Search Results (182)

A transparent, end-to-end pathway from learning-level training to deployable fixed-point hardware is presented and framed as gradients to gates. A didactic XOR convolutional network is first employed so that backpropagation, post-training quantization in INT8, and fixed-point arithmetic can be made concrete and verified with exact checks. The same methodology was applied to a compact LeNet-5 case study. On the software side, the training-to-export flow was formalized, and a bit-accurate Python reference was constructed for the quantized network. On the hardware side, a synthesizable INT8 datapath was implemented in Verilog, including multiply–accumulate units, sigmoid activation stages, and per-layer requantization with rounding and saturation. Test benches are provided so that the exported weights and activations can be ingested, and layer-wise matches can be reported. A co-simulation harness was used to coordinate framework inference, quantization, file conversion, HDL simulation, and regression checks, which enabled deterministic comparisons of the activations, partial sums and outputs. The complete loop was mapped to Artix-7 on the CMOD A7 development board, and the resource usage, maximum clock frequency, inference latency, and throughput were determined. The approach aligns with an educational HDL-to-Caffe pipeline by using reusable parameterized Verilog primitives for convolution, pooling, activation, and fully connected layers, training in Colab with AccDNN, Caffe, quantization, and an automated bit-for-bit verification regime before FPGA synthesis. Methodological contributions are provided, including a minimal and auditable XOR CNN that exposes scales, shifts, and saturation; a practical quantization recipe with INT32 accumulation and unit tests that guarantee agreement within one least significant bit between RTL and the INT8 reference; and a scalable mapping to LeNet-5 using a row-stationary and line-buffered dataflow on an Artix-7 FPGA. Empirical evidence shows feasibility at 100 MHz with representative utilization, millisecond-scale latency and zero mismatches across large test sets, which validates the quantization configuration and the verification strategy. Full article

Gas Metal Arc Welding (GMAW) is widely adopted in automated manufacturing industries where the accurate prediction of thermal fields and welding-induced distortions is essential to ensure joint integrity of the parts; however, finite element modeling, as the most reliable non-destructive predictive approach, remains time-consuming and highly user-specialized. This work presents an automated and low computational cost thermo-mechanical finite element methodology implemented in Ansys Parametric Design Language (APDL) for the parametric analysis of GMAW T-joints, integrating automated geometry generation, meshing, heat source implementation, and thermo-mechanical modeling for different beam and weld seam dimensions under continuous or intermittent single-pass configurations. A volume element selection strategy is introduced to limit heat input calculations to the active weld pool region, achieving up to a 50% computational time reduction while maintaining high predictive accuracy, in contrast with conventional and partial selection methods. Overall script performance was validated through temperature and displacement comparisons between the numerical and experimental results of two T-joint configurations using SM490A structural steel specimens. The results demonstrate that the developed macro provides a useful tool for automated thermo-mechanical welding analysis, significantly reducing model preparation effort while enabling the evaluation of parametric T-joint geometries and welding conditions with a low computational cost focus. Full article

In recent years, the concentration risk of the Internet has intensified, with traffic being concentrated in the hands of a few service providers. However, existing research focuses on the client-side perspective and lacks a centralized measurement of the public resolver in terms of operation strategies and software functionality implementation. Therefore, we propose a dual-end measurement framework to measure the public resolver from both the client and authoritative perspectives, stably matching the active nodes of the public resolver pool with their providers, and using probes to evaluate the diversity of its functionality implementation and configuration schemes. The study analyzed the operation plans of different suppliers and revealed the regional nature of the public resolver service scope, enabling the localization of specific resolver instances, thereby achieving a concentration assessment for specific suppliers. In actual measurements from the perspectives of 5 countries and regions using 14 probes on 4 large public resolvers and 7 regional resolvers, we found that although anycast provides geographical redundancy, the software implementation logic of the public resolver cluster in a single region tends to be somewhat homogeneous. The characteristic entropy of Google in five regions was 1.435, while in the Silicon Valley region of the United States, there was only one software implementation. Full article

