Search Results (677)

In hardware/software (HW/SW) partitioning, the most commonly established objectives are execution time, power consumption, and hardware area. Surprisingly, memory usage, a critical resource in embedded systems, has received limited attention as a primary optimization objective. Moreover, the few studies that consider memory rarely provide an explicit, design-time estimation method. This work proposes a methodology for obtaining memory usage as a design metric, along with an objective function tailored to evaluate memory usage in systems-on-chip featuring a hard processor core and a Field-Programmable Gate Array suitable for a HW/SW partitioning problem. To validate the proposed methodology, HW/SW partitioning was carried out for a PD-type fuzzy control algorithm targeting a DC motor. The optimization problem was solved using the Non-dominated Sorting Genetic Algorithm II. The results demonstrate the feasibility and accuracy of the proposed approach, achieving more than 97.5% accuracy in predicting memory and hardware resource consumption. Additionally, the functional performance of the selected partition configuration was validated in real-time, where the tracking of different reference signals for the velocity of the motor was successfully achieved. Full article

Background: Endobronchial ultrasound-guided transbronchial biopsy (EBUS-TBB) is a valuable technique for diagnosing peripheral pulmonary lesions (PPLs). Although computer-aided diagnostic (CAD) systems have been explored for EBUS interpretation, most rely on manually selected 2D static frames and overlook temporal dynamics that may provide important cues for differentiating benign from malignant lesions. This study aimed to develop an artificial intelligence model that incorporates temporal modeling to analyze EBUS videos and improve lesion classification. Methods: We retrospectively collected EBUS videos from patients undergoing EBUS-TBB between November 2019 and January 2022. A dual-path 3D convolutional network (SlowFast) was employed for spatiotemporal feature extraction, and contrastive learning (SwAV) was integrated to enhance model generalizability on clinical data. Results: A total of 465 patients with corresponding EBUS videos were included. On the validation set, the SlowFast + SwAV_Frame model achieved an AUC of 0.857, accuracy of 82.26%, sensitivity of 93.18%, specificity of 55.56%, and F1-score of 88.17%, outperforming pulmonologists (accuracy 70.97%, sensitivity 77.27%, specificity 55.56%, F1-score 79.07%). On the test set, the model achieved an AUC of 0.823, accuracy of 76.92%, sensitivity of 84.85%, specificity of 63.16%, and F1-score of 82.35%. The proposed model also demonstrated superior performance compared with conventional 2D architectures. Conclusions: This study introduces the first CAD framework for real-time malignancy classification from full-length EBUS videos, which reduces reliance on manual image selection and improves diagnostic efficiency. In addition, given its higher accuracy compared with pulmonologists’ assessments, the framework shows strong potential for clinical applicability. Full article

Reinforced concrete (RC) structural walls are essential for ensuring adequate lateral stiffness and strength in buildings located in seismic regions. However, many older structures incorporate nonconforming walls constructed with low-strength concrete, plain longitudinal reinforcement, and insufficient boundary confinement, and experimental data on such systems remain limited. This study investigates the seismic performance of two full-scale, relatively slender nonconforming RC wall specimens representative of older construction: one with no boundary confinement (SW-NC-FF) and one with insufficient confinement (SW-IC-FF). Both specimens exhibited flexure-controlled behavior, with initial yielding of boundary longitudinal bars occurring at an approximately 0.30% drift ratio and maximum reinforcement tensile strains of 0.006 (SW-IC-FF) and 0.015 (SW-NC-FF). Rocking governed the lateral response due to progressive debonding of the plain bars along the wall height, producing pronounced pinching and self-centering behavior. Failure occurred through longitudinal bar buckling and concrete crushing, with ultimate drift ratios of 2.0% and 1.5% and displacement ductility values of 4.0 and 4.3 for SW-IC-FF and SW-NC-FF, respectively. Experimental results were compared with backbone predictions from ASCE 41:2023, NZ C5:2025, and EN 1998-3:2025. While all three guidelines captured initial stiffness and yield rotations, their rotation-capacity predictions diverged, underscoring the need for improved assessment approaches for rocking-dominated, plain-reinforced walls. Full article

