Genetic Programming Research Articles

Evolving routing policies for electric vehicles by means of genetic programming

AbstractIn recent years, the growing interest in environmental sustainability has led to Electric Vehicle Routing Problems (EVRPs) attracting more and more attention. EVRPs involve the use of electric vehicles, which have additional constraints, such as range and recharging time, compared to conventional Vehicle Routing Problems (VRPs). The complexity and dynamic nature of solving VRPs often lead to the introduction of Routing Policies (RPs), simple heuristics that incrementally build routes. However, manually designing efficient RPs proves to be a challenging and time-consuming task. Therefore, there is a pressing need to explore the application of hyper-heuristics, in particular Genetic Programming (GP), to automatically generate new RPs. Since this method has not yet been investigated in the literature in the context of EVRPs, this study explores the applicability of GP to automatically generate new RPs for EVRP. To this end, three RP variants (serial, semiparallel, and parallel) are introduced in this study, along with a set of domain-specific terminal nodes to optimise three criteria: the number of vehicles, energy consumption, and total tardiness. The experimental analysis shows that the serial variant performs best in terms of energy consumption and number of vehicles, while the parallel variant is most effective in minimising the total tardiness. A comprehensive analysis of the proposed method is conducted to determine its convergence properties and the impact of the proposed terminal nodes on performance and to describe several generated RPs. The results show that the automatically generated RPs perform commendably compared to traditional methods such as metaheuristics and exact methods, which usually require significantly more runtime. More specifically, depending on the scenario in which they are used, the generated RPs achieve results that are about 20%-37% worse compared to the best known results for the number of vehicles in almost negligible time, in just some milliseconds.

Reproductive characteristics of indigenous village chickens

BackgroundIndigenous village chickens (IVCs) show a wide variation in production performance and reproductive characteristics. This high variation is occurred due to genetic and environmental factors and gene-environment interactions.ResultsThis work reports the results of a cross-sectional study conducted using a face-to-face interview with 119 small-scale farmers regarding their insights on the production performance and reproductive characteristics of IVCs. It was reported that pullets were sexually mature on average at the age of 5.5 months and cockerels at 6 months. This comparatively early sexual maturity by the standards of IVCs might be associated with the impact of uncontrolled gene flow from production breeds. However, there is high variation in age at sexual maturity at an individual bird’s level. It was found that pullets mature earlier than cockerels (t = 3.250, df = 159, p = 0.001, 95% CI: -0.670, -0.166). According to the respondents, local hens laid on average 14 eggs per clutch. The average number of clutches per year was 4, which can culminate in the yearly production of 56 eggs. A significantly large proportion of the respondents reported that the dry season is appropriate for laying eggs (96.7%) and brooding chicks (94.1%). During the dry season, the scavenging feed resource is relatively abundant, the risk of infection is comparatively low, and the hot weather is suitable for laying eggs and brooding chicks. IVCs possess a long reproductive lifetime, that is, on average, 3 years in hens and 2 years in cocks, which makes them more productive than has been anticipated. For example, this study found that a hen, on average, can lay 174 eggs and hatch 58 chicks in its average reproductive lifetime of 3 years.ConclusionsThe high intrapopulation variation in the reproductive performance of IVCs is vital resource in genetic improvement programs.

Plant Immunity Modulation in Arbuscular Mycorrhizal Symbiosis and Its Impact on Pathogens and Pests

Arbuscular mycorrhizal (AM) symbiosis is the oldest and most widespread mutualistic association on Earth and involves plants and soil fungi belonging to Glomeromycotina. A complex molecular, cellular, and genetic developmental program enables partner recognition, fungal accommodation in plant tissues, and activation of symbiotic functions such as transfer of phosphorus in exchange for carbohydrates and lipids. AM fungi, as ancient obligate biotrophs, have evolved strategies to circumvent plant defense responses to guarantee an intimate and long-lasting mutualism. They are among those root-associated microorganisms able to boost plants’ ability to cope with biotic stresses leading to mycorrhiza-induced resistance (MIR), which can be effective across diverse hosts and against different attackers. Here, we examine the molecular mechanisms underlying the modulation of plant immunity during colonization by AM fungi and at the onset and display of MIR against belowground and aboveground pests and pathogens. Understanding the MIR efficiency spectrum and its regulation is of great importance to optimizing the biotechnological application of these beneficial microbes for sustainable crop protection.

