Search Results (249)

Bilayer transition metal dichalcogenides (TMDs) are promising materials for next-generation field-effect transistors (FETs) due to their atomically thin structure and favorable transport properties. In this study, we employ density functional theory (DFT) to compute the electronic band structures and phonon dispersions of bilayer WS${}_2$, WSe${}_2$, and MoS${}_2$, and the electron-phonon scattering rates using the EPW (electron-phonon Wannier) method. Carrier transport is then investigated within a semiclassical full-band Monte Carlo framework, explicitly including intrinsic electron-phonon scattering, dielectric screening, scattering with hybrid plasmon–phonon interface excitations (IPPs), and scattering with ionized impurities. Freestanding bilayers exhibit the highest mobilities, with hole mobilities reaching 2300 cm${}^2$/V·s in WS${}_2$ and 1300 cm${}^2$/V·s in WSe${}_2$. Using hBN as the top gate dielectric preserves or slightly enhances mobility, whereas HfO${}_2$ significantly reduces transport due to stronger IPP and remote phonon scattering. Device-level simulations of double-gate FETs indicate that series resistance strongly limits performance, with optimized WSe${}_2$ pFETs achieving ON currents of 820 A/m, and a 10% enhancement when hBN replaces HfO${}_2$. These results show the direct impact of first-principles electronic structure and scattering physics on device-level transport, underscoring the importance of material properties and the dielectric environment in bilayer TMDs. Full article

Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a specific deep learning backbone that addresses breast ultrasound classification needs. The proposed architecture combines a convolutional stem with learnable 2D positional embeddings, followed by a hybrid stage that unites MobileViT blocks with spatial gating and convolutional residuals and two progressively global stages that use a depth-aware composition of three components: (1) UltraScanUnit (a state-space module with selective scan gated convolutional residuals and low-rank projections), (2) ConvAttnMixers for spatial channel mixing, and (3) multi-head self-attention blocks for global reasoning. This research includes a detailed ablation study to evaluate the individual impact of each architectural component. The results demonstrate that UltraScanNet reaches 91.67% top-1 accuracy, a precision score of 0.9072, a recall score of 0.9174, and an F1-score of 0.9096 on the BUSI dataset, which make it a very competitive option among multiple state-of-the-art models, including ViT-Small (91.67%), MaxViT-Tiny (91.67%), MambaVision (91.02%), Swin-Tiny (90.38%), ConvNeXt-Tiny (89.74%), and ResNet-50 (85.90%). On top of this, the paper provides an extensive global and per-class analysis of the performance of these models, offering a comprehensive benchmark for future work. The code will be publicly available. Full article

In this study, the nonlinearity characteristics of bilayer graphene field-effect transistors (Bi-GFETs) are analyzed by using a small-signal equivalent circuit. The static nonlinearity is determined by applying mathematical operation on the drain current equation of Bi-GFETs. Furthermore, the closed expressions for the second- and third-order harmonic distortion (HD) and the intermodulation (IM) distortion of the second- and third-order for Bi-GFETs are analyzed graphically. Dynamic nonlinearity is studied and illustrated in the results by examining the input and output characteristics; i.e., the drain current versus the negative drain to the source voltage and the transfer characteristic curve at various gate voltages controlled by both the top gate as well as the back gate. The characteristic behavior of the gate voltage in Bi-GFETs at short channel lengths is observed and compared; that is, the characteristic curves exhibits strong nonlinearity, with a low power point with some kinks at high gate biasing and a constant linear region at low gate biasing. The quantitative values of the second-order harmonic distortion (HD) and intermodulation distortion (IM) of the proposed analytical model are −40 dB and −45 dB. Quantitative and qualitative outcomes of the characteristics of Bi-GFETs are compared with existing experimental data, which is available in the literature. Full article

Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials. Full article

Empty container depot (ECD) design significantly impacts maritime terminal efficiency, yet traditional evaluation approaches assess limited operational factors, constraining comprehensive performance optimization. This study develops an integrated discrete event simulation (DES) framework that simultaneously evaluates lifting equipment utilization, truck turnaround times, and potential collisions to support terminal decision-making. This study combines operational efficiency metrics with safety analytics for non-automated ECDs using Top Lifters and Reach Stackers. Additionally, a regression analysis examines efficiency metrics’ effect on safety risk. A case study at a Chilean multipurpose terminal reveals performance trade-offs between indicators under different operational scenarios, identifying substantial efficiency disparities between dry and refrigerated container operations. An analysis of four distinct collision zones with varying historical risk profiles showed the gate area had the highest potential collisions and a strong regression correlation with efficiency metrics. Similar models showed a poor fit in other conflict zones, evidencing the necessity for dedicated safety indicators complementing traditional measures. This integrated approach quantifies interdependencies between safety and efficiency metrics, helping terminal managers optimize layouts, expose traditional metric limitations, and reduce safety risks in space-constrained maritime terminals. Full article

This study presents a photoresist-free patterning method for solution-processed indium zinc oxide (IZO) thin films using two photochemical exposure techniques, namely pulsed ultraviolet (UV) light and UV-ozone, and a plasma-based method using oxygen (O₂) plasma. Pulsed UV light delivers short, high-intensity flashes of light that induce localised photochemical reactions with minimal thermal damage, whereas UV-ozone enables smooth and uniform surface oxidation through continuous low-pressure UV irradiation combined with in situ ozone generation. By contrast, O₂ plasma generates ionised oxygen species via radio frequency (RF) discharge, allowing rapid surface activation, although surface damage may occur because of energetic ion bombardment. All three approaches enabled pattern formation without the use of conventional photolithography or chemical developers, and the UV-ozone method produced the most uniform and clearly defined patterns. The patterned IZO films were applied as active layers in bottom-gate top-contact thin-film transistors, all of which exhibited functional operation, with the UV-ozone-patterned devices exhibiting the most favourable electrical performance. This comparative study demonstrates the potential of photochemical and plasma-assisted approaches as eco-friendly and scalable strategies for next-generation IZO patterning in electronic device applications. Full article

This research article examines the CO₂ laser cutting performance of Fused Filament Fabricated Acrylonitrile Styrene Acrylate (ASA) thermoplastics by analyzing the influence of plate thickness, laser power, and cutting speed on four quality characteristics: surface roughness (Ra), top kerf width (Top KW), bottom kerf width (Bottom KW), and bottom heat-affected zone (Bottom HAZ). Forty-five experiments were conducted using five thickness levels, three power levels, and three cutting speeds. To model and predict these outputs, seven machine learning approaches were employed: Autoencoder, Autoencoder–Gated Recurrent Unit, Autoencoder–Long Short-Term Memory, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression, and Linear Regression. Among them, XGBoost yielded the highest accuracy across all performance metrics. Analysis of Variance results revealed that Ra is mainly affected by plate thickness, Bottom KW by cutting speed, and Bottom HAZ by power, while Top KW is influenced by all three parameters. The study proposes an effective prediction framework using multi-output modeling and hybrid deep learning, offering a data-driven foundation for process optimization. The findings are expected to support intelligent manufacturing systems for real-time quality prediction and adaptive laser post-processing of engineering-grade thermoplastics such as ASA. This integrative approach also enables a deeper understanding of nonlinear dependencies in laser–material interactions. Full article

The construction of marine ranching is a crucial component of China’s Blue Granary strategy, yet the fragmented knowledge system in marine ranching equipment impedes intelligent management and operational efficiency. This study proposes the first knowledge graph (KG) framework tailored for marine ranching equipment, integrating hybrid ontology design, joint entity–relation extraction, and graph-based knowledge storage: (1) The limitations in existing KG are obtained through targeted questionnaires for diverse users and employees; (2) A domain ontology was constructed through a combination of the top-down and the bottom-up approach, defining seven key concepts and eight semantic relationships; (3) Semi-structured data from enterprises and standards, combined with unstructured data from the literature were systematically collected, cleaned via Scrapy and regular expression, and standardized into JSON format, forming a domain-specific corpus of 1456 annotated sentences; (4) A novel BERT-BiGRU-CRF model was developed, leveraging contextual embeddings from BERT, parameter-efficient sequence modeling via BiGRU (Bidirectional Gated Recurrent Unit), and label dependency optimization using CRF (Conditional Random Field). The TE + SE + R_i + BMESO tagging strategy was introduced to address multi-relation extraction challenges by linking theme entities to secondary entities; (5) The Neo4j-based KG encapsulated 2153 nodes and 3872 edges, enabling scalable visualization and dynamic updates. Experimental results demonstrated superior performance over BiLSTM-CRF and BERT-BiLSTM-CRF, achieving 86.58% precision, 77.82% recall, and 81.97% F1 score. This study not only proposes the first structured KG framework for marine ranching equipment but also offers a transferable methodology for vertical domain knowledge extraction. Full article

