Multidisciplinary Digital Publishing Institute (Switzerland)
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Tumor Characterization Using [18F]FDG PET Radiomics in a PD-L1-Positive NSCLC Cohort
No full textBackground: Durvalumab consolidation following radiochemotherapy is now the standard treatment for unresectable stage III non-small cell lung cancer (NSCLC). [18F]FDG PET/CT offers valuable insights not just for staging but also for tumor characterization via radiomics, which can potentially predict histology, immunophenotype, and prognosis. Methods: We conducted a retrospective analysis of [18F]FDG PET/CT scans from stage IIIA–IIIB NSCLC patients treated at the Clinical Centre, University of Pécs. All biopsy samples were classified histologically (squamous vs. adenocarcinoma) and tested for PD-L1. Lung tumors were segmented using MEDISO InterViewTM FUSION software (version 3.12.002.0000). with an SUVmax threshold of four. Imaging features were extracted and compared based on histology, PD-L1 status, and neutrophil-to-lymphocyte ratio (NLR)-based prognosis groups. Statistical analyses were performed with Jamovi (v2.6.44), using Shapiro–Wilk, t-test/ANOVA, Mann–Whitney/Kruskal–Wallis, or Chi-square tests as appropriate. Results: Fifty-six patients were included (38 PD-L1-positive, 18 -negative). Among PD-L1-positive cases, poor versus good NLR prognosis groups differed in maximum diameter (p = 0.046), short-zone emphasis (p = 0.026), and zone-length non-uniformity (p = 0.027). Focusing on PD-L1-positive squamous carcinoma, maximum diameter, metabolic tumor volume, busyness, and coarseness showed significant differences (all p < 0.05). SUVmax, mean SUV, SUVpeak, and complexity were higher in squamous than in adenocarcinoma subtypes. PD-L1-positive and -negative squamous tumors differed in zone percentage (p = 0.039) and long-zone high gray-level emphasis (p = 0.024), while no significant differences were observed among adenocarcinomas. Conclusions: [18F]FDG PET/CT radiomics showed potential for differentiating NSCLC histological subtypes and for identifying PD-L1-associated imaging patterns in squamous cell carcinoma. In addition, certain metabolic features were associated with NLR-based prognostic groups in PD-L1-positive patients
How Does Big Data Analytics Drive Supply Chain Resilience in Pharmaceuticals? Exploring the Roles of Supply Chain Risk and Ambidexterity
No full textBackground: The primary objective of this study is to investigate the influence of big data analytics (BDA) on supply chain (SC) risk, SC ambidexterity, and SC resilience. It further examines the effects of SC risk and SC ambidexterity on SC resilience and explores their mediating roles in the BDA–SC resilience relationship. Despite growing interest in BDA and resilience, limited empirical research has addressed these linkages in pharmaceutical distribution, particularly in emerging economies such as Jordan. Methods: A quantitative research strategy was adopted, employing a survey-based methodology. Data were obtained from 204 managers in pharmaceutical distribution companies in Jordan. Results: The findings indicate that BDA reduces SC risk and positively influences SC ambidexterity and SC resilience. Furthermore, SC risk and SC ambidexterity positively affect SC resilience. Notably, both variables partially mediate the BDA–SC resilience relationship, with ambidexterity showing a stronger effect. Conclusions: Grounded in the resource-based view and the dynamic capability view, this study provides empirical evidence that BDA enhances SC resilience primarily by fostering ambidexterity and mitigating risks. By clarifying the distinct mediating roles of SC risk and SC ambidexterity, the research extends theory and offers practical insights for managers seeking to build more resilient pharmaceutical SCs
Numerical Investigation of Material Flow and Defect Formation in FRAM-6061 Al Alloy Ring Component Using CEL Simulation
No full textIn this study, a novel and efficient solid-state additive manufacturing technique, friction rolling additive manufacturing (FRAM), was employed to fabricate an aluminum alloy ring component, significantly reducing process complexity and mitigating solidification defects typical of melt-based techniques. However, previous studies on FRAM have primarily focused on the microstructural characteristics and mechanical properties of flat components, with limited attention paid to ring-shaped components. Owing to the unique geometric constraints imposed during the forming process, ring components exhibit markedly different microstructural evolution and defect formation mechanisms compared with flat counterparts, and