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The synthesis of Al-ZnO nanoparticles (NPs) was achieved using a green synthesis approach, utilizing leaf extract from Anisomeles indica (L.) in a straightforward co-precipitation method. The goal of this study was to investigate the production of Al-ZnO nanoparticles through the reduction and capping method utilizing Anisomeles indica (L.) leaf extract. The powder X-ray diffraction, UV spectroscopy, Fourier transform infrared spectroscopy, and scanning electron microscopy with EDAX analysis were used to analyze the nanoparticles. X-ray diffraction analysis confirmed the presence of spherical structures with an average grain size of 40 nm in diameter, while UV–visible spectroscopy revealed a prominent absorption peak at 360 nm. FTIR spectra demonstrated the presence of stretching vibrations associated with O-H, N-H, C=C, C-N, and C=O as well as C-Cl groups indicating their involvement in the reduction and stabilization of nanoparticles. SEM image revealed the presence of spongy, spherical, porous agglomerated nanoparticles, confirming the chemical composition of Al-ZnO nanoparticles through the use of the EDAX technique. Al-ZnO nanoparticles showed increased bactericidal activity against both Gram-positive and Gram-negative bacteria. The antioxidant property of the green synthesized Al-ZnO nanoparticles was confirmed by DPPH radical scavenging with an IC50 value of 23.52 indicating excellent antioxidant capability. Green synthesized Al-ZnO nanoparticles were shown in in vivo studies on HeLa cell lines to be effective for cancer treatment. Additionally, α-amylase inhibition assay and α-glucosidase inhibition assay demonstrated their potent anti-diabetic activities. Moving forward, the current methodology suggests that the presence of phenolic groups, flavonoids, and amines in Al-ZnO nanoparticles synthesized with Anisomeles indica (L.) extract exhibit significant promise for eliciting biological responses, including antioxidant and anti-diabetic effects, in the realms of biomedical and pharmaceutical applications. [ABSTRACT FROM AUTHOR]

Climate change, a pressing global concern, poses significant challenges to agricultural systems worldwide. Among the myriad impacts of climate change, the cultivation of kiwifruit trees (Actinidia spp.) faces multifaceted challenges. In this review, we delve into the intricate effects of climate change on kiwifruit production, which span phenological shifts, distributional changes, physiological responses, and ecological interactions. Understanding these complexities is crucial for devising effective adaptation and mitigation strategies to safeguard kiwifruit production amidst climate variability. This review scrutinizes the influence of rising global temperatures, altered precipitation patterns, and a heightened frequency of extreme weather events on the regions where kiwifruits are cultivated. Additionally, it delves into the ramifications of changing climatic conditions on kiwifruit tree physiology, phenology, and susceptibility to pests and diseases. The economic and social repercussions of climate change on kiwifruit production, including yield losses, livelihood impacts, and market dynamics, are thoroughly examined. In response to these challenges, this review proposes tailored adaptation and mitigation strategies for kiwifruit cultivation. This includes breeding climate-resilient kiwifruit cultivars of the Actinidia species that could withstand drought and high temperatures. Additional measures would involve implementing sustainable farming practices like irrigation, mulching, rain shelters, and shade management, as well as conserving soil and water resources. Through an examination of the literature, this review showcases the existing innovative approaches for climate change adaptation in kiwifruit farming. It concludes with recommendations for future research directions aimed at promoting the sustainability and resilience of fruit production, particularly in the context of kiwifruit cultivation, amid a changing climate. [ABSTRACT FROM AUTHOR]

This research aims to use energy harvested from conductive materials to power microelectronic components. The proposed method involves using vibration-based energy harvesting to increase the natural vibration frequency, reduce the need for battery replacement, and minimise chemical waste. Piezoelectric transduction, known for its high-power density and ease of application, has garnered significant attention. Additionally, graphene, a non-piezoelectric material, exhibits good piezoelectric properties. The research explores a novel method of printing graphene material using 3D printing, specifically Direct Ink Writing (DIW) and fused deposition modelling (FDM). Both simulation and experimental techniques were used to analyse energy harvesting. The experimental technique involved using the cantilever beam-based vibration energy harvesting method. The results showed that the DIW-derived 3D-printed prototype achieved a peak power output of 12.2 µW, surpassing the 6.4 µW output of the FDM-derived 3D-printed prototype. Furthermore, the simulation using COMSOL Multiphysics yielded a harvested output of 0.69 µV. [ABSTRACT FROM AUTHOR]

