Search Results (27,424)

Secure communication is critically dependent on high-speed and high-security quantum random number generation (QRNG). In this work, we present a responsive approach to enhance the efficiency and security of QRNG by leveraging polarization-controlled heterodyne detection to simultaneously measure the quadrature amplitude and phase fluctuations of vacuum shot noise. To address the practical non-idealities inherent in QRNG systems, we investigate the critical impacts of imbalanced heterodyne detection, amplitude–phase overlap, finite-size effects, and security parameters on quantum conditional min-entropy derived from the entropy uncertainty principle. It effectively mitigates the overestimation of randomness and fortifies the system against potential eavesdropping attacks. For a high-security parameter of ${10}^{-20}$, QRNG achieves a true random bit extraction ratio of 83.16% with a corresponding real-time speed of 37.25 Gbps following a 16-bit analog-to-digital converter quantization and 1.4 GHz bandwidth extraction. Furthermore, we develop a deep convolutional neural network for rapid and accurate entropy evaluation. The entropy evaluation of 13,473 sets of quadrature data is processed in 68.89 s with a mean absolute percentage error of 0.004, achieving an acceleration of two orders of magnitude in evaluation speed. Extracting the shot noise with full detection bandwidth, the generation rate of QRNG using dual-quadrature heterodyne detection exceeds 85 Gbps. The research contributes to advancing the practical deployment of QRNG and expediting rapid entropy assessment. Full article

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

This study investigates the application of the Analytic Hierarchy Process (AHP) as a decision-support mechanism for managing complex sustainability issues in industrial settings, specifically within the framework of circular economy principles. Focusing on a case from the brewery sector, developed under the EU ECOFACT initiative, this research evaluates ten distinct configurations for the must cooling process. These alternatives are assessed using environmental, economic, and technical criteria, drawing on data from life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The findings indicate that selecting an optimal scenario involves balancing trade-offs among electricity and water consumption, operational efficiency, and overall environmental impacts. Notably, Scenario 3 emerges as the most balanced option, consistently demonstrating superior performance across the primary evaluation criteria. The use of AHP in this context proves valuable by introducing structure and transparency to a multifaceted decision-making process where quantitative metrics and sustainability objectives intersect. By integrating empirical industrial data with an established multi-criteria decision approach, this study highlights both the practical utility and existing limitations of conventional AHP, particularly its diminished ability to discriminate between alternatives when their scores are closely aligned. These insights suggest that hybrid or advanced AHP methodologies may be necessary to facilitate more nuanced decision-making for circular economy transitions in industrial environments. Full article

Aiming at the problems of low thermal analysis efficiency and high computational cost of traditional computational fluid dynamics (CFD) methods for modular fault-tolerant permanent magnet synchronous motors (MFT-PMSMs) under complex working conditions, this paper proposes a fast modeling and calculation method of motor temperature field based on a high-efficiency and high-precision network algorithm. In this method, the physical structure of the motor is equivalent to a parameterized network model, and the computational efficiency is significantly improved by model partitioning and Fourth-order Runge Kutta method. The temperature change of the cooling medium is further considered, and the temperature rise change of the motor at different spatial positions is effectively considered. Based on the finite element method (FEM), the space loss distribution under rated, single-phase open circuit and overload conditions is obtained and mapped to the thermal network nodes. Through the transient thermal network solution, the rapid calculation of the temperature rise law of key components such as windings and permanent magnets is realized. The accuracy of the thermal network model was verified by using fluid-structure coupling simulation and prototype test for temperature analysis. This method provides an efficient tool for thermal safety assessment and optimization in the motor fault-tolerant design stage, especially for heat capacity check under extreme conditions and fault modes. Full article

