Multiple Engineering Research Articles

ORI-Explorer: a unified cell-specific tool for origin of replication sites prediction by feature fusion.

The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithful transfer of genetic data from generation to generation. The advent of experimental techniques has aided in the discovery of ORIs in many species. Experimentation, on the other hand, is often more time-consuming and pricey than computational approaches, and it necessitates specific equipment and knowledge. Recently, ORI sites have been predicted using computational techniques like motif-based searches and artificial intelligence algorithms based on sequence characteristics and chromatin states. In this article, we developed ORI-Explorer, a unique artificial intelligence-based technique that combines multiple feature engineering techniques to train CatBoost Classifier for recognizing ORIs from four distinct eukaryotic species. ORI-Explorer was created by utilizing a unique combination of three traditional feature-encoding techniques and a feature set obtained from a deep-learning neural network model. The ORI-Explorer has significantly outperformed current predictors on the testing dataset. Furthermore, by employing the sophisticated SHapley Additive exPlanation method, we give crucial insights that aid in comprehending model success, highlighting the most relevant features vital for forecasting cell-specific ORIs. ORI-Explorer is also intended to aid community-wide attempts in discovering potential ORIs and developing innovative verifiable biological hypotheses. The used datasets along with the source code are made available through https://github.com/Z-Abbas/ORI-Explorer and https://zenodo.org/record/8358679.

Stem Cell-Based Tissue Engineering Approaches for Diabetic Foot Ulcer: a Review from Mechanism to Clinical Trial.

Diabetic foot ulcer (DFU) is a complication from incomplete or prolonged wound healing, at times requires amputation, putting substantial health and socioeconomic burden. Wound healing is a dynamic overlapping process that can be regulated by arrays of molecular factors showing redundancy in function. However, dysregulation in the mechanism of angiogenesis, extra cellular matrix (ECM) formation and immune modulation are the major causes for impair wound healing in hyperglycaemic patients. Despite development of wound care research, there is a lack of well-accepted targeted therapy with multidisciplinary approach for DFU treatment. Stem cell therapy holds a promising outcome both in preclinical and clinical trials because of its ability to promote healing via regeneration and specialized tissue differentiation. Among different types of stem cells, regenerative potential of mesenchymal stem cell (MSC) is well demonstrated in both experimental and clinical trial. Still there is a huge knowledge gap among medical practitioners for deciding the best stem cell source, administration route, and safety. This review strengthens the fact that why stem cell therapy is a promising candidate to treat DFU and cited multiple tissue engineering and biomaterial-based approaches for delivering stem cells and their aftermath paracrine events. Based on the pre-clinical and clinical studies, the review tried to come up with optimum stem cell source and delivery route for the treatment of DFU.At last, the review glances on possible direction to enhance therapeutics strategy for the same, including different approaches like: phytocompounds, exosomes,scaffold geometry, cell preconditioning and licensing etc.

Stock price prediction on Australian companies under Chinas trade restriction based on the LSTM model

The volatility of Australian companies' stock prices in 2020, caused by China's trade restrictions, poses a significant challenge for predicting financial gain or loss. This research contributes to future scholarship in predicting stock prices under specific circumstances or during special time periods. The study proposes a novel approach to stock price prediction, incorporating news sentiment analysis into a deep learning model. The research collected news items potentially affecting the stock price, incorporating them into an analysis model to generate a new feature for the Long Short-Term Memory (LSTM) model. The LSTM model used in this study was bidirectional, with two sets of gates per layer, and a three-layer model with different units. Each layer employed a dropout layer and a dense layer in the final stage. The study also utilized the feature engineering of lookback, selecting a window of time in the past to predict the next day's stock prices. Following multiple hyperparameter tunings and feature engineering adjustments, the results and graphs demonstrate a successful prediction for all three of the chosen companies, even during an unstable stock market. The overall trend lines achieve optimal predictions for the stock prices, illustrating both upward and downward trends.

Bioinspired Design Rules from Highly Mineralized Natural Composites for Two-Dimensional Composite Design.

