Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria for strength, stiffness, and permeability. High workability and consistency are essential attributes for BPC because it is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting the slump of BPC. In addition, prediction models offer valuable tools to estimate various strength parameters, enabling adjustments to BPC mixing designs to optimize project construction, leading to cost and time savings. Therefore, this study explores the multi-expression programming (MEP) technique to predict the key characteristics of BPC, such as slump, compressive strength (fc), and elastic modulus (Ec). In the present study, 158, 169, and 111 data points were collected from the experimental studies for the slump, fc, and Ec, respectively. The dataset was divided into three sets: 70% for training, 15% for testing, and another 15% for model validation. The MEP models exhibited excellent accuracy with a correlation coefficient (R) of 0.9999 for slump, 0.9831 for fc, and 0.9300 for Ec. Furthermore, the comparative analysis between MEP models and conventional linear and non-linear regression models revealed remarkable precision in the predictions of the proposed MEP models, surpassing the accuracy of traditional regression methods. SHapley Additive exPlanation analysis indicated that water, cement, and bentonite exert significant influence on slump, with water having the greatest impact on compressive strength, while curing time and cement exhibit a higher influence on elastic modulus. In summary, the application of machine learning algorithms offers the capability to deliver prompt and precise early estimates of BPC properties, thus optimizing the efficiency of construction and design processes.
The parasite Plasmodium knowlesi has been the sole cause of malaria in Malaysia from 2018 to 2022. The persistence of this zoonotic species has hampered Malaysia's progress towards achieving the malaria-free status awarded by the World Health Organisation (WHO). Due to the zoonotic nature of P. knowlesi infections, it is important to study the prevalence of the parasite in the macaque host, the long-tailed macaque (Macaca fascicularis). Apart from P. knowlesi, the long-tailed macaque is also able to harbour Plasmodium cynomolgi, Plasmodium inui, Plasmodium caotneyi and Plasmodium fieldi. Here we report the prevalence of the 5 simian malaria parasites in the wild long-tailed macaque population in 12 out of the 13 states in Peninsular Malaysia using a nested PCR approach targeting the 18s ribosomal RNA (18s rRNA) gene. It was found that all five Plasmodium species were widely distributed throughout Peninsular Malaysia except for states with major cities such as Kuala Lumpur and Putrajaya. Of note, Pahang reported a malaria prevalence of 100% in the long-tailed macaque population, identifying it as a potential hotspot for zoonotic transmission. Overall, this study shows the distribution of the 5 simian malaria parasite species throughout Peninsular Malaysia, the data of which could be used to guide future malaria control interventions to target zoonotic malaria.
The antifungal efficacy and cytotoxicity of a novel nano-antifungal agent, the Fe3O4@SiO2/Schiff-base complex of Cu(II) magnetic nanoparticles (MNPs), have been assessed for targeting drug-resistant Candida species. Due to the rising issue of fungal infections, especially candidiasis, and resistance to traditional antifungals, there is an urgent need for new therapeutic strategies. Utilizing Schiff-base ligands known for their broad-spectrum antimicrobial activity, the Fe3O4@SiO2/Schiff-base/Cu(II) MNPs have been synthesized. The Fe3O4@SiO2/Schiff-base/Cu(II) MNPs was characterized by Fourier Transform-Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM), Scanning Electron Microscopy (SEM), Dynamic Light Scattering (DLS), Energy-dispersive X-ray (EDX), Vibrating Sample Magnetometer (VSM), and Thermogravimetric analysis (TGA), demonstrating successful synthesis. The antifungal potential was evaluated against six Candida species (C. dubliniensis, C. krusei, C. tropicalis, C. parapsilosis, C. glabrata, and C. albicans) using the broth microdilution method. The results indicated strong antifungal activity in the range of 8-64 μg/mL with the lowest MIC (8 μg/mL) observed against C. parapsilosis. The result showed the MIC of 32 μg/mL against C. albicans as the most common infection source. The antifungal mechanism is likely due to the disruption of the fungal cell wall and membrane, along with increased reactive oxygen species (ROS) generation leading to cell death. The MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide) assay for cytotoxicity on mouse L929 fibroblastic cells suggested low toxicity and even enhanced cell proliferation at certain concentrations. This study demonstrates the promise of Fe3O4@SiO2/Schiff-base/Cu(II) MNPs as a potent antifungal agent with potential applications in the treatment of life-threatening fungal infections, healthcare-associated infections, and beyond.
