Search Results (247)

University orientation programmes are a primary mechanism through which new students become familiar with campus facilities, academic spaces, and institutional procedures. However, many orientation activities are delivered as single in-person sessions, limiting opportunities for students to revisit spatial and procedural information after the event. To help address this constraint, a digital twin-inspired metaverse orientation application, the Digital Twin Metaverse Orientation (DTMO), was designed in Unity and hosted on Spatial.io as a spatially faithful virtual replica of a faculty environment. An exploratory pilot evaluation was conducted with 30 university students from multiple faculties after a facilitator-guided orientation session. The System Usability Scale (SUS), Net Promoter Score (NPS), and two open-ended questions were used to examine perceived usability, recommendation intention, and the reasons underpinning recommendation decisions. The application obtained a mean SUS score of 86.83, corresponding to an excellent perceived-usability rating, and an NPS of 53.33, indicating positive immediate recommendation intention. Qualitative responses suggested that participants valued the DTMO for engagement, accessibility, ease of navigation, and support for spatial familiarisation, while some participants emphasised that it should complement rather than replace physical orientation. These pilot findings indicate promising user reception in a small, guided-session sample, but they do not establish orientation effectiveness, learning transfer, wayfinding performance, retention, belonging, institutional integration, or sustained use. Further research with broader samples and outcome-based measures is therefore needed. Full article

Micro-structured SAE H13 tool steel inserts for polymer injection molding require accurate replication of sub-millimeter features while retaining adequate densification and heat-treatment response. This study compared two powder-based routes on the same hemispherical insert containing pyramidal features of approximately 0.145 mm base width: hot embossing (HE) of water-atomized SAE H13 powder (supplier d50 = 5.7 µm, irregular morphology) compounded with a commercial M1 binder, and material extrusion (MEX) of a commercial gas-atomized SAE H13 filament processed on a Markforged Metal X. Rheological screening selected a 57:43 vol% powder-to-binder ratio for the in-house HE feedstock, and DSC/TGA measurements defined two-step debinding windows. The best HE conditions were 220 °C, 8 MPa, and 45 min for the in-house mixture, and 210 °C, 8 MPa, and 30 min for the granulated commercial filament; the latter showed a 0.15% linear deviation from the silicone replica diameter among the best-rated samples. Under the tested commercial MEX configuration, the pyramidal features were not resolved because the 0.40 mm deposition line width exceeded the target feature base width, causing the slicer to omit the sub-line-width geometry. The defect populations differed qualitatively: HE specimens showed porosity and local cracking associated with powder morphology and pressureless sintering, whereas MEX specimens showed build-direction-aligned inter-raster voids associated with the toolpath. Microhardness and tensile data are therefore interpreted as process-history-specific results rather than as a direct route ranking, because sintering conditions were not uniform across all specimens. The study defines an experimentally bound process-selection limit for SAE H13 micro-tooling: HE remains preferable for sub-nozzle surface features, whereas MEX remains attractive for macro-scale geometric freedom, if resolution, densification, and post-sintering consolidation are addressed. Full article

This paper describes design, implementation and initial evaluation of Digital Twin Metaverse Classroom for higher education. Digital Twin Metaverse Classroom refers to highly realistic digital replicas or virtual replicas or prototypes of university classrooms or learning spaces. This paper focuses on creating high-fidelity digital replica of typical university lecture room. The main purpose of the Digital Twin Metaverse Classroom is to support teaching and learning in addition to traditional videoconferencing. The pilot involved thirty-two undergraduate students. A single-group pre-test/post-test quiz measured short-term learning, while the Technology Acceptance Model (TAM) measured acceptance through perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention. A single session raised the mean quiz score from 6.41 to 9.19, a within-session gain that reached statistical significance, while all four TAM constructs scored highly. Because the sample was small and confined to one institution, with neither a control group nor a follow-up, these findings are best read as early evidence of feasibility, short-term improvement, and favorable acceptance rather than as proof of comparative effectiveness. Full article