Applying Field Programmable Gate Array (FPGA) technology in cloud infrastructure and heterogeneous computations is of great interest today. FPGA as a Service assumes that the programmable logic device (PLD) is used as a remote (available over the Internet) service with an FPGA silicon chip on board. During the prototyping of FPGA-based projects within modern design flow, it is necessary to consider the processing delays caused by various factors, including the delay of data transfer between the kernel and host computer, limited clock frequency, and multiple parallel-running FPGA accelerator cards. To address these challenges, three techniques are proposed to reduce the required modification efforts and improve project performance. Based on the proposed models, the analytical evaluation of the functioning process of FPGA as a Service is performed to determine possibilities of improving productivity and reducing the response time. The practical experience of porting FPGA projects to new integrated environments is considered. The evaluation of the response time of FPGA as a Service using the queueing theory is proposed. It is shown that scaling and parallelization at the top level of project hierarchy, pipelining, and parameterization allow for the effective deployment of such FPGA systems for data centers and cloud infrastructures. The proposed techniques and models allow for an evaluation of the performance and response time of FPGA as a Service for formulating recommendations to improve technical characteristics. Full article

Additive manufacturing, particularly Fused Filament Fabrication (FFF), provides notable advantages such as design flexibility and efficient material usage. However, components produced via FFF often exhibit suboptimal surface quality and dimensional inaccuracies. Acrylonitrile Butadiene Styrene (ABS), a widely used thermoplastic in FFF applications, commonly necessitates post-processing to enhance its surface finish and dimensional precision. This study investigates the effects of CO₂ laser cutting on FFF-printed ABS plates, focusing on surface roughness, top and bottom kerf width, and bottom heat-affected zone. Forty-five experimental trials were conducted using different combinations of plate thickness, cutting speed, and laser power. Measurements were analysed statistically, and analysis of variance was applied to determine the significance of each parameter. To enhance prediction capabilities, seven machine learning models—comprising traditional (Linear Regression and Support Vector Regression), ensemble (Extreme Gradient Boosting and Random Forest), and deep learning algorithms (Long Short-Term Memory (LSTM), LSTM-Gated Recurrent Unit (LSTM-GRU), LSTM-Extreme Gradient Boosting (LSTM-XGBoost))—were developed and compared. Among these, the LSTM-GRU model achieved the highest predictive performance across all output metrics. Results show that cutting speed is the dominant factor affecting cutting quality, followed by laser power and thickness. The proposed experimental-computational approach enables accurate prediction of laser cutting outcomes, facilitating optimisation of post-processing strategies for 3D-printed ABS parts and contributing to improved precision and efficiency in polymer-based additive manufacturing. Full article

Multimodal emotion recognition faces substantial challenges due to the inherent heterogeneity of data sources, each with its own temporal resolution, noise characteristics, and potential for incompleteness. For example, physiological signals, audio features, and textual data capture complementary yet distinct aspects of emotion, requiring specialized processing to extract meaningful cues. These challenges include aligning disparate modalities, handling varying levels of noise and missing data, and effectively fusing features without diluting critical contextual information. In this work, we propose a novel Mixture of Experts (MoE) framework that addresses these challenges by integrating specialized transformer-based sub-expert networks, a dynamic gating mechanism with sparse Top-k activation, and a cross-modal attention module. Each modality is processed by multiple dedicated sub-experts designed to capture intricate temporal and contextual patterns, while the dynamic gating network selectively weights the contributions of the most relevant experts. Our cross-modal attention module further enhances the integration by facilitating precise exchange of information among modalities, thereby reinforcing robustness in the presence of noisy or incomplete data. Additionally, an auxiliary diversity loss encourages expert specialization, ensuring the fused representation remains highly discriminative. Extensive theoretical analysis and rigorous experiments on benchmark datasets—the Korean Emotion Multimodal Database (KEMDy20) and the ASCERTAIN dataset—demonstrate that our approach significantly outperforms state-of-the-art methods in emotion recognition, setting new performance baselines in affective computing. Full article