these mechanisms remain insufficiently and systematically understood. To address this knowledge gap, the coupled Eulerian–Lagrangian (CEL) method was introduced for the first time to numerically simulate the temperature distribution and residual stress evolution during the FRAM process of ring-shaped components. In addition, tracer particles were incorporated into the simulations to analyze the material flow behavior, thereby systematically elucidating the forming behavior and microstructural evolution characteristics under geometric constraint conditions. Moreover, scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) were employed to systematically characterize the microstructural evolution and defect morphology. The CEL numerical simulations exhibited good consistency with the experimental observations, demonstrating the reliability and accuracy of the simulation method. The results showed that the peak temperatures were primarily concentrated at the advancing side of the rotation tool, and the temperature on the outer diameter side of the ring was consistently higher than that on the inner diameter side. The lack of shoulder friction on the inner side led to an increased heat dissipation rate, thereby resulting in higher residual stress compared to other regions. The particle analysis revealed that, due to ring geometry, material flow varied across radial regions, resulting in distinct microstructures. Further EBSD analysis revealed that, after the rotating tool passed, the material first developed a preferential orientation with {111} planes parallel to the shear direction, and with more layers, dynamic recrystallization produced an equiaxed grain structure. This study provides a theoretical basis and process reference for the application of the FRAM technique in the manufacturing of large ring components
Applications of Artificial Intelligence in Selected Internal Medicine Specialties: A Critical Narrative Review of the Latest Clinical Evidence
No full textBackground: Artificial intelligence (AI) is rapidly transforming clinical medicine by enabling earlier disease detection, personalized risk stratification, precision diagnostics, and optimized therapeutic decision-making across multiple specialties. Methods: This narrative review synthesizes the most recent evidence from prospective randomized controlled trials, large cohort studies, and real-world implementations of AI in cardiology, pulmonology, neurology, hepatology, pancreatic diseases, and other key areas of internal medicine. Studies were selected based on clinical impact, external validation, and regulatory approval status where applicable. Results: AI systems now outperform traditional clinical tools in numerous high-stakes applications: >88% freedom from atrial fibrillation at 1 year with AI-guided ablation, noninferior stent optimization versus OCT guidance, >95% sensitivity for atrial fibrillation and low ejection fraction detection on single-lead ECG, substantial increases in adenoma detection rate and melanoma triage accuracy, automated pancreatic cancer detection on routine CT with 89–90% sensitivity, and significant improvements in palliative care consultation rates and post-PCI outcomes using AI-supported telemedicine. Over 850 FDA-cleared AI devices exist as of November 2025, with cardiology and radiology dominating clinical adoption. Conclusions: AI has transitioned from experimental to clinically indispensable in multiple specialties, delivering measurable reductions in mortality, morbidity, hospitalizations, and healthcare resource utilization. Remaining challenges include external validation gaps, bias mitigation, and the need for large-scale prospective trials before universal implementation
Dynamic Protocol Parse Based on a General Protocol Description Language
No full textReal-timenetwork protocol data are indispensable for network security analysis. However, the rapid evolution of protocol standards poses significant challenges to automated parsing and dynamic extensibility. While artificial intelligence (AI) techniques offer potential solutions, they often introduce semantic ambiguities and inconsistent results, thereby undermining parsing precision. To overcome these limitations, we propose PMDL (Protocol Model Description Language), a general-purpose protocol description language. PMDL abstracts protocols into structured sets of fields and attributes, enabling precise and unambiguous specification of protocol syntax and semantics. Based on PMDL descriptions, our execution engine dynamically instantiates and loads protocol templates on the fly, achieving accurate, automated, and dynamically extensible parsing of network traffic. We evaluate PMDL against representative tools such as Wireshark and Kelai, as well as approaches such as Nail and BIND. Experimental results demonstrate that PMDL provides concise yet expressive protocol specifications, and the execution engine achieves superior parsing throughput. Furthermore, performance evaluation using real-world HTTP, MySQL, and DNS traffic from a campus network confirms that our system robustly meets the throughput requirements of large-scale security analysis
