Search Results (247)

Microbiological analysis is crucial for identifying species, assessing infections, and diagnosing infectious diseases, thereby supporting both research studies and medical diagnosis. In response to these needs, accurate and efficient identification of bacterial colonies is essential. Conventionally, this process is performed through manual counting and visual inspection of colonies on agar plates. However, this approach is prone to several limitations arising from human error and external factors such as lighting conditions, surface reflections, and image resolution. To overcome these limitations, an automated bacterial colony counting and classification system was developed by integrating a custom-designed imaging device with advanced deep learning models. The imaging device incorporates controlled illumination, matte-coated surfaces, and a high-resolution camera to minimize reflections and external noise, thereby ensuring consistent and reliable image acquisition. Image-processing algorithms implemented in MATLAB were employed to detect bacterial colonies, remove background artifacts, and generate cropped colony images for subsequent classification. A dataset comprising nine bacterial species was compiled and systematically evaluated using five deep learning architectures: ResNet-18, ResNet-50, Inception V3, GoogLeNet, and the state-of-the-art EfficientNet-B0. Experimental results demonstrated high colony-counting accuracy, with a mean accuracy of 90.79% ± 5.25% compared to manual counting. The coefficient of determination (R² = 0.9083) indicated a strong correlation between automated and manual counting results. For colony classification, EfficientNet-B0 achieved the best performance, with an accuracy of 99.78% and a macro-F1 score of 0.99, demonstrating strong capability in distinguishing morphologically distinct colonies such as Serratia marcescens. Compared with previous studies, this research provides a time-efficient and scalable solution that balances high accuracy with computational efficiency. Overall, the findings highlight the potential of combining optimized imaging systems with modern lightweight deep learning models to advance microbiological diagnostics and improve routine laboratory workflows. Full article

Objectives: The aim of this study was to compare the stiffness-related mechanical response and peak von Mises stress distribution of low-profile 2.4 mm mandibular reconstruction systems (a conventional reconstruction plate, a 3D grid-type plate, and a customized plate) in a virtual atrophic mandible model with a 5 cm segmental defect. Materials and Methods: A CT-based three-dimensional mandible model was created and instrumented with three plate configurations (G1–G3). Linear static finite element analyses were performed under a 300-N masticatory load combined with literature-based muscle force vectors. Peak von Mises stresses were recorded for plates and screws, and the locations of maximum stress concentration were identified. Results: Peak plate stress was highest in the conventional reconstruction plate (G1: 695.5 MPa), followed by the 3D grid-type plate (G2: 595.6 MPa), and lowest in the customized plate (G3: 185.2 MPa). The peak screw stress was 692.9 MPa (G1), 898.0 MPa (G2), and 595.6 MPa (G3). The 3D grid-type plate increased construct stiffness but shifted stress concentration toward the mandibular angle and adjacent screws, whereas the customized plate reduced the peak plate stress and limited the extent of the high-stress region across the defect. Conclusions: Within the limitations of a linear static FEA (stiffness/stress distribution rather than failure load or fatigue resistance), the customized plate (G3) demonstrated the most favorable biomechanical performance (lowest peak plate stress). The 3D grid-type plate (G2) reduced peak plate stress compared with the conventional design (G1) but produced the highest peak screw stress. Practical considerations such as manufacturing lead time and resource requirements may favor off-the-shelf plates; however, a formal cost or operative-time analysis was not performed. Full article

Vertical cavity surface emitting lasers (VCSELs) have shown great potential in high-speed communication, quantum information processing, and 3D sensing due to their excellent beam quality and low power consumption. However, generating high-purity and controllable circularly polarized light usually requires external optical components such as quarter-wave plates, which undoubtedly increases system complexity and volume, hindering chip-level integration. To address this issue, we propose a monolithic integration scheme that directly integrates a custom-designed double-layer asymmetric metasurface onto the upper distributed Bragg reflector of a chiral VCSEL. This metasurface consists of a rotated GaAs elliptical nanocolumn array and an anisotropic grating above it. By precisely controlling the relative orientation between the two, the in-plane symmetry of the structure is effectively broken, introducing a significant optical chirality response at a wavelength of 1550 nm. Numerical simulations show that this structure can achieve a near 100% high reflectivity for the left circularly polarized light (LCP), while suppressing the reflectivity of the right circularly polarized light (RCP) to approximately 33%, thereby obtaining an efficient in-cavity circular polarization selection function. Based on this, the proposed VCSEL can directly emit high-purity RCP without any external polarization control components. This compact circularly polarized laser source provides a key solution for achieving the next generation of highly integrated photonic chips and will have a profound impact on frontier fields such as spin optics, secure communication, and chip-level quantum light sources. Full article