New coumarin derivatives were designed using a 2-(2-oxo-2H-chromen-4-yl)acetic acid scaffold conjugated with amino acid esters or tyramine. The anti-tyrosinase and anti-lipid peroxidation activities of the synthesized compounds were investigated. Coumarin derivatives 7,9, 11-13, 15-18 showed strong anti-lipid peroxidation activity. Compound 13 exhibited uncompetitive tyrosinase inhibitory activity with an IC50 value of 68.86 µM. Compound 14 (% activity = 123.41) showed stronger tyrosinase activating activity than 8-methoxypsolaren (8-MOP, % activity = 109.46). In silico studies revealed different poses between the inhibitors and activators near the tyrosinase catalytic site. Compounds 13 (25-50 μM) and 14 (25-100 μM) did not show cytotoxicity against B16F10 cells. In contrast to the tyrosinase inhibition assay, compound 13 (50 μM) suppressed melanogenesis in B16F10 cells with two times higher potency than KA (100 μM). Compound 14 at 100 μM showed melanogenesis enhancement in B16F10 cells in a dose-dependent manner, however, inferior to the 8-MOP. Based on the findings, compound 13 and 14 offer potential for development as skin-lightening agents and vitiligo therapy agents, respectively.
Previous research has found that parenting style influences academic resilience. Nonetheless, few studies have focused on the mechanism underlying the relationship between parenting style and academic resilience. This study aims to examine the relationship between adolescents' parenting style and academic resilience, drawing upon the framework of Social Cognitive Theory. Specifically, it wants to explore the mediating roles of self-efficacy and academic motivation in this relationship. The participants were 518 students chosen at random from educational institutions in the Chinese provinces of Zhejiang, Shanghai, and Jiangsu. Social Cognitive Theory was the theoretical foundation for the study, and the Parental Authority Questionnaire was used to measure parenting style. Out of the respondents, 55.5% were male and 45.5% female. The student allocation in the study sample was as follows: 62.34% undergraduate, 28.22% master's, and 9.44% PhD. More than 60% of participants were over 25 years old. Moreover, the findings revealed that parenting style was directly and positively related to academic resilience. Parenting style was also found to be indirectly and positively related to academic resilience via self-efficacy and academic motivation, respectively, and sequentially. More crucially, it was discovered that the direct association was far lower than the indirect effects, with self-efficacy being the most effective. The study indicates a relationship between parenting style and academic resilience in adolescents, with self-efficacy and academic motivation acting as the main mediators. These findings emphasize the significance of these intermediary elements, implying that they play a larger role than the direct influence of parenting style alone.
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.
This study compared the pathogenicity of monokaryotic (monokaryon) and dikaryotic (dikaryon) mycelia of the oil palm pathogen Ganoderma boninense via metabolomics approach. Ethyl acetate crude extracts of monokaryon and dikaryon were analysed by liquid chromatography quadrupole/time-of-flight-mass spectrometry (LC-Q/TOF-MS) coupled with multivariate data analysis using MetaboAnalyst. The mummichog algorithm was also used to identify the functional activities of monokaryon and dikaryon without a priori identification of all their secondary metabolites. Results revealed that monokaryon produced lesser fungal metabolites than dikaryon, suggesting that monokaryon had a lower possibility of inducing plant infection. These findings were further supported by the identified functional activities. Monokaryon exhibits tyrosine, phenylalanine, and tryptophan metabolism, which are important for fungal growth and development and to produce toxin precursors. In contrast, dikaryon exhibits the metabolism of cysteine and methionine, arginine and proline, and phenylalanine, which are important for fungal growth, development, virulence, and pathogenicity. As such, monokaryon is rendered non-pathogenic as it produces growth metabolites and toxin precursors, whereas dikaryon is pathogenic as it produces metabolites that are involved in fungal growth and pathogenicity. The LC-MS-based metabolomics approach contributes significantly to our understanding of the pathogenesis of Ganoderma boninense, which is essential for disease management in oil palm plantations.