Wheat possesses inherently low concentrations and bioavailability of the essential micronutrients (EMis) zinc (Zn), iron (Fe), manganese (Mn), and copper (Cu), limiting its capacity to sufficiently address human nutritional requirements. Biofortification of wheat with EMis through agricultural methods is a strategy aimed at addressing EMi deficiencies in human populations that emphasize cost-effectiveness and sustainability. All EMis are usually applied foliarly as sulfates, which indicates sulfur (S)-assisted biofortification. The formation of EMi complexes provides solubility as well as protection during long-distance transport. Several small molecules are possible candidates as ligands—the S-containing amino acids cysteine and methionine among them—linking EMi homeostasis to S homeostasis, which represents another aspect of S-assisted biofortification. In this study, we delve into the S-assisted agronomic biofortification strategy by applying sulfate micronutrients coupled with a sulfur-containing amino acid and we explore the effect of the selected accompanying cation (Zn, Fe, Mn, or Cu) on the EMi metallome of the grain, along with the biofortification effectiveness, whilst the type of the incorporated surface active agent seems to affect this approach. A field experiment was conducted for two years with durum wheat cultivation subjected to various interventions at the initiation of the dough stage, aiming to biofortify the grain with EMis provided as sulfate salts coupled with cysteine or methionine as potential biofortification enhancers. The mixtures were applied alone or in combination with commercial surfactants of the organosilicon ethoxylate (SiE) type or the alcohol ethoxylate (AE) type. The performance of two relevant preparations, FytoAmino-Bo (FABo) and Phillon, has been studied, too. The interventions affected the accumulation of the EMi metallome into the grains, along with the interactions of the EMis within this metallome. Several interventions increased the EMi metallome of the grain and affected the contribution of each EMi to this metallome. Many interventions have increased Zn and Fe, while they have decreased Mn and Cu. An increase in Zn corresponded (i) to a decrease in Cu, (ii) to an increase or no increase in Fe, and (iii) to a variable change in Mn. Cys increased the metallome by 34% and Zn and Fe within it. ZnSO₄ and FeSO₄ increased the metallome by 5% and 9%, whilst MnSO₄ and CuSO₄ increased the metallome by 36% and 33%, respectively. The additives improved the contribution to increasing the metallome in most cases. Without surfactant, the efficacy ranking proved to be MnSO₄ > CuSO₄ > ZnSO₄ > FeSO₄. The use of SW7 sustained the order CuSO₄ > MnSO₄ > ZnSO₄ > FeSO₄. The use of Saldo switched the order to CuSO₄ > ZnSO₄ > FeSO₄ > MnSO₄. In the case of Phillon, the order was CuSO₄ > FeSO₄ > ZnSO₄ > MnSO₄. The effect of Cys or Met was case-specific. The differentiations in the intensity of both the agronomic performance (grain weight, grain weight per spike, and yield) and the biofortification performance (concentrations vs. accumulations of each EMi within the grain) among the various combinations of EMis and additives are depicted by adopting a grading scale, which highlighted the intensity of the acclimation reaction of the biofortified grain to the applied intervention. Full article

Carbon steel casing material in high-temperature deep geothermal wells can be prone to severe corrosion and premature failure due to the oxidation capacity of H₂O, H₂S, CO₂, and more corrosive species in geothermal fluid. Due to the higher temperature and pressure and phase state of fluid in high-temperature deep geothermal wells, the rate and extent of corrosion can be expected to be different than in low-temperature geothermal wells. To reduce the extent of corrosion damage and corrosion rate, and increase the lifetime of geothermal wells, one mitigation method is to clad the internal surface of the geothermal casing with a more noble, corrosion-resistant material. Conventional cladding, however, has been an expensive and time-consuming process up to the current date, but recently, a more economical and productive method has been established, i.e., EHLA cladding. In this study, a 14-day corrosion performance test was conducted on stainless steel and nickel-based alloy clads on a carbon steel substrate in a 262 °C and 95 bar geothermal well in the Hellisheidi geothermal field (SW Iceland). Samples were partially or fully cladded, and some samples were stressed to investigate the clads’ susceptibility to general corrosion and stress corrosion cracking, as well as the substrate’s vulnerability to galvanic corrosion. Corrosion analysis of pure carbon steel substrate was also investigated for comparison. Samples were microstructurally analysed with SEM, and chemical analysis was performed with EDX. The results indicated that the clad materials have good corrosion resistance in the geothermal environment tested, suggesting that EHLA cladding is a more feasible option for strengthening the corrosion resistance of geothermal casing and equipment. Full article

