Search Results (13,333)

Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R² = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops. Full article

The present study aims to assess the sustainability of bioenergy potential from agriculture in the European Union in the period 2012–2021, with a particular focus on material flow and emissions management, bioenergy and recycling impacts, while assessing the potential of bioenergy from agriculture and analyzing the degree of self-sufficiency and import dependency in the biomass economy. While biomass has significant potential in the EU energy transition, its use is accompanied by challenges related to sustainability, carbon neutrality, efficiency and economic viability. Using a quantitative approach based on official statistical data, this research tracked the evolution of biomass imports, exports, domestic extraction and consumption, providing a comprehensive picture of the stability and adaptability of the biomass economy in the European Union. The results indicate a steady increase in domestic extraction and a stability in consumption, reflecting a high capacity of the European Union to manage biomass resources; thus, the degree of self-sufficiency has been high throughout the period, with a moderate dependence on imports, showing an adaptable economy. The conclusions suggest that in order to maintain this stability, the European Union must continue to develop balanced economic and environmental policies that support the sustainable use of biomass and contribute to the energy transition and environmental objectives. Full article

To address the issue of suboptimal spectral purity in Direct Digital Frequency Synthesis (DDFS) within resource-constrained environments, this paper proposes an optimized DDFS technique based on cubic Hermite interpolation. Initially, a DDFS hardware architecture is implemented on a Field-Programmable Gate Array (FPGA); subsequently, essential interpolation parameters are extracted by combining the derivative relations of sine and cosine functions with a dual-port Read-Only Memory (ROM) structure using the cubic Hermite interpolation method to reconstruct high-fidelity target waveforms. This approach effectively mitigates spurious issues caused by amplitude quantization during the DDFS digitalization process while reducing data node storage units. Moreover, this paper introduces single-quadrant ROM compression technology to further diminish the required storage space. Experimental results indicate that, compared to traditional DDFS methods, the optimization scheme proposed in this work achieves a ROM resource compression ratio of 1792:1 and a 14-bit output Spurious-Free Dynamic Range (SFDR) of −88.134 dBc, effectively enhancing amplitude quantization precision and significantly lowering spurious levels. This significantly improves amplitude quantization precision and reduces spurious levels. The proposed scheme demonstrates notable advantages in both spectral performance and resource utilization efficiency, making it highly suitable for resource-constrained embedded systems and high-performance applications such as radar and communication systems. Full article

The skin, recognized as the largest organ in the human body, serves a vital function in safeguarding against external threats. Severe damage to the skin can pose significant risks to human health. There is an urgent requirement for safe and effective therapies for wound healing. While phytotherapy has been widely utilized for various health conditions, the potential of Quercus glauca in promoting wound healing has not been thoroughly explored. Q. glauca is a cultivated crop known for its abundance of bioactive compounds. This study examined the wound-healing properties of Quercus glauca acorn seed coat water extract (QGASE). The findings from the study suggest that QGASE promotes wound closure in HF cells by upregulating essential markers related to the wound-healing process. Additionally, QGASE demonstrates antioxidant effects, mitigating oxidative stress and aiding in recovery from injuries induced by H₂O₂. In vivo experiments provide additional substantiation supporting the efficacy of QGASE in enhancing wound healing. The collective results indicate that QGASE may be a promising candidate for the development of innovative therapeutic strategies aimed at enhancing skin wound repair. Full article

The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback to measure patients’ experiences. However, the power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. We used the “COVID-19 Tweets” Dataset, which has over 28 million tweets, and extracted tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. We evaluated patient experience using natural language processing (NLP) techniques such as word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. The pandemic showcased the worries of mothers and providers on the risks of COVID-19. However, many people also shared how they survived the pandemic. Both providers and maternal patients had concerns regarding the pregnancy risks due to COVID-19. This model will help process improvement experts without domain expertise to understand the various domain challenges efficiently. Such insights can help decision-makers improve the patient care system. Full article