With the continuous scaling of CMOS technology, stress engineering has become increasingly critical at advanced technology nodes, especially in tall and narrow FinFET structures. Asymmetric layout environments (such as dual-Fin structures or poly cuts) can introduce stress imbalance originating from shallow trench isolation (STI), which in turn affects device performance. In this study, TCAD simulations were performed on n-type FinFETs representative of the 10 nm technology node, with a physical gate length of 20 nm, to investigate the correlation between asymmetric stress and device drive current. As the Fin width decreases, the asymmetric stress from STI induces noticeable performance fluctuations, with the mobility enhancement under saturation bias reaching a maximum of 8.42% at W = 6 nm. Similarly, as the Fin body angle deviates from 90° and the Fin top narrows, with W_top = 6 nm and W_bottom = 8 nm, the mobility enhancement peaks at 7.65%. The simulation results confirm that STI-induced asymmetric stress has a significant impact on the Fin sidewall channel, while its effect on the top channel is minimal. To mitigate these effects, CESL stress engineering is proposed as an effective solution to amplify the top channel current, thereby reducing the influence of asymmetric stress on device performance. A CESL stress of 2.0 GPa is shown to improve device stability by approximately 20%. Full article

Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behaviors, along with the high dimensionality, sparsity, and complex noise in the data. To address these issues, this paper proposes a Heterogeneous Graph Attention Network (GEVEHGAN) model based on Lite Gate Recurrent Unit (Lite-GRU) embedding and Variational Autoencoder (VAE) enhancement. The model constructs a heterogeneous graph with two types of nodes and three types of edges; combines Skip-Gram and Lite-GRU to learn Point of Interest (POI) and user node embeddings; introduces VAE for dimensionality reduction and denoising of the embeddings; and employs edge-level attention mechanisms to enhance information propagation and feature aggregation. Experiments are conducted on the publicly available Foursquare dataset. The results show that the GEVEHGAN model outperforms other comparative models in evaluation metrics such as AUC, AP, and Top@K accuracy, demonstrating its superior performance in the friend link prediction task. Full article

This paper presents two cryogenic low-noise amplifiers (LNAs) based on the WIN’s 0.18 $\mathrm{\mu }$m gate length gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) process designed for radio telescope receivers. Discrete transistors with gate peripheries spanning 50–600 $\mathrm{\mu }$m were DC-characterized at 290 K and 15 K, respectively. The LNAs underwent on-chip noise characterization under 15 K using a Y-factor measurement setup, which integrated a calibrated noise source and a noise figure analyzer. This approach directly quantified the noise temperature—critical metrics for radio telescope receiver front-ends. The top-performing LNA variant identified through on-chip characterization was packaged and evaluated in a cryogenic test-bed. This LNA, spanning a bandwidth of 0.3–15 GHz, demonstrated a gain of 26 dB and a minimum noise temperature of 6 K when operated at an ambient temperature of 15 K. In contrast, a second LNA architecture, tested solely on-chip, demonstrated a gain of 30 dB and a minimum noise temperature of 15 K across the 0.3–7 GHz range. Full article

In this paper, we design a frequency reconfigurable antenna for terahertz communication. The antenna is based on a Yagi design, with the main radiating elements being a pair of dipole antennas printed on the top and bottom of a dielectric substrate, respectively. The director and reflector elements give the antenna end-fire characteristics. The ends of the two arms of the dipole are constructed by staggered metal and graphene parasitic patches. By utilizing the effect of gate voltage on the conductivity of graphene, the equivalent length of the dipole antenna arms are altered and thereby adjust the antenna’s operating frequency. The proposed reconfigurable hybrid Yagi–Uda antenna can operate in five frequency bands separately at a peak gain of 4.53 dB. This reconfigurable antenna can meet the diverse requirements of the system without changing its structure and can reduce the size and cost while improving the performance. Full article