The Eurasian lynx (Lynx lynx) exhibits geographic variability and phylogenetic intraspecific relationships. Previous morphological studies have suggested the existence of multiple lynx subspecies, but recent genetic research has questioned this classification, particularly in Central Asia. In this study, we aimed to analyse the geographic and genetic variation in Central Asian lynx populations, particularly the Turkestan lynx and Altai lynx populations, using morphometric data and mtDNA sequences to contribute to their taxonomic classification. The comparative analysis of morphometric data revealed limited clinal variability between lynx samples from the Altai and Tien Shan regions. By examining mtDNA fragments (control region and cytochrome b) obtained from Kazakhstani lynx populations, two subspecies were identified: L. l. isabellinus (represented by a unique haplotype of the South clade, H46) and L. l. wrangeli (represented by haplotypes H36, H45, and H47 of the East clade). L. l. isabellinus was recognized only in Tien Shan Mountain, while Altai lynx was likely identical to L. l. wrangeli and found in northern Kazakhstan, Altai Mountain, Saur and Tarbagatai Mountains, and Tien Shan Mountain. The morphological and mtDNA evidence presented in this study, although limited in sample size and number of genetic markers, renders the differentiation of the two subspecies challenging. Further sampling and compilation of whole-genome sequencing data are necessary to confirm whether the proposed subspecies warrant taxonomic standing.
Rice husk, an agricultural waste from the rice industry, can cause serious environmental pollution if not properly managed. However, rice husk ash (RHA) has been found to have many positive properties, making it a potential replacement for non-renewable peat in soilless planting. Thus, this study investigated the impact of a RHA composite substrate on the growth, photosynthetic parameters, and fruit quality of cucumber (Yuyi longxiang variety) and melon (Yutian yangjiaomi variety). The RHA, peat, vermiculite, and perlite were blended in varying proportions, with the conventional seedling substrate (peat:vermiculite:perlite = 1:1:1 volume ratio) serving as the control (CK). All plants were cultivated in barrels filled with 10L of the mixed substrates. The results from this study found that RHA 40 (RHA:peat:vermiculite:perlite = 4:4:1:1 volume ratio) significantly enhanced substrate ventilation and positively influenced the stem diameter, root activity, seedling index, chlorophyll content, net photosynthetic rate (Pn), stomatal conductance (Gs), and transpiration rate (Tr) of cucumber and melon plants. Additionally, plant planted using RHA 40, the individual fruit weight of cucumber and melon found to increase by 34.62% and 21.67%, respectively, as compared to the control. Aside from that, both cucumber and melon fruits had significantly higher sucrose, total soluble sugar, vitamin C, and soluble protein levels. This subsequently improved the activity of sucrose synthase and sucrose phosphate synthase in both cucumber and melon. In conclusion, the RHA 40 found to best promote cucumber and melon plant growth, increase plant leaf photosynthesis, and improve cucumber and melon fruit quality, making it a suitable substrate formula for cucumber and melon cultivation in place of peat.