Discoveries of two-dimensional (2D) materials, exemplified by the recent entry of MXene, have ushered in a new era of multifunctional materials for applications from electronics to biomedical sensors due to their superior combination of mechanical, chemical, and electrical properties. MXene, for example, can be designed for specialized applications using a plethora of element combinations and surface termination layers, making them attractive for highly optimized multifunctional composites. Although multiple critical engineering applications demand that such composites balance specialized functions with mechanical demands, the current knowledge of the mechanical performance and optimized traits necessary for such composite design is severely limited. In response to this pressing need, this paper critically reviews structure-function connections for highly mineralized 2D natural composites, such as nacre and exoskeletal of windowpane oysters, to extract fundamental bioinspired design principles that provide pathways for multifunctional 2D-based engineered systems. This paper highlights key bioinspired design features, including controlling flake geometry, enhancing interface interlocks, and utilizing polymer interphases, to address the limitations of the current design. Challenges in processing, such as flake size control and incorporating interlocking mechanisms of tablet stitching and nanotube forest, are discussed along with alternative potential solutions, such as roughened interfaces and surface waviness. Finally, this paper discusses future perspectives and opportunities, including bridging the gap between theory and practice with multiscale modeling and machine learning design approaches. Overall, this review underscores the potential of bioinspired design for engineered 2D composites while acknowledging the complexities involved and providing valuable insights for researchers and engineers in this rapidly evolving field.

Recent Advances in the 3D Printing of Pure Copper Functional Structures for Thermal Management Devices

Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around conventional manufacturing constraints, and thermal performance has plateaued as a result. While 3D printing offers the design freedom to address these limitations, there has been a notable lack in high thermal conductivity materials beyond aluminium alloys. Recently, the 3D printing of pure copper to sufficiently high densities has finally taken off, due to the emergence of commercial-grade printers which are now equipped with 1 kW high-power lasers or short-wavelength lasers. Although the capabilities of these new systems appear ideal for processing pure copper as a bulk material, the performance of advanced thermal management devices are strongly dependent on topology-optimised filigree structures, which can require a very different processing window. Hence, this article presents a broad overview of the state-of-the-art in various additive manufacturing technologies used to fabricate pure copper functional filigree geometries comprising thin walls, lattice structures, and porous foams, and identifies opportunities for future developments in the 3D printing of pure copper for advanced thermal management devices.

Classification of integers based on residue classes via modern deep learning algorithms

Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but it can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches to classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We also evaluated automated machine learning (AutoML) platforms from Amazon, Google, and Microsoft and found that, without appropriately engineered features, they failed on this task. Furthermore, we introduced a method that utilizes linear regression on Fourier series basis vectors and demonstrated its effectiveness. Finally, we evaluated large language models (LLMs) such as GPT-4, GPT-J, LLaMA, and Falcon, and we demonstrated their failures. In conclusion, feature engineering remains an important task to improve performance and increase interpretability of machine learning models, even in the era of AutoML and LLMs.

Impact of 3D-printed laboratory equipment in vibrations and controls courses on student engineering identity, motivation, and mindset

This paper describes the implementation of innovative 3D-printed laboratory equipment linked to inquiry-based learning activities designed to improve learning, increase engineering identity and motivation, and foster a growth mindset in students taking undergraduate level mechanical vibrations courses, control theory courses, and associated laboratories. These innovative designs create new opportunities for hands-on learning, are low-cost, portable, and can be adapted for use in multiple science and engineering disciplines. The learning activities are based on the POGIL model, which has been used across a variety of disciplines including engineering. We describe the features of three separate devices (spring-connected sliding carts, compliant parallel arms with fixed-free ends and a slider mass, and a pendulum with variable tip load) implemented using a quasi-experimental approach with 510 duplicated students across three semesters during the COVID-19 pandemic in multiple lecture courses and laboratory sections. We also present an assessment of impact based on descriptive statistical analyses of survey data for student-reported learning gains and pre-post paired comparison tests on validated instruments measuring perceptions of engineering identity, engineering motivation, and growth mindset. Further, we conducted a student focus group and include salient instructor observations. Results show most students participating in the learning activities using these devices report that it supported their learning “a lot” or “a great deal.” In addition, on six of seven surveyed learning outcomes, most students reported feeling confident enough to complete them on their own or even teach them to someone else. Our data did not show a measurable impact on engineering identity, engineering motivation, or growth mindset, though it does suggest further investigation is merited.

Advances in E-Skin research: materials, functions and applications

The need for long-term physiological signal surveillance during everyday life is brought on by the ageing of the population and the rise in health-related worries. Electronic skin is a system that replicates the properties of human skin as well as performs comparable sensory tasks. It has recently demonstrated significant promise in the fields of health monitoring, human-machine interface, and machine perception as a result of its high abrasion resistance and adaptability. The material qualities, functional aspects, as well as typical applications of contemporary electronic skin research are reviewed in this paper. The goal of the project is to make e-skins stretchable, self-healing, and bio-compatible while simultaneously monitoring physical, chemical, and electrophysiological signals in real-time. Finally, the difficulties and potential solutions for e-development skin’s are examined. In order to realize its full potential, e-skin requires the scientists collaboration from multiple areas, including materials, information, engineering, and life, to realize its full potential.

