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Because of the theoretical challenges posed by the Efficient Market Hypothesis with respect to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where the effects of the microstructure of the market dominate. This
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Because of the theoretical challenges posed by the Efficient Market Hypothesis with respect to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where the effects of the microstructure of the market dominate. This study evaluates the integration of traditional technical indicators with Random Forest regression models using minute-level SPY data, analyzing 13 distinct model configurations. Our empirical results reveal a stark contrast between in-sample and out-of-sample performance, with values deteriorating from 0.749–0.812 during training to negative values in testing. A feature importance analysis demonstrates that primary price-based features dominate the predictions made by the model, accounting for over 60% of the importance, while established technical indicators, such as RSI and Bollinger Bands, account for only 14–15%. Although the indicator-enhanced models achieved superior risk-adjusted metrics, with Rachev ratios between 0.919 and 0.961, they consistently underperformed a simple buy-and-hold strategy, generating returns ranging from −2.4% to −3.9%. These findings challenge conventional assumptions about the usefulness of technical indicators in algorithmic trading, suggesting that in high-frequency contexts, they may be more relevant to risk management rather than to predicting returns. For practitioners and researchers, our findings indicate that successful high-frequency trading strategies should focus on adaptive feature selection and regime-specific modeling rather than relying on traditional technical indicators, as well as indicating the critical importance of robust out-of-sample testing in the development of a model.
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Talk shows have a vital role in framing legislative issues to influence public attitudes, in a way that serves and achieves the interests of those in charge of these talk shows. The coverage of the talk show Voice of the Kingdom during the
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Talk shows have a vital role in framing legislative issues to influence public attitudes, in a way that serves and achieves the interests of those in charge of these talk shows. The coverage of the talk show Voice of the Kingdom during the second regular session of the National Assembly reveals the spotlight on the performance of the legislative authority, which is the basis for approving, rejecting, or amending laws in Jordan. It also highlights the way talk shows address the performance of the legislative authority in exercising its legislative and oversight role and analyzes the elements on which these talk shows rely in framing legislative issues, as these treatments reflect the public’s interpretations and priorities towards the performance of the legislative authority. To achieve these objectives, the media content analysis approach was used to analyze the media frames of 37 episodes of the Voice of the Kingdom talk show during the second regular session of the 19th National Assembly, which extended from 13 November 2022 to 7 May 2023. This article reveals that the Voice of the Kingdom talk show framed the performance of the legislative authority within the framework of economic results, responsibility, and human interests. The methods of treatment were characterized by the analytical approach, while it relied on rational persuasion methods in treating the performance of the legislative authority. The results concluded that the process of framing the Voice of the Kingdom talk show for the performance of the legislative authority acquires its characteristics from the nature of the television channel’s orientations, the vision of the talk show makers, and the goals they seek to achieve, which gives the communicator an influential role in framing legislative performance.
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Background: Minimally invasive thoracic surgery has advanced since the introduction of multiportal video-assisted thoracoscopic surgery (mVATS) in 1991. Primary spontaneous pneumothorax (PSP) is an ideal condition for refining minimally invasive techniques owing to its straightforward procedures and predictable bullae distributions. Methods: Uniportal VATS
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Background: Minimally invasive thoracic surgery has advanced since the introduction of multiportal video-assisted thoracoscopic surgery (mVATS) in 1991. Primary spontaneous pneumothorax (PSP) is an ideal condition for refining minimally invasive techniques owing to its straightforward procedures and predictable bullae distributions. Methods: Uniportal VATS (uVATS), which involves a single incision, is an alternative to mVATS, offering reduced postoperative pain, lower paresthesia rates, and comparable recurrence outcomes. This review explores two main uVATS approaches: intercostal and subxiphoid. Results: The intercostal approach is common to surgeons trained in mVATS, easier to adopt, and provides excellent cosmetic outcomes. Innovations such as the chest wall pulley method and anchoring sutures further enhance its operability and prevent recurrence. Subxiphoid uVATS minimizes intercostal nerve damage and postoperative pain, making it advantageous for bilateral PSP surgeries. However, it poses challenges such as longer operative times and limited dorsal visualization. Emerging strategies, including drainless postoperative management and two-lung ventilation with CO2 insufflation, have reduced surgical invasiveness. Additionally, cosmetic techniques such as subaxillary incisions enhance patient satisfaction. Conclusions: uVATS continues to redefine PSP surgery, prioritize patient-centered outcomes, and integrate novel strategies to achieve superior results.