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising chemotherapeutic agent because of its selective apoptotic action on cancer cells. However, resistance to TRAIL-induced apoptosis remains a challenge in many cancers. The gintonin-enriched Panax ginseng extract fraction (GEF) has diverse pharmacological benefits. We explored the combined efficacy of GEF and TRAIL in inducing apoptosis in human renal cell carcinoma (RCC) cells. The effect of GEF treatment on the viability, clonogenic potential, wound healing, and TRAIL-induced apoptotic signaling of RCC cells was studied in vitro. Our investigation revealed that GEF pre-treatment sensitized RCC cells to TRAIL-induced apoptosis, as evidenced by DNA fragmentation and cell proliferation, colony formation, and migration inhibition. This sensitization was linked to the upregulation of death receptors 4 and 5 and alterations in apoptotic protein expression, notably, the decreased expression of the Mu-2-related death-inducing gene, a novel anti-apoptotic protein. Our findings underscore the necessity of caspase activation for GEF/TRAIL-induced apoptosis using the pan-caspase inhibitor Z-VAD-FMK. This study demonstrates that GEF sensitizes human RCC cells to TRAIL-induced apoptosis by upregulating DR4/5 and modulating apoptotic protein expression. These findings suggest a promising strategy for overcoming TRAIL resistance in cancer therapy and highlight the potential of GEF as a valuable adjunct to TRAIL-based treatments. Full article

The detection of ship fires is a critical aspect of maritime safety and surveillance, demanding high accuracy in both identification and response mechanisms. However, the scarcity of ship fire images poses a significant challenge to the development and training of effective machine learning models. This research paper addresses this challenge by exploring advanced data augmentation techniques aimed at enhancing the training datasets for ship and ship fire detection. We have curated a dataset comprising ship images (both fire and non-fire) and various oceanic images, which serve as target and source images. By employing diverse image blending methods, we randomly integrate target images of ships with source images of oceanic environments under various conditions, such as windy, rainy, hazy, cloudy, or open-sky scenarios. This approach not only increases the quantity but also the diversity of the training data, thus improving the robustness and performance of machine learning models in detecting ship fires across different contexts. Furthermore, we developed a Gradio web interface application that facilitates selective augmentation of images. The key contribution of this work is related to object extraction-based blending. We propose basic and advanced data augmentation techniques while applying blending and selective randomness. Overall, we cover eight critical steps for dataset creation. We collected 9200 ship fire and 4100 ship non-fire images. From the images, we augmented 90 ship fire images with 13 background images and achieved 11,440 augmented images. To test the augmented dataset performance, we trained Yolo-v8 and Yolo-v10 models with “Fire” and “No-fire” augmented ship images. In the Yolo-v8 case, the precision-recall curve achieved 96.6% (Fire), 98.2% (No-fire), and 97.4% mAP score achievement in all classes at a 0.5 rate. In Yolo-v10 model training achievement, we got 90.3% (Fire), 93.7 (No-fire), and 92% mAP score achievement in all classes at 0.5 rate. In comparison, both trained models’ performance is outperforming other Yolo-based SOTA ship fire detection models in overall and mAP scores. Full article

Bridges are prone to damage from various factors, impacting the overall safety of transportation networks. Accurate damage identification is crucial for maintaining bridge integrity. This study proposes a novel method using encoded images and a convolutional neural network (CNN) for bridge damage identification. By converting raw acceleration data into encoded images, the data can be represented from multiple perspectives, enhancing the extraction of essential features related to bridge damage states. The method was validated using data simulated from a continuous rigid-frame bridge model. The results demonstrate that using encoded images as inputs yields a higher recall rate, precision, and F1-score compared to using acceleration responses as inputs, achieving a comprehensive accuracy of 92%. This study concludes that the combination of encoded images and CNN provides a robust approach for accurate and efficient bridge damage identification. Full article

Equivalent circuit models represent one of the most efficient virtual representations of battery systems, with numerous applications supporting the design of electric vehicles, such as powertrain evaluation, power electronics development, and model-based state estimation. Due to their popularity, their parameter extraction and model parametrization procedures present high interest within the research community, with novel approaches at an elementary level still being identified. This article introduces and compares in detail two novel parameter extraction methods based on the distinct application of least squares linear regression in relation to the autoregressive exogenous as well as the state-space equations of the double polarization equivalent circuit model in an iterative optimization-type manner. Following their application using experimental data obtained from an NCA Sony VTC6 cell, the results are benchmarked against a method employing differential evolution. The results indicate the least squares linear regression applied to the state-space format of the model as the best overall solution, providing excellent accuracy similar to the results of differential evolution, but averaging only 1.32% of the computational cost. In contrast, the same linear solver applied to the autoregressive exogenous format proves complementary characteristics by being the fastest process but presenting a penalty over the accuracy of the results. Full article

