Esophageal varices (EV) and variceal hemorrhages are major causes of mortality in liver cirrhosis patients. Detecting EVs early is crucial for effective management. Computed tomography (CT) scans, commonly performed for various liver-related indications, provide an opportunity for non-invasive EV assessment. However, previous CT studies focused on variceal diameter, neglecting the three-dimensional (3D) nature of varices and shunt vessels. This study aims to evaluate the potential of 3D volumetric shunt-vessel measurements from routine CT scans for detecting high-risk esophageal varices in portal hypertension. Methods: 3D volumetric measurements of esophageal varices were conducted using routine CT scans and compared to endoscopic variceal grading. Receiver operating characteristic (ROC) analyses were performed to determine the optimal cutoff value for identifying high-risk varices based on shunt volume. The study included 142 patients who underwent both esophagogastroduodenoscopy (EGD) and contrast-enhanced CT within six months. Results: The study established a cutoff value for identifying high-risk varices. The CT measurements exhibited a significant correlation with endoscopic EV grading (correlation coefficient r = 0.417, p < 0.001). A CT cutoff value of 2060 mm³ for variceal volume showed a sensitivity of 72.1% and a specificity of 65.5% for detecting high-risk varices during endoscopy. Conclusions: This study demonstrates the feasibility of opportunistically measuring variceal volumes from routine CT scans. CT volumetry for assessing EVs may have prognostic value, especially in cirrhosis patients who are not suitable candidates for endoscopy. Full article

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios. Full article

Thanks to the development of geodetic methods and equipment, there has been a transition from conventional methods to modern technologies, which can efficiently and accurately acquire a large amount of data in a short time without the need for direct contact with the measured object. Combined technologies such as Structure from Motion (SfM), Multi-View Stereo (MVS) photogrammetry using Unmanned Aerial Systems (UAS), and terrestrial laser scanning (TLS) are often used for monitoring geohazards and documenting objects in quarries to obtain detailed and accurate information about their condition and changes. This article deals with the analysis of point clouds obtained with different settings in terms of average absolute point distance, average point density, and time range for surveying and office work. The numerical and graphical results of the research lead to conclusions for scientific and practical applications for activities in the mining industry. Full article

This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges. Full article

Muscle growth stands as a pivotal economic trait within pig production, governed by a complex interplay of multiple genes, each playing a role in its quantitative manifestation. Understanding the intricate regulatory mechanisms of porcine muscle development is crucial for enhancing both pork yield and quality. This study used the GSE99749 dataset downloaded from the GEO database, conducting a detailed analysis of the RNA-seq results from the longissimus dorsi muscle (LD) of Tibetan pigs (TP), Wujin pigs (WJ) and large white pigs (LW) at 60 days of gestation, representing diverse body sizes and growth rates. Comparative analyses between TPvsWJ and TPvsLW, along with differential gene expression (DEG) analysis, functional enrichment analysis, and protein–protein interaction (PPI) network analysis, revealed 1048 and 1157 significantly differentially expressed genes (p < 0.001) in TPvsWJ and TPvsLW, respectively. With stricter screening criteria, 37 DEGs were found to overlap between the 2 groups. PPI analysis identified MYL5, MYL4, and ACTC1 as the three core genes. This article focuses on exploring the MYL4 gene. Molecular-level experimental validation, through overexpression and interference of the MYL4 gene combined with EDU staining experiments, demonstrated that overexpression of MYL4 significantly promoted the proliferation of porcine skeletal muscle satellite cells (PSMSC), while interference with MYL4 inhibited their proliferation. Furthermore, by examining the effects of overexpressing and interfering with the MYL4 gene on the muscle hypertrophy marker Fst gene and the muscle degradation marker FOXO3 gene, the pivotal role of the MYL4 gene in promoting muscle growth and preventing muscle degradation was further confirmed. These findings offer a new perspective on the molecular mechanisms behind porcine muscle growth and development, furnishing valuable data and insights for muscle biology research. Full article

Rosa roxburghii Tratt pomace is rich in insoluble dietary fiber (IDF). This study aimed to investigate the influence of three modification methods on Rosa roxburghii Tratt pomace insoluble dietary fiber (RIDF). The three modified RIDFs, named U-RIDF, C-RIDF, and UC-RIDF, were prepared using ultrasound, cellulase, and a combination of ultrasound and cellulase methods, respectively. The structure, physicochemical characteristics, and functional properties of the raw RIDF and modified RIDF were comparatively analyzed. The results showed that all three modification methods, especially the ultrasound–cellulase combination treatment, increased the soluble dietary fiber (SDF) content of RIDF, while also causing a transition in surface morphology from smooth and dense to wrinkled and loose structures. Compared with the raw RIDF, the modified RIDF, particularly UC-RIDF, displayed significantly improved water-holding capacity (WHC), oil-binding capacity (OHC), and swelling capacity (SC), with increases of 12.0%, 84.7%, and 91.3%, respectively. Additionally, UC-RIDF demonstrated the highest nitrite ion adsorption capacity (NIAC), cholesterol adsorption capacity (CAC), and bile salt adsorption capacity (BSAC). In summary, the combination of ultrasound and cellulase treatment proved to be an efficient approach for modifying IDF from RRTP, with the potential for developing a functional food ingredient. Full article