Fatigue crack propagation is a critical failure mechanism in engineering structures, requiring meticulous monitoring for timely maintenance. This research introduces a deep learning framework for estimating fatigue fracture length in metallic plates through acoustic emission (AE) signals. AE waveforms recorded during crack growth are transformed into time-frequency images using the Choi–Williams distribution. First, a clustering system is developed to analyze the distribution of the AE image-based dataset. This system employs a CNN-based model to extract features from the input images. The AE dataset is then divided into three categories according to fatigue lengths using the K-means algorithm. Principal Component Analysis (PCA) is used to reduce the feature vectors to two dimensions for display. The results show how close together the data points are in the clusters. Second, convolutional neural network (CNN) models are trained using the AE dataset to categorize fracture lengths into three separate ranges. Using the pre-trained models ResNet50V2 and VGG16, we compare the performance of a bespoke CNN using transfer learning. It is clear from the data that transfer learning models outperform the custom CNN by a wide margin, with an accuracy of approximately 99% compared to 93%. This research confirms that convolutional neural networks (CNNs), particularly when trained with transfer learning, are highly successful at understanding AE data for data-driven structural health monitoring. Full article

Atlantoaxial instability (AAI) in toy- and small-breed dogs remains a significant clinical challenge, as the restricted anatomical space and risk of complications complicate the selection of implants. This study aimed to compare three patient-specific Ti-6Al-4V stabilizers for the C1–C2 region: a clinically used ventral C1–C3 plate, a shortened ventral C1–C2 plate, and a dorsal C1–C2 implant. Computed tomography, segmentation, virtual reduction, CAD/CAM design, and finite element analysis were employed to evaluate the linear-static mechanical behavior of each construct under loading ranging from 5 to 25 N, with a focus on displacements, von Mises stresses, and peri-screw bone strains. Additionally, cadaver procedures were performed in nine small-breed dogs using custom drill guides and additively manufactured implants to evaluate procedural feasibility and implantation time. Finite element models demonstrated that all stabilizers operated within material and biological safety limits. The C1–C3 plate exhibited the highest implant stresses, while the C1–C2 plate demonstrated an intermediate response, and the dorsal implant minimized implant stresses, albeit by increasing bone stresses. Cadaver experiments revealed that dorsal fixation required less implantation time than ventral fixation. Collectively, the findings indicate that all evaluated constructs represent safe stabilization options, and the choice of implant should reflect the preferred load-transfer pathway as well as anatomical or surgical constraints that may limit ventral access. Full article

This article presents the design, fabrication, and experimental validation of a centimeter-scale autonomous robot that achieves bidirectional locomotion and trajectory control through 3D-printed resonators actuated by piezoelectricity and integrated with miniature legs. Building on previous works that employed piezoelectric bimorphs, the proposed system replaces them with custom-designed 3D-printed resonant plates that exploit the excitation of standing waves (SW) to generate motion. Each resonator is equipped with strategically positioned passive legs that convert vibratory energy into effective thrust, enabling both linear and rotational movement. A differential drive configuration, implemented through two independently actuated resonators, allows precise guidance and the execution of complex trajectories. The robot integrates onboard control electronics consisting of a microcontroller and inertial sensors, which enable closed-loop trajectory correction via a PD controller and allow autonomous navigation. The experimental results demonstrate high-precision motion control, achieving linear displacement speeds of 8.87 mm/s and a maximum angular velocity of 37.88°/s, while maintaining low power consumption and a compact form factor. Furthermore, the evaluation using the mean absolute error (MAE) yielded a value of 0.83° in trajectory tracking. This work advances the field of robotics and automatic control at the insect scale by integrating efficient piezoelectric actuation, additive manufacturing, and embedded sensing into a single autonomous platform capable of agile and programmable locomotion. Full article