Anodized aluminum oxide (AAO) molds were used for the production of large-area and inexpensive superhydrophobic polymer films. A controlled anodization methodology was developed for the fabrication of hierarchical micro–nanoporous (HMN) AAO imprint molds (HMN-AAO), where phosphoric acid was used as both an electrolyte and a widening agent. Heat generated upon repetitive high-voltage (195 V) anodization steps is effectively dissipated by establishing a cooling channel. On the HMN-AAO, within the hemispherical micropores, arrays of hexagonal nanopores are formed. The diameter and depth of the micro- and nanopores are 18/8 and 0.3/1.25 µm, respectively. The gradual removal of micropatterns during etching in both the vertical and horizontal directions is crucial for fabricating HMN-AAO with a high aspect ratio. HMN-AAO rendered polycarbonate (PC) and polymethyl methacrylate (PMMA) films with respective water contact angles (WCAs) of 153° and 151°, respectively. The increase in the WCA is 80% for PC (85°) and 89% for PMMA (80°). On the PC and PMMA films, mechanically robust arrays of nanopillars are observed within the hemispherical micropillars. The micro–nanopillars on these polymer films are mechanically robust and durable. Regular nanoporous AAO molds resulted in only a hydrophobic polymer film (WCA = 113–118°). Collectively, the phosphoric acid-based controlled anodization strategy can be effectively utilized for the manufacturing of HMN-AAO molds and roll-to-roll production of durable superhydrophobic surfaces. [ABSTRACT FROM AUTHOR]

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were performed using two different domain adaptation architectures. The domain adversarial neural network with two gradient reversal layers and VGG13 as a feature extractor achieved the highest accuracy and F1 score of 0.7567 and 0.75, respectively, representing an 18.47% improvement in accuracy over the baseline model. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on the in vitro mass propagation of pear (Pyrus spp.) trees through vegetative propagation, demonstrating high efficiency in shoot multiplication across various pear species. However, the low in vitro rooting rates remain a significant barrier to the practical application and commercialization of mass propagation. This study aims to determine the favorable conditions for inducing root formation in the in vitro microshoots of Pyrus genotypes. The base of the microshoots was exposed to a high concentration (2 mg L−1) of auxins (a combination of IBA and NAA) for initial root induction at the moment when callus formation begins. The microshoots were then transferred to an R1 medium (1/2 MS with 30 g L−1 sucrose without PGRs) to promote root development. This method successfully induced rooting in three European pear varieties, one Asian pear variety, and a European–Asian hybrid, resulting in rooting rates of 66.7%, 87.2%, and 100% for the European pear (P. communis), 60% for the Asian pear (P. pyrifolia), and 83.3% for the hybrid pear (P. pyrifolia × P. communis) with an average of 25 days. In contrast, the control group (MS medium) exhibited rooting rates of 0–13.3% after 60 days of culture. These findings will enhance in vitro root induction for various pear varieties and support the mass propagation and acclimatization of pear. The in vitro root induction method developed in this study has the potential for global commercial application in pear cultivation. [ABSTRACT FROM AUTHOR]

This study presents a promising method for creating high-performance supercapacitor electrodes. The approach involves crafting a unique composite material—nickel-cobalt-layered double hydroxides (NiCo-LDH) grown on carbon nanoballs (CNBs). This is achieved by first creating a special carbon material rich in oxygen and nitrogen from a polybenzoxazine source. At first, eugenol, ethylene diamine and paraformaldehyde undergo Mannich condensation to form the benzoxazine monomer, which undergoes self-polymerization in the presence of heat to produce polybenzoxazine. This was then carbonized and activated to produce CNBs containing heteroatoms. Then, through a hydrothermal technique, NiCo-LDH nanocages are directly deposited onto the CNBs, eliminating the need for complicated templates. The amount of CNBs used plays a crucial role in performance. By optimizing the CNB content to 50%, a remarkable specific capacitance of 1220 F g−1 was achieved, along with excellent rate capability and impressive cycling stability, retaining 86% of its capacitance after 5000 cycles. Furthermore, this NiCo-LDH/CNB composite, when combined with active carbon in a supercapacitor configuration, delivered outstanding overall performance. The exceptional properties of this composite, combined with its simple and scalable synthesis process, position it as a strong contender for next-generation sustainable energy storage devices. The ease of fabrication also opens doors for its practical application in advancing energy storage technologies. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback–Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks. [ABSTRACT FROM AUTHOR]