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by
Káriton Magalhães Bronze, Uener Ribeiro dos Santos, Galileu Barbosa Costa, Anaiá da Paixão Sevá, Maíra Guimarães Kersul, Cathianne Sacramento Pinto, George Rego Albuquerque, Ana Paula Melo Mariano and Sandra Rocha Gadelha
Viruses2025, 17(3), 389; https://doi.org/10.3390/v17030389 (registering DOI) - 9 Mar 2025
In recent years, the incidence of Severe Acute Respiratory Infection (SARI) has increased due to the emergence of SARS-CoV-2. However, the impact of the COVID-19 pandemic extends beyond mortality rates. Recent analyses suggest that the introduction and spread of SARS-CoV-2 have significantly affected
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In recent years, the incidence of Severe Acute Respiratory Infection (SARI) has increased due to the emergence of SARS-CoV-2. However, the impact of the COVID-19 pandemic extends beyond mortality rates. Recent analyses suggest that the introduction and spread of SARS-CoV-2 have significantly affected the epidemiology of other key respiratory viruses, such as influenza virus (FLUV), respiratory syncytial virus (RSV), and rhinovirus (RV). These changes raise new questions about the dynamics and incidence of post-COVID-19 respiratory infections, as well as potential alterations in symptom profiles and clinical outcomes. In this study, we analyzed data from the Epidemiological Surveillance Information System of Respiratory Viral Agents (SIVEP-Gripe), established by the Brazilian Ministry of Health, to examine the profile of SARI before and during the COVID-19 pandemic in Brazil. Our data reveal a distinct epidemiological pattern, with a significant decrease in FLUV notifications during the pandemic, accompanied by peaks in RSV and RV cases in late 2020. Additionally, there was a shift in the age distribution of RSV and other viral infections, with individuals infected during the pandemic being older than those infected before the pandemic. Interestingly, the introduction and spread of SARS-CoV-2 in Bahia State resulted in a reduction in the frequency of symptoms associated with non-SARS-CoV-2 SARI, without altering clinical outcomes. Our findings suggest that the circulation of SARS-CoV-2 has contributed to a clinical and epidemiological shift, particularly for FLUV, RSV, and other viruses, marked by a reduction in symptoms such as fever, dyspnea, respiratory distress, and the need for ventilatory support. The underlying mechanisms driving these changes remain unclear. These insights are crucial for public health authorities and policymakers to refine surveillance strategies and enhance control measures for respiratory viruses, particularly those causing SARI.
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Aiming at the problem of the nonlinear, strongly coupled, and underdriven trajectory tracking instability of a quadrotor unmanned aerial vehicle (UAV), this thesis proposes a feedback linearization and fractional order model predictive control strategy based on feedback linearization by modeling the dynamics of
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Aiming at the problem of the nonlinear, strongly coupled, and underdriven trajectory tracking instability of a quadrotor unmanned aerial vehicle (UAV), this thesis proposes a feedback linearization and fractional order model predictive control strategy based on feedback linearization by modeling the dynamics of the UAV control system and linearizing the nonlinear model of attitude control. A dual closed-loop control structure, feedback linearization control (FLC) for a position loop, and fractional order model predictive control (FOMPC) for an attitude loop are adopted to realize fast position tracking and attitude response. In addition, considering that the fractional order method has the advantage of flexible regulation, the fractional order integral operator is added to the cost function of model predictive control. Finally, the simulation results and the calculation of the root mean square error verify that the proposed method has a fast response speed, small overshoot, stable flight, and good track tracking performance in UAV track tracking.
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Over 3 billion people live in rural, unincorporated areas globally, which are vital for habitation and production. The perceived safety of these landscapes significantly impacts health and well-being. However, rural areas, as natural environments for urban populations to connect with nature, have not
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Over 3 billion people live in rural, unincorporated areas globally, which are vital for habitation and production. The perceived safety of these landscapes significantly impacts health and well-being. However, rural areas, as natural environments for urban populations to connect with nature, have not been sufficiently addressed in terms of safety concerns. Negative factors often outweigh those promoting safety, limiting the restorative potential of rural landscapes. This study collected rural audio–visual samples through photography and recording, captured facial emotional responses using facial expression recognition models, collected psychological response data using the rural perceived unsafety scale, and statistically evaluated safety perceptions in rural landscapes. Results indicate that (1) audio stimuli exert a stronger influence on perceived unsafety than visual stimuli, with an EUPI (Emotional Unsafety Perception Index) value 44.8% higher under audio conditions than visual conditions; (2) artificial sounds amplify perceived unsafety by 30.9% compared to natural sounds; (3) different animal sounds show significant variations in reducing perceived unsafety, with birds and pigs identified as positive factors; (4) visual factors like plant shading and buildings strongly increase perceived unsafety; and (5) audio–visual matching complicates perceived safety. For the first time, we identify auditory stimuli as the dominant factor in perceived safety in rural landscapes. These insights establish a scientific foundation and practical guidance for improving perceived safety in rural environments.
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Background/Objectives: Scaphoid fractures are notorious for a high rate of nonunion, resulting in chronic pain and impaired wrist function. The decision for surgical intervention often involves extensive imaging and prolonged conservative management, leading to delays in definitive treatment. The effectiveness of such
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Background/Objectives: Scaphoid fractures are notorious for a high rate of nonunion, resulting in chronic pain and impaired wrist function. The decision for surgical intervention often involves extensive imaging and prolonged conservative management, leading to delays in definitive treatment. The effectiveness of such treatment remains a subject of ongoing clinical debate, with no universally accepted predictive tool for surgical success. The objective of this study was to train a deep learning algorithm to reliably identify cases of nonunion with a high probability of subsequent union following operative revision. Methods: This study utilized a comprehensive database of 346 patients diagnosed with scaphoid nonunions, with preoperative and postoperative X-rays available for analysis. A classical logistic regression for clinical parameters was used, as well as a TensorFlow deep learning algorithm on X-rays. The latter was developed and applied to these imaging datasets to predict the likelihood of surgical success based solely on the preoperative anteroposterior (AP) X-ray view. The model was trained and validated over six epochs to optimize its predictive accuracy. Results: The logistic regression yielded an accuracy of 66.3% in predicting the surgical outcome based on patient parameters. The deep learning model demonstrated remarkable predictive accuracy, achieving a success rate of 93.6%, suggesting its potential as a reliable tool for guiding clinical decision-making in scaphoid nonunion management. Conclusions: The findings of this study indicate that the preoperative AP X-ray of a scaphoid nonunion provides sufficient information to predict the likelihood of surgical success when analyzed using our deep learning model. This approach has the potential to streamline decision-making and reduce reliance on extensive imaging and prolonged conservative treatment.