Background/Objectives: Marrubium vulgare L. (M. vulgare), the white horehound, is well known for treating inflammation-related diseases. Methods: In this context, we investigated the efficacy of M. vulgare ingredients in treating Alzheimer’s disease using various in vitro and in silico antioxidant, anti-inflammatory, anti-cholinesterase, and anti-tyrosinase mechanisms. Results: In our results, sixty-one components were tentatively identified using gas and liquid chromatography (GC-MS and LC-MSⁿ) and categorized as hydrocarbons, fatty acids, and polyphenolics. The extract inhibited linoleic oxidation with an IC₅₀ value of 114.72 µg/mL, captured iron (Fe²⁺) ions with an IC₅₀ value of 164.19 µg/mL, and displayed reducing power. In addition, the extract showed radical-scavenging ability towards DPPH^•, NO^•, ABTS^•+, and H₂O₂ assays compared to L-ascorbic acid and butylated hydroxytoluene. The DPPH^• was scavenged by 77.62% at 100 µg/mL, and NO^•, ABTS^•+, and H₂O₂ were scavenged with IC₅₀ values of 531.66, 117.51, and 143.10 µg/mL, respectively. M. vulgare also exhibited discriminating anti-inflammatory potency against cyclooxygenase (COX-2) with IC₅₀ values of 619.15 µg/mL compared to celecoxib (p > 0.05). Notably, three Alzheimer’s biomarkers, acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and tyrosinase were significantly inhibited. The molecular docking study supposed that the phenylethanoid glycosides of samioside and forsythoside B inhibited AChE and tyrosinase enzymes with low binding affinities of −9.969 and −8.804 kcal/mol, respectively. Marruboside was a proper inhibitor of COX and BChE enzymes with a binding score of −10.218 and −10.306 kcal/mol, respectively. Conclusions: M. vulgare extract showed significant inhibitory actions, which suggest that it could have a promising potential as an anti-Alzheimer agent. Full article

Background: Rosa canina L. (rosehip) is used worldwide in traditional medicine as a plant with medicinal properties. However, its anti-obesity effects are not fully explained on a transcriptional level. Methods: In the present work, the 3T3-L preadipocytes were utilized to explore the impact of R. canina fruit extract (RCE) on the cellular and molecular pathways involved in adipocyte hypertrophy. Results: Obtained results showed the ability of RCE to reduce lipid overloads in hypertrophic adipocytes associated with the down-regulation of mRNA expressions of adipogenic transcription factors such as PPARγ, C/EBPα, and SREBP-1c as well as genes involved in lipid biosyntheses such as FAS, LPL, and aP2. Moreover, obesity-associated oxidative stress (antioxidant enzyme activities and ROS generation) and inflammation were ameliorated in RCE-treated hypertrophic adipocytes. The mRNA and protein levels of adipokines such as leptin, resistin, and adiponectin were restored to more favorable levels. Conclusions: Rosa canina fruit might be a valuable source of phytochemicals in preventing obesity and obesity-related metabolic complications. Full article

This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) with a maximum-overlap discrete wavelet transform (MODWT) layer and an adaptive neuro-fuzzy network (ANFN) layer. The proposed method has the advantage of developing existing deep networks and machine learning to reconstruct signals through CW radars to acquire ECG biological information in a non-contact manner. The fully connected (FC) layer of the CNN is replaced by an ANFN layer suitable for resolving black boxes and handling complex nonlinear data. The MODWT layer is activated via discrete wavelet transform frequency decomposition with maximum-overlap to extract ECG-related frequency components from radar signals to generate essential information. In order to evaluate the performance of the proposed model, we use a dataset of clinically recorded vital signs with a synchronized reference sensor signal measured simultaneously. As a result of the experiment, the performance is evaluated by the mean squared error (MSE) between the measured and reconstructed ECG signals. The experimental results reveal that the proposed model shows good performance in comparison to the existing deep learning model. From the performance comparison, we confirm that the ANFN layer preserves the nonlinearity of information received from the model by replacing the fully connected layer used in the conventional deep learning model. Full article