Background/Objectives: Fibrillarin (FBL) is a key nucleolar methyltransferase involved in ribosome biogenesis through 2′-O-ribose methylation of rRNA. While its oncogenic role has been reported in several cancer types, its expression and function in human colorectal cancer (CRC) have remained largely unexplored. This study aims to investigate the expression of FBL in human CRC tissues and cell lines and to determine its functional role in tumor progression and metastasis. Methods: We examined FBL expression in paired human CRC primary tumors and liver metastases using immunohistochemistry. Functional studies were performed using SW-480 (primary tumor) and SW-620 (lymph node metastasis) CRC cell lines derived from the same patient. Cell migration, invasion, and 3D spheroid growth were analyzed following FBL downregulation. In vivo tumor growth was assessed in SCID mice xenografted with FBL-deficient cells. Molecular changes were explored through phosphorylation arrays and Western blotting. Results: FBL expression was significantly higher in human metastatic lesions than in primary tumors. FBL downregulation impaired migration, invasion, and spheroid growth in SW-480 and SW-620 cells and reduced tumor growth in vivo. Mechanistically, FBL inhibition decreased activation of MAPK/ERK, PI3K/AKT, and JNK/p38 pathways and reduced phosphorylation of the transcription factor CREB. Conclusions: Our study identifies FBL as a potential contributor to colorectal cancer progression, with elevated expression associated particularly with metastatic disease. By demonstrating that FBL expression is elevated in patient-derived metastatic tissues and functionally promotes migration, invasion, and tumor growth, our findings expand the role of ribosome biogenesis factors beyond protein synthesis. The observed suppression of key oncogenic pathways and CREB phosphorylation upon FBL inhibition suggests that FBL integrates ribosomal regulation with cancer cell signaling. These insights open new avenues for targeting nucleolar activity in advanced CRC and highlight FBL as a potential biomarker and therapeutic target in metastatic disease. Full article

As embedded and IoT systems demand secure and compact encryption, developing cryptographic solutions that are both lightweight and efficient remains a major challenge. Many existing AES implementations either lack flexibility or consume excessive hardware resources. This paper presents an area-efficient and flexible AES-128 implementation based on a hardware/software (HW/SW) co-design, specifically optimized for platforms with limited hardware resources, resulting in reduced power consumption. In this approach, key expansion is performed in software on a lightweight MicroBlaze processor, while encryption and decryption are accelerated by dedicated hardware IP cores optimized at the Look-up Table (LuT) level. The design is implemented on a Xilinx XC5VLX50T Virtex-5 FPGA, synthesized using Xilinx ISE 14.7, and tested at a 100 MHz system clock. It achieves a throughput of 13.3 Gbps and an area efficiency of 5.44 Gbps per slice, requiring only 2303 logic slices and 7 BRAMs on a Xilinx FPGA. It is particularly well-suited for resource-constrained applications such as IoT nodes, secure mobile devices, and smart cards. Since key expansion is executed only once per session, the runtime is dominated by AES core operations, enabling efficient processing of large data volumes. Although the present implementation targets AES-128, the HW/SW partitioning allows straightforward extension to AES-192 and AES-256 by modifying only the software Key expansion module, ensuring practical scalability with no hardware changes. Moreover, the architecture offers a balanced trade-off between performance, flexibility and resource utilization without relying on complex pipelining. Experimental results demonstrate the effectiveness and flexibility of the proposed lightweight design. Full article