The mentee report: outcomes from implementing a mentorship program for international genetic counseling applicants.

The first genetic counseling (GC) graduate training program was established in the United States (U.S.) in 1969 and GC is an emerging field of healthcare in many countries. Each year, at least 7% of applicants to U.S.-based GC graduate programs come from countries outside the U.S. ("international GC applicants''). To address the unique needs of international GC applicants, volunteers from the International Special Interest Group (ISIG) of the National Society of Genetic Counselors (NSGC) launched a semi-structured mentorship program (the "International Genetic Counseling Mentorship Program'' (IGCMP)) in 2021, which provides individualized mentorship and optional group activities for networking and learning. Fifty-two people from 19 countries signed up for the IGCMP across three application cycles. Of these, 47 were eligible to participate as mentees, and most were interested in one-on-one virtual meetings with international GCs in the U.S. (n = 41/47, 87.2%). An assessment form was sent to 17 mentees who applied to GC graduate school after participating in the first or second cycle of the IGCMP. Of the 12 responses received, 10 (83.3%) reported being extremely satisfied with their individual mentor(s), and the one-on-one meeting with a mentor was considered helpful to both the application and interview process by nine (75.0%) respondents. Importantly, feedback about program improvement revealed an interest in connecting with additional international applicants and mentors and all respondents expressed interest in receiving mentorship throughout graduate school. Future directions include collaborating with other mentorship and graduate programs to further enhance support for international applicants.

The decision for or against mycoparasitic attack by Trichoderma spp. is taken already at a distance in a prey-specific manner and benefits plant-beneficial interactions

BackgroundThe application of plant-beneficial microorganisms as bio-fertilizer and biocontrol agents has gained traction in recent years, as both agriculture and forestry are facing the challenges of poor soils and climate change. Trichoderma spp. are gaining popularity in agriculture and forestry due to their multifaceted roles in promoting plant growth through e.g. nutrient translocation, hormone production, induction of plant systemic resistance, but also direct antagonism of other fungi. However, the mycotrophic nature of the genus bears the risk of possible interference with other native plant-beneficial fungi, such as ectomycorrhiza, in the rhizosphere. Such interference could yield unpredictable consequences for the host plants of these ecosystems. So far, it remains unclear, whether Trichoderma is able to differentiate between plant-beneficial and plant-pathogenic fungi during the process of plant colonization.ResultsWe investigated whether Trichoderma spp. can differentiate between beneficial ectomycorrhizal fungi (represented by Laccaria bicolor and Hebeloma cylindrosporum) and pathogenic fungi (represented by Fusarium graminearum and Alternaria alternata) in different confrontation scenarios, including a newly developed olfactometer “race tube”-like system. Using two independent species, T. harzianum and T. atrobrunneum, with plant-growth-promoting and immune-stimulating properties towards Populus x canescens, our study revealed robustly accelerated growth towards phytopathogens, while showing a contrary response to ectomycorrhizal fungi. Transcriptomic analyses identified distinct genetic programs during interaction corresponding to the lifestyles, emphasizing the expression of mycoparasitism-related genes only in the presence of phytopathogens.ConclusionThe findings reveal a critical mode of fungal community interactions belowground and suggest that Trichoderma spp. can distinguish between fungal partners of different lifestyles already at a distance. This sheds light on the entangled interactions of fungi in the rhizosphere and emphasizes the potential benefits of using Trichoderma spp. as a biocontrol agent and bio-fertilizer in tree plantations.