Untargeted metabolomics faces significant challenges in standardization due to variability introduced by sample preparation and analytical workflows. We systematically evaluated the impact of biological matrices, extraction protocols, and chromatographic configurations to establish a mechanism-informed framework aimed at improving reproducibility in large-scale clinical and epidemiological studies. Three extraction protocols were compared using an in-house pooled heparin plasma: monophasic protein precipitation with isopropanol (IPA), methanol:acetonitrile (MeOH:ACN), and a modified Matyash biphasic method. The most reproducible protocol was then applied to four blood matrices. Samples were analysed using untargeted metabolomics on hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) HPLC columns, with mass spectrometry data processed using Compound Discoverer. Both IPA and MeOH:ACN extractions achieved over 80% of features with coefficient of variation (CV%) ≤ 30% for both RP and HILIC, whereas the Matyash method showed higher variability, with a larger proportion of metabolites exhibiting CV% > 30%. Across matrices, RP chromatography detected over 80% of metabolites with CV% < 30%, while HILIC showed higher variability, with at least 20% of metabolites above this threshold. Among matrices, serum and heparin plasma outperformed EDTA and citrate in reproducibility. We propose a standardized workflow in which monophasic extractions combined with RP chromatography maximize reproducibility and metabolite coverage, minimizing methodological artefacts and providing a reliable framework for robust biological discovery in large-scale untargeted metabolomics studies. Full article

Oil spills represent a severe threat to aquatic ecosystems, requiring rapid and reliable detection methods to support environmental response. Hyperspectral imaging (HSI) offers high spectral resolution for distinguishing hydrocarbon types, but its effective use depends on the performance and robustness of deep learning (DL) models, especially under data-limited conditions. This study presents a systematic evaluation of convolutional neural network (CNN) configurations for oil spill classification in visible-near-infrared (VNIR) hyperspectral data, examining the influence of architectural depth and hyperparameters such as the number of convolutional kernels, neuron density, and dropout rate. Two architectures were tested across 54 configurations and two training set sizes (259 and 518 samples). Results show that a compact architecture with an additional max pooling layer achieved near-perfect accuracy (>0.99) with reduced complexity and greater robustness, outperforming its deeper counterpart. Importantly, this study reveals that under small-sample scenarios, optimal performance can still be achieved by carefully balancing model capacity, favouring moderate convolutional depth and high neuron density, while avoiding over-regularisation. These findings provide practical guidance for designing efficient CNNs for UAV-based oil spill monitoring and lay the groundwork for future integration into local real-time processing pipelines and transfer learning applications. Full article

Institutions play a central role in shaping entrepreneurial behavior, yet much of the existing literature, even with the foundational insights of institutional economists such as Veblen, Mitchell, Commons, Coase, Ostrom, Williamson, and North, continues to view institutions as monolithic entities rather than as differentiated governance systems. This study addresses this gap by reconceptualizing institutions as multi-branch governance architectures in which legislative, executive, and judicial mechanisms interact to shape entrepreneurial outcomes, particularly in volatile emerging economies. The research asks how these disaggregated governance branches, mediated by institutional quality and external shocks, jointly influence entrepreneurial activity. Using Global Entrepreneurship Monitor (GEM) microdata for Iran over the period 2008–2020, merged with governance indicators and shock variables including sanctions and COVID-19, we employ pooled logistic regression to estimate the effects of governance functions and their policy mix interactions on Total Entrepreneurial Activity. The results show that executive policy quality has the strongest positive association with entrepreneurship, legislative coherence strengthens opportunity-driven activity, and judicial inefficiencies suppress entrepreneurial engagement by increasing uncertainty. Interaction effects further reveal that misalignment among governance branches weakens entrepreneurial activity, while coherent policy mixes mitigate the negative impact of external shocks. By integrating conceptual synthesis with empirical evidence, the study advances institutional theory, clarifies deficiencies in prevailing models, and demonstrates that entrepreneurial dynamism depends on the configuration and coordination of governance branches rather than on aggregate institutional scores. These insights provide policymakers with actionable guidance for designing coherent, adaptive, and resilient entrepreneurship-supporting ecosystems. Full article