Hydrogels are gaining popularity for use in wearable electronics owing to their inherent biomimetic characteristics, flexible physicochemical properties, and excellent biocompatibility. Among various hydrogels, conductive polymer-based hydrogels (CP HGs) have emerged as excellent candidates for future wearable sensor designs. These hydrogels can attain desired properties through various tuning strategies extending from molecular design to microstructural configuration. However, significant challenges remain, such as the limited strain-sensing range, significant hysteresis of sensing signals, dehydration-induced functional failure, and surface/interfacial malfunction during manufacturing/processing. This review summarizes the recent developments in polymer-hydrogel-based wearable electrochemical biosensors over the past five years. Initially serving as carriers for biomolecules, polymer-hydrogel-based sensors have advanced to encompass a wider range of applications, including the development of non-enzymatic sensors facilitated by the integration of nanomaterials such as metals, metal oxides, and carbon-based materials. Beyond the numerous existing reports that primarily focus on biomolecule detection, we extend the scope to include the fabrication of nanocomposite conductive polymer hydrogels and explore their varied conductivity mechanisms in electrochemical sensing applications. This comprehensive evaluation is instrumental in determining the readiness of these polymer hydrogels for point-of-care translation and state-of-the-art applications in wearable electrochemical sensing technology. [ABSTRACT FROM AUTHOR]

The increasing burden of Alzheimer's disease (AD) emphasizes the need for effective diagnostic and therapeutic strategies. Despite available treatments targeting amyloid beta (Aβ) plaques, disease-modifying therapies remain elusive. Early detection of mild cognitive impairment (MCI) patients at risk for AD conversion is crucial, especially with anti-Aβ therapy. While plasma biomarkers hold promise in differentiating AD from MCI, evidence on predicting cognitive decline is lacking. This study's objectives were to evaluate whether plasma protein biomarkers could predict both cognitive decline in non-demented individuals and the conversion to AD in patients with MCI. This study was conducted as part of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a prospective, community-based cohort. Participants were based on plasma biomarker availability and clinical diagnosis at baseline. The study included MCI (n = 50), MCI-to-AD (n = 21), and cognitively unimpaired (CU, n = 40) participants. Baseline plasma concentrations of six proteins—total tau (tTau), phosphorylated tau at residue 181 (pTau181), amyloid beta 42 (Aβ42), amyloid beta 40 (Aβ40), neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP)—along with three derivative ratios (pTau181/tTau, Aβ42/Aβ40, pTau181/Aβ42) were analyzed to predict cognitive decline over a six-year follow-up period. Baseline protein biomarkers were stratified into tertiles (low, intermediate, and high) and analyzed using a linear mixed model (LMM) to predict longitudinal cognitive changes. In addition, Kaplan–Meier analysis was performed to discern whether protein biomarkers could predict AD conversion in the MCI subgroup. This prospective cohort study revealed that plasma NFL may predict longitudinal declines in Mini-Mental State Examination (MMSE) scores. In participants categorized as amyloid positive, the NFL biomarker demonstrated predictive performance for both MMSE and total scores of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-TS) longitudinally. Additionally, as a baseline predictor, GFAP exhibited a significant association with cross-sectional cognitive impairment in the CERAD-TS measure, particularly in amyloid positive participants. Kaplan–Meier curve analysis indicated predictive performance of NFL, GFAP, tTau, and Aβ42/Aβ40 on MCI-to-AD conversion. This study suggests that plasma GFAP in non-demented participants may reflect baseline cross-sectional CERAD-TS scores, a measure of global cognitive function. Conversely, plasma NFL may predict longitudinal decline in MMSE and CERAD-TS scores in participants categorized as amyloid positive. Kaplan–Meier curve analysis suggests that NFL, GFAP, tTau, and Aβ42/Aβ40 are potentially robust predictors of future AD conversion. [ABSTRACT FROM AUTHOR]