Genetic variation in susceptibility of Phytophthora cinnamomi-infected holm oak in the absence or presence of severe drought

Abstract The evergreen oaks Quercus ilex and Quercus suber are exposed to widespread Phytophthora infestation in natural forests. To restore diseased forests, deploying trees less susceptible to combined stress is the most promising approach. We aimed to determine whether drought affects the susceptibility of Q. ilex and Q. suber seedlings to Phytophthora cinnamomi (Pc) differently. Additionally, to provide scientific support for a genetic improvement program to reduce the susceptibility of holm oak to decline, genetic variation and heritability in susceptibility to Pc in Q. ilex in the absence or presence of drought were estimated. About 7000 seedlings of 66 Q. ilex and 9 Q. suber trees from the Extremadura region (Spain) were inoculated with Pc at age 1. The following year, half the experimental blocks were regularly watered and half were exposed to severe drought, and inoculated again with Pc. In the absence of drought, Q. ilex was more susceptible than Q. suber to Pc (mortality 97% and 59%, respectively), but in the presence of drought after Pc infection the species were equally susceptible (~97% plant mortality). It could therefore be expected that under the climate change scenarios predicted for the south of the Iberian Peninsula, offspring of Q. suber will be as compromised as offspring of Q. ilex to soil infestation by Pc. Significant additive genetic variation and heritability in the susceptibility of Q. ilex to combined Pc infection and drought were observed (hi2 = 0.46 for time to death of plants), indicating that breeding for tolerance to combined stress is possible. Family variance component estimates of time to death in Q. ilex were highest in the presence of drought, and genetic control of susceptibility in Q. ilex decreased over time as plant stress increased. This is the first study to define a production population against combined stress in oak.

Predicting maximum deflection of N-Edged thin-shelled hyperbolic-Paraboloid umbrella using machine learning techniques

Many thin-shelled hyperbolic paraboloid (hypar) umbrella forms have been built in the last 60 years as roof coverings. While the stresses in these forms remain relatively low, the deflections are a critical design parameter, and one that must be considered by architects and engineers in the conceptual design phase, meaning the design stage when the scale and form is given to the structure. To-date, there is no closed-form solution that can predict the maximum deflection of umbrellas due to their highly varying and complex geometry. The best option for predicting deflection is via finite element analysis, which is time-consuming for conceptual (preliminary) design purposes. In response, this paper uses machine learning via genetic programming (GP) and gene expression programming (GEP) to develop closed-form equations that predict the maximum corner deflection of N-edged hypar umbrellas – where N = 3, 4, 5, 6, 7, and 8. For a given a boundary condition and material, geometry is the most significant parameter influencing a shell's stiffness; thus, elastic finite element (FE) models use geometric properties as input variables (N, projected area, normalized rise, and shell thickness). The maximum corner deflection is recorded as an output and the FE analyses generate a large dataset of 53,754 results. It is observed that both GP and GEP can effectively parameterize the maximum deflection of N-edged hypar umbrellas, with GEP producing more concise, but relatively less accurate, equations than GP. While the formulations are trained using concrete material, a material factor multiplier transforms the results to other material properties within the assumption of elastic limits. The results of the study can be used to assist with conceptual design of hypar umbrellas and to validate complex FE models of hypar umbrellas. This research also illustrates the use of machine learning techniques as applied to the conceptual design of structures with highly varying geometries.

An explainable model for predicting Worsening Heart Failure based on genetic programming

Heart Failure (HF) poses a challenge for our health systems, and early detection of Worsening HF (WHF), defined as a deterioration in symptoms and clinical and instrumental signs of HF, is vital to improving prognosis. Predicting WHF in a phase that is currently undiagnosable by physicians would enable prompt treatment of such events in patients at a higher risk of WHF. Although the role of Artificial Intelligence in cardiovascular diseases is becoming part of clinical practice, especially for diagnostic and prognostic purposes, its usage is often considered not completely reliable due to the incapacity of these models to provide a valid explanation about their output results. Physicians are often reluctant to make decisions based on unjustified results and see these models as black boxes. This study aims to develop a novel diagnostic model capable of predicting WHF while also providing an easy interpretation of the outcomes. We propose a threshold-based binary classifier built on a mathematical model derived from the Genetic Programming approach. This model clearly indicates that WHF is closely linked to creatinine, sPAP, and CAD, even though the relationship of these variables and WHF is almost complex. However, the proposed mathematical model allows for providing a 3D graphical representation, which medical staff can use to better understand the clinical situation of patients. Experiments conducted using retrospectively collected data from 519 patients treated at the HF Clinic of the University Hospital of Salerno have demonstrated the effectiveness of our model, surpassing the most commonly used machine learning algorithms. Indeed, the proposed GP-based classifier achieved a 96% average score for all considered evaluation metrics and fully supported the controls of medical staff. Our solution has the potential to impact clinical practice for HF by identifying patients at high risk of WHF and facilitating more rapid diagnosis, targeted treatment, and a reduction in hospitalizations.