This study examines multi-celebrity deployment as a destination branding practice, using Seoul as an empirical case. The analysis draws on 172 official tourism promotional videos released by the Seoul Tourism Organization between 2011 and 2025, featuring 67 identifiable celebrities and 438 destination references. A qualitative content analysis examines how celebrity endorsement is organized as a branding mechanism, focusing on who appears, what is represented, and how representations are communicated across media formats over time. The findings show that Seoul’s tourism promotion operates through a structured multi-celebrity branding system in which multiple endorsers are coordinated across campaigns and periods. Endorser selection is anchored in Hallyu-affiliated celebrities who function as primary carriers of destination meaning, while emerging, non-Hallyu, and heritage-linked figures occupy complementary roles that broaden representational scope and reduce reliance on individual figures. Celebrity endorsement continues to emphasize major and symbolically dense attractions, while also extending visibility to everyday neighborhoods and locally oriented urban landscapes. Long-term ambassador-led campaigns coexist with travel vlogs and other creative video formats, enabling variation in narrative tone and experiential framing. Theoretically, the study extends celebrity endorsement research by conceptualizing multi-celebrity deployment as a coordinated branding system. Practically, the findings show how destination marketing organizations can mobilize a broad pool of celebrity resources to structure endorsement portfolios over time. Coordinated use of celebrities with different levels of familiarity supports wider spatial representation, enables ongoing narrative renewal, and maintains promotional continuity across changing media environments. This configuration is most applicable to destinations with strong cultural visibility and an established celebrity ecosystem, and may be less transferable to destinations with limited access to influential figures. Full article

Background: Robot-assisted partial nephrectomy (RAPN) can be done using either a three-arm or four-arm configuration. However, the evidence comparing the perioperative, functional, and oncological outcomes between these two approaches is inconsistent. Therefore, we aimed to quantitatively compare the outcomes of three-arm versus four-arm RAPN. Methods: A comprehensive search of multiple databases, including PubMed, Embase, Scopus, Web of Science, and Cochrane, was conducted, adhering to the PRISMA guidelines. Studies comparing three-arm and four-arm RAPN were included. Continuous outcomes were assessed using mean differences (MD), and dichotomous outcomes were evaluated using risk ratios (RR). The ROBINS-I tool was used to determine the risk of bias. Results: Five studies that met the selection criteria were included in the final review and analysis. The pooled analyses demonstrated no significant difference in estimated blood loss, warm ischemia time, transfusion rates, overall complications, major complications, or positive surgical margins between the three-arm and four-arm RAPN. Although the initial primary analysis showed a shorter length of stay within the three-arm RAPN technique, the sensitivity analysis did not reflect this finding. Conclusions: The three-arm and four-arm RAPN demonstrated comparable perioperative, functional, and oncologic outcomes. As both techniques appear to be effective, the choice of configuration may be decided by the institutional resources, case complexity, and the surgeon’s preference. Full article

This study examines the joining characteristics of Cu foil stacks to lead tabs using green-laser welding in the main-welding step of a sequential welding process for lithium-ion pouch cells. The influence of lap configuration, line and wobble seam strategies, and process parameters was systematically investigated in terms of bead morphology, mechanical performance, metallurgical characteristics, and electrical resistance. Under the present line-welding parameter window (2.0 kW, 100–200 mm/s), humping, pinholes, and porosity were observed, particularly in the upper lead-tab configuration, which is attributed to melt-pool/keyhole instability under the applied conditions. Wobble welding effectively suppressed these defects in the foil-stack configuration by promoting stable melt flow and efficient bubble expulsion. Mechanical tests revealed that the wobble-based seam strategy achieved a maximum tensile–shear load of approximately 1.28 kN at a wobble amplitude of 0.8 mm. Fracture analysis confirmed a transition from seam-type interfacial failure in line welding to ductile tearing in the heat-affected zone with wobble welding. In electrical performance, wobble welding reduced resistance to as low as 45 µΩ at a wobble amplitude of 1.2 mm, while line welding yielded higher and scattered values. These results should be interpreted as the combined outcome of the wobble-based seam strategy (beam oscillation together with overlapped stitch welding at a lower travel speed) under the present processing windows. A strictly matched A/B comparison at identical linear energy density and seam layout will be investigated in future work to isolate the effect of oscillation. Full article

