In the ex situ conservation of chondrichthyan species, successful reproduction in aquaria is essential. However, these species often exhibit reduced reproductive success under human care. A key aspect is that conventional sperm analyses do not provide insights into the functional competence of sperm. However, proteomics analysis enables a better understanding of male physiology, gaining relevance as a powerful tool for discovering protein biomarkers related to fertility. The present work aims to build the first proteome database for shark semen and to investigate the proteomic profiles of seminal plasma and spermatozoa from small-spotted catsharks (Scyliorhinus canicula) related to the underlying adaptations to both natural and aquarium environments, thereby identifying the reproductive impact in aquarium specimens. A total of 305 seminal plasma and 535 spermatozoa proteins were identified. Among these, 89 proteins (29.2% of the seminal plasma set) were common to both spermatozoa and seminal plasma. In the seminal plasma, only adenosylhomocysteinase protein showed differential abundance (DAP) between wild and aquarium animals. With respect to the spermatozoa proteins, a total of 107 DAPs were found between groups. Gene Ontology enrichment analysis highlighted the primary functional roles of these DAPs involved in oxidoreductase activity. Additionally, KEGG analysis indicated that these DAPs were primarily associated with metabolic pathways and carbon metabolism. In conclusion, we have successfully generated an initial proteome database for S. canicula seminal plasma and spermatozoa. Furthermore, we have identified protein variations, predominantly within spermatozoa, between aquarium and wild populations of S. canicula. These findings provide a foundation for future biomarker discovery in shark reproduction studies. However, additional research is required to determine whether these protein variations correlate with reproductive declines in captive sharks. Full article

There are currently no disease-modifying therapies for Parkinson’s disease (PD), a progressive neurodegenerative disorder associated with dopaminergic neuronal loss. There is increasing evidence that endogenous dopamine (DA) can be a pathological factor in neurodegeneration in PD. Tyrosine hydroxylase (TH) is the key rate-limiting enzyme for DA generation. Drugs that inhibit TH, such as alpha-methyltyrosine (α-MT), have recently been shown to protect against neurodegeneration in various PD models. DA receptor agonists can activate post-synaptic DA receptors to alleviate DA-deficiency-induced PD symptoms. However, DA receptor agonists have no therapeutic effects against neurodegeneration. Thus, a combination therapy with DA receptor agonists plus TH inhibitors may be an attractive therapeutic approach. TH inhibitors can protect and promote the survival of remaining dopaminergic neurons in PD patients’ brains, whereas DA receptor agonists activate post-synaptic DA receptors to alleviate PD symptoms. Additionally, other PD drugs, such as N-acetylcysteine (NAC) and anticholinergic drugs, may be used as adjunctive medications to improve therapeutic effects. This multi-drug cocktail may represent a novel strategy to protect against progressive dopaminergic neurodegeneration and alleviate PD disease progression. Full article

Genes from Perilla frutescens and Ocimum basilicum were introduced into N. tabacum L. var. HHY via distant hybridization, and the new-type tobacco varieties “Zisu” and “Luole” were developed, with noticeable differences in chemical composition. Smoking is the leading cause of chronic obstructive pulmonary disease (COPD), and its pathogenesis is complex. In the present study, 48 male Sprague-Dawley (SD) rats were randomly divided into four groups, namely, the control, “HHY”, “Zisu” and “Luole”, and then exposed to fresh air/cigarette smoke (CS) for 30 days and 60 days. The COPD model was constructed, and their health hazards were compared and evaluated. CS from different tobacco varieties influenced rats in varying degrees at the tissue, cell and molecular levels. The rats in the “HHY” group showed obvious symptoms, such as cough and dyspnea, which were less severe in the “Zisu” and “Luole” groups. Pathological and morphological analyses, including scores, MLI, MAN, WAt/Pbm and WAm/Pbm, showed that “Zisu” and “Luole” caused less damage to the airways and lung parenchyma than “HHY”. Significant increases in the numbers of total leukocytes and neutrophils in the BALF were found in “HHY” compared to those in “Zisu” and “Luole”. Moreover, they caused less oxidative stress and apoptosis in lung tissues, as reflected by indicators such as ROS, MDA, T-AOC, GSH, the apoptotic index and the ratio of Bcl-2 to Bax. “Zisu” and “Luole” even altered the ratios of MMP-9/TIMP-1 and IFN-γ/IL-4 in lung tissues to a lesser degree. These differences between CS-exposed rats may be closely related to the altered expression of Nrf2, p38 MAPK and p-p38 MAPK. Changes in chemical composition via introducing genes from some medicinal plants may be an attractive strategy for tobacco harm reduction. Full article

Nuclear power is a high-quality clean energy source, but nuclear waste is generated during operation. The waste continuously releases heat during disposal, increasing the adjoining rock temperature and affecting the safety of the disposal site. Basalt is widely considered a commonly used rock type in the repository. This study of basalt’s mechanical characteristics and damage evolution after thermal damage, with its far-reaching engineering value, was conducted by combining experimental work and theory. Uniaxial compression tests were conducted on basalt exposed to 25 °C, 500 °C, 700 °C, 900 °C, and 1100 °C conditions, and acoustic emission (AE) equipment was utilized to observe the acoustic emission phenomenon during deformation. This study was carried out to examine the mechanical characteristics, the sound emission features, the progression of damage laws, and the stress–strain framework of basalt after exposure to different types of thermal harm. As the temperature rises, the rock’s maximum strength declines steadily, the peak strain rises in tandem, the rock sample’s ductility is augmented, the failure mode changes from shear to tensile failure, and cracks in the failure area are observed. At room temperature, the acoustic emission signal is more vigorous than in the initial stage of rock sample loading due to thermal damage; however, after the linear elastic stage is entered, its activity is lessened. In cases where the rock approaches collapse, there is a significant surge in acoustic emission activity, leading to the peak frequency of acoustic emission ringing. The cumulative ring count of acoustic emission serves as the basis for the definition of the damage variable. At room temperature, the damage evolution of rock samples can be broken down into four distinct stages. This defined damage variable is more reflective of the entire failure process. After exposure to high temperatures, the initial damage of the rock sample becomes more extensive, and the damage variable tends to be stable with strain evolution. The stress–strain constitutive model of basalt deformation is derived based on the crack axial strain law and acoustic emission parameters. A powerful relationship between theoretical and experimental curves is evident. Full article

Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes. Full article

The use of alternative fuels remains an important factor in solving the problem of reducing harmful substances caused by vehicles and decarbonising transport. It is also important to ensure the energy efficiency of vehicle power plants when using different fuels at a sufficient level. The article presents the results of theoretical and experimental studies of the conversion of diesel engine to alternative fuels with hydrogen admixtures. Methanol is considered as an alternative fuel which is a cheaper alternative to commercial diesel fuel. The chemical essence of improving the calorific value of alternative methanol fuel was investigated. Studies showed that the energy effect of burning an alternative mixture with hydrogen additives exceeds the effect of burning the same amount of methanol fuel. The increase in combustion energy and engine power is achieved as a result of heat from efficient use of the engine exhaust gases and chemical conversion of methanol. An experimental installation was created to study the work of a converted diesel engine on hydrogen–methanol mixtures and thermochemical regeneration processes. Experimental studies of the energy and environmental parameters of diesel engine converted to work on an alternative fuel with hydrogen admixtures have shown that engine power increases by 10–14% and emissions of harmful substances decrease. Full article

In the present article, we build the excitedcoherent states associated with deformed $su(1,1)$ algebra (DSUA), called photon-added deformed Perelomov coherent states (PA-DPCSs). The constructed coherent states are obtained by using an alterationof the Holstein–Primakoff realization (HPR) for DSUA. A general method to resolve of the problem of the unitary operator was developed for these kinds of quantum states. The Mandel parameter is considered to examine the statistical properties of PA-DPCSs. Furthermore, we offer a physical method to generate the PA-DPCSs in the framework of interaction among fields and atoms. Finally, we introduce the concept of entangled states for PA-DPCSs and examine the entanglement properties for entangled PA-DPCSs. Full article

The Han River Estuary (HRE), Yellow Sea, forms part of the border between South Korea and North Korea, and these two countries are militarily hostile. Since the HRE has quite excellent ecological integrity, the task of preserving it well is emerging as important. Thus, the South Korean Government is attempting to preserve the ecological integrity of the HRE through interstate cooperation. By employing contingent valuation, this study delves into South Korean households’ willingness to pay (WTP) for this preservation. One thousand households nationwide were sampled and surveyed through face-to-face individual interviews. Annual household income tax was selected as the payment vehicle. Dichotomous choice questioning was chosen as the WTP induction method. A spike model was selected as a method for modelling a WTP of zero. The main results showed statistical significance. Annual WTP per household and national WTP were obtained as KRW 4487 (USD 3.92) and KRW 125.75 billion (USD 109.83 million), respectively. When a 10-year payment period and a 4.5% discount rate were adopted, the value was KRW 766.14 billion (USD 669.12 million). South Korean households placed considerable value on the preservation of the ecological integrity of the HRE through interstate cooperation. Full article

Bimetallic composites have a wide range of application prospects in various industries. Different bonding temperatures, as one of the influencing factors, directly affect the bonding effectiveness as well as the performance and application of the materials. Using metallurgical bonding techniques ensures a strong bond at the interface of bimetallic materials, resulting in high-quality composite pipe billets. This paper describes an Incoloy825/P110 steel bimetal composite material made by the solid–liquid composite method. By utilizing ProCAST 14.5 software for simulation and deriving theoretical formulas, an initial range of temperatures for bimetallic preparation has been tentatively determined. And this temperature range will be utilized for on-site experiments to prepare bimetallic samples. After the preparation process is completed, samples will be selected. The influence of the external mold temperature on the interface bonding of Incoloy825/P110 steel solid–liquid composite material is studied using an ultra-depth three-dimensional morphology microscope and a scanning electron microscope. Through research, the optimal preheating temperature range for the solid–liquid composite outer mold of Incoloy825/P110 bimetallic composite material has been determined. The casting temperature of the inner mold has a significant impact on the interface bonding of this bimetal composite material. As the casting temperature of the inner mold increases, the interface thickness gradually increases. At lower temperatures, the interface thickness is lower and the bonding is poorer. At higher temperatures, melting may occur, leading to coarse grains at the interface. When the temperatures of the inner and outer molds are within a certain range, a new phase appears at the interface. Indeed, it increases the strength of the interface bonding. Due to co-melting of the bimetal near the interface, element migration occurs, resulting in increased Ni and Cr content at the interface and enhanced corrosion resistance. Full article

Assessing immune responses to cytomegalovirus (CMV) after liver transplant in patients on immunosuppressive therapy remains challenging. In this study, employing ELISPOT assays, 52 liver-transplant recipients were evaluated for antiviral T-cell activity in peripheral blood mononuclear cells (PBMCs), measuring interferon-γ (IFN-γ) secretion upon stimulation with CMV-specific peptides (CMV peptide pool, CMV IE-1, and pp65 antigens). Parameters such as stimulation index, mean spot size, and mean spot count were measured. The study found that heightened immunosuppression, especially with prednisolone in triple therapy, significantly dampened CMV-specific immune responses. This was demonstrated by decreased IFN-γ production by CMV-specific T-cells (CMV peptide pool: p = 0.036; OR = 0.065 [95% CI: 0.005–0.840], pp65 antigen: p = 0.026; OR = 0.048 [95% CI: 0.003–0.699]). Increased immunosuppression correlated with reduced IFN-γ secretion per cell, reflected in smaller mean spot sizes for the CMV peptide pool (p = 0.019). Notably, shorter post-transplant intervals correlated with diminished antiviral T-cell IFN-γ release at two years (CMV peptide pool: p = 0.019; IE antigen: p = 0.010) and five years (CMV peptide pool: p = 0.0001; IE antigen: p = 0.002; pp65 antigen: p = 0.047), as did advancing age (pp65 antigen: p = 0.016, OR = 0.932, 95% CI: 0.881–0.987). Patients with undetectable CMV antigens had a notably higher risk of CMV reactivation within six months from blood collection, closely linked with triple immunosuppression and prednisolone use. These findings highlight the intricate interplay between immunosuppression, immune response dynamics, and CMV reactivation risk, emphasizing the necessity for tailored immunosuppressive strategies to mitigate CMV reactivation in liver-transplant recipients. It can be concluded that, particularly in the early months post-transplantation, the use of prednisolone as a third immunosuppressant should be critically reconsidered. Additionally, the use of prophylactic antiviral therapy effective against CMV in this context holds significant importance. Full article

In recent years, machine learning models have become a potential approach in accurately predicting the concrete compressive strength, which is essential for the real-world application of geopolymer concrete. However, the precursor system of geopolymer concrete is known to be more heterogeneous compared to Ordinary Portland Cement (OPC) concrete, adversely affecting the data generated and the performance of the models. To its advantage, data enrichment through deep learning can effectively enhance the performance of prediction models. Therefore, this study investigates the capability of tabular generative adversarial networks (TGANs) to generate data on mixtures and compressive strength of geopolymer concrete. It assesses the impact of using synthetic data with various models, including tree-based, support vector machines, and neural networks. For this purpose, 930 instances with 11 variables were collected from the open literature. In particular, 10 variables including content of fly ash, slag, sodium silicate, sodium hydroxide, superplasticizer, fine aggregate, coarse aggregate, added water, curing temperature, and specimen age are considered as inputs, while compressive strength is the output of the models. A TGAN was employed to generate an additional 1000 data points based on the original dataset for training new predictive models. These models were evaluated on real data test sets and compared with models trained on the original data. The results indicate that the developed models significantly improve performance, particularly neural networks, followed by tree-based models and support vector machines. Moreover, data characteristics greatly influence model performance, both before and after data augmentation. Full article

Different excavation methods lead to substantial changes in the relaxation zone of the surrounding rock. The stress characteristics of the support structure become more complex during the process of excavation method conversion. It is essential to design a well-considered construction plan to minimize the disruptions caused by excavation method conversion. This work takes a tunnel in Jiangsu Province, China, as the engineering background and establishes a numerical model for the conversion from the double-side-drift method to the three-bench method. Based on a numerical investigation, this study analyzed the deformation of surrounding rock and the stress state of the support structure during the excavation method conversion. The results showed that excavation method conversion can accelerate the deformation rate of the surrounding rock, causing the support structure to bear greater pressure. This leads to a significant increase in the circumferential stress at the junction of two excavation methods. Setting up a reinforced area during the process of excavation method conversion can improve the stress state of the support structure. Finally, an analysis of the parameters of the reinforced area was conducted, and a reasonable construction plan was proposed. This study can provide guidance for subsequent construction projects. Full article

This article describes a tool that can be used to improve the effectiveness of the ISCO (in situ chemical oxidation) method. It is an Excel-based application that uses Visual Basic, PHREEQC, and Python. The main functions are feedback control solutions. There are several ideas that can optimise ISCO treatment when using the geochemical model: (i) looping real-time data into the geochemical model and using them to estimate the actual rate, (ii) using spatial distribution maps for delineating zones that are susceptible or resistant to oxidation, (iii) visualising the permanganate consumption that could indicate the right time for further action, and (iv) using alarm reports of the abnormal physico-chemical conditions that jeopardise successful injection. Full article

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other. Full article

With the rapid development of automobiles, energy shortages and environmental pollution have become a growing concern. In order to decrease the energy consumption of electric vehicles (EVs), this study aims to improve EV efficiency with AMT and dual-motor systems. Firstly, the paper establishes an Automated Manual Transmission (AMT) model for EVs, which is then simulated using MATLAB R2022a software. In order to eliminate the impact of gear ratio selection, the genetic algorithm is used to optimize the AMT gear ratios. Meanwhile, a dual-motor EV model is constructed, and three different torque distribution schemes are simulated and analyzed. The results indicate that due to the elongation of the energy transmission chain in AMT-equipped EVs, energy losses increase, leading to some improvement in optimized power consumption. However, these EVs remain inferior to those with only a single-stage main reducer. The study also found that the torque distribution based on optimal efficiency further improves results. Full article

Primary progressive apraxia of speech (PPAOS) is a neurodegenerative syndrome characterized by the progressive and initially isolated or predominant onset of difficulties in the planning/programming of movements necessary for speech production and can be accompanied by dysarthria. To date, no study has used an evidence-based treatment to address phonation control in patients with PPAOS. The aim of this study was to evaluate the feasibility and efficacy of LSVT LOUD^® as a treatment for phonatory control in speakers with PPAOS. Three speakers with PPAOS received LSVT LOUD^® therapy, and changes in phonatory control, voice quality and prosody were measured immediately, and one, four and eight weeks after the end of the treatment. Overall, the results suggest that the treatment is feasible and could improve voice quality, intensity, and control in some patients with PPAOS. The generalization of the results is also discussed. Full article

This paper presents a comprehensive study on ultra-high-performance concrete (UHPC) beams without stirrups, where the test data of 487 beams were collected, and an experimental database was established. Four distinct shear strength calculation models for UHPC beams were examined in the study. These models were created from national specification guides. The results indicate that while the code equation is useful for predicting UHPC beam shear capacity, it consistently underestimates actual values, with a mean experimental-to-calculated ratio above 1.5. The database was also used to study the impacts of the compressive strength of UHPC, the shear span-to-depth ratio, the fiber volume fraction, and the reinforcement ratio on the shear strength of UHPC beams. The findings showed that the shear span-to-depth ratio significantly affected the shear load-bearing capacity of UHPC beams. The increase in the compressive strength of UHPC, fiber volume fraction, and reinforcement ratio positively affected the shear strength of UHPC beams to varying degrees. Additionally, there were size effects for beams with a shear span-to-depth ratio of less than 1.5 and an effective depth of more than 300. In addition, coefficients accounting for fiber influence and the shear span-to-depth ratio were incorporated to develop an enhanced formula for UHPC beams. The empirical data from the database tests revealed that the average ratio of the beams’ experimental shear capacity to the values predicted by the modified equation is 1.3, with a standard deviation of 0.74. These results suggest that the refined equation offers improved calculation precision and broader applicability. Eventually, a summary of the issues pertaining to the shear performance of UHPC beams and the key future research directions is provided to facilitate a clearer comprehension and awareness of emerging concepts for scholars within the discipline. Full article

SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, has revealed a broader impact beyond the respiratory system, predominantly affecting the vascular system with various adverse manifestations. The infection induces endothelial dysfunction and immune system dysregulation, creating an inflammatory and hypercoagulable state. It affects both microvasculature and macrovasculature, leading to thromboembolic events, cardiovascular manifestations, impaired arterial stiffness, cerebrovascular complications, and nephropathy, as well as retinopathy—frequently observed in cases of severe illness. Evidence suggests that SARS-CoV-2 infection may result in persistent effects on the vascular system, identified as long-term COVID-19. This is characterized by prolonged inflammation, endotheliopathy, and an increased risk of vascular complications. Various imaging modalities, histopathological studies, and diagnostic tools such as video capillaroscopy and magnetic resonance imaging have been employed to visualize vascular alterations. This review aims to comprehensively summarize the evidence concerning short and long-term vascular alterations following COVID-19 infection, investigating their impact on patients’ prognosis, and providing an overview of preventive strategies to mitigate associated vascular complications. Full article

Colombia hosts the largest number of refugees and migrants fleeing the humanitarian emergency in Venezuela, many of whom experience high levels of displacement-related trauma and adversity. Yet, Colombian mental health services do not meet the needs of this population. Scalable, task-sharing interventions, such as Group Problem Management Plus (Group PM+), have the potential to bridge this gap by utilizing lay workers to provide the intervention. However, the current literature lacks a comprehensive understanding of how and for whom Group PM+ is most effective. This mixed methods study utilized data from a randomized effectiveness-implementation trial to examine the mediators and moderators of Group PM+ on mental health outcomes. One hundred twenty-eight migrant and refugee women in northern Colombia participated in Group PM+ delivered by trained community members. Patterns in moderation effects showed that participants in more stable, less marginalized positions improved the most. Results from linear regression models showed that Group PM+-related skill acquisition was not a significant mediator of the association between session attendance and mental health outcomes. Participants and facilitators reported additional possible mediators and community-level moderators that warrant future research. Further studies are needed to examine mediators and moderators contributing to the effectiveness of task-shared, scalable, psychological interventions in diverse contexts. Full article

Initially reported as pneumonia of unknown origin, COVID-19 is increasingly being recognized for its impact on the nervous system, despite nervous system invasions being extremely rare. As a result, numerous studies have been conducted to elucidate the mechanisms of nervous system damage and propose appropriate coping strategies. This review summarizes the mechanisms by which SARS-CoV-2 invades and damages the central nervous system, with a specific focus on aspects apart from the immune response and inflammatory storm. The latest research findings on these mechanisms are presented, providing new insights for further in-depth research. Full article

Magnetic resonance (MR) with sodium (²³Na) is a noninvasive tool providing quantitative biochemical information regarding physiology, cellular metabolism, and viability, with the potential to extend MR beyond anatomical proton imaging. However, when using clinical scanners, the low detectable ²³Na signal and the low ²³Na gyromagnetic ratio require the design of dedicated radiofrequency (RF) coils tuned to the ²³Na Larmor frequency and sequences, as well as the development of dedicated phantoms for testing the image quality, and an MR scanner with multinuclear spectroscopy (MNS) capabilities. In this work, we propose a hardware and software setup for evaluating the potential of ²³Na magnetic resonance imaging (MRI) with a clinical scanner. In particular, the reliability of the proposed setup and the reproducibility of the measurements were verified by multiple acquisitions from a 3T MR scanner using a homebuilt RF volume coil and a dedicated sequence for the imaging of a phantom specifically designed for evaluating the accuracy of the technique. The final goal of this study is to propose a setup for standardizing clinical and research ²³Na MRI protocols. Full article

Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of data and density. This research prioritized the development of 3D point cloud datasets specifically for heavy construction equipment, including dump trucks, rollers, graders, excavators, and dozers; all of which are extensively used in earthwork sites. The aim was to enhance the efficiency and productivity of machine learning (ML) and deep learning (DL) research that relies on 3D point cloud data in the construction industry. Notably, unlike conventional approaches to acquiring point cloud data using UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), the datasets for the five types of heavy construction equipment established in this research were generated using 3D-scanned diecast models of heavy construction equipment to create point cloud data. Full article