Short-fiber-reinforced composites (SFRCs) are widely used for their high strength-to-weight ratio. In the Additive Manufacturing (AM) field, Material Extrusion (MEX) processes inherently induce anisotropy, primarily due to fiber alignment along the deposition path, making printing direction and layer orientation critical for mechanical performance. In this study, specimens made of Onyx^®, a carbon short-fiber-reinforced polyamide, were fabricated by varying their orientation on the build platform, thereby producing different infill deposition directions. Each replica contained 25 layers. Two deposition strategies were investigated: a conventional alternating ±45° raster pattern and a 0°/90° configuration. Owing to the odd number of deposited layers, the latter resulted in two distinct stacking configurations, namely 0°/90° and 90°/0°, depending on the orientation of the first deposited layer. With such a strategy, it was possible to obtain configurations with a predominance of fibers either aligned with or transverse to the loading direction, depending on the orientation of the first-deposited layer. Mechanical test results were systematically compared to evaluate the influence of deposition strategy and fiber orientation on tensile performances. The effect of extrusion on fiber alignment was evaluated using Scanning Electron Microscopy (SEM). Mechanical behavior was evaluated using replicated tensile testing (five specimens per condition) and SEM-based fiber-orientation analysis. The investigation confirms the anisotropic nature of MEX-produced SFRCs. In particular, the 0°/90° configuration showed reductions of approximately 24% in tensile strength and 58% in elongation at break compared with the ±45° configuration. These results demonstrate that both extrusion-induced fiber orientation and layer-wise deposition strategy play a crucial role in defining the mechanical response of the material. Full article

Kubernetes Horizontal Pod Autoscaler (HPA) primarily relies on resource-based metrics, such as CPU utilization, which are poorly suited to capturing the latency variability of AI inference workloads. In this paper, we propose a custom-metric-driven autoscaling approach that leverages inference latency histograms as first-class scaling signals for Kubernetes HPA. The proposed framework integrates a Prometheus Operator (PO)-based observability stack with the Prometheus Adapter to expose and aggregate per-pod inference latency metrics, enabling workload-aware scaling decisions. We evaluate the approach using four mid-scale transformer-based inference services, comprising two reasoning-like and two latency-stable workloads, under high-concurrency conditions. The experiments analyze latency variation, tail behavior, and replica dynamics across multiple autoscaling policies, including variations in scale-up aggressiveness (3 pods/30 s, 3 pods/60 s, 6 pods/60 s), inference-time thresholds, and stabilization windows. Compared to CPU-based autoscaling, inference-driven policies reduce mean response time by 18–27% for reasoning-like workloads and 12–20% for stable workloads. The results show that latency-variable workloads exhibit wider tails and higher variance, indicating the need for moderately aggressive scale-up strategies to avoid long-lasting latency spikes. Overall, the findings show that inference-latency-driven custom metrics significantly improve autoscaling efficiency and stability for transformer-based inference workloads in cloud-native environments. Full article

Serverless computing has emerged as a prominent research focus in cloud computing because it provides infrastructure-transparent development and elastic resource management. However, this computing paradigm still faces the inherent challenge of cold start. Existing approaches have two major limitations: insufficient workload prediction accuracy and inefficient allocation of reusable container replicas to incoming function requests. To address these challenges, we propose a container scheduling approach based on Workload Prediction and Particle Swarm Optimization (PSO), named WPPSO. WPPSO first leverages a code-pre-trained large language model (LLM) to extract intrinsic function features and then uses a spatio-temporal fusion-based temporal neural network (STF-TNN) to predict serverless workloads. It subsequently employs a greedy algorithm to construct a high-quality initial matching state and uses PSO to refine the container scheduling strategy. Finally, WPPSO introduces a hierarchical container recycling mechanism to reduce idle resource waste. Extensive experiments show that WPPSO reduces startup latency by up to 72.2% and memory footprint by 63.4% compared with the native Knative platform. Compared with RainbowCake, WPPSO achieves a 15.6% lower mean startup latency without statistical significance and a statistically significant 31% reduction in idle memory consumption. Full article