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Cell culture in two dimensions has been the main instrument in cellular and molecular biology. But there are limitations to two-dimensional culture when it comes to tissue engineering and in vivo reproduction. Tissue engineering technology enabled the creation of biomedical scaffolds, which are
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Cell culture in two dimensions has been the main instrument in cellular and molecular biology. But there are limitations to two-dimensional culture when it comes to tissue engineering and in vivo reproduction. Tissue engineering technology enabled the creation of biomedical scaffolds, which are mostly utilized to biofabricate different artificial human organs. Tissue architecture that encourage cell proliferation can be produced using direct bioprinting technology. The development of bioinks for 3D bioprinting is consistently seen as a problem in the domains of biofabrication and tissue engineering. This study aimed to determine if Fibroblasts and Keratinocytes could grow on hydrogel scaffolds as efficiently as they can in the culture plates. Melanocytes were co-cultured, and the production of melanin was assessed in a two- and three-dimensional culture system. Scaffolds were fabricated using 8% alginate and 6% gelatin and 3D-printed using a cell link printer. FTIR was used to determine the precise composition of the gels. SEM analysis was performed for the cells present in gel and the topology of the cells. In addition, 8% alginate and 6% alginate gel scaffolds were analyzed for swelling and degradation over time in the cell growth medium and PBS. Furthermore, a gene expression study of cell cultures on scaffolds was performed through qPCR. A live/dead assay was performed to determine cell viability for cells grown on scaffolds for 7, 14, and 21 days. Most of the cells were shown to be viable, similar to the control cells grown on a plate. The findings from the SEM showed that cells were grown on the gel surface, remained viable even after 21 days, and displayed circular cells stacked three-dimensionally on the gel surface in the 3D scaffold. The MTT assay was performed to check the viability of cells cultured on a 3D-printed scaffold for 1, 5, and 15 days. We observed about 40% viable cells after 15 days, as shown by the MTT assay. Furthermore, a co-culture study with Melanocyte showed an increased production of melanin in a 3D culture as compared to a 2D culture. Our findings suggest that an alginate and gelatin polymer can be used as a cellular matrix for epithelial cell culture. Further, in vivo and ex vivo experiments are needed to validate the results for future applications in tissue engineering for wound healing and other tissue engineering domains.
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Pectin is a complex and versatile polysaccharide crucial for various industries. It functions as a thickener, gelling agent, emulsifier, and low-calorie food. Its anti-inflammatory and immunomodulatory properties have attracted biomedical interest, while its biodegradability and biocompatibility make it valuable for biomaterial applications. The
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Pectin is a complex and versatile polysaccharide crucial for various industries. It functions as a thickener, gelling agent, emulsifier, and low-calorie food. Its anti-inflammatory and immunomodulatory properties have attracted biomedical interest, while its biodegradability and biocompatibility make it valuable for biomaterial applications. The effectiveness of these applications depends on the quality of pectin extraction procedures. While traditional extraction methods exist, green methodologies and alternative techniques have improved pectin’s physicochemical properties—a significant advantage for industrial applications. Pectin can be extracted from various sources, with its molecular structure and functional groups analyzed through different characterization techniques. Modern green extraction methods include ultrasound-assisted extraction, pulsed ultrasound-assisted extraction, pulsed electric field, moderate electric field mediated extraction, microwave-assisted extraction, subcritical water extraction, enzyme-assisted extraction, ohmic heating-assisted extraction, ultrasound-assisted microwave extraction, ultrasound-assisted ohmic heating extraction, hydrothermal processing, high-pressure processing extraction, and dielectric barrier discharge extraction. This review examines these methods’ advantages and disadvantages, along with their applications and future possibilities; it serves as a comprehensive guide for researchers exploring new pectin-rich sources and green extraction technologies for commercial applications.