As the most common psychiatric symptom, depression represents a subject of high interest for the medical community. Background/Objectives: International guidelines consider selective serotonin reuptake inhibitors (SSRIs) the first-line treatment of depression. Although having better efficacy and tolerability in comparison to tricyclic antidepressants or monoamine oxidase inhibitors, the diversity and potential severity of adverse effects and interactions manifested by SSRIs, combined with the frequency of prescriptions, lead to the necessity of evaluating real-world data. The aim of this study was to identify and evaluate the drug interactions reported in EudraVigilance (EV) for the six SSRIs representatives that are authorized in Europe: fluoxetine (FXT), fluvoxamine (FVM), citalopram (CIT), escitalopram (ESC), paroxetine (PAR) and sertraline (SER). The entire class of SSRIs was examined as a comparator to identify whether one of the representatives was more prone to reporting. Methods: Descriptive analysis and disproportionality analysis were conducted on data extracted from the EV database. Results: A total of 326,450 adverse reactions (ADRs) were reported for the SSRIs group. Approximately a quarter of these (n = 83,201; 25.46%) were reported for SER and 22.37% (n = 73,131) for PAR. Of the total ADRs reported, 2.12% (n = 6925) represent preferred terms related to drug-drug interactions (DDIs): SER (n = 1474; 22.37%), CIT (n = 1272, 19.86), and FXT (n = 1309, 19.83%). Specific ADRs related to inhibitory activity represent 0.98%, and for potentiating activity, 1.89%. Conclusions: Although representing a small value of the total ADRs, DDIs may be related to severe outcomes. Awareness should be raised for this category of ADRs that can be reduced by the joined efforts of physicians and pharmacists. Full article

Background/Objectives: Colorectal cancer (CRC) is the third most diagnosed cancer globally. Radiotherapy is a common treatment strategy for patients but factors such as gene expressions and molecular mechanism effects may affect tumor radioresponse. The aim of this review is to systematically identify genes suggested to have molecular mechanism effects on the radioresponsiveness of CRC patients. Methods: By following the PRISMA guidelines, a comprehensive literature search was conducted on Pubmed, EMBASE and Cochrane Library. After exclusion and inclusion criteria sorting and critical appraisal for study quality, data were extracted from seven studies. A gene set analysis was conducted on reported genes. Results: From the seven studies, 56 genes were found to have an effect on CRC radioresponsiveness. Gene set analysis show that out of these 56 genes, 24 genes have roles in pathways which could affect cancer radioresponse. These are AKT1, APC, ATM, BRAF, CDKN2A, CTNNB1, EGFR, ERBB2, FLT3, KRAS, MET, mTOR, MYC, NFKB1, KRAS, PDGFRA, PIK3CA, PTEN, PTGS1, PTGS2, RAF1, RET, SMAD4 and TP53. The current project was conducted between the period May 2024 to August 2024. Conclusions: The current review systematically presented 56 genes which have been reported to be related to RT or CRT treatment effectiveness in rectal cancer patients. Gene set analysis shows that nearly half of the genes were involved in apoptosis, DNA damage response and repair, inflammation and cancer metabolism molecular pathways that could affect cancer radioresponse. The gene cohort identified in this study may be used as a foundation for future works focusing on the molecular mechanism of specific pathways contributing to the radioresponse of CRC. Full article

Introduction: The choice of prosthesis for vertebral body reconstruction (VBR) remains a controversial issue due to the lack of a reliable solution. The subsidence rate of the most commonly used titanium mesh cages (TMC) ranges from 42.5% to 79.7%. This problem is primarily caused by the differences in the elastic modulus between the TMC and bone. This review aims to summarize the clinical and radiological outcomes of new 3D-printed artificial vertebral bodies (3DP AVB). Methods: A literature search of PubMed, Scopus and Google Scholar was conducted to extract relevant studies. After screening the titles and abstracts, a total of 50 articles were selected for full-text analysis. Results: Preliminary data suggest fewer implant-related complications with 3DP AVB. Most comparative studies indicate significantly lower subsidence rates, reduced operation times and decreased intraoperative blood loss. However, the scarcity of randomized clinical trials and the high variability of the results warrant caution. Conclusion: Most literature data show an advantage of 3DP AVB in terms of the operation time, intraoperative blood loss and subsidence rate. However, long manufacturing times, high costs and regulatory issues are this technology’s main drawbacks. Full article