Optimized V‐Doped Defective TiO2/α‐(Fe2O3)1‐x(Cr2O3)x Heterojunctions for Photo‐Assisted Supercapacitor Devices: Insights on the Materials Integrity and Dual Conversion‐Storage Mechanism

AbstractEnergy conversion and storage integrated power units suffer from multiple engineering issues. Replacing two devices (solar cell and supercapacitor assembly) with one device (photo‐enhanced supercapacitor‐ PSC) requires materials with emerged dual solar‐electrochemical storage attributes. Herein, a propitious approach is developed to fabricate all visible light‐enhanced semisolid flexible PCS. Nanoflakes‐based p‐n junction α‐(Fe2O3)1‐x(Cr2O3)x photocathode is synthesized directly on industrial waste stainless steel mesh (316L‐SS). Alongside, three TiO2‐based electrodes are utilized as positive photoactive electrodes. Tuning the optical properties of TiO2 is displayed via doping with mixed valence vanadium (V4+/V5+) together with thermal hydrogen annealing. This is revealed via the reduction of the bandgap energy from 2.89 to 2.15 eV, which can be ascribed to the induced oxygen vacancies. The device can sustain up to 1.6 V potential window with 91% stability after 350 successive charge/discharge cycles with the possibility of performance regeneration to its 100% retention. An illustration of the photo‐storage mechanism is proposed based on the X–ray photoelectron spectroscopy, X–ray crystallography, and band position/alignment results. Quasi‐reversible water splitting/formation is concluded as the main storage mechanism in the semisolid state electrolyte under illumination conditions.

Exploration of phase change material melt front during latent heat thermal energy storage charging

Latent Heat Thermal Energy Storage (LHTES) is a technology that utilises the phase change energy of a material to store energy and is used in multiple engineering disciplines. The use of this technology at high temperatures presents new material challenges that need to be considered. The exploration of Phase Change Material (PCMs) melting phenomena was completed with finite volume methods to model advection-diffusion interactions of different PCM classes undergoing pure substance melting. This study modelled a two-dimensional system with heat flux elicited by an isothermal wall – producing a melt front in the PCM. The investigation focussed on retention of a Solid Salt Barrier (SSB) between the liquid PCM and the storage tank wall. The importance of such a barrier is to mitigate the corrosion between the liquid PCM and the containment vessel. Results reveal the possibility of SSB retention through the adaptation of a LHTES system aspect ratio. A thermal storage inefficiency via unmelted PCM has been demonstrated and quantified when an SSB is maintained. Through varying geometry and PCM choice (carbonate, fluoride, and chloride eutectics), it was also shown that SSB retention is possible; however, when optimising the rate of internal enthalpy change, the corresponding aspect causes the SSB protected wall to be insignificant compared to exposed surfaces at the top and base. This paper provides insight into the implications of aspect ratio choice for thermal energy storage tank designs; as well, suggestions are made regarding exploration of divergent geometries to greater utilise the PCM energy storage capacity available in SSB systems.

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain.