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Based on the positions of 1027 typhoons that passed through the Western Pacific (WP), East China Sea (ECS), and South China Sea (SCS), the results indicate that the category of marine heatwaves (MHWs) significantly decreases or dissipates after a typhoon’s passage, with stronger
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Based on the positions of 1027 typhoons that passed through the Western Pacific (WP), East China Sea (ECS), and South China Sea (SCS), the results indicate that the category of marine heatwaves (MHWs) significantly decreases or dissipates after a typhoon’s passage, with stronger typhoons causing more pronounced dissipation. The presence of MHWs does not necessarily enhance typhoon intensity; in as many as 151 cases, typhoons weakened despite the presence of MHWs. Furthermore, case studies were conducted using three typhoons that traversed different regions—Hinnamnor (2022), Mawar (2023), and Koinu (2023)—to investigate the dual effects of MHWs on typhoon intensity and their dissipation using satellite observations and ocean reanalysis datasets. Results show that MHWs enhance typhoon intensity by increasing sea surface temperature (SST) and ocean heat content (OHC), while also strengthening stratification through a shallower mixed layer depth (MLD), creating favorable conditions for intensification. While MHWs may initially enhance typhoon intensity, the passage of a typhoon triggers intense vertical mixing and upwelling, which disrupts MHW structures and alters heat distribution, potentially leading to intensity fluctuations. The impact of MHWs on typhoon intensity varies in time and space, MHWs can sustain typhoon strength despite heat loss induced by the typhoon. Additionally, variations in OHC and the mean upper 100 m temperature () were more pronounced in the inner-core region (R50) than in the outer-core region (R30), indicating that energy exchange is concentrated in the inner core, while broader air–sea interactions occur in the outer core. The results show that MHWs can enhance typhoon development by increasing stratification and SST but are also highly susceptible to rapid dissipation due to typhoon-induced impacts, forming a highly dynamic two-way interaction.
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This article provides detailed geological and reservoir data on the existing underground gas storage (UGS) facilities in Ukraine and their prospects for hydrogen (H2) and carbon dioxide (CO2) storage. The H2 and CO2 storage issue is an
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This article provides detailed geological and reservoir data on the existing underground gas storage (UGS) facilities in Ukraine and their prospects for hydrogen (H2) and carbon dioxide (CO2) storage. The H2 and CO2 storage issue is an integral part of the decarbonisation of Ukraine and Europe as a whole. A detailed assessment of UGS in Ukraine was carried out in the framework of the EU Horizon 2020 project Hystories, which is about the possibility of the geological storage of H2. A database of the available geological data on reservoir and caprock properties was compiled and standardised (reservoir geometry, petrophysics, tectonics, and reservoir fluids). General environmental criteria were defined in terms of geology and surface context. The total estimated H2 energy storage capacity in 13 studied UGS facilities is about 89.8 TWh, with 459.6 and 228.2 Mt of H2 using the total (cushion and working gas) and working gas volumes, respectively. The estimated optimistic and conservative CO2 storage capacities in the 13 studied UGS facilities are about 37.6/18.8 Gt, respectively. The largest and deepest UGS facilities are favourable for H2 and CO2 storage, while shallower UGS facilities are suitable only for H2 storage. Studies could be conducted to determine if CO2 and H2 storage could be applied in synergy with CO2 being used as a cushion gas for H2 storage. The underground storage of H2 and CO2 plays key roles in reducing greenhouse gas emissions and supporting clean energy while enhancing energy security. Increasing the share of renewable energy and integrating sustainable development across various sectors of the economy is crucial for achieving climate goals.
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Background: Depressive symptoms (DepS) are prevalent among patients with breast cancer. Offering an anti-inflammatory diet is a promising strategy for DepS management, but it is costly and difficult to scale up. Instead, anti-inflammatory dietary education is cost-effective and may be more conducive
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Background: Depressive symptoms (DepS) are prevalent among patients with breast cancer. Offering an anti-inflammatory diet is a promising strategy for DepS management, but it is costly and difficult to scale up. Instead, anti-inflammatory dietary education is cost-effective and may be more conducive to the promotion of an anti-inflammatory diet strategy. Methods: A prospective, assessor-blinded, two-arm randomized controlled trial was designed to determine the effects of 12-week anti-inflammatory dietary education on DepS in breast cancer patients with depression. Adult female patients with depression and receiving adjuvant chemotherapy were recruited. Participants in the intervention group received anti-inflammatory dietary education, while the control group received routine nursing care. Outcomes included the Center for Epidemiologic Studies Depression Scale (CES-D) score, energy-adjusted dietary inflammatory index (E-DII), plasma inflammatory biomarkers, and quality of life (QoL), which were all assessed at baseline and after a 12-week follow-up. The robustness of the estimates was investigated through sensitivity analyses. A post hoc power analysis was conducted to establish the observed effect sizes for the primary outcomes. Results: A total of 88.6% (62/70) of the participants completed the entire 12-week follow-up. No statistically significant between-group differences were found in the baseline characteristics, including sociodemographic factors, disease-related characteristics, and lifestyle factors. After the intervention, both the CES-D score (p = 0.040) and E-DII (p < 0.001) in the intervention group were significantly lower than in the control group, while the QoL was significantly increased (p < 0.001). Compared with the baseline, the tumor necrosis factor-α (TNF-α) (p = 0.002) and C-reactive protein (CRP) (p = 0.045) levels were significantly lower in the intervention group but not in the control group. Conclusions: Anti-inflammatory dietary education may improve DepS and QoL in breast cancer patients with depression and undergoing chemotherapy by regulating inflammation. Given its acceptability and practicality, this strategy may be incorporated into routine cancer care.