One potential approach for treating obesity is to increase energy expenditure in brown and white adipose tissue. Here we aimed to achieve this outcome by targeting mitochondrial uncoupler compounds selectively to adipose tissue, thus avoiding side effects from uncoupling in other tissues. Selective drug accumulation in adipose tissue has been observed with many lipophilic compounds and dyes. Hence, we explored the feasibility of conjugating uncoupler compounds with a lipophilic C8-hydrocarbon chain via an ether bond. We found that substituting the trifluoromethoxy group in the uncoupler FCCP with a C8-hydrocarbon chain resulted in potent uncoupling activity. Nonetheless, the compound did not elicit therapeutic effects in mice, likely as a consequence of metabolic instability resulting from rapid ether bond cleavage. A lipophilic analog of the uncoupler compound 2,6-dinitrophenol, in which a C8-hydrocarbon chain was conjugated via an ether bond in the para-position (2,6-dinitro-4-(octyloxy)phenol), exhibited increased uncoupling activity compared to the parent compound. However, in vivo pharmacokinetics studies suggested that 2,6-dinitro-4-(octyloxy)phenol was also metabolically unstable. In conclusion, conjugation of a hydrophobic hydrocarbon chain to uncoupler compounds resulted in sustained or improved uncoupling activity. However, an ether bond linkage led to metabolic instability, indicating the need to conjugate lipophilic groups via other chemical bonds.
Plastic waste is being manufactured for the production of hydrogen. The amount of plastic waste collected annually is 189,953 tonnes from adjacent nations like Indonesia and Malaysia. Polyethylene (PE), Polypropylene (PP), Polyethylene Terephthalate (PET), Polyvinyl chloride (PVC), and Polystyrene (PS) are the five most prevalent forms of plastic found in most waste. Pyrolysis, water gas shift and steam reforming reaction, and pressure swing adsorption are the three main phases utilized and studied. In this research, authors examines the energy consumption on every stage. The plastic waste can be utilized to manufacture many hydrocarbons using the pyrolysis reaction. For this process, fast pyrolysis is being used at a temperature of 500 °C. A neutralization process is also needed due to the presence of Hydrochloric acid from the pyrolysis reaction, with the addition of sodium hydroxide. This is being carried to prevent any damage to the reactor during the process. Secondly, the steam reforming process continues after the water gas shift reaction has produced steam and carbon monoxide, followed by carbon dioxide and hydrogen formation. Lastly, pressure swing adsorption is designed to extract H2S and CO2 from the water gas shift and steam reforming reaction for greater purity of hydrogen. From the simulation study, it is observed that using various types of plastic waste procured (total input of 20,000 kg per hour of plastics) from, Brunei Darussalam, Malaysia and Indonesia, can produce about 340,000 tons of Hydrogen per year. Additionally, the annual profit of the Hydrogen production is estimated to be between $ 271,158,100 and $ 358,480,200. As per the economic analysis, it can be said that its a good to start hydrogen production plant in these regions.
A Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems (EMSs) and ESSs, which further enhanced the operational performance of NGs. It was seen that EMS acts as the distributed energy source in the NG setup and assists in power generation, usage, dissemination, and differential pricing. Hence this study employed the MATLAB Simulink software for modelling the grid-connected NG that included HRES; such as wind and PV; in addition to 3 Battery Storage Devices (BSDs) to design an effective EMS for the NG system and decrease its overall costs. For this purpose, a Rule-Based EMS (RB-EMS) that employs State Flow (SF) to guarantee a safe and reliable operating power flow to the NG has been developed. In addition to that, a Genetic Algorithm (GA)-based optimization system and Simulated Annealing optimization Algorithm (SAA) were proposed to determine an economical solution for decreasing the cost of the NG system depending on its operational constraints. Lastly, comparison about the cost between RB-EMS, GA and SAA has been presented. According to the simulation results, the proposed GA displayed an economical performance since it could achieve a 40% cost saving whereas the SAA system showed a 19.3% cost saving compared to the RB-EMS. It can be concluded from the findings that the GA-based optimization technique was very cost-effective displays many important features, like rapid convergence, simple design, and very few controlling factors.
Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.