Editorial

ARAI is placed at the center of critical juncture where the entire automotive industry is evolving from conventional combustion engine centered to a software centered smart mobility Industry. ARAI journal of mobility technology captures this essence of transition very clearly. The readers will indeed get a glimpse of these technologies that are built in India and across global institutions and organizations.
 As I recollect my memories of walking through the mist-covered hilltop centre of ARAI back in the 1990s for calibration of the smoke meter which was the only measuring equipment used to check and control the visible pollutants from diesel engines. Today’s ARAI has morphed itself into an institution where every modern aspect of these software-driven automotive products are created, assessed and regulated to create a sustainable future.
 Any nation's progress purely relies on the utilization of its full potential. Our capability to create products and services that fulfil the aspiration and needs of our consumers. We are the home to the software industry that designs software module or software packages that runs in machines today be it consumer appliances, automobiles, aircraft, banks, security systems, defence equipment and many more where software is an integral part of a product.
 Crystallization of ideas and products which helps individuals to move or for the general public to move in large numbers within our growing cities and in between cities is very well encompassed in this technical journal which helps researchers and engineers to visualize different products for the mobility industry.
 As a nation having the largest pool of young, qualified and highly capable engineers, the education system that propels the youth into a capable engineer with skills across multiple engineering domains is the fuel for the growth of both the component and vehicle industries. This human capital combined with the core research into components like sensors, actuators, magnetics, multiple cell chemistries, fast acting low-loss switches getting enabled through the latest communication technologies is the strength of our ecosystem to create software-defined automobiles of the future. This Journal from ARAI places a critical role in all these areas.
 The partnership between components and product creators in the automotive industry is in a transition phase through which Industry is undergoing will definitely witness a sea change in its capabilities to support and create next-gen mobility products. To identify, inform and eliminate the risk of failure on the part, every mobility product of today and in future has a vast amount of electronics and software inbuilt to effectively use such products to its fullest potential. These components will configure and change over a period of time inside these intelligent products to optimize the product performance over its lifetime.
 This journal of mobility technology and the authors of various articles herein bring great insight into every technical aspect and align leaders in the mission of creating software-centred autonomous and semi-autonomous products.
 Wishing all success to the editorial team for bringing this together and happy reading to all the readers.

Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes

The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters – flow rates and mixing configurations, type and concentrations of the reagents – contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.

Integration of Deep Learning for Automatic Recognition of 2D Engineering Drawings

In an environment where manufacturing precision requirements are increasing, complete project plans can consist of hundreds of engineering drawings. The presentation of these drawings often varies based on personal preferences, leading to inconsistencies in format and symbols. The lack of standardization in these aspects can result in inconsistent interpretations during subsequent analysis. Therefore, proper annotation of engineering drawings is crucial as it determines product quality, subsequent inspections, and processing costs. To reduce the time and cost associated with interpreting and analyzing drawings, as well as to minimize human errors in judgment, we developed an engineering drawing recognition system. This study employs geometric dimensioning and tolerancing (GD&T) in accordance with the ASME (American Society of Mechanical Engineers) Y14.5 2018 specification to describe the language of engineering drawings. Additionally, PyTorch, OpenCV, and You Only Look Once (YOLO) are utilized for training. Existing 2D engineering drawings serve as the training data, and image segmentation is performed to identify objects such as dimensions, tolerances, functional frames, and geometric symbols in the drawings using the network model. By reading the coordinates corresponding to each object, the correct values are displayed. Real-world cases are utilized to train the model with multiple engineering drawings containing mixed features, resulting in recognition capabilities surpassing those of single-feature identification. This approach improves the recognition accuracy of deep learning models and makes engineering drawing and image recognition more practical. The recognition results are directly stored in a database, reducing product verification time and preventing errors that may occur due to manual data entry, thereby avoiding subsequent quality control issues. The accuracy rates achieved are as follows: 85% accuracy in detecting views in 2D engineering drawings, 70% accuracy in detecting annotation groups and annotations, and 80% accuracy in text and symbol recognition.

Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction.

The study of non-natural biocatalytic transformations relies heavily on empirical methods, such as directed evolution, for identifying improved variants. Although exceptionally effective, this approach provides limited insight into the molecular mechanisms behind the transformations and necessitates multiple protein engineering campaigns for new reactants. To address this limitation, we disclose a strategy to explore the biocatalytic reaction space and garner insight into the molecular mechanisms driving enzymatic transformations. Specifically, we explored the selectivity of an "ene"-reductase, GluER-T36A, to create a data-driven toolset that explores reaction space and rationalizes the observed and predicted selectivities of substrate/mutant combinations. The resultant statistical models related structural features of the enzyme and substrate to selectivity and were used to effectively predict selectivity in reactions with out-of-sample substrates and mutants. Our approach provided a deeper understanding of enantioinduction by GluER-T36A and holds the potential to enhance the virtual screening of enzyme mutants.

Dynamics of Amino Acid Metabolism, Gene Expression, and Circulomics in a Recombinant Chinese Hamster Ovary Cell Line Adapted to Moderate and High Levels of Extracellular Lactate.