Background/Objectives: Three-dimensional (3D) cell cultures are increasingly used because 3D cell aggregates better mimic tissue-level biological mechanisms and support studies of tissue physiology and drug screening. However, existing laboratory methods and commercial microwell platforms often yield inconsistent results and can be error-prone, time-consuming, or costly. The objective of this work was to develop a reproducible, high-yield, and cost-effective approach for generating homogeneous cell aggregates using custom 3D-printed microwell stamps. Methods: Custom conical and semi-spherical microwell stamps were fabricated using 3D printing. Stamp resolution was characterized by scanning electron microscopy (SEM). Negative imprints were cast in polydimethylsiloxane (PDMS), a biocompatible and hydrophobic polymer conducive to cell aggregation. These PDMS microwells were then used to generate pluripotent stem cell aggregates (embryoid bodies, EBs) and tumor spheroids from adherent cancer cell lines. Results: The 3D-printed stamps produced high-resolution conical and semi-spherical microwells in PDMS. Semi-spherical microwells enabled rapid, simple, and cost-effective formation of pluripotent stem cell aggregates that were homogeneous in size and shape. These aggregates outperformed those produced using commercial microwell plates and ultra-low attachment plates. The fabricated microwells also generated uniform tumor spheroids from adherent cancer cells, demonstrating their versatility. Conclusions: The in-house 3D-printed microwell stamps offer a reproducible, efficient, and economical platform for producing homogeneous cell aggregates. This system improves upon commercial alternatives and supports a broad range of applications, including pluripotent stem cell embryoid body formation and tumor spheroid generation. Full article

The conservation and breeding of the western honey bee (Apis mellifera) is central dependent on accurate subspecies assignment, but the most commonly used methods are labor-intensive classical morphometrics and costly molecular assays. We developed an XGBoost-based classification framework using a compact set of routinely measurable characters. A curated dataset of labeled workers was measured under harmonized protocols; features were screened according to embedded importance, and model performance was assessed using five-fold cross-validation, outperforming standard machine learning baselines. The resulting model using only the top 10 characters—primarily forewing venation angles and abdominal plate metrics—achieved high performance (accuracy = 0.98; F1 = 0.99) and an area under the receiver operating characteristic curve (AUC) of 0.99 (95% CI = 0.995–0.999). SHAP analyses confirmed the discriminatory contributions of these features, while error inspection suggested that misclassifications were concentrated in morphologically overlapping lineages. The model’s performance supports its use as a rapid triage tool alongside genetic testing, providing a scalable and interpretable tool for researchers to create and deploy custom morphometric models, demonstrated here for A. mellifera but portable to other insect taxa. Full article

Ejector-based cooling systems have gathered scientific interest as a low-cost alternative for solar-assisted cooling applications, especially in regions with solar abundance. This work presents the experimental investigation of a solar ejector cooling prototype system. The system, developed at the National Technical University of Athens, includes a custom-made ejector and is powered by a 48 m² flat plate solar collector field, assisted by an auxiliary natural gas boiler. Experimental testing under varying operating conditions was conducted to assess the system’s performance, focusing on the influence of evaporation and condensation temperatures. The maximum coefficient of performance (COP) was measured at approximately 0.160–0.165, corresponding to an entrainment ratio of 0.19 at an evaporation temperature of 9 °C and condensation temperatures of 26–27 °C. Ejector performance substantially declined with increased condensation temperatures. However, the influence of the evaporator pressure on system performance was less significant. These findings demonstrate the feasibility of ejector-based solar cooling as a sustainable solution for reducing electricity use in cooling applications, highlighting the critical influence of operating parameters in the system’s performance optimization. Full article