The growing interest in high-power amplifiers for the terahertz (THz) radar system leads to significant performance improvements of THz wave traveling-wave tubes (TWT). This article presents a detailed development of a G-band pulsed wave TWT with 120 W output power. Three approaches have been combined to improve the tube’s output power including proposing the modified folded waveguide (MFWG) slow wave structure (SWS), using large beam current, and adopting phase velocity tapering (PVT). Firstly, the MFWG SWS circuit has an additional degree of freedom that can be used to achieve approximately 36% higher interaction impedance than that in the conventional folded waveguide (CFWG). Subsequently, the electron beam current was increased to approximately 100 mA to boost the DC power of the electron beam. Finally, the PVT technology dramatically enhanced the output power from 98 W to 143 W, concomitant with a notable increase in electronic efficiency from 4.75% to 7.03%. Hot experimental results show that the measured output power can be over 100 W at 20% duty cycle within a bandwidth of 5 GHz when the operation voltage and the current are 22.48 kV and 103.5 mA, respectively. In addition, the maximum power is 121 W with the corresponding electronic efficiency of 5.1%. The proposed G-band 100 W TWT will have broad applications in far-distance high-resolution imaging. Full article

Accurate prediction of land surface temperature (LST) is critical for remote sensing applications, yet remains hindered by in situ data scarcity, limited input variables, and regional variability. To address these limitations, we introduce a three-stage strategic fine-tuning-based transfer learning (SFTL) framework that integrates a large simulated dataset (430 K samples), in situ measurements from the Heihe and Huailai regions in China, and high-resolution imagery from the GF5-02 Visible and Infrared Multispectral Imager (VIMI). The key novelty of this study is the combination of large-scale simulation, an engineered humidity-sensitive feature, and multiple parameter-efficient tuning strategies—full, head, gradual, adapter, and low-rank adaptation (LoRA)—within a unified transfer-learning framework for cross-site LST estimation. In Stage 1, pre-training with 5-fold cross-validation on the simulated dataset produced strong baseline models, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), Deep Neural Network (DNN), Transformer (TrF), and Convolutional Neural Network (CNN). In Stage 2, strategic fine-tuning was conducted under two cross-regional scenarios—Heihe-to-Huailai and Huailai-to-Heihe—and model transfer for tree-based learners. Fine-tuning achieved competitive in-domain performance while materially improving cross-site transfer. When trained on Huailai and tested on Heihe, DNN-gradual attained RMSE 2.89 K (R² ≈ 0.96); when trained on Heihe and tested on Huailai, TrF-head achieved RMSE 3.34 K (R² ≈ 0.94). In Stage 3, sensitivity analyses confirmed stability across IQR multipliers of 1.0–1.5, with <1% RMSE variation across models and sites, indicating robustness against outliers. Additionally, application to real GF5-02 VIMI imagery demonstrated that the best SFTL configurations aligned with spatiotemporal in situ observations at both sites, capturing the expected spatial gradients. Overall, the proposed SFTL framework—anchored in cross-validation, strategic fine-tuning, and large-scale simulation—outperforms the widely used Split-Window (SW) algorithm (Huailai: RMSE = 3.64 K; Heihe: RMSE = 4.22 K) as well as direct-training Machine Learning (ML) models, underscoring their limitations in modeling complex regional variability. Full article