ACDNet: Adaptive Citrus Detection Network Based on Improved YOLOv8 for Robotic Harvesting
No full textTo address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) module that combines fruit-aware partial convolution with illumination-adaptive attention mechanisms to enhance feature representation with improved efficiency; (2) Dynamic Multi-Scale Sampling (DMS) operator that adaptively focuses sampling points on fruit regions while suppressing background interference through content-aware offset generation; and (3) Fruit-Shape Aware IoU (FSA-IoU) loss function that incorporates citrus morphological priors and occlusion patterns to improve localization accuracy. Extensive experiments on our newly constructed CitrusSet dataset, which comprises 2887 images capturing diverse lighting conditions, occlusion levels, and fruit overlapping scenarios, demonstrate that ACDNet achieves superior performance with [email protected] of 97.5%, precision of 92.1%, and recall of 92.8%, while maintaining real-time inference at 55.6 FPS. Compared to the baseline YOLOv8n model, ACDNet achieves improvements of 1.7%, 3.4%, and 3.6% in [email protected], precision, and recall, respectively, while reducing model parameters by 11% (to 2.67 M) and computational cost by 20% (to 6.5 G FLOPs), making it highly suitable for deployment in resource-constrained robotic harvesting systems. However, the current study is primarily validated on citrus fruits, and future work will focus on extending ACDNet to other spherical fruits and exploring its generalization under extreme weather conditions
A Gas Production Classification Method for Cable Insulation Materials Based on Deep Convolutional Neural Networks
No full textAs a non-invasive diagnostic technique, evolved gas analysis (EGA) holds significant value in assessing the insulation conditions of critical equipment such as power cables. Current analytical methods face two major challenges: insulation materials may undergo multiple aging mechanisms simultaneously, leading to interfering characteristic gases; and traditional approaches lack the multi-label recognition capability to address concurrent fault patterns when processing mixed-gas data. These limitations hinder the accuracy and comprehensiveness of insulation condition assessment, underscoring the urgent need for intelligent analytical methods. This study proposes a deep convolutional neural network (DCNN)-based multi-label classification framework to accurately identify the gas generation characteristics of five typical power cable insulation materials—ethylene propylene diene monomer (EPDM), ethylene-vinyl acetate copolymer (EVA), silicone rubber (SR), polyamide (PA), and cross-linked polyethylene (XLPE)—under fault conditions. The method leverages concentration data of six characteristic gases (CO2, C2H4, C2H6, CH4, CO, and H2), integrating modern data analysis and deep learning techniques, including logarithmic transformation, Z-score normalization, multi-scale convolution, residual connections, channel attention mechanisms, and weighted binary cross-entropy loss functions, to enable simultaneous prediction of multiple degradation states or concurrent fault pattern combinations. By constructing a gas dataset covering diverse materials and operating conditions and conducting comparative experiments to validate the proposed DCNN model’s performance, the results demonstrate that the model can effectively learn material-specific gas generation patterns and accurately identify complex label co-occurrence scenarios. This approach provides technical support for improving the accuracy of insulation condition assessment in power cable equipment
Direct Transformation of Laplace Equation’s Solution from Spherical to Cartesian Representation