Flexible pressure sensors with a porous architecture are highly desirable for wearable health monitoring and intelligent human–machine interaction, owing to their excellent comfort and conformability to human motion. However, conventional porous sensors often suffer from poor signal accuracy and unstable output, which limit their capability for precision sensing. To address these challenges, we designed and fabricated a flexible pressure sensor with exceptional linearity by mimicking the unique surface structure of Iron Cross Begonia (Begonia masoniana) leaves. The sensor is constructed using a readily available melamine foam as the backbone: a porous sensing scaffold is first obtained via a simple dip-coating process, and a film featuring bioinspired protrusions is fabricated by repeated replica molding. Lamination of these two components yields a stacked sensor device. Characterization demonstrates that the sensor achieves a broad pressure detection range of up to 350 kPa, with a minimum resolvable pressure of 250 Pa, and exhibits an excellent linearity of 0.999 over its entire working range (0–350 kPa). Moreover, the sensor shows stable responses under varying loading frequencies, is capable of detecting low-frequency signals, and retains its performance without notable degradation even after 5000 repeated loading-unloading cycles. In practical applications, the sensor accurately monitors flexion and extension movements of the wrist, finger, neck, and knee, capturing human motion signals with high fidelity. Furthermore, it enables information encoding and transmission through finger gestures. The proposed bioinspired structural design strategy effectively enhances the overall performance of porous pressure sensors, offering a new paradigm for the development of flexible sensing devices with promising applications in wearable health monitoring, human motion detection, and human–machine interaction. Full article

Identifying primary ceramic forming techniques is often problematic when surface traces are altered or erased by secondary shaping on the potter’s wheel, particularly in vessels combining hand-building and wheel use. This study aims to develop a quantitative, non-destructive method to distinguish wheel-throwing and wheel-coiling techniques by analyzing internal fabric features. Experimental replicas of Middle Minoan handleless conical cups (18th cent. BC), produced using wheel-throwing-off-the-hump and wheel-coiling techniques, were investigated using X-ray micro-computed tomography (µCT). Macropores were segmented from complete 3D µCT datasets and their shape preferred orientation was quantitatively assessed through ellipsoid fitting, orientation distribution functions, and pole figure analysis. The results reveal systematic and reproducible differences between the two forming techniques: wheel-coiled vessels show predominantly horizontal pore elongation, expressed as equatorial girdle textures and vertically clustered short axes, whereas wheel-thrown vessels display inclined pore orientations, forming displaced girdles and ring-like short-axis distributions. These contrasting orientation patterns reflect distinct deformation fields imposed during vessel shaping. The study demonstrates that quantitative 3D analysis of pore orientation in whole vessels provides reliable criteria for identifying ceramic forming techniques and confirms previous qualitative observations. This approach offers a robust framework for technological analysis of ceramics and can be applied to both complete vessels and suitably oriented fragments. Full article

In veterinary education, many exercises are performed on animals. Palpating the mucosa of the Ruminoreticulum in ruminants is a necessary preparatory exercise for future surgery. However, there are legal and ethical obligations to reduce the use of animals and improve animal welfare. This can be achieved using 3D models and simulators. To allow students to practice palpating the goat’s forestomach, a simulator is being developed. The aim of the present study was to produce replicas of the mucosal surface of the Ruminoreticulum for the inner lining of this simulator. Two methods were applied and compared: 3D printing and surface casting. For 3D printing, computed tomography-based virtual templates were created and printed after appropriate post-processing. For the surface cast, a negative mold of the mucosal surfaces was created using epoxy resin. The positive mucosal cast was then created using silicone. The results showed a clear advantage of surface casting compared to 3D printing. The virtual templates and 3D prints lacked fine anatomical structures. In contrast, the surface casting method yielded detailed replicas of the mucosal surfaces of Rumen and Reticulum, including even finer anatomical structures. Since the silicone casts also allowed for haptic differentiation of mucosal formations, they can be considered a suitable inner lining for the planned simulator. Full article

