Search Results (1,374)

Background/Objectives: Primary sclerosing cholangitis (PSC) is a rare, incurable liver disease characterized by chronic biliary inflammation and fibrosis. PSC is a significant risk factor for biliary tract cancer (BTC). This study aims to evaluate STAT3 expression in BTC and its prognostic significance as well as explore the potential of organoids derived from PSC and liver tumor patients as an in vitro model for testing novel therapeutic strategies in both PSC and BTC. Methods: Fresh tissue samples obtained from 10 PSC patients through targeted endoscopic retrograde cholangiography (ERC) and biopsy samples from liver tumor patients were used to establish organoid cultures. Organoids were treated with different agents and the therapeutic effect was measured by CellTiterGlo. Treatment with the JAK inhibitor baricitinib was followed by the measurement of cytokine concentrations in the supernatant. Archived formalin-fixed paraffin-embedded (FFPE) samples from 55 surgically resected BTC tumors were analyzed for STAT3 expression using immunohistochemistry. Results: We successfully established organoid cultures from all ERC samples. STAT3 protein expression was detected in 56% of tumor samples and 69% of the immune microenvironment. STAT3 positivity in the immune cell compartment was associated with longer disease-free survival, although the multivariate analysis could not confirm its value as an independent prognostic factor. Chemotherapy testing on liver tumor organoids showed various degrees of decreases in viability after treatment with gemcitabine, cisplatin, and cabozantinib. Baricitinib treatment significantly reduced IL-6 and MCP-1 secretion in cholangiocarcinoma Conclusions: The patient-derived organoid model of PSC and liver tumors is a valuable tool for testing novel and established therapeutic strategies, including JAK inhibitors and chemotherapy regimens. STAT3 expression in the immune microenvironment of BTC may serve as a prognostic marker. Further studies are needed to explore the integration of co-cultured organoid systems with stromal and immune components to improve physiological relevance. Full article

Colorectal cancer (CRC) progression occurs through three stages: adenoma (pre-cancerous lesion), carcinoma in situ (CIS) and adenocarcinoma, with tumor stage playing a pivotal role in the prognosis and treatment outcomes. Despite therapeutic advancements, the lack of stage-specific biomarkers hinders the development of accurate diagnostic tools and effective therapeutic strategies. This study aims to identify stage-specific gene expression profiles and key molecular mechanisms in CRC providing insights into molecular alterations across disease progression. Our methodological approach integrates the use of absolute gene set enrichment analysis (absGSEA) on formalin-fixed paraffin-embedded (FFPE)-derived transcriptomic data, combined with large-scale clinical validation and experimental confirmation. A comparative whole transcriptomic analysis (RNA-seq) was performed on FFPE samples including adenoma (n = 10), carcinoma in situ (CIS) (n = 8) and adenocarcinoma (n = 11) samples. Using absGSEA, we identified significant cellular pathways and putative molecular biomarkers associated with each stage of CRC progression. Key findings were then validated in a large independent CRC patient cohort (n = 1926), with survival analysis conducted from 1336 patients to assess the prognostic relevance of the candidate biomarkers. The key differentially expressed genes were experimentally validated using real-time PCR (RT-qPCR). Pathway analysis revealed that in CIS, apoptotic processes and Wnt signaling pathways were more prominent than in adenoma samples, while in adenocarcinoma, transcriptional co-regulatory mechanisms and protein kinase activity, which are critical for tumor growth and metastasis, were significantly enriched compared to adenoma. Additionally, extracellular matrix organization pathways were significantly enriched in adenocarcinoma compared to CIS. Distinct gene signatures were identified across CRC stages that differentiate between adenoma, CIS and adenocarcinoma. In adenoma, ARRB1, CTBP1 and CTBP2 were overexpressed, suggesting their involvement in early tumorigenesis, whereas in CIS, RPS3A and COL4A5 were overexpressed, suggesting their involvement in the transition from benign to malignant stage. In adenocarcinoma, COL1A2, CEBPZ, MED10 and PAWR were overexpressed, suggesting their involvement in advanced disease progression. Functional analysis confirmed that ARRB1 and CTBP1/2 were associated with early tumor development, while COL1A2 and CEBPZ were involved in extracellular matrix remodeling and transcriptional regulation, respectively. Experimental validation with RT-qPCR confirmed the differential expression of the candidate biomarkers (ARRB1, RPS3A, COL4A5, COL1A2 and MED10) across the three CRC stages reinforcing their potential as stage-specific biomarkers in CRC progression. These findings provide a foundation to distinguish between the CRC stages and for the development of accurate stage-specific diagnostic and prognostic biomarkers, which helps in the development of more effective therapeutic strategies for CRC. Full article