No full textThe description of the Earth’s gravitational field, governed by the fundamental potential equation (the Laplace equation), is conventionally expressed using spherical harmonics, yet the Cartesian formulation, using a Taylor series representation, offers significant algebraic advantages. This paper proposes a novel Direct Cartesian Method for generating spherical basis functions and coefficients directly within the Cartesian coordinate system, utilising the partial derivatives of the inverse distance (1/R) function. The present study investigates the structural correspondence between the Cartesian form of spherical basis functions and the high-order partial derivatives of 1/R. The study reveals that spherical basis functions can be categorised into four distinct groups based on the parity of the degree n and order m. It is demonstrated that each spherical basis function is equivalent to a weighted summation of the partial derivatives of the inverse distance (1/R) with respect to Cartesian coordinates. Specifically, the basis functions are combined with those derivatives that share the same order of Z-differentiation and possess matching parities in their orders of differentiation with respect to X and Y. In order to facilitate the practical calculation of these high-degree derivatives, a recursive numerical algorithm has been developed. The method generates the polynomial coefficients for the numerator of the 1/R derivatives. A pivotal innovation is the implementation of a step-wise normalization scheme within the recursive relations. The integration of the recursive ratios of global normalization factors (including full Schmidt normalization) into each step of the algorithm effectively neutralises factorial growth, rendering the process immune to numerical overflow. The validity and numerical stability of the proposed method are demonstrated through a detailed step-by-step derivation of a sectorial basis function (n=8,m=2)
Effects of Cultivation Systems and Mulching on Yield and Fruit Quality of Highbush Blueberry (Vaccinium corymbosum L.)
No full textHighbush blueberry (Vaccinium corymbosum L.) is a major berry crop valued for its nutritional and bioactive properties. This study evaluated the influence of cultivation systems and genotypes on fruit quality and antioxidant potential in a two-factorial field experiment (four cultivars × four systems). ‘Sunrise’, ‘Draper’, ‘Ozark Blue’, and ‘Aurora’ were assessed for physicochemical traits, total polyphenols (TPC), vitamin C, nitrates, and antioxidant capacity (2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cation decolourisation (ABTS•+), 2,2-diphenyl-1-picrylhydrazyl (DPPH•), and ferric-reducing antioxidant power (FRAP))). The maximum fruit weight was recorded in cv. Aurora grew under the raised-bed with agrotextile system (353 g per 100 berries), while Draper produced the smallest fruits (227 g). Soluble solids ranged from 12.2 to 16.9 °Brix, acidity from 0.53 to 0.97 g/100 g FW, and TPC from 318 to 544 mg/100 g FW. Agrotextile treatments stabilised microclimate and reduced stress, resulting in lower ABTS (17.9 vs. 24.0), DPPH (19.8 vs. 22.3), and FRAP (11.6 vs. 13.9 mmol TE/100 g FW) values, indicating stronger radical scavenging activity. Ozark showed the highest TPC, vitamin C (123 mg/1000 g FW), and firmness (420 g/mm), whereas Aurora and Sunrise had brighter fruits (L = 37.6–36.1). Nitrate concentrations remained low (42–68 mg/1000 g FW). Genotype × system interactions significantly influenced secondary metabolite synthesis and stress adaptation. Raised beds with agrotextile improved fresh-market quality, while traditional systems favoured storage stability, providing practical, sustainable cultivation guidelines
Comparative Simulation and Optimization of “Continuous Membrane Column” Cascades for Post-Combustion CO2 Capture
No full textThis study presents a comprehensive evaluation of a modified membrane cascade operating in “Continuous Membrane Column” mode for selective CO2 capture in combined heat power plants. For the first time, a novel membrane cascade configuration for separating four-component wet flue gases is analyzed and compared with existing technologies in terms of the capital and operating costs required to capture one ton of CO2. The proposed membrane cascade generates two countercurrent recirculating streams: one continuously depleted of the permeate component and the other enriched in it. Because the internal recirculation streams significantly exceed the bypass product streams, the system demonstrates a multiplicative increase in separation efficiency. As a result, the required membrane area and compression energy can be significantly reduced. The analysis demonstrates that the proposed cascade configuration meets all current performance requirements for CO2 recovery and the target composition of the product and residual streams. Furthermore, due to its balanced material and energy cost ratio, the system can serve as a competitive alternative to previously developed membrane CO2 capture technologies, offering lower overall capture losses