This paper presents a compact output-stage current-limiting architecture intended for reliable overcurrent protection in CMOS analog and mixed-signal circuits. In modern integrated systems, the output stages of blocks such as operational amplifiers, drivers, buffers, and reference circuits may be exposed to overload conditions, low-impedance loads, or short circuits that can lead to excessive power dissipation and device degradation. The proposed architecture employs scaled replicas of the output transistors together with local negative feedback to sense the delivered load current and independently limit both sinking and sourcing currents. The circuit is demonstrated by integration into a two-stage folded-cascode operational amplifier with a class-AB output stage and evaluated through circuit-level simulations in 130 nm CMOS technology. The results confirm a well-defined current limit across the supply and temperature corners that are relevant to high-reliability applications, spanning 2 V and 5 V supplies and a temperature range from −55 °C to 175 °C. The proposed current-limiting scheme constrains both pull-down and pull-up currents to approximately 9–12 mA across the investigated operating domain. Monte Carlo analysis further shows bounded dispersion and symmetric single-mode distributions, indicating predictable operation under device mismatch. These results demonstrate that the proposed architecture provides a compact and scalable solution for deterministic current limiting in reliability-critical CMOS systems. Full article

The need for reliable software for data acquisition, processing and communication with laboratory instruments, as well as for extending laboratory findings to real-scale structures, is imperative. In this context, ReplicaXLite is presented: an open-source software framework designed to facilitate and organize structural experimental testing on seismic tables. The software enables the creation of digital twin models and real-time sensor data recording. Furthermore, it allows for the processing, storage and visualization of results within a graphical interface. It features two primary modes of operation: (a) via terminal with specific Application Programming Interfaces (APIs) and (b) via a Graphical User Interface (GUI), adapting to the user’s expertise level. The software lies on top of open-source libraries like OpenSeesPy and opstool. It supports many material types, such as concrete, steel, fibers and composites, among others. Models produced by ReplicaXLite demonstrate strong agreement with experimental data across varying structural configurations. For both acceleration and displacement, the framework yielded satisfactory accuracy at the top slab with mean envelope correlations ranging from 0.91 to 0.97 and mean Pearson correlations generally between 0.83 and 0.95 for varying seismic intensities (0.1 g to 1.4 g). The numerical framework successfully captured global stiffness degradation, with Normalized Root Mean Square Errors (NRMSE) well-constrained between 2.3% and 7.9% across both acceleration and displacement response metrics. The architecture allows for the one-click execution of custom user codes, providing full access to the source code and the ability to perform live toolkit modifications via the “app.” terminal variable. Finally, it provides mid-simulation modification of the mass and elements of the model. Full article

We describe the formation of LIPSS by fs laser irradiation on polished titanium or steel samples, from which polymer replicas can be produced. The irradiation of inclined samples allows a variation in the periodicity of the LIPSS in a range between about 500 and 1000 nm, depending on the angle of incidence and the orientation of the laser polarization relative to the plane of incidence, either parallel (p-polarization) or perpendicular (s-polarization). For p-polarization, a second larger-size LIPSS feature with periodicities between about 1300 and 2200 nm is observed at medium angles. LIPSS lines are oriented perpendicular to the light polarization, except for s-polarization on steel samples, where a rotation of up to 35° is observed. In a two-step process, the LIPSS are replicated in polymers. We investigate the attachment of Schwann cells and fibroblasts seeded thereon, which show no direct dependence on the variation in the LIPSS periodicities. Full article

