like receptor 4 (TLR4). Together, they trigger the activation of IFN regulatory factors (IRFs) and signal transducer and activator of transcription (STAT)s. Based on their promoting role in atherosclerosis, we hypothesized that the inhibition of pro-inflammatory target gene expression through multi-IRF inhibitors may be a promising strategy to treat CVDs. Using comparative in silico docking of multiple IRF-DNA-binding domain (DBD) models on a multi-million natural compound library, we identified the novel multi-IRF inhibitor, ALEKSIN. This compound targets the DBD of IRF1, IRF2, and IRF8 with the same affinity and simultaneously inhibits the expression of multiple IRF target genes in human microvascular endothelial cells (HMECs) in response to IIFNα and IFNγ. Under the same conditions, ALEKSIN also inhibited the phosphorylation of STATs, potentially through low-affinity STAT-SH2 binding but with lower potency than the known multi-STAT inhibitor STATTIC. This was in line with the common inhibition of ALEKSIN and STATTIC observed on the genome-wide expression of pro-inflammatory IRF/STAT/NF-κB target genes, as well as on the migration of HMECs. Finally, we identified a novel signature of 46 ALEKSIN and STATTIC commonly inhibited pro-atherogenic target genes, which was upregulated in atherosclerotic plaques in the aortas of high-fat diet-fed ApoEKO mice and associated with inflammation, proliferation, adhesion, chemotaxis, and response to lipids. Interestingly, the majority of these genes could be linked to macrophage subtypes present in aortic plaques in HFD-fed LDLR-KO mice. Together, this suggests that ALEKSIN represents a novel class of multi-IRF inhibitors, which inhibits IRF-, STAT-, and NF-κB-mediated transcription and could offer great promise for the treatment of CVDs. Furthermore, the ALEKSIN and STATTIC commonly inhibited pro-inflammatory gene signature could help monitor plaque progression during experimental atherosclerosis.

Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression.

Short linear peptide motifs play important roles in cell signaling. They can act as modification sites for enzymes and as recognition sites for peptide binding domains. SH2 domains bind specifically to tyrosine-phosphorylated proteins, with the affinity of the interaction depending strongly on the flanking sequence. Quantifying this sequence specificity is critical for deciphering phosphotyrosine-dependent signaling networks. In recent years, protein display technologies and deep sequencing have allowed researchers to profile SH2 domain binding across thousands of candidate ligands. Here, we present a concerted experimental and computational strategy that improves the predictive power of SH2 specificity profiling. Through multi-round affinity selection and deep sequencing with large randomized phosphopeptide libraries, we produce suitable data to train an additive binding free energy model that covers the full theoretical ligand sequence space. Our models can be used to predict signaling network connectivity and the impact of missense variants in phosphoproteins on SH2 binding.

Hybrid Laplace-time domain model for coupled dynamic analysis of multibody mating system in topside float-over installations

The mating operation is the most critical phase during the topside float-over installation for jacket platforms, involving complex load transfer process and nonlinear couplings among the topside, float-over barge, jacket, and other buffering devices. It is very important and yet challenging to accurately and efficiently capture the coupled dynamics of the multibody mating system, as the time domain (TD) model requires a sufficiently small time interval to clearly capture the strong impacts on the buffering devices, resulting in a worse computational efficiency. This paper innovatively proposes a hybrid Laplace-time domain (HLTD) model to improve the computational efficiency in the coupled dynamic analysis while the computational accuracy is remained. The HLTD model computes the coupled dynamics by the time-domain iterative operations, mixing the Laplace-domain pole-residue operations for computing the motion responses in each iteration. Since the tedious differential and integral solutions in the TD model are replaced by the simple algebraic pole-residue calculations, the HLTD model has much higher computational efficiency with the same computational accuracy as the TD model. By comparing with the commercial software OrcaFlex, the proposed HLTD model shows satisfactory accuracy and superior efficiency in the heave-only mating analysis during the topside float-over installation of a jacket platform.

Applicability of AlphaFold2 in the modeling of dimeric, trimeric, and tetrameric coiled-coil domains.

Coiled coils are a common protein structural motif involved in cellular functions ranging from mediating protein-protein interactions to facilitating processes such as signal transduction or regulation of gene expression. They are formed by two or more alpha helices that wind around a central axis to form a buried hydrophobic core. Various forms of coiled-coil bundles have been reported, each characterized by the number, orientation, and degree of winding of the constituent helices. This variability is underpinned by short sequence repeats that form coiled coils and whose properties determine both their overall topology and the local geometry of the hydrophobic core. The strikingly repetitive sequence has enabled the development of accurate sequence-based coiled-coil prediction methods; however, the modeling of coiled-coil domains remains a challenging task. In this work, we evaluated the accuracy of AlphaFold2 in modeling coiled-coil domains, both in modeling local geometry and in predicting global topological properties. Furthermore, we show that the prediction of the oligomeric state of coiled-coil bundles can be achieved by using the internal representations of AlphaFold2, with a performance better than any previous state-of-the-art method (code available at https://github.com/labstructbioinf/dc2_oligo).