Cryopreservation is a promising method for the long-term preservation of plant germplasm, especially for vegetatively propagated species like freesias. In this study, we investigate streamlining the cryopreservation process for 'Sunny Gold' Freesia, starting from effective in vitro initiation and proliferation using various plant growth regulator combinations. We also assess the impact of subculture on regrowth rates after cryopreservation. The shoot tips were successfully initiated in vitro after sterilization. The shoots were multiplied an average of three times in media containing N6-benzyladenine and kinetin. The regrowth rates of non-cryopreserved shoot tips excised from different subculture cycles did not differ significantly, with rates of 44% observed for plants from more than five subcultures and 47% for those from three subcultures. However, only the shoot tips excised from cultures subjected to three subculture cycles were able to recover after cryopreservation, with a regrowth rate of 31%. Our findings lay the groundwork for the development of an efficient cryopreservation protocol for freesias in the future. [ABSTRACT FROM AUTHOR]

In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky–Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R2 = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R2 = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing. [ABSTRACT FROM AUTHOR]

Background: An evaluation of the persistence of symptoms following COVID-19 in economically active young and middle-aged adults is crucial due to its significant socioeconomic impact resulting from compromised work performance. Methods: A prospective, multicenter study at 12 South Korean hospitals from January to December 2022 involved telephone interviews along with validated questionnaires. Results: Among 696 participants with a median age of 32 and no prior diagnoses, 30% of participants experienced persistent fatigue, while 21.4% suffered from sleep disturbance at 6 months following infection. Additionally, approximately 25% of the participants exhibited depression that endured for up to 6 months. Symptomatic individuals at 3 months exhibited a significantly higher prevalence of persistent fatigue, sleep disturbances, and depression at 6 months compared to those who remained asymptomatic. Notably, sleep disturbance and persistent fatigue at 3 months emerged as significant independent predictors of the presence of depression at 6 months. Conclusions: Even among young and middle-aged healthy adults, prolonged fatigue, sleep disturbance, and depression exhibit a significant prevalence and persisted for up to 6 months. Therefore, implementing a workplace management protocol for these symptoms is essential to mitigate the socioeconomic burden caused by the impairment of work efficiency. [ABSTRACT FROM AUTHOR]

Background: This study aimed to analyze the correlation between the cycle threshold (Ct) values of severe fever with thrombocytopenia syndrome (SFTS) virus small (S) and middle (M) segments and the SFTS viral load, aiming to estimate the initial viral load and predict prognosis in the early clinical course. Method: A retrospective study was conducted with confirmed SFTS patients at Jeju National University Hospital (2016–2022). Patients were categorized into non-fatal and fatal groups. Results: This study included 49 patients with confirmed SFTS (non-fatal group, n = 42; fatal group, n = 7). A significant negative correlation (−0.783) was observed between the log SFTS viral load and Ct values (p < 0.001). This negative correlation was notably stronger in the fatal group (correlation coefficient −0.940) than in the non-fatal group (correlation coefficient −0.345). Conclusion: In this study, we established a correlation between SFTS viral load and Ct values for estimating the initial viral load and early predicting prognosis. These results are expected to offer valuable insights for SFTS patient treatment and prognosis prediction. [ABSTRACT FROM AUTHOR]

In this research work, a coated paper was prepared with poly (butylene adipate-co-terephthalate) (PBAT) film to explore its use in eco-friendly food packaging. The paper was coated with PBAT film for packaging using hot pressing, a production method currently employed in the packaging industry. The coated papers were evaluated for their structural, mechanical, thermal, and barrier properties. The structural morphology and chemical analysis of the coated paper confirmed the consistent formation of PBAT bi-layered on paper surfaces. Surface coating with PBAT film increased the water resistance of the paper samples, as demonstrated by tests of barrier characteristics, including the water vapor transmission rate (WVTR), oxygen transmission rate (OTR), and water contact angle (WCA) of water drops. The transmission rate of the clean paper was 2010.40 cc m−2 per 24 h for OTR and 110.24 g m−2 per 24 h for WVTR. If the PBAT-film was coated, the value decreased to 91.79 g m−2 per 24 h and 992.86 cc m−2 per 24 h. The hydrophobic nature of PBAT, confirmed by WCA measurements, contributed to the enhanced water resistance of PBAT-coated paper. This result presents an improved PBAT-coated paper material, eliminating the need for adhesives and allowing for the fabrication of bi-layered packaging. [ABSTRACT FROM AUTHOR]