Background and Objectives: Meniscal pathologies are common abnormalities of the knee joint and a frequent cause of knee pain. Prompt and accurate diagnosis is essential to ensure appropriate treatment. Ultrasonography is increasingly used due to its accessibility, cost- and time-efficiency, and capacity for dynamic assessment. This study aimed to evaluate the usefulness of ultrasonography in identifying specific types of meniscal tears and to assess their frequency of occurrence. Materials and Methods: A retrospective study was conducted to assess the frequency and sonographic appearance of various meniscal pathologies. The study population included all patients who underwent ultrasonographic examination of the knee in our clinic over one year for various indications (n = 430). Archived ultrasound images were retrospectively reviewed and analyzed. Results: Meniscal pathologies were identified in 134 patients. The findings included 95 cases of degenerative lesions (70.9%), 18 meniscal cyst-related pathologies (13.4%), 8 complex tears (6.0%), 5 flap tears (3.7%), 3 vertical pericapsular tears (2.2%), 3 partial thickness tears (2.2%), and 2 bucket-handle-type tears (1.5%). Each lesion type was characterized and illustrated through representative ultrasound images. Conclusions: Ultrasound imaging of meniscal pathology offers a valuable diagnostic option. By characterizing and visually documenting different meniscal lesions, this study highlights the practical potential of ultrasonography in routine clinical settings. These findings may enhance diagnostic accuracy and guide more targeted management strategies. Moreover, the results contribute to the expanding body of research on musculoskeletal ultrasonography and may encourage broader adoption of ultrasound in orthopedic diagnostics. Full article

Hydrogen produced from renewable sources has the potential to tackle various energy challenges, from allowing cost-effective transportation of renewable energy from production to consumption regions to decarbonizing intensive energy consumption industries. Due to its application versatility and non-greenhouse gaseous emissions characteristics, it is expected that hydrogen will play an important role in the decarbonization strategies set out for 2050. Currently, there are some barriers and challenges that need to be addressed to fully take advantage of the opportunities associated with hydrogen. The present work aims to characterize the state of the art of different hydrogen production, storage, transport, and distribution technologies, which compose the hydrogen value chain. Based on the information collected it was possible to conclude the following: (i) Electrolysis is the frontrunner to produce green hydrogen at a large scale (efficiency up to 80%) since some of the production technologies under this category have already achieved a commercially available state; (ii) in the storage phase, various technologies may be suitable based on specific conditions and purposes. Technologies of the physical-based type are the ones mostly used in real applications; (iii) transportation and distribution options should be viewed as complementary rather than competitive, as the most suitable option varies based on transportation distance and hydrogen quantity; and (iv) a single value chain configuration cannot be universally applied. Therefore, each case requires a comprehensive analysis of the entire value chain. Methodologies, like life cycle assessment, should be utilized to support the decision-making process. Full article

Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka–Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka–Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge–Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Our method achieves RMSEs of 0.0687 (BTC), 0.0268 (ETH), and 0.0558 (ALTs)—an over 50% reduction in error relative to ARIMA(2,1,2) and over 25% relative to a standard NN–ODE model—thereby underscoring its effectiveness for cryptocurrency-market forecasting. The entire framework, including neural network training and Runge–Kutta integration, was implemented in MATLAB R2024a (version 24.1). Full article

The integration of digital technologies is catalysing a fundamental transformation of modern energy systems, enhancing operational efficiency, adaptability, and sustainability. Despite significant progress, the existing literature often addresses digital innovations in isolation, with limited consideration of their synergistic potential within Advanced Energy Systems (AES). This paper presents a systematic review of key digital technologies—such as artificial intelligence, the Internet of Things, blockchain, and digital twins—employed in AES, providing a critical assessment of their individual functionalities, interdependencies, and collective contributions to the energy sector. The analysis highlights the capacity of integrated digital solutions to augment system intelligence, strengthen operational resilience, and increase flexibility across various layers of the energy infrastructure. In addressing persistent challenges—including demand-side variability, supply intermittency, and regulatory complexity—the coordinated implementation of these technologies enables real-time optimization, predictive maintenance, and data-informed decision-making. The findings demonstrate that the synergistic deployment of digital technologies not only enhances system performance but also contributes to measurable improvements in reliability, cost-effectiveness, and environmental sustainability. The review concludes that establishing a cohesive and interoperable digital ecosystem is essential for the development of future-ready energy systems that are robust, efficient, and responsive to the evolving dynamics of the global energy landscape. Full article