This paper presents a novel forensic watermarking method for digital evidence distribution in non-cloud environments. The approach addresses the critical need for the secure sharing of Joint Photographic Experts Group (JPEG) images in forensic investigations. The method utilises an adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) domain technique to embed a 64-bit watermark in both stand-alone JPEGs and those within forensic disk images. This occurs without alterations to disk structure or complications to the chain of custody. The system implements uniform secure randomisation and recipient-specific watermarks to balance security with forensic workflow efficiency. This work presents the first implementation of forensic watermarking at the disk image level that preserves structural integrity and enables precise leak source attribution. It addresses a critical gap in secure evidence distribution methodologies. The evaluation occurred on extensive datasets: 1124 JPEGs in a forensic disk image, 10,000 each of BOSSBase 256 × 256 and 512 × 512 greyscale images, and 10,000 COCO2017 coloured images. The results demonstrate high imperceptibility with average Peak Signal-to-Noise Ratio (PSNR) values ranging from 46.13 dB to 49.37 dB across datasets. The method exhibits robust performance against geometric attacks with perfect watermark recovery (Bit Error Rate (BER) = 0) for rotations up to 90° and scaling factors between 0.6 and 1.5. The approach maintains compatibility with forensic tools like Forensic Toolkit FTK and Autopsy. It performs effectively under attacks including JPEG compression (QF ≥ 60), filtering, and noise addition. The technique achieves high feature match ratios between 0.684 and 0.690 for a threshold of 0.70, with efficient processing times (embedding: 0.0347 s to 0.1187 s; extraction: 0.0077 s to 0.0366 s). This watermarking technique improves forensic investigation processes, particularly those that involve sensitive JPEG files. It supports leak source attribution, preserves evidence integrity, and provides traceability throughout forensic procedures. Full article

Nowadays, accessibility to academic papers has been significantly improved with electric publications on the internet, where open access has become common. At the same time, it has increased workloads in literature surveys for researchers who usually manually download PDF files and check their contents. To solve this drawback, we have proposed a reference paper collection system using a web scraping technology and natural language models. However, our previous system often finds a limited number of relevant reference papers after taking long time, since it relies on one paper search website and runs on a single thread at a multi-core CPU. In this paper, we present an improved reference paper collection system with three enhancements to solve them: (1) integrating the APIs from multiple paper search web sites, namely, the bulk search endpoint in the Semantic Scholar API, the article search endpoint in the DOAJ API, and the search and fetch endpoint in the PubMed API to retrieve article metadata, (2) running the program on multiple threads for multi-core CPU, and (3) implementing Dynamic URL Redirection, Regex-based URL Parsing, and HTML Scraping with URL Extraction for fast checking of PDF file accessibility, along with sentence embedding to assess relevance based on semantic similarity. For evaluations, we compare the number of obtained reference papers and the response time between the proposal, our previous work, and common literature search tools in five reference paper queries. The results show that the proposal increases the number of relevant reference papers by 64.38% and reduces the time by 59.78% on average compared to our previous work, while outperforming common literature search tools in reference papers. Thus, the effectiveness of the proposed system has been demonstrated in our experiments. Full article

Structural health monitoring (SHM) plays a critical role in ensuring the safety and longevity of civil infrastructure by enabling the early detection of structural changes and supporting preventive maintenance strategies. In recent years, deep learning techniques have emerged as powerful tools for analyzing the complex data generated by SHM systems. This study investigates the use of deep belief networks (DBNs) for classifying structural conditions before and after retrofitting, using both ambient and train-induced acceleration data. Dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) enabled a clear separation between structural states, emphasizing the DBN’s ability to capture relevant classification features. The DBN architecture, based on stacked restricted Boltzmann machines (RBMs) and supervised fine-tuning, was optimized via grid search and cross-validation. Compared to traditional unsupervised methods like K-means and PCA, DBNs demonstrated a superior performance in feature representation and classification accuracy. Experimental results showed median cross-validation accuracies of $98.04\%$ for ambient data and $96.96\%$ for train-induced data, with low variability. Although random forests slightly outperformed DBNs in classifying ambient data ($99.19\%$), DBNs achieved better results with more complex train-induced signals ($95.91\%$). Robustness analysis under Gaussian noise further demonstrated the DBN’s resilience, maintaining over $90\%$ accuracy for ambient data at noise levels up to ${\sigma }_{\mathrm{noise}}=0.5$. These findings confirm that DBNs are a reliable and effective approach for data-driven structural condition assessment in SHM systems. Full article