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We previously reported that 3-(trihydroxygermyl)propanoic acid (THGP) suppresses inflammasome activation in THP-1 cells following stimulation with lipopolysaccharide (LPS) and ATP (signals 1 and 2) by forming a complex with ATP, thereby inhibiting IL-1β secretion. Our findings also suggested that THGP inhibits inflammasome activation
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We previously reported that 3-(trihydroxygermyl)propanoic acid (THGP) suppresses inflammasome activation in THP-1 cells following stimulation with lipopolysaccharide (LPS) and ATP (signals 1 and 2) by forming a complex with ATP, thereby inhibiting IL-1β secretion. Our findings also suggested that THGP inhibits inflammasome activation through mechanisms independent of ATP complex formation. This study investigated the anti-inflammatory effects of THGP on signal 1 (ATP-independent) of inflammasome activation. THGP suppressed NF-κB nuclear translocation in LPS-stimulated THP-1 cells, which reduced the mRNA expression of the proinflammatory cytokines TNF-α and IL-6, as well as IL-1β secretion. This mechanism was mediated by the formation of a THGP–adenosine complex, which inhibited adenosine degradation and subsequently activated adenosine–NR4A2 signaling. Thus, THGP exerts anti-inflammatory effects by forming a complex with adenosine, leading to adenosine–NR4A2 signaling pathway activation. This mechanism is distinct from the ATP-dependent pathway by which THGP was previously reported to function. By targeting both ATP-dependent and ATP-independent inflammasome activation pathways, THGP has potential as a broad-spectrum therapeutic agent for various inflammatory diseases.
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This study examined the effects of selenium (Se) fertilization, applied via foliar and rhizosphere methods, on the physiological and biochemical characteristics of ‘Muscat Hamburg’ grapes. Sodium selenite (Na2SeO3) treatments were administered at three concentrations (50, 100, and 150 ppm)
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This study examined the effects of selenium (Se) fertilization, applied via foliar and rhizosphere methods, on the physiological and biochemical characteristics of ‘Muscat Hamburg’ grapes. Sodium selenite (Na2SeO3) treatments were administered at three concentrations (50, 100, and 150 ppm) during critical phenological stages. The results showed that Se at 50 ppm effectively increased the chlorophyll content and enhanced chlorophyll fluorescence parameters. Se significantly elevated total soluble solid content and reduced titratable acidity, thereby increasing the TSS/TA ratio. Foliar fertilization with 50 ppm Se enhanced cluster size without affecting berry dimensions, whereas rhizosphere fertilization increased both with increasing Se concentrations, albeit with negative impacts on berry size at higher concentrations. Se increased flavonoid content in grape peels, with rhizosphere fertilization exerting more pronounced effects. Se—via rhizosphere fertilization at 100 and 150 ppm—significantly influenced VOCs derived from fatty acid and isoprene metabolic pathways. Mantel’s test confirmed that foliar fertilization significantly increased chlorophyll content and fluorescence indices, while rhizosphere fertilization had more marked effects on flavonoid content, berry and cluster size, and VOCs, particularly through fatty acid metabolism. These findings suggest that Se can enhance grape quality, but optimal concentrations and fertilization methods must be carefully determined to avoid adverse effects.
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Reprogramming of metabolic pathways is crucial to guarantee the bioenergetic and biosynthetic demands of rapidly proliferating cancer cells and might be related to treatment resistance. We have previously demonstrated the deregulation of the succinate pathway in head and neck squamous cell carcinoma (HNSCC)
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Reprogramming of metabolic pathways is crucial to guarantee the bioenergetic and biosynthetic demands of rapidly proliferating cancer cells and might be related to treatment resistance. We have previously demonstrated the deregulation of the succinate pathway in head and neck squamous cell carcinoma (HNSCC) and its potential as a diagnostic and prognostic marker. Now we aim to identify biomarkers of resistance to radiotherapy (RT) by analyzing the expression of genes related to the succinate pathway and nutrient flux across the cell membrane. We determined the transcriptional expression of succinate receptor 1 (SUCNR1), succinate dehydrogenase A (SDHA), and the solute carrier (SLC) superfamily transporters responsible for the influx or efflux of a wide variety of nutrients (SLC2A3 and SLC16A3) in tumoral tissue from 120 HNSCC patients treated with RT or chemoradiotherapy (CRT). Our results indicated that the transcriptional expression of the glucose transporter SLC2A3 together with SDHA had the best predictive capacity for local response after treatment with RT or CRT. High SLC2A3 and SDHA expression predicted poor outcomes after RT or CRT, with these patients having a 4.2 times higher risk of local recurrence compared to the rest of the patients. These results might indicate that tumors that shifted toward a higher glucose influx and a higher oxidation of succinate via mitochondrial complex II present an ideal environment for radioresistance development. Patients with a high transcriptional expression of both SLC2A3 and SDHA had a significantly higher risk of local recurrence after treatment with RT or CRT.
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Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews
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Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews the role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges. Crop management has progressed significantly from early agricultural methods to the advanced capabilities of Ag5.0, marking a notable leap in precision agriculture. Emerging technologies such as collaborative robots, 6G, digital twins, the Internet of Things (IoT), blockchain, cloud computing, and quantum technologies are central to this evolution. The paper also highlights how machine learning and modern agricultural tools are improving the way we perceive, analyze, and manage crop growth. Additionally, it explores real-world case studies showcasing the application of machine learning and deep learning in crop monitoring. Innovations in smart sensors, AI-based robotics, and advanced communication systems are driving the next phase of agricultural digitalization and decision-making. The paper addresses the opportunities and challenges that come with adopting Ag5.0, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues. Finally, as Agriculture 5.0 is the future of agriculture, we highlight future trends and research needs such as multidisciplinary approaches, regional adaptation, and advancements in AI and robotics. Ag5.0 represents a paradigm shift towards precision crop management, fostering sustainable, data-driven farming systems that optimize productivity while minimizing environmental impact.