Imperative Genetic Programming

Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes.

Phosphorylation of the DNA damage repair factor 53BP1 by ATM kinase controls neurodevelopmental programs in cortical brain organoids.

53BP1 is a well-established DNA damage repair factor that has recently emerged to critically regulate gene expression for tumor suppression and neural development. However, its precise function and regulatory mechanisms remain unclear. Here, we showed that phosphorylation of 53BP1 at serine 25 by ATM is required for neural progenitor cell proliferation and neuronal differentiation in cortical brain organoids. Dynamic phosphorylation of 53BP1-serine 25 controls 53BP1 target genes governing neuronal differentiation and function, cellular response to stress, and apoptosis. Mechanistically, ATM and RNF168 govern 53BP1's binding to gene loci to directly affect gene regulation, especially at genes for neuronal differentiation and maturation. 53BP1 serine 25 phosphorylation effectively impedes its binding to bivalent or H3K27me3-occupied promoters, especially at genes regulating H3K4 methylation, neuronal functions, and cell proliferation. Beyond 53BP1, ATM-dependent phosphorylation displays wide-ranging effects, regulating factors in neuronal differentiation, cytoskeleton, p53 regulation, as well as key signaling pathways such as ATM, BDNF, and WNT during cortical organoid differentiation. Together, our data suggest that the interplay between 53BP1 and ATM orchestrates essential genetic programs for cell morphogenesis, tissue organization, and developmental pathways crucial for human cortical development.

Machine learning-assisted design of high-entropy alloys for optimal strength and ductility

High Entropy Alloys (HEAs) are a novel class of multi-component alloys with compositional flexibility, presenting a promising alternative to traditional alloys. This research aims to enhance the strength of HEAs while achieving adequate ductility, a challenging objective due to their conflicting nature. We applied evolutionary data-driven models and Bi-Objective Genetic Programming (BioGP) with genetic algorithms (GA) to accurately predict and optimize yield strength and ductility. The predictions were validated through experimental methods, including casting by vacuum arc melting and comprehensive mechanical and microstructural characterization. Our integrated approach successfully developed an HEA exhibiting a strength of 1795 ± 21 MPa and a ductility of 31.45 %. This study highlights the effectiveness of combining data-driven models with experimental validation to advance the development of high-performance materials.

Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods

AbstractThe broad‐crested weir is convenient to construct and has a small amount of excavation, widely used in practical engineering. Discharge computing has been the focus of research on this structure, thus developing generalized regression neural network (GRNN), genetic programming (GP), and extreme learning machine (ELM) are used to predict the discharge coefficient (Cd) of the triangular broad‐crested weir. The comprehensive analysis shows that the ELM model has high stability, predictive ability, and computational speed. The coefficient of determination (R^2) is 0.99982, the mean absolute percentage error (MAPE) is 0.000261, the Nash‐Sutcliffe coefficient (NSE) is 0.99977, and the root means square error (RMSE) is 4.17E‐05 in the testing phase. The apex angle θ is the most critical parameter affecting the Cd, and the contribution to the Cd is 52.45%. A new computational formula is proposed and compared with the accuracy of empirical formulas, showing that the intelligent method has higher accuracy and efficiency.