Sorghum (Sorghum bicolor L.)–soybean (Glycine max L.) intercropping produces a significant yield advantage over monocropping. However, a comprehensive synthesis is lacking to quantify yield benefits. This article provides a systematic review, a primary meta-analysis, and an exploratory meta-analysis to quantify the land productivity advantage of sorghum–soybean intercropping, explore the impact of planting configuration, and critically assess the methodological robustness of the existing literature. A random-effect meta-analysis of Land Equivalent Ratio (LER), with a primary analysis on studies with reported and calculated variance only (n = 23 treatments from six studies) and an exploratory analysis on the full dataset, which includes studies with imputed variances (n = 103 treatments from 21 studies). Group-specific analyses examined row configurations. The exploratory meta-analysis showed a pooled LER of 1.31 (95% CI: 1.25–1.36), suggesting an approximately 31% average land productivity gain (LER > 1). Configuration beyond a 1:1 row ratio showed potential for higher yield gains (LER = 1.43 for 2:2). Critically, over 75% of studies required variance data imputation. The analysis, limited to studies with reported or calculated variance data, showed a higher LER of 1.55 (95% CI: 1.41–1.69), but with extreme heterogeneity (I² = 96.2%). This highlights substantial outcome variability and inconsistent statistical reporting in the literature, limiting robust synthesis. Future research must prioritize long-term, well-replicated experiments with reported standardized variance and configuration evaluations to enable precise, locally relevant intercropping recommendations. Full article

This paper proposes a robust convolutional neural network (CNN) architecture for human activity recognition (HAR) using smartphone accelerometer data, evaluated on the WISDM dataset. We introduce two novel pooling mechanisms—Pooling A (Extrema Contrast Pooling (ECP)) and Pooling B (Center Minus Variation (CMV))—that enhance feature discrimination and noise robustness. ECP emphasizes sharp signal transitions through a nonlinear penalty based on the squared range between extrema, while CMV Pooling penalizes local variability by subtracting the standard deviation, improving resilience to noise. Input data are normalized to the [0, 1] range to ensure bounded and interpretable pooled outputs. The proposed framework is evaluated in two separate configurations: (1) a 1D CNN applied to raw tri-axial sensor streams with the proposed pooling layers, and (2) a histogram-based image encoding pipeline that transforms segment-level sensor redundancy into RGB representations for a 2D CNN with fully connected layers. Ablation studies show that histogram encoding provides the largest improvement, while the combination of ECP and CMV further enhances classification performance. Across six activity classes, the 2D CNN system achieves up to 96.84% weighted classification accuracy, outperforming baseline models and traditional average pooling. Under Gaussian, salt-and-pepper, and mixed noise conditions, the proposed pooling layers consistently reduce performance degradation, demonstrating improved stability in real-world sensing environments. These results highlight the benefits of redundancy-aware pooling and histogram-based representations for accurate and robust mobile HAR systems. Full article

In the Internet of Things (IoT), ultra-wideband (UWB) plays an essential role in localization and navigation. However, in indoor environments, UWB signals are often blocked by obstacles, leading to non-line-of-sight (NLOS) propagation. Thus, reliable line-of-sight (LOS)/NLOS identification is essential for reducing errors and enhancing the robustness of localization. This paper focuses on a single-anchor UWB configuration and proposes a temporal deep learning framework that jointly exploits two-way ranging (TWR) and angle-of-arrival (AOA) measurements for LOS/NLOS identification. At the core of the model is a temporal convolutional network (TCN) augmented with a self-attentive pooling mechanism, which enables the extraction of dynamic propagation patterns and temporal contextual information. Experimental evaluations on real-world measurement data show that the proposed method achieves an accuracy of 96.65% on the collected dataset and yields accuracies ranging from 88.72% to 93.56% across the three scenes, outperforming representative deep learning baselines. These results indicate that jointly exploiting geometric and temporal information in a single-anchor configuration is an effective approach for robust UWB indoor positioning. Full article