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Background: Accurate segmentation of the left ventricle in echocardiograms is crucial for the diagnosis and monitoring of cardiovascular diseases. However, this process is hindered by the limited availability of high-quality annotated datasets and the inherent complexities of echocardiogram images. Traditional methods often
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Background: Accurate segmentation of the left ventricle in echocardiograms is crucial for the diagnosis and monitoring of cardiovascular diseases. However, this process is hindered by the limited availability of high-quality annotated datasets and the inherent complexities of echocardiogram images. Traditional methods often struggle to generalize across varying image qualities and conditions, necessitating a more robust solution. Objectives: This study aims to enhance left ventricular segmentation in echocardiograms by developing a framework that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation with a MultiResUNet architecture, providing a more accurate and reliable segmentation method. Methods: We propose a GAN-based framework that generates synthetic echocardiogram images and their corresponding segmentation masks, augmenting the available training data. The synthetic data, along with real echocardiograms from the EchoNet-Dynamic dataset, were used to train the MultiResUNet architecture. MultiResUNet incorporates multi-resolution blocks, residual connections, and attention mechanisms to effectively capture fine details at multiple scales. Additional enhancements include atrous spatial pyramid pooling (ASPP) and scaled exponential linear units (SELUs) to further improve segmentation accuracy. Results: The proposed approach significantly outperforms existing methods, achieving a Dice Similarity Coefficient of 95.68% and an Intersection over Union (IoU) of 91.62%. This represents improvements of 2.58% in Dice and 4.84% in IoU over previous segmentation techniques, demonstrating the effectiveness of GAN-based augmentation in overcoming data scarcity and improving segmentation performance. Conclusions: The integration of GAN-generated synthetic data and the MultiResUNet architecture provides a robust and accurate solution for left ventricular segmentation in echocardiograms. This approach has the potential to enhance clinical decision-making in cardiovascular medicine by improving the accuracy of automated diagnostic tools, even in the presence of limited and complex training data.
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In marine engineering, the study of underwater explosion effects on naval and offshore structures has gained significant attention due to its critical impact on structural integrity and safety. In practical applications, a crucial aspect is determining the precise point at which an underwater
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In marine engineering, the study of underwater explosion effects on naval and offshore structures has gained significant attention due to its critical impact on structural integrity and safety. In practical applications, a crucial aspect is determining the precise point at which an underwater explosive charge has detonated. This information is vital for assessing damage, implementing defensive and security strategies, and ensuring the structural integrity of marine structures. This paper presents a novel approach that combines coupled numerical simulations performed using the MSC Dytran suite with machine learning techniques to reconstruct the trigger point of underwater explosions based on onboard sensor data and leverage seabed wave reflection information. A Multi-Layer Neural Network (MLNN) was devised to identify the position of the denotation point of the charge using a classification task based on a user-defined two-dimensional grid of potential triggering locations. The MLNN underwent training, validation, and testing phases using simulation data from different underwater blast-loading scenarios for metallic target plates. Different positions of the charge, seabed typologies, and distances between the structure and the seabed are considered. The ability to accurately identify a detonation point using measurable data from onboard systems enhances the knowledge of ship and offshore structures’ response strategies and the overall safety of naval operations.
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Geometric and mechanical analyses were performed on 82 selenium-rich eggs, which underwent hydrostatic testing as 2 raw eggs, 60 steamed eggs, and 20 emptied eggshells. By analyzing the geometric and mechanical properties of the egg, we can draw inspiration from its structural design
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Geometric and mechanical analyses were performed on 82 selenium-rich eggs, which underwent hydrostatic testing as 2 raw eggs, 60 steamed eggs, and 20 emptied eggshells. By analyzing the geometric and mechanical properties of the egg, we can draw inspiration from its structural design to create a pressure shell capable of effectively withstanding the immense water pressure in deep-sea environments. The major axis, minor axis, egg-shape coefficient, weight, thickness, volume, superficial area, and ultimate compressive strength were measured, and their correlations were analyzed. The thickness, egg-shape coefficient, and ultimate compressive strength were normally distributed, and many parameters were strongly correlated. Moreover, finite element analysis was conducted to evaluate the compressive resistance of egg-like pressure shells made from different materials, including metal, ceramic, resin, and selenium-enriched eggshell materials. The performance ratio of the ceramic shells was 2.6 times higher than that of eggshells, and eggshells outperformed metal and resin shells by factors of 2.14 and 4.49, respectively. The eggshells had excellent compression resistance. These findings offer novel insights into the design and optimization of egg-like pressure shells.