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R² = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R² as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP. Full article

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs. Full article

Adolescents and young adults (AYAs; 15–39 years) diagnosed with cancer face disparities in outcomes and survival. Patient advocacy organizations can play a pivotal role in advancing outcomes for underserved health conditions, such as AYA cancer. In 2018 a group of AYA patient advocates founded AYA Canada (later renamed to “AYA Can—Canadian Cancer Advocacy”), a peer-led national organization aimed at improving the experiences and outcomes of Canadian AYAs affected by cancer. The aim of this article is to describe the development and impact of AYA Can. AYA Can was incorporated as a not-for-profit organization in 2021 and became a registered charity in 2023. Since 2018, AYA Can has established a thriving community of practice comprising nearly 300 patients, healthcare providers, researchers, and charitable organizations with an interest in advocacy for AYA cancer. Other activities have included advocacy at academic conferences and on scientific committees, collaboration with scientists to advance AYA cancer research, training the next generation of AYA patient advocates through a “patient ambassador program,” and developing a national resource hub to centralize knowledge and information on AYA cancer. Through its work to foster collaboration and amplify patient priorities on a national scale, AYA Can has become a leading voice for AYA cancer advocacy in Canada. Full article

Introduction: The management of aortic dissection has evolved significantly over the decades, with the frozen elephant trunk (FET) procedure emerging as a key technique for treating complex aortic pathologies. Recent practices involve deploying the FET prosthesis more proximally in the aorta (Zone 0) to reduce complications, leading to questions about its impact on long-term aortic remodeling compared to traditional Zone 2 deployment. Methods: This retrospective analysis utilized 3D segmentation software to assess the volumetric changes in aortic remodeling after acute Type A aortic dissections, comparing FET stent graft deployment in Zone 0 and Zone 2. The study included 27 patients operated on between 2020 and 2022, with volumetric measurements taken from postoperative and 6-month follow-up CT scans. Statistical analyses were performed to evaluate the differences in the aortic true lumen (TL) and the perfused false lumen (PFL) between the two groups. Results: Both Zone 0 and Zone 2 deployments resulted in significant true lumen (TL) increases (Z0 p = 0.001, Z2 p < 0.001) and perfused false lumen (PFL) decreases (Z0 p = 0.02, Z2 p = 0.04), with no significant differences in volumetric changes between the groups (p = 0.7 post op and p = 0.9 after 6 months). The distal anastomosis in Zone 0 did not compromise the aortic remodeling outcomes and was associated with reduced distal ischemia and cerebral perfusion times (p = 0.041). The angle measurements in Zone 0 did not show any significant changes after the 6-month control (p = 0.2). However, in Zone 2, a significant change was detected. (p = 0.022). The part comparison analyses did not indicate significant differences in aortic deviation between the groups (p = 0.62), suggesting comparable effectiveness in aortic remodeling. Conclusions: Performing the distal anastomosis more proximally in Zone 0 offers technical advantages without compromising the effectiveness of aortic remodeling compared to the traditional Zone 2 deployment. This finding supports the continued recommendation of Zone 0 deployment in the management of acute Type A aortic dissections, with ongoing studies being needed to confirm the long-term outcomes and survival benefits. Full article

Traditional roadway lighting is intended to provide safe guidance for drivers and pedestrians, but the large-scale application of roadway lighting has resulted in significant energy consumption and light pollution. However, road markings prepared by luminous coating are a kind of multi-functional road marking that can meet the needs of highway lighting at night and save energy. Here, CaAl₂O₄:Eu²⁺,Nd³⁺,Gd³⁺ blue long-afterglow phosphor is obtained by the high-temperature solid-state method, and the blue luminescent coating is synthesized by the blending method. The phase composition, microscopic morphology, luminescence properties and water resistance of the phosphor and luminescent coatings are characterized. The best components and processes of the luminescent coating are explored to meet the application of an expressway. Considering the afterglow’s performance, the optimal calcination temperature of the phosphor is determined to be 1300 °C. The afterglow of the phosphor can be over 8 h after 2 h of daylight excitation. The addition of 1.25% SiO₂ to the luminescent coating improves the uniformity of the components, and the incorporation of 3.5% CaCO₃ improves the denseness of the coating. When the coating thickness is 0.8mm, the luminescent coating can achieve the best luminous effect. After 120 h of immersion in water, the afterglow intensity of the luminescent coating reduced to 70% of the original, which has excellent water resistance. The blue luminescent coating with the addition of appropriate amounts of CaCO₃ and SiO₂ improves the dispersion as well as the densification of the components in the coating to achieve the best luminescent effect. In the Shenyang area, different weather conditions (cloudy, sunny, rainy) have no significant effect on the afterglow performance of the luminescent coatings, all of which can achieve over 5 h of afterglow and are suitable for expressways. Full article