Strawberries in a warming world: examining the ecological niche of Fragaria×ananassa Duch – Across different climate scenarios

BACKGROUND: Strawberry (Fragaria×ananassa Duch.) is a species of great economic and nutritional importance. It is widely cultivated in different regions of the world. However, climatic factors have a significant influence on its production. OBJECTIVE: To evaluate the ecoclimatic suitability and growth index of F.×ananassa in the face of the impacts of climate change in Brazil and the world. METHODS: The potential global distribution of F.×ananassa it was based on predicted global climate changes using CLIMEX and the A1B and A2 emissions scenario for the years 2050 and 2100. CLIMEX weekly growth index (GIw) was applied, and productivity data in commercial strawberry cultivation in Brazilian areas were collected. RESULTS: The results indicate a progressive reduction in areas suitable for strawberry cultivation globally, mainly on the African continent, Australia, China, and the Americas for the years 2050 and 2100. In Brazil, only the South region and part of the Southeast will continue to be suitable for strawberry production. CONCLUSIONS: The CLIMEX model predicts a reduction in the suitable area for cultivating Fragaria×ananassa Duch between 2050 and 2100 due to climate change. The generated maps can help identify new cultivation areas and support genetic improvement programmes to develop cultivars more tolerant to heat.

A Novel Multiobjective Genetic Programming Approach to High-Dimensional Data Classification.

The development of data sensing technology has generated a vast amount of high-dimensional data, posing great challenges for machine learning models. Over the past decades, despite demonstrating its effectiveness in data classification, genetic programming (GP) has still encountered three major challenges when dealing with high-dimensional data: 1) solution diversity; 2) multiclass imbalance; and 3) large feature space. In this article, we have developed a problem-specific multiobjective GP framework (PS-MOGP) for handling classification tasks with high-dimensional data. To reduce the large solution space caused by high dimensionality, we incorporate the recursive feature elimination strategy based on mining the archive of evolved GP solutions. A progressive domination Pareto archive evolution strategy (PD-PAES), which optimizes the objectives in a specific order according to their objectives, is proposed to evaluate the GP individuals and maintain a better diversity of solutions. Besides, to address the seriously imbalanced class issue caused by traditional binary decomposition (BD) one versus rest (OVR) for multiclass classification problems, we design a method named BD with a similar positive and negative class size (BD-SPNCS) to generate a set of auxiliary classifiers. Experimental results on benchmark and real-world datasets demonstrate that our proposed PS-MOGP outperforms state-of-the-art traditional and evolutionary classification methods in the context of high-dimensional data classification.

Business failure prediction models with high and stable predictive power over time using genetic programming

This study focuses on the deterioration of the predictive power and the analysis of the predictive stability of business failure prediction models, an aspect not sufficiently analysed in previous research. Insolvency prediction is considered with three temporal horizons (1 year, 3 years and 5 years prior to failure). The Genetic Programming (GP) tool has been used to achieve prediction models with high performance and stability over time, considering a long post-learning period in the stability analysis. In addition, novel scenarios representative of actual model use are proposed and considered, as well as metrics to assess the deterioration of the models’ predictive power. The optimised GP prediction models (in the three temporal horizons) present a higher performance with respect to external references and, more importantly in relation to the objective of our study, the selected GP models substantially improve on the stability reported in previous studies, meeting the pursued requirements of degree of deterioration (less than 5%) and stability (Pearson’s coefficient of variation less than 5%). Thus, the predictions of the GP models after the learning are very stable (period 2008–2019), to a certain extent immune, with respect to their environment, responding adequately in both procyclical and countercyclical modes, all of which is particularly relevant as this period includes a strong recession and a strong recovery. This should help to increase the reliability of business failure prediction models. Moreover, the relevance of including variables other than the usual financial ratios as predictors of failure is confirmed.

Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments

Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algorithms. In this study, we introduce data-driven machine learning methods to enhance container port truck dispatching simulation accuracy. These methods effectively surrogate the intersections within the simulation, thereby increasing the accuracy of simulated outcomes without imposing significant computational overhead in sparse data environments. We incorporate three data-driven learning methods: genetic programming (GP), reinforcement learning (RL), and a GP and RL hybrid heuristic (GPRL-H) approach. The GPRL-H method proved the most efficacious through a detailed comparative study, striking an effective balance between simulation accuracy and computational efficiency. It reduced the error rate of simulation from approximately 35% to about 7%, while also halving the simulation time compared to the RL-based method. Our proposed method also does not rely on precise Global Positioning System (GPS) data to simulate truck operations within a port accurately. Demonstrating robustness and adaptability, this approach holds promise for extending beyond port operations to improve the simulation accuracy of vehicle operations in various scenarios characterized by sparse data.