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Agroecosystems form a natural ecosystem component, allowing the proper classification of a regional biome at a global scale. It is important to view agroecosystems from a micro-environmental perspective given that they are characterised by a combination of factors, including the interaction of soil–plant–atmosphere
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Agroecosystems form a natural ecosystem component, allowing the proper classification of a regional biome at a global scale. It is important to view agroecosystems from a micro-environmental perspective given that they are characterised by a combination of factors, including the interaction of soil–plant–atmosphere conditions, which are largely responsive to human management practices. The published literature generally provides a limited explanation of the multidimensional nature of agroecosystems. In combination, agroecosystem practices promote efficient water use and nutrient cycling in defence of regenerative agriculture ethos. Sustainable agroecosystem practices can be combined to explain how to mitigate the risks to biodiversity. This study aims to present a review of predominant advances in sustainable crop production from the perspective of the agroecosystem. A hybrid methodology of data mining and interpretation was used to establish the meaning and relationships of the major research areas that have emerged over time and dominate the narrative of sustainable agroecosystem definition and practices. Crop diversification, sustainable soil management, integrated pest management, sustainable water resource management, and precision agriculture were selected using document summarisation and entity relation modelling to generate and explain relationships between various components of sustainable agroecosystems based on the existing literature. A major finding is the confirmation of comparable applications in different regions, whose explanation is enhanced by recent advances in data summation. This review concludes that sustainable agroecosystems are separable in meaning and impact. However, it is reasonable to recommend the need for future research into their integration for implementation and interpretation.
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Based on the analysis of the motion law of 3D five-directional circular transverse braided fibers, this paper obtains the angle calculation formula between fibers and the local polar coordinate system in various cell models by transforming the position coordinates of fiber nodes. The
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Based on the analysis of the motion law of 3D five-directional circular transverse braided fibers, this paper obtains the angle calculation formula between fibers and the local polar coordinate system in various cell models by transforming the position coordinates of fiber nodes. The stress transformation matrix between the local coordinate system and the global coordinate system of any fiber in the circular braided single cell is derived without considering the physical force on the single-cell micro-hexahedron unit. The calculation formulas of braided parameters such as the overall stiffness matrix and fiber volume content of the circular braided composite material after considering the matrix are derived by using the volume average method; the length of braided knuckles is 2 mm, the inner diameter of inner cells is 7 mm, the number of radial and axial braided yarns is 80, the height of inner cells is 0.5 mm, and the filling coefficient is 0.61. Comparing the results of the numerical prediction model with the experimental results in reference, it is found that the error of the numerical prediction model deduced in this paper is small. Therefore, this model can be used to fully study the effects of braided parameters such as cell inner diameter, cell height, and node length on the mechanical properties of composites.
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Non-alcoholic fatty liver disease (NAFLD) is considered a significant global health issue related to serious metabolic disorders. However, effective pharmacological treatments are still limited. Flavonoids, a wide group of polyphenol substances, exert anti-inflammatory and lipid-lowering effects in preclinical data. Thus, implementing these research
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Non-alcoholic fatty liver disease (NAFLD) is considered a significant global health issue related to serious metabolic disorders. However, effective pharmacological treatments are still limited. Flavonoids, a wide group of polyphenol substances, exert anti-inflammatory and lipid-lowering effects in preclinical data. Thus, implementing these research findings in clinical practice could significantly help manage NAFLD and its consequences. This narrative review assesses the therapeutic potential of flavonoids in managing NAFLD. The research collected randomized controlled trials (RCTs) and meta-analyses of RCTs from the past five years concerning the impact of flavonoids on NAFLD. A total of 20 studies were selected according to predetermined inclusion criteria, comprising thirteen randomized controlled trials (RCTs) and seven meta-analyses. The research underscores the beneficial effects of flavonoids in the management of NAFLD through the enhancement of lipid metabolism, the reduction in hepatic steatosis, and the provision of anti-inflammatory actions. Clinical trials demonstrate that interventions rich in flavonoids, including quercetin, epigallocatechin gallate, naringenin, and isoflavones, substantially reduce liver fat content and enhance liver enzyme profiles, with certain compounds exhibiting superior efficacy in particular subgroups, such as older adults and females. Nonetheless, whereas these therapies significantly diminish hepatic steatosis, their effect on fibrosis is constrained. To sum up, flavonoids exhibit significant potential as supplementary treatments for NAFLD by enhancing liver function, lipid metabolism, and inflammation. Additional extensive controlled clinical trials are necessary to create uniform treatment methods and ascertain their long-term therapeutic advantages.
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To address chromatic dispersion (CD) and nonlinear impairments in coherent optical four-level pulse amplitude modulation (PAM4) systems, we propose a magnitude-assisted decision feedback equalizer (MA-DFE). The proposed scheme utilizes signal amplitude information to construct the error function, which is robust to carrier phase
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To address chromatic dispersion (CD) and nonlinear impairments in coherent optical four-level pulse amplitude modulation (PAM4) systems, we propose a magnitude-assisted decision feedback equalizer (MA-DFE). The proposed scheme utilizes signal amplitude information to construct the error function, which is robust to carrier phase noise. Therefore, no additional carrier phase recovery operation is required during digital signal processing (DSP). Under conditions without CD pre-compensation, MA-DFE achieves 80 Gb/s single-wavelength transmission over a 25 km standard single-mode fiber (SSMF) in the C-band. When considering a bit error rate (BER) of 1 × 10−2 for the soft decision threshold, the link budget achieves 27 dB. In addition, we incorporate the phase into the error function, proposing the phase-assisted decision feedback equalizer (PA-DFE). PA-DFE is also unaffected by carrier phase noise and demonstrates better performance than MA-DFE when equalizing the more severe signal impairments caused by SOA gain saturation. Ultimately, we achieve a link budget of 29 dB using PA-DFE.