Reproductive efficiency in pigs is regulated by hormonal pathways that control follicular development at Day 15 of the estrous cycle, during corpus luteum regression. Miniature pigs are extensively employed as human-relevant models in biomedical research, yet their reproductive characteristics during mid-luteal regression remain inadequately characterized, limiting assessments of their translational reliability. Differences in follicular morphology, hormonal signaling, and vascular development may underlie their lower fertility compared to conventional pigs. In this study, follicular development after corpus luteum formation was compared between conventional pigs and minipigs using histological staining, immunofluorescence, hormonal assays, and transcriptomic profiling. The expression of VEGF, mTOR, LH, FSH, PAPP-A, and apoptosis markers was evaluated across the granulosa and thecal regions. Differential gene expression was analyzed using microarray data followed by GO categorization. Minipigs exhibited smaller follicles, reduced vascularization, and lower VEGF and MMP activity compared to conventional pigs. Expression of LH and PAPP-A was higher in conventional pigs, while minipigs showed relatively elevated E2 and FSH levels. Transcriptomic data revealed greater upregulation of cell-survival- and angiogenesis-related genes in conventional pigs, including genes involved in IGF pathways. Apoptosis and poor extracellular matrix remodeling were more pronounced in minipigs. Minipigs demonstrated impaired follicular remodeling and weaker hormonal signaling after corpus luteum formation, which likely contributed to their reduced reproductive efficiency. Understanding these species differences can guide breeding strategies and fertility management in biomedical and agricultural settings. Full article

Plant growth monitoring is a complex and challenging task, which depends on a variety of environmental variables, such as temperature, humidity, nutrient availability, and solar radiation. Advances in optical sensors have significantly enhanced data collection on plant growth. These developments enable the optimization of agricultural practices and crop management through the integration of artificial vision techniques. Despite advances in the application of these technologies, limitations and challenges persist. This review aims to analyze the current state-of-the-art methodologies for using artificial vision and optical sensors in plant growth assessment. The systematic review was conducted following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies were analyzed from the Scopus and Web of Science databases. The main findings indicate that data collection in agricultural environments is challenging. This is due to the variability of climatic conditions, the heterogeneity of crops, and the difficulty in obtaining accurately and homogeneously labeled datasets. Additionally, the integration of artificial vision models and advanced sensors would enable the assessment of plant responses to these environmental factors. The advantages and limitations were examined, as well as proposed research areas to further contribute to the improvement and expansion of these emerging technologies for plant growth assessment. Finally, a relevant research line focuses on evaluating AI-based models on low-power embedded platforms to develop accessible and efficient decision-making solutions in both agricultural and urban environments. This systematic review was registered in the Open Science Framework (OSF). Full article