This study aims to critically assess different micromechanical analysis models applied to carbon-fiber-reinforced plastic (CFRP) composites, employing micromechanics-based homogenization to accurately predict their effective properties. The paper begins with the simplest Voigt and Reuss models and progresses to more sophisticated micromechanics-based models, including the Mori–Tanaka and Method of Cells (MOC) models. It provides a critical review of the areas in which these micromechanics-based models are effective and analyses of their limitations. The numerical analysis results were confirmed through finite element simulations of the periodic representative volume element (RVE). Furthermore, the effective properties predicted by these micromechanics-based models were validated via experiments conducted on IM7/5320-1 composite material with a fiber volume fraction of 0.62. [ABSTRACT FROM AUTHOR]

Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called "TimeTector-Twin-Branch Shared LSTM Autoencoder" which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models. [ABSTRACT FROM AUTHOR]

In this study, we utilized NDVI data from the moderate resolution imaging spectroradiometer (MODIS) alongside climatic variables obtained from a reanalyzed dataset to analyze Arctic greening during the summer months (June–September) of the last two decades. This investigation entailed a detailed analysis of these changes across various temporal scales. The data indicated a continuous trend of Arctic greening, evidenced by a 1.8% per decade increment in the NDVI. Notably, significant change points were identified in June 2012 and September 2013. A comparative assessment of NDVI pre- and post-these inflection points revealed an elongation of the Arctic greening trend. Furthermore, an anomalous increase in NDVI of 2% per decade was observed, suggesting an acceleration in greening. A comprehensive analysis was conducted to decipher the correlation between NDVI, temperature, and energy budget parameters to elucidate the underlying causes of these change points. Although the correlation between these variables was relatively low throughout the summer months, a distinct pattern emerged when these periods were dissected and examined in the context of the identified change points. Preceding the change point, a strong correlation (approximately 0.6) was observed between all variables; however, this correlation significantly diminished after the change point, dropping to less than half. This shift implies an introduction of additional external factors influencing the Arctic greening trend after the change point. Our findings provide foundational data for estimating the tipping point in Arctic terrestrial ecosystems. This is achieved by integrating the observed NDVI change points with their relationship with climatic variables, which are essential in comprehensively understanding the dynamics of Arctic climate change, particularly with alterations in tundra vegetation. [ABSTRACT FROM AUTHOR]

This study aimed to review the considerations for choosing a suitable sealer according to various endodontic scenarios. An electronic search of PubMed, Scopus, and the Web of Science was undertaken for the keywords of 'sealer choosing', 'appropriate sealer', 'suitable sealer', 'sealer for clinical scenario', and 'sealer for clinical situations'. However, the literature review revealed a lack of studies with practical clinical recommendations regarding the choice of appropriate endodontic root canal sealers for particular clinical situations of root canal treatment. Therefore, a narrative review was undertaken under the basis of the characteristics of an epoxy resin-based sealer (ERS) versus a calcium silicate-based sealer (CSS). Based on the evidence found through the review, the choice of an appropriate sealer in a variety of clinical scenarios was proposed. An ERS is recommended for one-visit non-vital cases, teeth with periodontal involvement, cracked teeth, and internal root resorption without root perforation. A CSS is recommended for vital or non-vital cases in multiple visits, teeth with internal root resorption with perforation or internal approach for external cervical resorption, teeth with open apices, and teeth with iatrogenic aberrations. [ABSTRACT FROM AUTHOR]

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance. [ABSTRACT FROM AUTHOR]

Biobased plastics provide a sustainable alternative to conventional food packaging materials, thereby reducing the environmental impact. The present study investigated the effectiveness of chitosan with varying levels of Moringa oleifera seed powder (MOSP) and tannic acid (TA). Chitosan (CS) biocomposite films with tannic acid acted as a cross-linker, and Moringa oleifera seed powder served as reinforcement. To enhance food packaging and film performance, Moringa oleifera seed powder was introduced at various loadings of 1.0, 3.0, 5.0, and 10.0 wt.%. Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy analyses were performed to study the structure and morphology of the CS/TA/MOSP films. The scanning electron microscopy results confirmed that chitosan/TA with 10.0 wt.% of MOSP produced a lightly miscible droplet/matrix structure. Furthermore, mechanical properties, swelling, water solubility, optical barrier, and water contact angle properties of the film were also calculated. With increasing Moringa oleifera seed powder contents, the biocomposite films' antimicrobial and antifungal activity increased at the 10.0 wt.% MOSP level; all of the observed bacteria [Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), Aspergillus niger (A. niger), and Candida albicans (C. albicans)] had a notably increased percentage of growth. The film, with 10.0 wt.% MOSP content, effectively preserves strawberries' freshness, making it an ideal food packaging material. [ABSTRACT FROM AUTHOR]