Electrical heating ice removal pavement represents a promising technology for pavement ice melting. Existing studies primarily focus on optimizing cable-heated asphalt pavement through indoor model tests or finite element results. To obtain more accurate and reasonable temperature rise processes and heat transfer results, we propose a new evaluation metric for heat transfer capability and optimization in electric heating asphalt pavement. Firstly, a three-dimensional heat transfer model considering environmental heat exchange is established, and the accuracy of the model is verified by outdoor measured data. A dual-variable control experiment was carried out between the cable buried depth and insulation layer configuration to specifically analyze their influence on the temperature field of the asphalt layer. We further investigated heat transfer performance metrics (entransy dissipation and entransy dissipation thermal resistance), with results indicating that shallower cable burial depths reduce environmental interference on pavement heat transfer; the thermal insulation layer most significantly enhances pavement surface temperature (35.66% improvement) when cables are embedded in the lower asphalt layer. Placing cables within corresponding pavement layers according to burial depth reduces heat transfer loss capacity and thermal resistance, and positioning cables in the lower asphalt layer with a thermal insulation layer significantly decreases thermal resistance in both concrete and lower asphalt layers while reducing heat transfer capacity loss, demonstrating that installing thermal insulation layers under this structure improves heat transfer efficiency. The combined experimental and simulation verification method and fire dissipation evaluation system proposed in this study provide a new theoretical tool and design criterion for the optimization of electric heating road systems. Full article

Mathematical models and methods serve as fundamental tools for studying ice-related phenomena in the Yellow River. River ice is driven and constrained by hydrometeorological and geographical conditions, creating a complex system. Regarding the Yellow River, there are some uncertainties that manifest in unique features in this context, including ice–water–sediment mixed transport processes and the distribution of sediment both within the ice and on its surface. These distinctive characteristics are considered to different degrees across different scales. Mathematical models for Yellow River ice developed over the past few decades not only encompass models for the large-scale deterministic evolution of river ice formation and melting, but also uncertainty parameter schemes for deterministic mathematical models reflecting the Yellow River’s particular ice-related characteristics. Moreover, there are modern mathematical results quantitatively describing these characteristics with uncertainty, allowing for a better understanding of the unique ice phenomena in the Yellow River. This review summarizes (a) universal equations established according to thermodynamic and hydrodynamic principles in river ice mathematical models, as well as (b) uncertainty sources caused by the river’s characteristics, ice properties, and hydrometeorological conditions, embedded in parametric schemes reflecting the Yellow River’s ice. The intractable uncertainty-related problems in space–sky–ground telemetric image segmentation and the current status of mathematical processing methods are reviewed. In particular, the current status and difficulties faced by various mathematical models in terms of predicting the freeze-up and break-up times, the formation of ice jams and dams, and the early warning of ice disasters are presented. This review discusses the prospects related to the uncertainties in research results regarding the simulation and prediction of Yellow River ice while also exploring potential future trends in research related to mathematical methods for uncertain problems. Full article

Agricultural pest and disease monitoring has recently become a crucial aspect of modern agriculture. Toward this end, this study investigates methodologies for implementing low-power, high-performance convolutional neural networks (CNNs) on agricultural edge detection devices. Recognizing the potential of field-programmable gate arrays (FPGAs) to enhance inference parallelism, we leveraged their computational capabilities and intensive storage to propose an embedded FPGA-based CNN accelerator design aimed at optimizing rice leaf disease image classification. Additionally, we trained the MobileNetV2 network using multimodal image data and employed knowledge distillation from a stronger teacher (DIST) as the hardware benchmark. The solution was deployed on the ZYNQ-AC7Z020 hardware platform using High-Level Synthesis (HLS) design tools. Through a combination of fine-grained pipelining, matrix blocking, and linear buffering optimizations, the proposed system achieved a power consumption of 3.21 W, an accuracy of 97.41%, and an inference speed of 43 ms per frame, making it a practical solution for edge-based rice leaf disease classification. Full article

The emergent field of embedded computing presents a challenging scenario for ubiquitous music (ubimus) design. Available tools demand specific technical knowledge—as exemplified in the techniques involved in programming integrated circuits of configurable logic units, known as field-programmable gate arrays (FPGAs). Low-level hardware description languages used for handling FPGAs involve a steep learning curve. Hence, FPGA programming offers a unique challenge to probe the boundaries of ubimus frameworks as enablers of fast and versatile prototyping. State-of-the-art hardware-oriented approaches point to the use of high-level synthesis as a promising programming technique. Furthermore, current FPGA system-on-chip (SoC) hardware with an associated onboard general-purpose processor may foster the development of flexible platforms for musical signal processing. Taking into account the emergence of an FPGA-based ecology of tools, we introduce the ubimus plugging framework. The procedures employed in the construction of a modular- synthesis library based on field-programmable gate array hardware, ModFPGA, are documented, and examples of musical projects applying key design principles are discussed. Full article