The interconnections among water, energy, and food (WEF) systems are growing increasingly complex, making it essential to understand their evolutionary mechanisms and coordination barriers to enhance regional resilience and sustainability. In this study, we investigated the WEF system in Northeast China by constructing a comprehensive indicator system encompassing resource endowment and utilization efficiency. The coupling coordination degree (CCD) of the WEF system was quantitatively assessed from 2001 to 2022. An obstacle degree model was employed to identify key constraints, while grey relational analysis was used to evaluate the driving influence of individual indicators. Furthermore, a co-evolution model based on logistic growth and competition–cooperation dynamics was developed to simulate system interactions. The results reveal the following: (1) the regional WEF-CCD increased from 0.627 in 2001 to 0.769 in 2022, reaching the intermediate coordination level, with the CCDs of the food, water, and energy subsystems rising from 0.39 to 0.62, 0.38 to 0.60, and 0.40 to 0.55, respectively, highlighting that the food subsystem had the most stable and significant improvement; (2) Jilin Province attained the highest WEF-CCD, 0.850, in 2022, while that for Heilongjiang remained the lowest, at 0.715, indicating substantial interprovincial disparities; (3) key indicators, such as food self-sufficiency rate, electricity generation, and ecological water use, functioned as both core constraints and major drivers of system performance; (4) co-evolution modeling revealed that the food subsystem exhibited the fastest growth, followed by water and energy (α3 > α1 > α2 > 0), with mutual promotion between water and energy subsystems and inhibitory effects from the food subsystem, ultimately converging toward a stable equilibrium state; and (5) interprovincial co-evolution modeling indicated that Jilin leads in WEF system development, followed by Liaoning and Heilongjiang, with predominantly cooperative interactions among provinces driving convergence toward a stable and coordinated equilibrium despite structural asymmetries. This study proposes a transferable, multi-method analytical framework for evaluating WEF coordination, offering practical insights into bottlenecks, key drivers, and co-evolutionary dynamics for sustainable resource governance. Full article

Knowledge about the composition (volatile and non-volatile) and functionality of natural extracts from Mediterranean plants serves as a basis for their further application. In this study, five selected plants were used for the extraction of plant metabolites. Leaves and flowers of Critmum maritimum, Rosmarinus officinalis, Olea europea, Phylliera latifolia and Mellisa officinalis were collected, and a total of 12 extracts were prepared. Extractions were performed under microwave-assisted conditions, with two solvent types: water (W) and a hydroalcoholic (ethanolic) solution (HA). Detailed extract analysis was conducted. Phenolics were analyzed by detecting individual bioactive compounds using high-performance liquid chromatography and by calculating total phenolic and total flavonoid content through spectrophotometric analysis. Higher concentrations of total phenolics and total flavonoids were obtained in the hydroalcoholic extracts, with the significantly highest total phenolic and flavonoid values in the rosemary hydroalcoholic extract (3321.21 mg_GAE/L) and sea fennel flower extract (1794.63 mg_QE/L), respectively; and the lowest phenolics in the water extract of olive leaves (204.55 mg_GAE/L) and flavonoids in the water extracts of sea fennel leaves, rosemary, olive and mock privet (around 100 mg_QE/L). Volatile organic compounds (VOC) were detected using HS-SPME/GC–MS (Headspace Solid-Phase Microextraction coupled with Gas Chromatography-Mass Spectrometry), and antioxidant capacity was estimated using DPPH (2,2-diphenyl-1-picrylhydrazyl assay) and FRAP (Ferric Reducing Antioxidant Power) methods. HS-SPME/GC–MS analysis of samples revealed that sea fennel had more versatile profile, with the presence of 66 and 36 VOCs in W and HA sea fennel leaf extracts, 52 and 25 in W and HA sea fennel flower extracts, 57 in rosemary W and 40 in HA, 20 in olive leaf W and 9 in HA, 27 in W mock privet and 11 in HA, and 35 in lemon balm W and 10 in HA extract. The lowest values of chlorophyll a were observed in sea fennel leaves (2.52 mg/L) and rosemary (2.21 mg/L), and chlorophyll b was lowest in sea fennel leaf and flower (2.47 and 2.25 mg/L, respectively), while the highest was determined in olive (6.62 mg/L). Highest values for antioxidant activity, determined via the FRAP method, were obtained in the HA plant extracts (up to 11216 mg_AAE/L for lemon balm), excluding the sea fennel leaf (2758 mg_AAE/L) and rosemary (2616 mg_AAE/L). Considering the application of these plants for fresh fish preservation, antimicrobial activity of water extracts was assessed against Vibrio fischeri JCM 18803, Vibrio alginolyticus 3050, Aeromonas hydrophila JCM 1027, Moraxella lacunata JCM 20914 and Yersinia ruckeri JCM 15110. No activity was observed against Y. ruckeri and P. aeruginosa, while the sea fennel leaf showed inhibition against V. fisheri (inhibition zone of 24 mm); sea fennel flower was active against M. lacunata (inhibition zone of 14.5 mm) and A. hydrophila (inhibition zone of 20 mm); and rosemary and lemon balm showed inhibition only against V. fisheri (inhibition zone from 18 to 30 mm). This study supports the preparation of natural extracts from Mediterranean plants using green technology, resulting in extracts rich in polyphenolics with strong antioxidant potential, but with no clear significant antimicrobial efficiency at the tested concentrations. Full article