We study the pinning transition in a (1+1)-dimensional lattice model of a fluctuating interface interacting with a corrugated impenetrable wall. The interface is modeled as an N-step directed one-dimensional random walk on the half-line $x\ge 0$. Its interaction with the wall is described by a quenched, site-dependent, short-ranged random potential $u_j$ ($j=1,\dots ,N$), distributed according to $Q\left(u_j\right)$ and localized at $x=0$. By computing the first two disorder-averaged moments of the partition function, $⟨G_N⟩$ and $⟨G_N^2⟩$, and by analyzing the analytic structure of the resulting expressions, we derive an explicit criterion for the coincidence or distinction of the pinning transitions in annealed and quenched systems. We show that, although the transition points of the annealed and quenched systems are always different in the thermodynamic limit, for finite systems there exists a “gray zone” in which this difference is hardly detectable. Our results may help reconcile conflicting views on whether quenched disorder is marginally relevant. Full article

Gun violence in U.S. schools not only causes loss of life and physical injury but also leaves enduring psychological trauma, damages property, and results in significant economic losses. One way to reduce this loss is to detect the gun early, notify the police as soon as possible, and implement lockdown procedures immediately. In this project, a novel gun detector Internet of Things (IoT) system is developed that automatically detects the presence of a gun either from images or from gunshot sounds, and sends notifications with exact location information to the first responder’s smartphones using the Internet within a second. The device also sends wireless commands using Message Queuing Telemetry Transport (MQTT) protocol to close the smart door locks in classrooms and announce to act using public address (PA) system automatically. The proposed system will remove the burden of manually calling the police and implementing the lockdown procedure during such traumatic situations. Police will arrive sooner, and thus it will help to stop the shooter early, the injured people can be taken to the hospital quickly, and more lives can be saved. Two custom deep learning AI models are used: (a) to detect guns from image data having an accuracy of 94.6%, and (b) the gunshot sounds from audio data having an accuracy of 99%. No single gun detector device is available in the literature that can detect guns from both image and audio data, implement lockdown and make PA announcement automatically. A prototype of the proposed gunshot detector IoT system, and a smartphone app is developed, and tested with gun replicas and blank guns in real-time. Full article

Accurate immobilization is critical in head and neck (H&N) radiotherapy to ensure precise dose delivery while minimizing irradiation of surrounding healthy tissues. However, conventional thermoplastic masks cannot secure 100% replicas of the patient’s surface and are often limited by mechanical weakness, patient discomfort, and workflow inefficiencies. Recently, the best replicas of the patient’s face have been obtained by exploring personal CT or MRI scans of patients that are used for manufacturing of immobilization masks. This study aimed to design and evaluate customizable immobilization masks using acrylonitrile butadiene styrene (ABS)-based composites reinforced with bismuth oxide (Bi₂O₃) and to compare their mechanical performance against commercial thermoplastic masks. ABS and ABS/Bi₂O₃ composite filaments (5, 10, and 20 wt%) were fabricated and characterized by tensile testing. A patient-specific virtual mask was modeled and subjected to finite element analysis (FEA) under clinically relevant loading scenarios, including neck flexion and lateral bending. Results were benchmarked against two commercial thermoplastic masks. ABS and ABS-based composites exhibited significantly higher stiffness (1.7–2.5 GPa) and yield strength (20–25 MPa) compared to commercial thermoplastics (0.25–0.3 GPa, ~7 MPa; p < 0.001). FEA simulations revealed markedly reduced displacement in ABS masks (1–5 mm at 2 mm thickness; <1 mm at 4 mm thickness) relative to commercial masks, which exceeded 20 mm under lateral load. Hybrid configurations with reinforced edges further optimized rigidity while limiting material usage. Customized ABS-based immobilization masks outperform conventional thermoplastics in mechanical stability and displacement control, with the potential to reduce planning margins and improve patient comfort. In addition, ABS-based masks can be recycled, and Bi₂O₃-filled composites can be reused for printing new immobilization masks, thus contributing to a reduced amount of plastic waste. These findings support their promise as next-generation immobilization devices for precision radiotherapy, warranting further clinical validation, workflow integration and sustainable implementation within a circular economy. Full article