Egg-laying performance in hens is regulated by complex molecular mechanisms within the hypothalamic–pituitary–gonadal (HPG) axis and ovarian follicles. This study employed integrative transcriptome profiling of primordial (PR), primary (PM), small white (SW), and small yellow (SY) follicles in hens with low and high egg-laying capacities to explain regulatory pathways influencing reproductive outcomes. Specific gene expression patterns were observed that correlated with follicular growth, steroidogenesis, and granulosa cell proliferation. Heatmap clustering and principal component analysis revealed transcriptional divergence between low- and high-laying hens, suggesting that coordinated changes in signaling pathways influence egg-laying performance. High-laying hens intricated an upregulation of the PI3K-AKT-FOXO3, TGF-β, and Wnt/β-catenin pathways, which facilitate early follicular development, granulosa cell proliferation, and folliculogenesis. Higher phosphorylation of AKT and reduced nuclear FOXO3 activity were associated with enhanced primordial follicle growth. Increased TGF-β signaling, as demonstrated by higher levels of SMAD2/3/4 and cell cycle regulators, promoted granulosa cell proliferation in primary follicles (PMF). In SWF, higher levels of β-catenin and its downstream genes, such as c-Myc and cyclin D1, promoted follicle development. High-laying hens revealed increased expression of FSHR, CYP19A1, 17β-HSD, CYP1A1, and CYP1B1 in SYF, signifying enhanced FSH level and steroidogenesis. Similarly, low-laying hens exhibited downregulation of key genes, suggesting reduced follicular development and hormone signaling. These findings identify key regulatory networks and molecular markers associated with reproductive performance, providing targets for genetic selection and interventions to enhance egg production while reducing the risk of hormonal overstimulation. Full article

Anaerobic co-digestion of agro-industrial and municipal biowastes can enhance methane production, but the optimal mixture depends on nonlinear interactions among substrates. This study evaluated swine wastewater (SW), cheese whey (CW), and the organic fraction of municipal solid waste (OFMSW) under mesophilic batch conditions to quantify composition–response relationships and identify a robust operating window. A restricted simplex-centroid mixture design was tested; linear, quadratic, and special cubic models were fitted and evaluated using ANOVA, diagnostic plots, and optimization with desirability mapping. Cumulative methane yield (CMY) ranged between 251 and 295 NmL CH₄ g VS⁻¹ in the mixtures, outperforming SW as single component. All mixtures maintained neutral pH and moderate alkalinity ratios. The special cubic model provided the best performance (high R² and R²pred) and revealed significant ternary interaction. The optimization indicated a composition near 63% SW, 10% CW, and 27% OFMSW with a predicted CMY of 300 NmL CH₄ g VS⁻¹; a high-performance band (desirability 0.90–1.00; corresponding to CMY ≥ 294.8) defined a robust window of ~60–66% SW, 6–20% CW, and 20–31% OFMSW. Overall, balanced ternary co-digestion showed synergistic effects beyond additive expectations, and the response surface model based on mixture design proved effective in capturing interactions and providing flexible guidance for practical implementation. Full article

This study aimed to identify electrophysiological markers (event-related potentials, ERPs) of intentional, need-related mental activity under controlled gaze fixation, with potential applications in brain–computer interface (BCI) development for individuals with severe motor impairments. Methods: Using stimuli from the PAIN Pictionary—a pictogram database for non-verbal communication in locked-in syndrome (LIS) contexts—neural responses were recorded via high-density EEG in 30 neurologically healthy adults (25 included after artifact-based exclusion). Participants viewed randomized sequences of pictograms representing ten fundamental need categories (e.g., “I am cold”, “I’m in pain”), with one category designated as the target per sequence. Each pictogram was followed by a visual cue prompting a button press: during training, participants executed the press; during the main task, they performed right-hand motor imagery while maintaining central fixation. Results: ERP analyses revealed a robust P300 response (450–650 ms; p < 0.0002) over centro-parietal regions for target cues, reflecting enhanced attentional allocation and stimulus choice. An early Contingent Negative Variation (CNV, 450–750 ms; p = 0.008) over fronto-lateral sites indicated anticipatory attention and motor preparation, while a left-lateralized late CNV (2250–2750 ms; p = 0.035) appeared to embody the preparation of a finalized motor plan for the forthcoming right-hand imagined response. A centro-parietal P600 component (600–800 ms; p = 0.044) emerged during response monitoring, reflecting evaluative and decisional processes. SwLORETA source analyses localized activity within a distributed network spanning prefrontal, premotor, motor, parietal, and limbic areas. Conclusions: These findings demonstrate that motor imagery alone can modulate pattern-onset ERP components without overt movement or gaze shifts, supporting the translational potential of decoding need-related intentions for thought-driven communication systems in individuals with profound motor impairments. Full article