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Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite
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Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite Systems (GNSS)-level performance standards is limited. As the awareness of potential disruptions to GNSS due to adversarial actions grows, the current reliance on GNSS-level timing appears costly and outdated. This is especially relevant given the benefits of developing robust and stable time scale references in orbit, especially as various alternatives to GNSS are being explored. The onboard realization of clock ensembles is particularly promising for applications such as those providing the on-demand dissemination of a reference time scale for navigation services via a proliferated Low-Earth Orbit (pLEO) constellation. This article investigates potential inter-satellite network architectures for coordinating time and frequency across pLEO platforms. These architectures dynamically allocate radio resources for clock data transport based on the requirements for pLEO time scale formations. Additionally, this work proposes a model-based control system for wireless networked timekeeping systems. It envisions the optimal placement of critical information concerning the implicit ensemble mean (IEM) estimation across a multi-platform clock ensemble, which can offer better stability than relying on any single ensemble member. This approach aims to reduce data traffic flexibly. By making the IEM estimation sensor more intelligent and running it on the anchor platform while also optimizing the steering of remote frequency standards on participating platforms, the networked control system can better predict the future behavior of local reference clocks paired with low-noise oscillators. This system would then send precise IEM estimation information at critical moments to ensure a common pLEO time scale is realized across all participating platforms. Clock steering is essential for establishing these time scales, and the effectiveness of the realization depends on the selected control intervals and steering techniques. To enhance performance reliability beyond what the existing Linear Quadratic Gaussian (LQG) control technique can provide, the minimal-cost-variance (MCV) control theory is proposed for clock steering operations. The steering process enabled by the MCV control technique significantly impacts the overall performance reliability of the time scale, which is generated by the onboard ensemble of compact, lightweight, and low-power clocks. This is achieved by minimizing the variance of the chi-squared random performance of LQG control while maintaining a constraint on its mean.
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Background/Objectives: A vitamin D deficiency is prevalent in post-stroke patients and may impair neurological recovery. While observational studies highlight the neuroprotective role of vitamin D, there is limited evidence from interventional studies evaluating its impact on functional recovery during stroke rehabilitation. This
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Background/Objectives: A vitamin D deficiency is prevalent in post-stroke patients and may impair neurological recovery. While observational studies highlight the neuroprotective role of vitamin D, there is limited evidence from interventional studies evaluating its impact on functional recovery during stroke rehabilitation. This study aimed to assess whether daily vitamin D3 supplementation enhances functional recovery. Methods: This prospective, randomized, placebo-controlled, single-blind study included 159 patients (mean age: 62.5 ± 8.4 years) with a first ischemic stroke that were admitted for early rehabilitation. The participants were randomly allocated to receive 2000 IU of vitamin D3 daily (n = 79) or a placebo (n = 80) for six weeks. The functional outcomes were measured using the Barthel index (BI) and modified Rankin scale (mRS) at baseline and after 42 days. The serum 25-hydroxyvitamin D [25(OH)D] and insulin-like growth factor 1 (IGF-1) levels were analyzed. Results: Vitamin D3 supplementation significantly increased the serum 25(OH)D levels (p < 0.001). Supplementation was associated with improved BI scores (β = 0.07, p = 0.006). A higher BMI (β = −0.06, p = 0.033), higher NIHSS scores (β = −0.18, p = 0.036), hypertension, and statin use negatively impacted functional recovery. Anticoagulant use was correlated with higher mRS scores, indicating greater disability (p = 0.04). Conclusions: Vitamin D3 supplementation positively influences the functional outcomes during post-stroke rehabilitation, supporting its potential role in enhancing neuroplasticity and recovery. Larger multi-center trials are needed to confirm these findings and optimize vitamin D supplementation strategies.
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Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid
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Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to predict rehabilitation success based on clinical and patient-reported outcome measures (CROMs and PROMs). Using a dataset of 1047 rehabilitation patients encompassing diverse musculoskeletal conditions and treatment protocols, we compare the performance of deep learning models with previously established machine learning approaches such as Random Forest and Extra Trees classifiers. Our findings reveal that deep learning significantly enhances predictive performance. The weighted F1-score for direct classification improved from 65% to 74% using a CNN-RNN architecture, and the mean absolute error (MAE) for regression-based success metrics decreased by 12%, translating to more precise estimations of functional recovery. These improvements hold clinical significance as they enhance the ability to tailor rehabilitation interventions to individual patient needs, potentially optimizing recovery timelines and resource allocation. Moreover, attention mechanisms integrated into the deep learning models provided improved interpretability, highlighting key predictors such as age, range of motion, and PROM indices. This study underscores the potential of deep learning to advance outcome prediction in rehabilitation, offering more precise and interpretable tools for clinical decision-making. Future work will explore real-time applications and the integration of multimodal data to further refine these models.
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