Flowable resin composites are extensively used in restorative dentistry, where linear polymerization shrinkage and the resulting shrinkage stress are critical for clinical success. This study investigated the relationship between viscosity, linear polymerization shrinkage, and shrinkage stress in flowable resin composite materials. Two low-flow resin composites (Beautifil Flow Plus F00, Estelite Universal Flow SuperLow), two medium-flow resin composites (Tetric EvoFlow, Estelite Universal Flow Medium), and two high-flow resin composites (Beautifil Flow F10, Estelite Universal Flow High) were examined. Viscosity (n = 3) of the unset materials was determined using a cone–plate rheometer. The composites were photoactivated for 20 s at 1226 mW/cm², and linear polymerization shrinkage (n = 8) and shrinkage stress (n = 8) of 1.5 mm-thick specimens were recorded in real time for 5 min using a custom-made linometer and stress analyzer, respectively. Data were analyzed with Kruskal–Wallis rank tests followed by Conover post hoc tests, and Spearman correlation analyses were conducted to assess relationships between parameters (α = 0.05). A significant negative correlation was observed between viscosity and shrinkage stress (r = −0.943, p = 0.017). Beautifil Flow F10 exhibited the significantly lowest viscosity (14.60 ± 0.17 Pa·s) and the highest shrinkage stress (0.83 ± 0.14 MPa) among the materials, whereas low-flow composite Estelite Universal Flow SuperLow showed the lowest shrinkage stress (0.65 ± 0.10 MPa). Linear shrinkage ranged from 1.89 ± 0.13% to 3.18 ± 0.21%, but was not correlated with viscosity or stress (p > 0.05). In conclusion, viscosity critically influences polymerization-induced shrinkage stress development in flowable resin composites. Higher-viscosity flowable composites might be beneficial regarding stress build-up during polymerization compared with high-flow composites. Full article

Binder jet 3D printing of calcium sulfate-based materials combined with phase transformation offers a versatile route for fabricating customized bone grafts; however, controlling the transformation process remains a key challenge. This study investigates the effect of post-printing hydration in sodium chloride (NaCl) solutions on the phase transformation, dimension, and compressive properties of binder jet-printed calcium sulfate (3DPCaS) toward hydroxyapatite (3DPHA) formation. The as-printed 3DPCaS primarily consisted of bassanite with minor gypsum, which progressively transformed into gypsum upon immersion in NaCl solutions of varying concentrations (1–5 M) and durations (2–30 min). Increased immersion time and moderate NaCl concentrations (2–4 M) promoted gypsum formation without inducing dimensional instability. Subsequent transformation in phosphate solution produced 3DPHA with high hydroxyapatite (HA) purity, reaching 100% conversion. Microstructural analysis revealed recrystallized, plate-like gypsum crystals that served as favorable templates for HA nucleation. The resulting 3DPHA exhibited enhanced specific modulus (up to 274.9 MPa.m³/kg) and specific strength (up to 7.5 MPa.m³/kg). The optimal condition, immersion in 4 M NaCl solution for 30 min, achieved a balance between complete HA transformation, mechanical enhancement, and dimensional stability. Controlled ionic hydration thus represents a simple, low-cost, and effective strategy for improving properties of 3DPHA bone grafts. Full article

This study investigates the thermal performance of a passive vertical aluminum heat sink with plate fins through combined experimental measurements and numerical simulations. Using a custom-made experimental apparatus which used water as the heat source, heat transfer rate was determined, and heat transfer coefficient was compared against established empirical correlations, demonstrating good agreement. A 3D steady-state mathematical model was developed to capture the conjugate heat transfer problem of conduction and natural convection, with buoyancy-driven airflow modeled with the incompressible ideal gas law. The problem was solved numerically using the finite volume method through ANSYS Fluent 18.2 solver and validated against experimental data and analytical correlations, exhibiting good agreement throughout. Parametric analysis followed, investigating the influence of various base (50, 65, 80 °C) and ambient (19, 24, 29 °C) temperatures, resulting in base-to-ambient temperature differences from 21 to 61 °C. Increasing this temperature difference led to a significant increase in heat transfer rate, while heat transfer coefficient increased and overall thermal resistance decreased moderately. Additionally, a Nusselt–Rayleigh (Nu–Ra) number correlation, consistent with ranges reported in the literature, was derived, providing the scaling to predict the thermal performance of similar natural convection-governed heat sinks. The validated computational methodology, combined with obtained experimental and numerical results, presents a foundation for future studies focused on more complex heat sink geometries and physics. Full article