The green synthesis of zinc oxide nanoparticles (ZnO NPs) using plants has grown in significance in recent years. ZnO NPs were synthesized in this work via a chemical precipitation method with Jasminum sambac (JS) leaf extract serving as a capping agent. These NPs were characterized using UV-vis spectroscopy, FT-IR, XRD, SEM, TEM, TGA, and DTA. The results from UV-vis and FT-IR confirmed the band gap energies (3.37 eV and 3.50 eV) and the presence of the following functional groups: CN, OH, C=O, and NH. A spherical structure and an average grain size of 26 nm were confirmed via XRD. The size and surface morphology of the ZnO NPs were confirmed through the use of SEM analysis. According to the TEM images, the ZnO NPs had an average mean size of 26 nm and were spherical in shape. The TGA curve indicated that the weight loss starts at 100 °C, rising to 900 °C, as a result of the evaporation of water molecules. An exothermic peak was seen during the DTA analysis at 480 °C. Effective antibacterial activity was found at 7.32 ± 0.44 mm in Gram-positive bacteria (S. aureus) and at 15.54 ± 0.031 mm in Gram-negative (E. coli) bacteria against the ZnO NPs. Antispasmodic activity: the 0.3 mL/mL sample solution demonstrated significant reductions in stimulant effects induced by histamine (at a concentration of 1 µg/mL) by (78.19%), acetylcholine (at a concentration of 1 µM) by (67.57%), and nicotine (at a concentration of 2 µg/mL) by (84.35%). The antipyretic activity was identified using the specific Shodhan vidhi method, and their anti-inflammatory properties were effectively evaluated with a denaturation test. A 0.3 mL/mL sample solution demonstrated significant reductions in stimulant effects induced by histamine (at a concentration of 1 µg/mL) by 78.19%, acetylcholine (at a concentration of 1 µM) by 67.57%, and nicotine (at a concentration of 2 µg/mL) by 84.35%. These results underscore the sample solution's potential as an effective therapeutic agent, showcasing its notable antispasmodic activity. Among the administered doses, the 150 mg/kg sample dose exhibited the most potent antipyretic effects. The anti-inflammatory activity of the synthesized NPs showed a remarkable inhibition percentage of (97.14 ± 0.005) at higher concentrations (250 µg/mL). Furthermore, a cytotoxic effect was noted when the biologically synthesized ZnO NPs were introduced to treated cells. [ABSTRACT FROM AUTHOR]

With the growing demand for orthognathic surgery and other facial treatments, the accurate identification of anatomical landmarks has become crucial. Recent advancements have shifted towards using three-dimensional radiologic analysis instead of traditional two-dimensional methods, as it allows for more precise treatment planning, primarily relying on direct identification by clinicians. However, manual tracing can be time-consuming, mainly when dealing with a large number of patients. This study compared the accuracy and reliability of identifying anatomical landmarks using artificial intelligence (AI) and manual identification. Thirty patients over 19 years old who underwent pre-orthodontic and orthognathic surgery treatment and had pre-orthodontic three-dimensional radiologic scans were selected. Thirteen anatomical indicators were identified using both AI and manual methods. The landmarks were identified by AI and four experienced clinicians, and multiple ANOVA was performed to analyze the results. The study results revealed minimal significant differences between AI and manual tracing, with a maximum deviation of less than 2.83 mm. This indicates that utilizing AI to identify anatomical landmarks can be a reliable method in planning orthognathic surgery. Our findings suggest that using AI for anatomical landmark identification can enhance treatment accuracy and reliability, ultimately benefiting clinicians and patients. [ABSTRACT FROM AUTHOR]

The Metaverse and Natural Language Processing (NLP) technologies have combined to fundamentally change the nature of digital sociability. Our understanding of social interaction needs to be reevaluated as the Metaverse's influence spreads into more areas of daily life, such as AI-driven gaming, interactive training companions, museum exhibits, personalized fitness coaching, virtual mental health assistance, language translation services, virtual tour guiding, and virtual conferencing. This study analyzes how NLP is changing social relationships in these Metaverse applications. We examine how NLP algorithms influence societal norms, individual behaviors, interpersonal connections, and improve the user experience using a multi-method approach incorporating user surveys and sentiment analysis. Our study's findings show how NLP can enhance interactive experiences while also pointing out related issues like potential bias and moral problems. Our study provides a foundational analysis, shedding light on the challenges of negotiating a social environment in the Metaverse that is molded by cutting-edge NLP. It offers stakeholders in academia and public policy essential assistance that helps them understand and manage the complex ramifications of this changing socio-technological paradigm. [ABSTRACT FROM AUTHOR]