Three different Gas Tungsten Arc Welding methods—DC single electrode, DC double electrode, and PC double electrode—were analyzed using SS304 steel as the base material. Numerical models were developed to simulate the arc plasmas and calculate heat flux, current density, and wall shear stress on the surface of the workpiece. These data were used as input to simulate the weld pools across all three configurations. Experimental validation showed a good agreement with the numerical results. In the double-electrode setup, electromagnetic interaction caused the arcs to deflect, resulting in an 8% reduction in the maximum heat flux and a 4% decrease in the maximum current density. Marangoni stress had a notable effect on the weld pool shape, creating a -shaped pool with the stationary single-electrode setup, whereas the double-electrode setup produced a -shaped pool after 2 s. In the moving weld pool configurations, the sizes of the pools were maximum at the trailing electrodes. The pool was 1.7 mm deep and 5.6 mm wide in DC double- and 1.4 mm deep and 5.4 mm wide in PC double-electrode configurations. The pool depth and width were only 1.0 mm and 4.2 mm when a DC single-electrode setup was used. Comparing the three methods, the DC double-electrode setup produced the largest pool size. The findings of this research offer guidance for enhancing different arc settings and electrode arrangements to attain the intended welding quality and performance. Full article

Additive manufacturing (AM) has emerged as a pivotal technology in component fabrication, renowned for its capabilities in freeform fabrication, material efficiency, and integrated design-to-manufacturing processes. As a critical branch of AM, metal additive manufacturing (MAM) has garnered significant attention for producing metal parts. However, process anomalies during MAM can pose safety risks, while internal defects in as-built parts detrimentally affect their service performance. These concerns underscore the necessity for robust in-process monitoring of both the MAM process and the quality of the resulting components. This review first delineates common MAM techniques and popular in-process monitoring methods. It then elaborates on the fundamental principles of acoustic emission (AE), including the configuration of AE systems and methods for extracting characteristic AE parameters. The core of the review synthesizes applications of AE technology in MAM, categorizing them into three key aspects: (1) hardware setup, which involves a comparative analysis of sensor selection, mounting strategies, and noise suppression techniques; (2) parametric characterization, which establishes correlations between AE features and process dynamics (e.g., process parameter deviations, spattering, melting/pool stability) as well as defect formation (e.g., porosity and cracking); and (3) intelligent monitoring, which focuses on the development of classification models and the integration of feedback control systems. By providing a systematic overview, this review aims to highlight the potential of AE as a powerful tool for real-time quality assurance in MAM. Full article

Shielding gas flow in metal Laser Powder Bed Fusion (PBF-LB/M) removes ejecta and byproducts from the build plate and the optical path, preventing laser interference and loss of part quality. Previous research conducted on an EOS M290 used Magnetic Resonance Velocimetry (MRV) to resolve the three-component, three-dimensional flow field and identified a region of recirculation below the lower vent. The present work demonstrates the correction of this recirculation through practical chamber modifications: raising the build platform and optical assembly, and redesigning the recoater and the lower inlet to reflect the new build plate position. MRV was leveraged to generate flow distribution maps and velocity profiles of the modified configuration, showing a marked change in the overall flow field. Plate scans across the build area characterized the impact of gas flow improvements on process response. Specimens from the original configuration showed progressively shallower melt pools toward the vent, whereas those from the modified configuration exhibited a ~10% higher average melt pool depth in the region most affected by prior recirculation. Qualification artifacts built under both conditions provided preliminary evidence of improved part performance via enhanced gas flow distribution. These results highlight potential benefits of uniform gas flow distribution across the build plate through simple EOS M290 chamber modifications. Full article