The accumulation of metabolic wastes in cell cultures can diminish product quality, reduce productivity, and trigger apoptosis. The limitation or removal of unintended waste products from Chinese hamster ovary (CHO) cell cultures has been attempted through multiple process and genetic engineering avenues with varied levels of success. One study demonstrated a simple method to reduce lactate and ammonia production in CHO cells with adaptation to extracellular lactate; however, the mechanism behind adaptation was not certain. To address this profound gap, this study characterizes the phenotype of a recombinant CHO K-1 cell line that was gradually adapted to moderate and high levels of extracellular lactate and examines the genomic content and role of extrachromosomal circular DNA (eccDNA) and gene expression on the adaptation process. More than 500 genes were observed on eccDNAs. Notably, more than 1000 genes were observed to be differentially expressed at different levels of lactate adaptation, while only 137 genes were found to be differentially expressed between unadapted cells and cells adapted to grow in high levels of lactate; this suggests stochastic switching as a potential stress adaptation mechanism in CHO cells. Further, these data suggest alanine biosynthesis as a potential stress-mitigation mechanism for excess lactate in CHO cells.

Multiple intermolecular engineering of polymer electrolyte interphase by polyoxometalate-modified polydopamine filler for all-solid-state batteries

Polyethylene oxide (PEO)-based composite solid electrolytes (CPEs) are viewed as one class of the most promising candidates for all-solid-state batteries (ASSBs). Vast efforts have been made to address the ionic conductivity mainly by extending its amorphous phase, but the mobility of cations is strictly limited by the strong coordination with PEO. Herein, the design of polyoxometalate-modified polydopamine (PMo12@PDA) as composite filler is first proposed to simultaneously increase the ionic migration of PEO segments and intrinsic mobility of cations. PDA serves as versatile mediator and collaborates with PMo12 clusters to construct multiple Lewis acid-base interactions with the undissociated salts as well as the coordinated ether oxygen units. Raman spectra, solid-state nuclear magnetic resonance and the first principles calculations results confirm that more mobile cations are generated. The constructed CPEs exhibit wide utilization in all-solid-state Li/Na batteries by assembly with cathodes of LiFePO4, LiNi0.5Co0.2Mn0.3O2 or Na3V2(PO4)3@C, achieving long cycling stability and excellent rate performance. This work offers a viable route towards intermolecular engineering of CPE intrerphases at sub-nanometer scale and fast cationic movement in ASSBs applications.

Restoration of a fish-attracting flow field downstream of a dam based on the swimming ability of endemic fishes: A case study in the upper Yangtze River basin

The construction of fish passage facilities can mitigate the negative effects of dams and other water engineering construction on river connectivity and have a significant positive effect on the conservation of local fish diversity. To attract target fishes into fish passage facilities effectively, the optimal flow velocity range to attract fish must be determined. Three local endemic species of the Mishi Reservoir were considered as the protection targets. However, their swimming abilities remain unclear. Therefore, the induced swimming speed (Uind), critical swimming speed (Ucrit) and burst swimming speed (Uburst) of three fish species were tested. Based on these results, we identified the optimal flow velocity to attract fish, which falls within the range of 0.15–0.51 m/s. A validated three-dimensional hydrodynamic model was used to simulate different schemes. By comparing the flow field simulation results of different schemes, we obtained the optimal measure to restore the flow field, namely, a multiple engineering measure combining increased the fish attraction flow in the fish collection pond and the construction of a spur dike. This study offers a solution for the specific case and enhances the database of swimming characteristics of endemic fish in the upstream reaches of the Yangtze River. It also provides a valuable reference for designing fish-attracting flows and potential measures for restoring flow fields in similar future projects.

Computational instability analysis of inflated hyperelastic thin shells using subdivision surfaces

The inflation of hyperelastic thin shells is a highly nonlinear problem that arises in multiple important engineering applications. It is characterised by severe kinematic and constitutive nonlinearities and is subject to various forms of instabilities. To accurately simulate this challenging problem, we present an isogeometric approach to compute the inflation and associated large deformation of hyperelastic thin shells following the Kirchhoff–Love hypothesis. Both the geometry and the deformation field are discretized using Catmull–Clark subdivision bases which provide the required C1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C^1$$\\end{document}-continuous finite element approximation. To follow the complex nonlinear response exhibited by hyperelastic thin shells, inflation is simulated incrementally, and each incremental step is solved using the Newton–Raphson method enriched with arc-length control. An eigenvalue analysis of the linear system after each incremental step assesses the possibility of bifurcation to a lower energy mode upon loss of stability. The proposed method is first validated using benchmark problems and then applied to engineering applications, where the ability to simulate large deformation and associated complex instabilities is clearly demonstrated.

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques.

Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R2 score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R2 value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models' suitability and generalization strength for soil UCS investigations.