Considering the importance of the automotive industry, reducing the environmental impact of automotive component manufacturing is crucial. Additionally, lightening of the latter promotes a reduction in fuel consumption throughout the vehicle’s life cycle, limiting emissions. This study presents a comprehensive approach to optimizing and manufacturing a MacPherson steering knuckle using topology optimization (TO), additive manufacturing, and rapid investment casting (RIC). Static structural simulations confirmed the mechanical integrity of the optimized design, with stress and strain values remaining within the elastic limits of the SG A536 iron alloy. The TO process achieved a 30% reduction in mass, resulting in lower material use and production costs. Additive manufacturing of optimized geometry reduced resin consumption by 27% and printing time by 9%. RIC simulations validated efficient mold filling and solidification, with porosity confined to removable riser regions. Life cycle assessment (LCA) demonstrated a 27% reduction in manufacturing environmental impact and a 31% decrease throughout the component life cycle, largely due to vehicle lightweighting. The findings highlight the potential of integrated TO and advanced manufacturing techniques to produce structurally efficient and environmentally sustainable automotive components. This workflow offers promising implications for broader industrial applications that aim to balance mechanical performance with ecological responsibility. Full article

In response to growing environmental pressures and material constraints, circular economy principles are gaining traction across manufacturing sectors. However, most existing frameworks emphasize design and supply chain considerations, with limited focus on how circularity can be operationalized within internal manufacturing systems. This paper proposes a conceptual model that embeds circular operations at the core of production strategy. Grounded in circular economy theory, operations management, and socio-technical systems thinking, the model identifies four key operational pillars: circular input management, looping process and waste valorization, product-life extension, and reverse logistics. These are supported by enabling factors—digital infrastructure, organizational culture, and leadership—and mediated by operational flexibility, which facilitates adaptive, closed-loop performance. The model aims to align internal processes with long-term sustainability outcomes, specifically resource efficiency and operational resilience. Practical implications are outlined for resource-intensive industries such as automotive, electronics, and FMCG, along with a readiness assessment framework for guiding implementation. This study offers a pathway for future empirical research and policy development by integrating circular logic into the structural and behavioral dimensions of operations. The model contributes to advancing the Sustainable Development Goals (SDGs), particularly SDG 9 and SDG 12, by positioning circularity as a regenerative operational strategy rather than a peripheral initiative. Full article

Low-load conditioning activity with blood flow restriction has been addressed as an efficient method to enhance an individual’s performance during their main exercise activity. However, the optimal degree of blood flow restriction remains unclear. Therefore, this study investigated the acute effects of low-load conditioning activity with different degrees of blood flow restriction on muscle strength, power, and perceived exertion. Twenty recreationally trained men (20.9 ± 2.3 years) participated in a randomized crossover design including three conditions: control, low-load blood flow restriction at 50%, and 75% of total arterial occlusion pressure. Participants performed squats (three sets of ten reps) followed by isokinetic assessments of the knee flexor and extensor performance at 7 and 10-min post-exercise. The session rating of perceived exertion (SRPE) was recorded 30 min after each session. No significant effects were observed for condition, time, or their interaction on peak torque, total work, or average power (p < 0.05). However, SRPE was significantly higher in the 75% BFR condition compared to both the 50% BFR and control conditions (p < 0.05), with no difference between the 50% BFR and control. These findings suggest that low-load conditioning activity with blood flow restriction does not acutely enhance neuromuscular performance. However, a higher degree of restriction increases perceived exertion. Full article