Considering the rapid evolution of lithium silicate-based glass ceramics (LSCs) in dentistry, this review paper aims to present an updated overview of the recently introduced commercial novel LSCs. The clinical and in vitro English-language literature relating to the microstructure, manufacturing, strengthening, properties, surface treatments and clinical performance of LSC materials was obtained through an electronic search. Findings from relevant articles were extracted and summarised for this manuscript. There is considerable evidence supporting the mechanical and aesthetic competency of LSC variants, namely zirconia-reinforced lithium silicates and lithium–aluminium disilicates. Nonetheless, the literature assessing the biocompatibility and cytotoxicity of novel LSCs is scarce. An exploration of the chemical, mechanical and chemo-mechanical intaglio surface treatments—alternative to hydrofluoric acid etching—revealed promising adhesion performance for acid neutralisation and plasma treatment. The subtractive manufacturing methods of partially crystallised and fully crystallised LSC blocks and the additive manufacturing modalities pertaining to the fabrication of LSC dental restorations are addressed, wherein that challenges that could be encountered upon implementing novel additive manufacturing approaches using LSC print materials are highlighted. Furthermore, the short-term clinical performance of zirconia-reinforced lithium silicates and lithium–aluminium disilicates is demonstrated to be comparable to that of lithium disilicate ceramics and reveals promising potential for their long-term clinical performance. Full article

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers. Full article

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts. Full article

The concept of symmetry has played a major role in Hilbert space setting owing to the structure of a complete inner product space. Subsequently, different studies pertaining to symmetry, including symmetric operators, have investigated real Hilbert spaces. In this paper, we study the solutions to multiple-set split feasibility problems for a pair of finite families of $\beta$-enriched, strictly pseudocontractive mappings in the setup of a real Hilbert space. In view of this, we constructed an iterative scheme that properly included these two mappings into the formula. Under this iterative scheme, an appropriate condition for the existence of solutions and strong and weak convergent results are presented. No sum condition is imposed on the countably finite family of the iteration parameters in obtaining our results unlike for several other results in this direction. In addition, we prove that a slight modification of our iterative scheme could be applied in studying hierarchical variational inequality problems in a real Hilbert space. Our results improve, extend and generalize several results currently existing in the literature. Full article

In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue. Full article

The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations. Full article

Based on 208 city-level data in China, this paper empirically analyzes the impact of ESG rating on carbon emissions through the SDM spatial metrology model, identifies the direct and indirect consequences and spatial spillover effects of ESG rating on carbon emissions, and compares the regional heterogeneity and city-size heterogeneity of such impacts. This paper draws three conclusions: (1) Empirical evidence shows that the ESG rating performance of enterprises has a significant inhibition effect on carbon dioxide emissions. Specifically, when the ESG rating performance increases by 1%, carbon emissions will decrease by 0.076; among other control variables, the effect of FDI on carbon emission reduction is that when ESG score performance increases by 1%, carbon emission decreases by 0.022. (2) In the decomposition of the total effects, indirect effects and direct effects have the same impact on carbon emissions, and the total effect is −0.393. (3) The inhibition effect is more significant in the Eastern Region and in megacities, where the effect of −0.096 in the Eastern Region is more obvious than that of −0.078 at the national level, and the effect of carbon reduction in megacities is significantly greater than 0.013 in big cities. This suggests regional heterogeneity in regards to the role of ESG ratings in reducing CO2 emissions. This paper reveals the specific effects and internal logic of the impact of ESG performance on CO2 emissions, which has certain implications for various regions to further promote the construction of an ESG system, according to local conditions, and to encourage enterprises to focus on emission reduction and high-quality development. Full article

Concept mapping is a valuable method to represent a domain of knowledge, also with the aim of supporting educational needs. Students are called upon to construct their own knowledge through a meaningful learning process, linking new concepts to concepts they have already learned, i.e., connecting new knowledge to knowledge they already possess. Moreover, the particular graphic form of a concept map makes it easy for the teacher to construct and interpret both. Consequently, for an educator, the ability to assess concept maps offered by students, facilitated by an automated system, can prove invaluable. This becomes even more apparent in educational settings where there is a large number of students, such as in Massive Open Online Courses. Here, we propose two new measures devised to evaluate the similarity between concept maps based on two deep-learning embedding models: InferSent and Universal Sentence Encoder. An experimental evaluation with a sample of teachers confirms the validity of one such deep-learning model as the baseline of the new similarity measure. Subsequently, we present a proof-of-concept dashboard where the measures are used to encode a concept map in a 2D space point, with the aim of helping teachers monitor students’ concept-mapping activity. Full article

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software. Full article