Horses are worthy of care: Horse sector participants’ attitudes towards animal sentience, welfare, and well-being

Abstract Organisations for which sentient animals are central to the business model need to demonstrate the safeguarding of animal welfare and well-being. This requires providing positive experiences for animals which is critical to maintaining the social licence to operate. A cross-sectional survey captured the attitudes of experienced horse sector participants regarding sentience, welfare and well-being. Almost all respondents (99.9%; n = 676/677), believed horses were sentient. Analysis of open-ended responses identified two themes: (1) Sentience is a pathway to understanding the mental state, welfare and well-being of horses; and (2) A moral obligation for humans to consider sentience. Respondents’ observations that horses reacted to stimuli and responded to their surroundings underpinned their belief that horses were sentient. Theme one related to respondents’ understanding of sentience and how sentience informed their interpretations of horse behaviours and the making of inferences regarding the equine mental state. Theme two related to a moral obligation believed to exist towards horses because humans manage the horses’ environment and need to consider the impact of their interactions with horses. These obligations were perceived as responsibilities to consider sentience when determining good welfare and well-being in horse activity settings, when interacting with horses and when training and competing with horses. The results suggested a sophisticated understanding of sentience existed among experienced horse sector participants, who recognised the sentient horse as worthy of care. We propose that leveraging experienced participants’ existing knowledge of sentience could support the implementation of the Five Domains model when updating organisational policies.

FAITH: Frequency-domain Attention In Two Horizons for time series forecasting

Time Series Forecasting (TSF) plays a crucial role in various sectors, including industrial maintenance, weather prediction, energy management, traffic control, and financial planning. Current deep learning-based predictive models often exhibit significant deviations between their forecasting outcomes and the ground truth because they lack an efficient method to extract both the global frequency-domain information of each variable and the relationships between different variables. To tackle this challenge, we introduce an innovative model—Frequency-domain Attention In Two Horizons (FAITH). FAITH decomposes time series into trend and seasonal components using a multi-scale adaptive decomposition and fusion architecture and processes them separately. It utilizes two modules—the Frequency Channel Feature Extraction Module (FCEM) and the Frequency Temporal Feature Extraction Module (FTEM)—to capture inter-channel relationships at a finer granularity and to extract global temporal information from the frequency domain of different variables. This significantly enhances its ability to handle long-term dependencies and complex patterns. Furthermore, FAITH achieves theoretically linear complexity by modifying the time-frequency domain transformation method, effectively reducing computational costs. Extensive experiments across six benchmarks for long-term forecasting and five benchmarks for short-term forecasting demonstrate that FAITH outperforms existing models in various domains, such as electricity, weather, and traffic. These results validate the effectiveness and superiority of FAITH in both long-term and short-term time series forecasting tasks. Our codes and data are available at https://github.com/LRQ577/FAITH

Logic-infused knowledge graph QA: Enhancing large language models for specialized domains through prolog integration

Logic-infused knowledge graph QA: Enhancing large language models for specialized domains through prolog integration

Analytical approach to control the irreversibilities in a chemically reactive nanofluid flow over a Darcy–Forchheimer medium with nonuniform heat source/sink

AbstractIn real‐world processes, such as heat transfer, fluid flow, and chemical reactions, irreversibility occurs frequently, which induces an augment in entropy. The optimization of the entropy generation in a mechanical system is one of the fundamental areas of research to channel the energy of the system for utilization. This research exhibits an entropy generation in a nanofluid flow drenched in a fluid‐saturated non‐Darcy porous medium toward a stagnation point. The flow line also experiences the existence of nonuniform heat generation/absorption following the first‐order chemical reaction, which enhances the applicability of this model in different domains of science and engineering. A special form of the Lie group approach is applied to revert the governing partial differential system into its self‐similar ordinary differential form. The solutions of the transformed nonlinear ODEs are acquired analytically through the DTM ‐Padé as well as numerically by the Runge–Kutta–Fehlberg (RKF‐45) method coupled with the shooting process. The fallouts are vividly sketched graphically. One of the upshots reveals that the escalation of space and temperature‐reliant heat absorption parameters can optimize the thermal irreversibility of the system and so as the total entropy generation. Meanwhile, by incrementing the Darcy parameter from 0 to 0.8, the wall shear stress decays by 23.7%, whereas with the same hike in the Forchheimer resistance factor, declines the rate of heat and mass transfer approximately by 2.6% and 3.6%, respectively. Another upshot reveals that with the escalation of the space‐dependent heat source parameter from 0.1 to 0.5, the Nusselt number significantly decreased by 23.9%. Meanwhile boosting the temperature‐reliant heat sink parameter from ‐0.1 to ‐0.5 causes an inclination in the rate of heat transportation by 10.5%. Moreover, it is found that mass transport irreversibility can be controlled by enhancing the constructive chemical reaction rate. We hope that this study will be beneficial for many engineering and industrial processes, particularly in geothermal energy extraction, nuclear waste disposal management, groundwater filtration machinery and food processing equipment.

Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review.

Organic fertilizers have been identified as a sustainable agricultural practice that can enhance productivity and reduce environmental impact. Recently, the European Union defined and accepted insect frass as an innovative and emerging organic fertilizer. In the wider domain of organic fertilizers, mathematical and computational models have been developed to optimize their production and application conditions. However, with the advancement in policies and regulations, modelling has shifted towards efficiencies in the deployment of these technologies. Therefore, this paper reviews and critically analyzes the recent developments in the mathematical and computation modeling that have promoted various organic fertilizer products including insect frass. We reviewed a total of 35 studies and discussed the methodologies, benefits, and challenges associated with the use of these models. The results show that mathematical and computational modeling can improve the efficiency and effectiveness of organic fertilizer production, leading to improved agricultural productivity and reduced environmental impact. Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. These models have been used in quantifying nutrients concentration/release, effects of nutrients in agro-production, and fertilizer treatment. This paper also discusses prospects for the use of these models, including the development of more comprehensive and accurate models and integration with emerging technologies such as Internet of Things.