A numerical analysis of a CdTe/Si dual-junction solar cell in terms of defect density introduced at various defect energy levels in the absorber layer is provided. The impact of defect concentration is analyzed against the thickness of the CdTe layer, and variation of the top and bottom cell bandgaps is studied. The results show that CdTe thin film with defects density between 1014 and 1015 cm-3 is acceptable for the top cell of the designed dual-junction solar cell. The variations of the defect concentrations against the thickness of the CdTe layer indicate that the open circuit voltage, short circuit current density, and efficiency (ƞ) are more affected by the defect density at higher CdTe thickness. In contrast, the Fill factor is mainly affected by the defect density, regardless of the thin film's thickness. An acceptable defect density of up to 1015 cm-3 at a CdTe thickness of 300 nm was obtained from this work. The bandgap variation shows optimal results for a CdTe with bandgaps ranging from 1.45 to 1.7 eV in tandem with a Si bandgap of about 1.1 eV. This study highlights the significance of tailoring defect density at different energy levels to realize viable CdTe/Si dual junction tandem solar cells. It also demonstrates how the impact of defect concentration changes with the thickness of the solar cell absorber layer.
Hindlimb suspension (HLS) mice exhibit osteoporosis of the hindlimb bones and may be an excellent model to test pharmacological interventions. We investigated the effects of inhibiting endoplasmic reticulum (ER) stress with 4-phenyl butyrate (4-PBA) on the morphology, physicochemical properties, and bone turnover markers of hindlimbs in HLS mice. We randomly divided 21 male C57BL/6J mice into three groups, ground-based controls, untreated HLS group and 4-PBA treated group (HLS+4PBA) (100mg/kg/day, intraperitoneal) for 21 days. We investigated histopathology, micro-CT imaging, Raman spectroscopic analysis, and gene expression. Untreated HLS mice exhibited reduced osteocyte density, multinucleated osteoclast-like cells, adipocyte infiltration, and reduced trabecular striations on micro-CT than the control group. Raman spectroscopy revealed higher levels of ER stress, hydroxyproline, non-collagenous proteins, phenylalanine, tyrosine, and CH2Wag as well as a reduction in proteoglycans and adenine. Furthermore, bone alkaline phosphatase and osteocalcin were downregulated, while Cathepsin K, TRAP, and sclerostin were upregulated. Treatment with 4-PBA partially restored normal bone histology, increased collagen crosslinking, and mineralization, promoted anti-inflammatory markers, and downregulated bone resorption markers. Our findings suggest that mitigating ER stress with 4-PBA could be a therapeutic intervention to offset osteoporosis in conditions mimicking hindlimb suspension.
The effects of IT and R.I.C.E. treatment on arm muscle performance in overhead athletes with elbow pain (EP) have been partially validated. However, there is a lack of research evidence regarding the efficacy of these two methods on arm muscle performance among swimmers with EP. The aim of this study was to investigate the trends and differences in the effects of IT and R.I.C.E. treatment on arm muscle performance among swimmers with EP. The main outcomes were the time effects and group effects of interventions on muscle voluntary contraction (MVC). Sixty elite freestyle swimmers from Tianjin, China, voluntarily participated in the study and completed a 10-week intervention program. Swimmers with EP in the IT group showed a positive trend in MVC, with an approximately 2% increase, whereas the MVC of subjects in the R.I.C.E. treatment group and control group decreased by approximately 4% and 5%, respectively. In comparison, the effects of the IT intervention on the MVC of the triceps and brachioradialis muscles in swimmers with EP were significant (p = 0.042
The evolution of Internet technology has led to an increase in online users. This study focuses on the pivotal role of visual elements in web content conveyance and their impact on user browsing behavior. Therefore, the use of visual elements in web design based on big data has aroused widespread concern among web designers, they apply visual elements to their web design works to make the web more attractive. This study examines the composition and distribution characteristics of key visual elements identified through user behavior data in a big data environment and discusses the use of visual elements in web design in the era of network economy. In addition, this paper issued 200 questionnaires to investigate the degree of attention to visual elements in web pages for users of different occupations and different educational backgrounds. Our survey indicated that visual elements captured the attention of 41% of corporate employees, whereas a mere 1% of social welfare workers focused on web content; 36% of undergraduates pay attention to visual elements of web pages, but only 5% and 4% of postgraduates and doctoral degrees and above. Therefore, the visual elements of the designed web page need to conform to the user's cultural background and professional background.
Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.