This paper proposes TreeHelper, an IoT solution that aims to improve authorization and monitoring practices, in order to help authorities act faster and save essential elements of the environment. It is composed of two important parts: a web platform and an edge AI device placed on the routes of tree logging trucks. The web platform is built using Spring Boot for the backend, React for the frontend and PostgreSQL as the database. It allows transporters to request wood transport authorizations in a straight-forward manner, while giving authorities the chance to review and decide upon these requests. The smart monitoring device consists of a Raspberry Pi for processing, a camera for capturing live video, a Coral USB Accelerator in order to accelerate model inference and a SIM7600 4G HAT for communication and GPS data acquisition. The model used is YOLOv11n and it is trained on a custom dataset of tree logging truck images. Model inference is run on the frames of the live camera feed and, if a truck is detected, the frame is sent to a cloud ALPR service in order to extract the license plate number. Then, using the 4G connection, the license plate number is sent to the backend and a check for an associated authorization is performed. If nothing is found, the authorities are alerted through an SMS message containing the license plate number and the GPS coordinates, so they can act accordingly. Edge TPU acceleration approximately doubles TreeHelper’s throughput (from around 5 FPS average to above 10 FPS) and halves its mean inference latency (from around 200 ms average to under 100 ms) compared with CPU-only execution. It also improves p95 latency and lowers CPU temperature. The YOLOv11n model, trained on 1752 images, delivers high validation performance (precision = 0.948; recall = 0.944; strong mAP: mAP50 = 0.967; mAP50-95 = 0.668), allowing for real-time monitoring. Full article

This study investigates the effect of rotation step size on the performance of flat-plate solar collectors (FPSC) equipped with single-axis tracking. Numerical simulations were carried out in EnergyPlus, coupled with a custom Python interface enabling dynamic control of collector orientation. The analysis was carried out for the city of Kragujevac in Serbia, located in a temperate continental climate zone, based on five representative summer days (3 July–29 September) to account for seasonal variability. Three collector types with different efficiency parameters were considered, and inlet water temperatures of 20 °C, 30 °C, and 40 °C were applied to represent typical operating conditions. The results show that single-axis tracking increased the incident irradiance by up to 28% and the useful seasonal heat gain by up to 25% compared to the fixed configuration. Continuous tracking (ψ = 1°) achieved the highest energy yield but required 181 daily movements, which makes it mechanically demanding. Stepwise tracking with ψ = 10–15° retained more than 90–95% of the energy benefit of continuous tracking while reducing the number of daily movements to 13–19. For larger steps (ψ = 45–90°), the advantage of tracking decreased sharply, with thermal output only 5–10% higher than the fixed case. Increasing the inlet temperature from 20 °C to 40 °C reduced seasonal heat gain by approximately 30% across all scenarios. Overall, the findings indicate that relative single-axis tracking with ψ between 10° and 15° provides the most practical balance between energy efficiency, reliability, and economic viability, making it well-suited for residential-scale solar thermal systems. This is the first study to quantify how discrete rotation steps in single-axis tracking affect both thermal and economic performance of flat-plate collectors. The proposed EnergyPlus–Python model demonstrates that a 10–15° step offers 90–95% of the continuous-tracking energy gain while reducing actuator motion by ~85%. The results provide practical guidance for optimizing low-cost solar-thermal tracking in continental climates. Full article

This paper describes a laboratory set-up designed to support hands-on learning of heat transfer principles in aerospace engineering education. Developed within the framework of experiential and project-based learning, the set-up enables students to experimentally characterize the convective coefficient of a cooling fan and the thermo-optical properties of aluminum plates with different surface coatings, specifically their absorptivity and emissivity. A custom-built, LED-based radiation source (the ESAT Sun simulator) and a calibrated temperature acquisition system are used to emulate and monitor radiative heating under controlled conditions. Simplified physical models are developed for both the ESAT Sun simulator and the plates that capture the dominant thermal dynamics via first-order energy balances. The laboratory workflow includes real-time data acquisition, curve fitting, and thermal model inversion to estimate the convective and thermo-optical coefficients. The results demonstrate good agreement between the model predictions and observed temperatures, which supports the suitability of the set-up for education. The proposed activities can strengthen the student’s understanding of convective and radiative heat transport in aerospace applications while also fostering skills in data analysis, physical and numerical reasoning, and system-level thinking. Opportunities exist to expand the material library, refine the physical modeling, and evaluate the long-term pedagogical impact of the educational set-up described here. Full article