Metallic implants in radiotherapy patients alter dose distributions due to their high density and unique composition, potentially compromising treatment precision. This study evaluates the effects of three metallic materials, Co-Cr-Mo alloy, titanium alloy, and stainless steel, on dose distribution across four radiotherapy modalities: 6 MV photons, 15 MeV electrons, 170 MeV protons, and very-high-energy electrons (100 and 150 MeV). Monte Carlo simulations in the TOol for PArticle Simulations Monte Carlo (TOPAS MC) generated percentage depth dose curves and dose profiles, with dosage data standardized to a reference point and uncertainties addressed via error propagation. Results revealed that the Co-Cr-Mo alloy produced the most significant alterations. For instance, at 100 MeV Very High Electron Energy (VHEE), the dose at a 15 cm depth was 34.57% lower than in water; 6 MV photons showed a 15.16% reduction, and the proton Bragg peak shifted 9.5 cm closer to the source. These pronounced changes along the central beam axis affected dose distributions anterior and posterior to the metal. A prostate cancer simulation further demonstrated considerable dose reduction with deeply embedded metallic implants. The findings underscore the critical impact of implant properties on radiotherapy dose distributions, emphasizing the need to integrate these factors into clinical protocols to improve dosimetric accuracy and treatment safety. Full article

Tool Chatter or Self-Excited Vibration is a common issue in machining processes. This phenomenon arises due to various factors, such as tool rigidity, depth of cut, spindle speed, etc., leading to poor surface finish, excessive tool wear, and premature deterioration of machine components. To prevent tool chatter, a real-time chatter detection algorithm was developed using a low-cost accelerometer in combination with internal machine variables. The algorithm operates without requiring a prior model of the specific tool characteristics, making it capable of detecting chatter by simply knowing the number of teeth of the active tool. Furthermore, the implementation of the detection algorithm meets the strict requirements of real-time embedded systems, ensuring high determinism, low latency, and minimal computational cost. This enables efficient and optimal integration into the machine. The developed chatter detection system was validated through machine-based experimental testing. Full article

This paper presents a grammarware-based approach to developing active video games (AVGs) for sensor-driven training systems. The GCGame domain-specific language (DSL) is introduced to define game logic, sensor interactions, and timing behavior formally. This approach ensures cross-platform consistency, supports real-time configurability, and simplifies the integration of optimization and visualization tools. The presented system, called GCBLE, serves as a case study, demonstrating how grammarware enhances modularity, maintainability, and adaptability in real-world physical interaction applications. The results highlight the potential of a DSL-driven design to bridge the gap between developers and domain experts in embedded interactive systems. Full article

Environmental disasters are becoming increasingly frequent and severe, disproportionately impacting vulnerable populations who face compounded risks due to intersectional factors such as gender, socioeconomic status, rural residence, and cultural identity. These events exacerbate mental health challenges, including post-traumatic stress disorder (PTSD), anxiety, and depression, particularly in low- and middle-income countries (LMICs) and underserved areas of high-income countries (HICs). Addressing these disparities necessitates inclusive, culturally competent, intersectional, and cost-effective strategies. Artificial intelligence (AI) presents transformative potential for delivering scalable and culturally tailored mental health interventions that account for these vulnerabilities. This perspective highlights the importance of co-designing AI tools with at-risk populations, integrating these solutions into disaster management frameworks, and ensuring their sustainability through research, training, and policy support. By embedding mental health resilience into climate adaptation strategies, stakeholders can foster equitable recovery and reduce the long-term mental health burden of environmental disasters. Full article

Breast cancer detection is a critical task in healthcare, requiring fast, accurate, and efficient diagnostic tools. However, the high computational demands and latency of deep learning models in medical imaging present significant challenges, especially in resource-constrained environments. This paper addresses these challenges by presenting an FPGA hardware accelerator tailored for breast cancer classification, leveraging the Zynq XC7Z020 SoC. The system integrates FPGA-accelerated layers with an ARM Cortex-A9 processor to optimize both performance and resource efficiency. We developed modular IP cores, including Conv2D, Average Pooling, and ReLU, using Vivado HLS to maximize FPGA resource utilization. By adopting 8-bit fixed-point arithmetic, the design achieves a 15.8% reduction in execution time compared to traditional CPU-based implementations while maintaining high classification accuracy. Additionally, our optimized approach significantly enhances energy efficiency, reducing power consumption from 3.8 W to 1.4 W a 63.15% reduction. This improvement makes our design highly suitable for real-time, power-sensitive applications, particularly in embedded and edge computing environments. Furthermore, it underscores the scalability and efficiency of FPGA-based AI solutions for healthcare diagnostics, enabling faster and more energy-efficient deep learning inference on resource-constrained devices. Full article

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article