A genetic programming approach with adaptive region detection to skin cancer image classification

Dermatologists typically require extensive experience to accurately classify skin cancer. In recent years, the development of computer vision and machine learning has provided new methods for assisted diagnosis. Existing skin cancer image classification methods have certain limitations, such as poor interpretability, the requirement of domain knowledge for feature extraction, and the neglect of lesion area information in skin images. This paper proposes a new genetic programming (GP) approach to automatically learn global and/or local features from skin images for classification. To achieve this, a new function set and a new terminal set have been developed. The proposed GP method can automatically and flexibly extract effective local/global features from different types of input images, thus providing a comprehensive description of skin images. A new region detection function has been developed to select the lesion areas from skin images for feature extraction. The performance of this approach is evaluated on three skin cancer image classification tasks, and compared with three GP methods and six non-GP methods. The experimental results show that the new approach achieves significantly better or similar performance in most cases. Further analysis validates the effectiveness of our parameter settings, visualizes the multiple region detection functions used in the individual evolved by the proposed approach, and demonstrates its good convergence ability.

Studying the Effect of Operating Conditions and NO2 Cathode Contamination on PEM Fuel Cells Using Distribution Function of Relaxation Times Analysis.

The effect of NO2, an air pollutant, on the durability of polymer electrolyte membrane fuel cells (PEMFCs) and the affected electrochemical processes in the PEMFC following the contamination were investigated. In-situElectrochemical Impedance Spectroscopy (EIS) measurements were conducted on PEMFCs under different operating conditions of temperature and relative humidity (RH). NO2 was introduced to the cathode inlet flow. Analyses of the EIS measurements were performed using a genetic algorithm called ISGP (Impedance Spectroscopy by Genetic Programming) to obtain the distribution function of relaxation times (DFRT, a.k.a. DRT) models. Utilizing ISGP enabled us to differentiate the various phenomena in PEMFC and study how they are affected by NO2 contamination. Moreover, the experiments demonstrate the effectiveness of the mitigation method to flush the PEMFC and regenerate its performance after being contaminated, particularly at low operating temperatures. Energy-dispersive X-ray spectroscopy (EDS) technique is performed on the contaminated PEMFC to detect the presence of any nitrogen components in the FC's gas diffusion layer and the catalyst layer post the mitigation step. Cyclic Voltammetry is also performed on the contaminated cell to determine the effect of the contamination on the electrochemically active surface area of the cathode by evaluating the double-layer capacitance.

INLEC: An involutive and low energy lightweight block cipher for internet of things

The Internet of Things (IoT) has emerged as a pivotal force in the global technological revolution and industrial transformation.Despite its advancements, IoT devices continue to face significant security challenges, particularly during data transmission, and are often constrained by limited battery life and energy resources.To address these challenges,a low energy lightweight block cipher (INLEC) is proposed to mitigate data leakage in IoT devices. In addition, the Structure and Components INvolution (SCIN) design is introduced. It is constructed using two similar round functions to achieve front–back symmetry.This design ensures coherence throughout the INLEC encryption and decryption processes and addresses the increased resource consumption during the decryption phase in Substitution Permutation Networks (SPN).Furthermore, a low area S-box is generated through a hardware gate-level circuit search method combined with Genetic Programming (GP).This optimization leads to a 47.02% reduction in area compared to the S0 of Midori, using UMC 0.18μm technology. Moreover, a chaotic function is used to generate the optimal nibble-based involutive permutation, further enhancing its efficiency.In terms of performs, the energy consumption for both encryption and decryption with INLEC is 6.88 μJ/bit, representing 25.21% reduction compared to Midori.Finally, INLEC is implemented using STM32L475 PanDuoLa and Nexys A7 FPGA development boards, establishing an encryption platform for IoT devices. This platform provides functions for data acquisition, transmission, and encryption.

Artificial intelligence-driven enhanced CBR modeling of sandy soils considering broad grain size variability

The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively.