Search Results (51)

To address the challenges of low automation in tunnel wet-spraying jumbos and the heavy reliance on manual expertise for ensuring the spraying quality, this study proposes a novel task planning method for tunnel spraying operations. First, the tunnel surface to be sprayed is aligned with the designed contour using a vehicle navigation method, enabling the estimation of the overbreak and underbreak volumes. These volumes are then utilized to hierarchically plan the spraying tasks (e.g., patching, filling, and surface smoothing). A concrete coating thickness prediction method is developed, incorporating static and dynamic coating accumulation models with key process parameters—spraying flow rate Q, air pressure P, and spraying distance H—as independent variables. Based on the required thickness for each task layer, operational parameters such as the spraying duration t and nozzle movement speed v are optimized. By analyzing the spray gun action combinations and integrating hierarchical task planning with parameter optimization, a Planning Domain Definition Language (PDDL) domain file and problem file are designed to generate the spray gun action sequences and paths via a planner. The experimental results demonstrate that the overbreak volume on the sprayed tunnel surface is reduced to approximately 3 cm after applying the planned sequences. The proposed method autonomously generates the task hierarchies and the corresponding spray gun actions based on the 3D morphology of the tunnel surface, effectively ensuring the spraying quality while significantly reducing the dependence on manual intervention. This approach provides a practical solution for enhancing automation and precision in tunnel spraying operations. Full article

The agri-food supply chain and other industries that convert agricultural raw materials into various consumer goods generate large quantities of by-products, most of which end up in landfills. This waste, rich in cellulose, provides a significant opportunity for the conversion of agricultural residues into valuable products. In this paper, soybean hulls and hemp waste were subjected to chemical treatments with alkaline (NaOH 2% w/v) and acidic solutions (HCl 1 M) to remove non-cellulosic components and isolate cellulose. The biomass was characterized after each chemical process through FTIR, SEM, EDX, elemental analysis, TGA, and XRD. Lignin was determined following two different procedures, a conventional TAPPI protocol and a method recently proposed in the literature (CASA method). The results indicated that the chemical treatments favored the removal of organic compounds and minerals, increasing the cellulose content in biomass after each step. The purified product of soybean hulls consists of fibers 35–50 µm long and 5–11 µm thick, containing nearly pure cellulose arranged in crystalline domains. Fibers of variable sizes, rich in crystalline cellulose, were isolated from hemp waste. These fibers have diameters ranging between 2 and 60 µm and lengths from 40 to 800 µm and contain considerable amounts of lignin (~14%). Full article

This work presents a solution to the nonlocal harmonically varying heat model in a magneto-thermoelastic thick plate. The classical, simple, and refined Lord and Shulman theories of thermoelasticity are applied. The medium is under a harmonic varying heat source with a constant strength and applied longitudinal magnetic field. Additionally, the nonlocal effect of thermoelastic materials is demonstrated using Eringen’s nonlocal theory. The Laplace transform technique is used to find the analytical solution. The numerical inversion approach of the Laplace transform is employed to determine the solution within the physical domain. The impacts of nonlocal, time parameters, and the angular frequency of thermal vibration on the field variables are presented graphically and analyzed in detail. The findings indicate that the responses of the magneto-thermoelastic thick plate to harmonically varying heat are significantly influenced by each one of the physical parameters. The refined Lord and Shulman model presents significant fluctuations in the results due to the theory’s additional terms. Full article

Thyroid-associated ophthalmopathy (TAO), or Graves’ orbitopathy (GO), is a complex autoimmune disorder affecting orbital tissues, often leading to vision-threatening complications such as dysthyroid optic neuropathy (DON). In this systematic review, conducted following PRISMA guidelines, 22 studies were evaluated to investigate the role of optical coherence tomography (OCT) in assessing retinal and choroidal changes in TAO. Parameters such as the retinal nerve fiber layer (RNFL), ganglion cell complex (GCC), ganglion cell layer (GCL), and choroidal thickness were analyzed. RNFL changes varied by disease severity, with significant thinning in DON due to nerve fiber loss and thickening in early DON due to optic disk edema. Subfoveal choroidal thickness (SFCT) was consistently higher in active TAO, correlating positively with the clinical activity score (CAS) and proptosis, suggesting its role as a marker of disease activity. Subgroup analysis revealed that spectral-domain OCT (SD-OCT) was the most sensitive for detecting retinal changes. The findings highlight the effectiveness of OCT in detecting minor retinal and choroidal alterations in TAO. However, the variability of study designs, as well as the lack of longitudinal data, limits the ability to draw broad conclusions. Further standardized, long-term investigations are required to properly understand OCT’s diagnostic and prognostic value in TAO. Full article

Background/Objective: The aim of this retrospective study was to compare corneal parameters and compliance using a Pentacam HR–Scheimpflug (Pentacam HR) and a swept-source OCT Casia (Casia) in keratoconus (KC) patients post penetrating keratoplasty (PKP) and KC patients without PKP, as well as a control group. Pachymetry measurements were also analyzed using a spectral domain OCT Solix (OCT Solix), Pentacam HR, and Casia. Methods: The study included 71 patients (136 keratoconic eyes; group A), 86 eyes with KC post-PKP (group B), 50 eyes with KC without PKP (group C), and 52 control participants (104 eyes). All participants were adults, Polish Caucasian, and met specific inclusion criteria. Patients with ophthalmological or systemic diseases, cognitive impairment, or pregnancy were excluded. Corneal parameters were measured using two devices (Casia and Pentacam HR), while pachymetry was assessed with three devices (Casia, Pentacam HR, and OCT Solix), with the inter-device agreement and group differences analyzed. Results: Significant differences (p < 0.05) were found across all groups. The post-PKP KC eyes showed significant differences in all front parameters and K2 and Astig. back, while the non-PKP KC eyes showed differences in the K1 back (p = 0.025). The controls displayed differences in all parameters except front astigmatism (p = 0.61). The Pentacam HR overestimated the thinnest corneal thickness (TCT) compared to the OCT Casia across groups. The inter-device agreement was excellent for the anterior parameters (ICC > 0.9) but good for the posterior parameters and TCT. Conclusions: This study highlights significant variability in corneal and pachymetry measurements across devices, with OCT Casia providing more consistent and clinically reliable results than Pentacam HR. Clinicians should exercise caution when using these devices interchangeably, particularly for posterior parameters and TCT. Full article

The article presents an analysis of the influence of building materials on the propagation of an electromagnetic wave and the values of the electric field intensity. The topics of the analysis were two types of walls (partition and load bearing) built of different building materials. Different variants of walls were considered due to the building material used: concrete, aerated concrete, solid brick, clinker bricks, and three types of hollow bricks. The requirements for structures in terms of wall thickness were taken into account. The article, using concrete as an example, also describes the influence of changes in the electrical parameters of the building material on wave propagation and the values of the field. The results concerning the influence of complex materials, such as hollow bricks, on the non-uniform distribution of the electric field were also included. Due to the different percentage share of ceramic mass in hollow bricks, the article discusses its influence on the values of the field, taking into account the variability of conductivity. The analysis was performed using the Finite Difference Time Domain (FDTD) method. The results were compared with the analytical solution. The analysis, among others, showed that with the increase in the ceramic mass in bricks, the electric field values are higher but result in an uneven distribution of the field. Using the example of hollow bricks used to build load-bearing walls, it was observed that a small modification of the hollows practically does not affect the field intensity (the difference is approx. 2%). When planning the installation of wireless networks, the best solution is walls made of ceramics with a large number of hollows, where the ceramic mass constitutes only approx. 30%. A multivariate analysis allows for a better understanding of field phenomena inside single-family homes. Full article

Integrated heat and roll forming (IHMRF) is a process that uses thermal and mechanical loads to produce localized plastic strains in plates to form complex curvature hull plates. The magnitude of the resulting plastic strain depends mainly on the following forming parameters: the machining parameters (power of the heat source, speed of the heat source, and the forming depth of the rollers), the thickness of the plate, and the thermo-physical and mechanical properties of the plate. Finding the correspondence between the plastic strain and forming parameters is the key to selecting the appropriate machining parameters for forming. A data-driven approach is ideal for this purpose. However, due to the characteristics of the IHMRF process, the forming process involves a large number of variables, and different materials have different temperature-dependent yield strengths. These high-dimensional input characteristics create a conflict between the required number of samples and the model training requirements. This paper presents a physically informed data-driven (PIDD) approach for modeling the relationship between forming parameters and plastic strains in IHMRF. Based on dimensional analysis and domain knowledge, the proposed method derives the basic thermal and mechanical relationships between the forming parameters, obtaining a much smaller number of physical parameters. These physical parameters are expressions of the physical knowledge of forming in low-dimensional space. Using the physical parameters yields higher accuracy on fewer sample data points than directly using the forming parameters as input features. Furthermore, the models trained on a variety of commonly used materials and plate thicknesses achieved comparable accuracy to the numerical simulation with unseen materials and plate thicknesses. Experimental and numerical simulations further verify the effectiveness of the proposed method by machining plates of various materials to the same shape. Full article

Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on the collection, preprocessing, and analysis of acoustic data streams from a Fused Deposition Modeling (FDM) 3D-printed sample cube (10 mm × 10 mm × 5 mm). Time and frequency-domain features were extracted at 10-s intervals at varying layer thicknesses. The audio samples were preprocessed using the Harmonic–Percussive Source Separation (HPSS) method, and the analysis of time and frequency features was performed using the Librosa module. Feature importance analysis was conducted, and machine learning (ML) prediction was implemented using eight different classifier algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM)) for the classification of print quality based on the labeled datasets. Three-dimensional-printed samples with varying layer thicknesses, representing two print quality levels, were used to generate audio samples. The extracted spectral features from these audio samples served as input variables for the supervised ML algorithms to predict print quality. The investigation revealed that the mean of the spectral flatness, spectral centroid, power spectral density, and RMS energy were the most critical acoustic features. Prediction metrics, including accuracy scores, F-1 scores, recall, precision, and ROC/AUC, were utilized to evaluate the models. The extreme gradient boosting algorithm stood out as the top model, attaining a prediction accuracy of 91.3%, precision of 88.8%, recall of 92.9%, F-1 score of 90.8%, and AUC of 96.3%. This research lays the foundation for acoustic based quality prediction and control of 3D printed parts using Fused Deposition Modeling and can be extended to other additive manufacturing techniques. Full article

Tinea cruris, a dermatophyte fungal infection predominantly caused by Trichophyton rubrum and Epidermophyton floccosum, primarily affects the groin, pubic region, and adjacent thigh. Its recurrence is frequent, attributable to repeated fungal infections in susceptible individuals, especially those with onychomycosis or tinea pedis, which act as reservoirs for dermatophytes. Given the persistent nature of tinea cruris, vaccination emerges as a promising strategy for fungal infection management, offering targeted, durable protection against various fungal species. Vaccines stimulate both humoral and cell-mediated immunity and are administered prophylactically to prevent infections while minimizing the risk of antifungal resistance development. Developing fungal vaccines is challenging due to the thick fungal cell wall, similarities between fungal and human cells, antigenic variation, and evolutionary resemblance to animals, complicating non-toxic target identification and T-cell response variability. No prior research has shown an mRNA vaccine for T. rubrum. Hence, this study proposes a novel mRNA-based vaccine for tinea cruris, potentially offering long-term immunity and reducing reliance on antifungal medications. This study explores the complete proteome of T. rubrum, identifying potential protein candidates for vaccine development through reverse vaccinology. Immunogenic epitopes from these candidates were mapped and integrated into multitope vaccines and reverse translated to construct mRNA vaccines. Then, the mRNA was translated and computationally assessed for physicochemical, chemical, and immunological attributes. Notably, 1,3-beta-glucanosyltransferase, CFEM domain-containing protein, cell wall galactomannoprotein, and LysM domain-containing protein emerged as promising vaccine targets. Antigenic, immunogenic, non-toxic, and non-allergenic cytotoxic T lymphocyte, helper T lymphocyte, and B lymphocyte epitopes were selected and linked with appropriate linkers and Toll-like receptor (TLR) agonist adjuvants to formulate vaccine candidates targeting T. rubrum. The protein-based vaccines underwent reverse translation to construct the mRNA vaccines, which, after inoculation, were translated again by host ribosomes to work as potential components for triggering the immune response. After that, molecular docking, normal mode analysis, and molecular dynamic simulation confirmed strong binding affinities and stable complexes between vaccines and TLR receptors. Furthermore, immune simulations of vaccines with and without adjuvant demonstrated activation of immune responses, evidenced by elevated levels of IgG1, IgG2, IgM antibodies, cytokines, and interleukins. There was no significant change in antibody production between vaccines with and without adjuvants, but adjuvants are crucial for activating the innate immune response via TLRs. Although mRNA vaccines hold promise against fungal infections, further research is essential to assess their safety and efficacy. Experimental validation is crucial for evaluating their immunogenicity, effectiveness, and safety. Full article

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material’s susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool. Full article

Carbon nanotubes are widely used as material reinforcement in diverse fields of engineering. Being that their contribution is significant to improving the mean properties of the resulting materials, it is important to assess the influence of the variability on carbon nanotubes’ material and geometrical properties to structures’ responses. This work considers functionally graded plates constituted by an aluminum continuous phase reinforced with single-walled or multi-walled carbon. The nanotubes' weight fraction evolution through the thickness is responsible for the plates’ functional gradient. The plates’ samples are simulated considering that only the nanotubes’ material and geometrical characteristics are affected by uncertainty. The results obtained from the multiple regression models developed allow us to conclude that the length of the nanotubes has no impact on the maximum transverse displacement of the plates in opposition to the carbon nanotubes’ weight fraction evolution, their internal and external diameters, and the Young’s modulus. The multiple regression models developed can be used as alternative prediction tools within the domain of the study. Full article

This study deals with two different aspects of the high-strength low-alloyed 1100 MC steel. The first is associated with the remarkable heterogeneity (linked with surface decarburization) in the surface state produced during sheet rolling with respect to the sheet width. The variable thickness surface layer exhibits a microstructure different from that of the deeper bulk. Variation in the thickness of the thermally softened near-surface region strongly affects Barkhausen noise as well. This technique can be considered a reliable tool for monitoring the aforementioned heterogeneity. It can also be reported that the opposite sides of the sheet are different with respect to the surface state, the heterogeneity distribution, and the corresponding Barkhausen noise. These aspects indicate different conditions during hot rolling followed by rapid quenching on the upper and lower rollers. Furthermore, it was found that the degree of decarburizing and the corresponding surface heterogeneity is also a function of C content, and steels with lower C content exhibit less pronounced surface heterogeneity. The second aspect is related to the remarkable asymmetry in Barkhausen noise emission with respect to two consecutive bursts. This asymmetry is due to the presence of remnant magnetization in the sheet produced during manufacturing. The remnant magnetization is coupled to the magnetic field produced by the excitation coil of the Barkhausen noise sensor and strongly contributes to the aforementioned asymmetry. The remnant magnetization attenuates the domain wall mobility, which results in weaker Barkhausen noise. Moreover, the Barkhausen noise envelopes and the extracted features such as the position of the envelope maximum and its width are strongly affected by the remnant magnetization. Insufficient demagnetization makes the body magnetically softer and makes a wider range of magnetic fields in which Barkhausen noise emission can be detected. As soon as sufficient removal of this remnant magnetization is carried out in the vanishing magnetic field (demagnetization), the aforementioned remarkable asymmetry is fully lost. Full article

This work develops a three-dimensional (3D) weak formulation, based on the consistent couple stress theory (CCST), for analyzing the size-dependent dynamic instability behavior of simply-supported, functionally graded (FG) cylindrical microshells that are subjected to combinations of periodic axial compression and external pressure. In our formulation, the microshells are artificially divided into n_l layers. The displacement components of each individual layer are selected as the primary variables, which are expanded as a double Fourier series in the in-plane domain and are interpolated with Hermitian C² polynomials in the thickness direction. Incorporating the layer-wise displacement models into our weak formulation, we develop a Hermitian C² finite layer method (FLM) for addressing the current issue. The accuracy and the convergence rate of our Hermitian C² FLM are validated by comparing the solutions it produces with the accurate two-dimensional solutions of critical loads and critical pressures of FG cylindrical macroshells and single-walled carbon nanotubes, which were reported in the literature. The numerical results show the effects of the material length-scale parameter, the inhomogeneity index, the radius-to-thickness and length-to-radius ratios, the load magnitude ratio, and the static and dynamic load factors on the first principal and first secondary instability regions of parametric resonance of simply-supported FG cylindrical microshells are significant. Full article

Lake ice thickness (LIT) is one of the key climate variables in the lake ice domain, but there are currently large uncertainties in the retrieval of LIT. We present and validate a new LIT retrieval method that utilizes ICESat-2 data to assist CryoSat-2 echo peak selection, aiming to improve the accuracy of LIT retrieval and enable data acquisition without on-site measurements. The method involves screening out similar ICESat-2 and CryoSat-2 tracks based on time and space constraints. It also involves dynamically adjusting the range constraint window of CryoSat-2 waveforms based on the high-precision lake ice surface ellipsoid height obtained from ICESat-2/ATL06 data. Within this range constraint window, the peak selection strategy is used to determine the scattering interfaces between snow-ice and ice-water. By utilizing the distance between the scattering horizons, the thickness of the lake ice can be determined. We performed the ice thickness retrieval experiment for Baker Lake in winter and verified it against the on-site measurement data. The results showed that the accuracy was about 0.143 m. At the same time, we performed the ice thickness retrieval experiment for Great Bear Lake (GBL), which does not have on-site measurement data, and compared it with the climate change trend of GBL. The results showed that the retrieval results were consistent with the climate change trend of GBL, confirming the validity of the proposed method. Full article

Domain wall mobility as a function of nonmagnetic interlayer thickness and temperature was studied in ultrathin exchange-coupled ferromagnetic layers using magneto-optic Kerr microscopy. The system under study is a Pt/Co/Pt/Co/Pt heterostructure having perpendicular magnetic anisotropy and a middle Pt layer with spatially variable thickness. The ferromagnetic interaction between the Co layers is observed when the Pt interlayer thickness varies from 5 to 6 nm in a temperature range of 200–300 K. There is a certain interval of Pt layer thickness where domain walls in both ferromagnetic layers move independently. Nonlinear dependence of the domain wall displacement on the applied field was measured. It is shown that an equilibrium position of the relaxed domain wall depends on field, temperature, and the nonmagnetic interlayer thickness. This position is determined by the energy balance: (i) domain wall displacement provided by the applied field, (ii) interlayer exchange interaction in the area swept by the domain wall, and (iii) domain wall coercivity. The mechanism of domain wall stabilization in terms of independent wall motion near critical thickness was considered. It is found that both the coercivity of the Co layer and the critical thickness decrease at higher temperature, while the interlayer exchange constant J is changed weakly. Full article