This study optimizes vehicle suspension dynamics by introducing a controllable degree of nonlinearity, characterized by a parameter ε, into the spring element of Inerter-Spring-Damper (ISD) systems. Quarter-vehicle models for parallel and series ISD configurations are established, and a multi-objective genetic algorithm optimizes the parameters under random road excitation to minimize body acceleration (BA), suspension working space (SWS), and dynamic tire load (DTL). Results demonstrate that optimizing ε brings advantages: compared to a conventional passive suspension, the optimized parallel ISD suspension reduces BA, SWS, and DTL by 7.98%, 8.57%, and 1.69%, respectively, with the BA reduction notably improving from 5.94% (achieved by the linear ISD with ε = 0) to 7.98%. Similarly, the optimized series ISD achieves reductions of 2.53%, 7.62%, and 6.42% in BA, SWS, and DTL, showing a more balanced enhancement over its linear counterpart. The analysis reveals how ε distinctly influences the performance trade-offs, validating that strategically tuning the spring nonlinearity degree, in synergy with the inerter and damper, provides an effective method for superior suspension performance customization. Full article

Background/Objectives: Shift work in healthcare professionals affects performance in high cognitive processing, especially in complex environments. However, the beneficial effects that working in complex environments may have on auditory–cognitive processing remain unknown. These professionals face increased challenges in decision-making due to factors such as noise exposure and sleep disturbances, which may lead to the development of enhanced auditory–cognitive resources. This study aims to investigate the associations between shift work and auditory–cognitive processing in middle-aged healthcare workers. Methods: Thirty middle-aged healthcare workers were equally allocated to a shift worker (SW) or a fixed-schedule worker (FSW) group. Performance on a cognitive test, and in pure-tone audiometry, speech in quiet and noise, and listening effort were used to explore whether correlations were specific to shift work. Results: Exploratory analyses indicated that shift workers tended to perform better in visuospatial/executive function, memory recall, memory index, orientation, and total MoCA score domains compared to fixed-schedule workers. In the SW group, hearing thresholds correlated with memory recall and memory index. In the FSW group, hearing thresholds correlated with orientation, memory index, and total MoCA score, while listening effort correlated with naming, and speech intelligibility in quiet correlated with total MoCA scores. Conclusions: These exploratory findings suggest that shift work may be linked to distinct auditory–cognitive patterns, with potential compensatory mechanisms in visuospatial/executive functions and memory among middle-aged healthcare workers. Larger, longitudinal studies are warranted to confirm whether these patterns reflect true adaptive mechanisms. Full article

As a major contributor to environmental pollution in recent years, solid waste has become an increasingly significant concern in the realm of sustainable development. Unmanned Aerial Vehicle (UAV) imagery, known for its high spatial resolution, has become a valuable data source for solid waste detection. However, manually interpreting solid waste in UAV images is inefficient, and object detection methods encounter serious challenges due to the patchy distribution, varied textures and colors, and fragmented edges of solid waste. In this study, we proposed an improved instance segmentation approach called Watershed Mask Network for Solid Waste (WMNet-SW) to accurately retrieve solid waste with precise edges from UAV images. This approach combined the well-established Mask R-CNN segmentation framework with the watershed transform edge detection algorithm. The benchmark Mask R-CNN was improved by optimizing the anchor size and Region of Interest (RoI) and integrating a new mask head of Layer Feature Aggregation (LFA) to initially detect solid waste. Subsequently, edges of the detected solid waste were precisely adjusted by overlaying the segments generated by the watershed transform algorithm. Experimental results show that WMNet-SW significantly enhances the performance of Mask R-CNN in solid waste retrieval, increasing the average precision from 36.91% to 58.10%, F1-score from 0.5 to 0.65, and AP from 63.04% to 64.42%. Furthermore, our method efficiently detects the details of solid waste edges, even overcoming the limitations of training Ground Truth (GT). This study provides a solution for retrieving solid waste with precise edges from UAV images, thereby contributing to the protection of the regional environment and ecosystem health. Full article