Diet management has long been an important practice in healthcare, enabling individuals to get an insight into their nutrient intake, prevent diseases, and stay healthy. Traditional methods based on self-reporting, food diaries, and periodic assessments have been used for a long time to control dietary habits. These methods have shown limitations in accuracy, compliance, and real-time analysis. The rapid advancement of digital technologies has revolutionized healthcare, including the diet control landscape, allowing for innovative solutions to control dietary patterns and generate accurate and personalized recommendations. This study examines the potential of digital technologies in diet management and their effectiveness in anti-aging healthcare. After underlining the importance of nutrition in the aging process, we explored the applications of mobile apps, web-based platforms, wearables devices, sensors, the Internet of Things, artificial intelligence, blockchain, and other technologies in managing dietary patterns and improving health outcomes. The research further examines the effects of digital dietary control on anti-aging healthcare, including improved nutritional monitoring, personalized recommendations, and behavioral and sustainable changes in habits, leading to an expansion of longevity and health span. The challenges and limitations of digital diet monitoring are discussed, and some future directions are provided. Although many digital tools are used in diet control, their accuracy, effectiveness, and impact on health outcomes are not discussed much. This review consolidates the existing literature on digital diet management using emerging digital technologies to analyze their practical implications, guiding researchers, healthcare professionals, and policy makers toward personalized dietary management and healthy aging. [ABSTRACT FROM AUTHOR]

Fractals are widely recognized as one of the best geometric models to depict soil roughness on various scales from tillage to micro-topography smaller than radar wavelength. However, most fractal approaches require an additional geometric description of experimental sites to be analysed by existing radiative transfer models. For example, fractal dimension or spectral parameter is often related to root-mean-square (RMS) height to be characterized as the microwave surface. However, field measurements hardly represent multi-scale roughness. In this study, we rescaled Power Spectral Density with Synthetic Aperture Radar (SAR)-inverted rms height, and estimated non-stationary fractal roughness to accommodate multi-scale roughness into a radiative transfer model structure. As a result, soil moisture was retrieved over the Yanco site in Australia. Local validation shows that the Integral Equation Model (IEM) poorly simulated backscatters using inverted roughness as compared to fractal roughness even in anisotropic conditions. This is considered due to a violation of time-invariance assumption used for inversion. Spatial analysis also shows that multi-scale fractal roughness better illustrated the hydrologically reasonable backscattering partitioning, as compared to inverted roughness. Fractal roughness showed a greater contribution of roughness to backscattering in dry conditions. Differences between IEM backscattering and measurement were lower, even when the isotropic assumption of the fractal model was violated. In wet conditions, the contribution of soil moisture to backscattering was shown more clearly by fractal roughness. These results suggest that the multi-scale fractal roughness can be better adapted to the IEM even in anisotropic conditions than the inversion to assume time-invariance of roughness. [ABSTRACT FROM AUTHOR]

The regulated and targeted administration of hydrophobic and hydrophilic drugs is both promising and challenging in the field of drug delivery. Developing a hydrogel which is responsive to dual stimuli is considered a promising and exciting research area of study. In this work, melamine functionalized poly-N-isopropyl acrylamide-co-glycidyl methacrylate copolymer has been developed by copolymerizing glycidyl methacrylate (GMA) monomer with N-isopropyl acrylamide (NIPAm) and further functionalized with melamine units (pNIPAm-co-pGMA-Mela). The prepared pNIPAm-co-pGMA-Mela copolymer hydrogel was characterized using various characterization techniques, including 1H NMR, FTIR, SEM, zeta potential, and particle size analysis. A hydrophobic drug (ibuprofen, Ibu) and hydrophilic drug (5-fluorouracil, 5-Fu) were selected as model drugs. Dual pH and temperature stimuli-responsive drug release behavior of the pNIPAm-co-pGMA-Mela hydrogel was evaluated under different pH (pH 7.4 and 4.0) and temperature (25 °C, 37 °C, and 45 °C) conditions. Furthermore, the in vitro biocompatibility of the developed pNIPAm-co-pGMA-Mela copolymer hydrogel was determined on MDA-MB-231 cells. The pH and temperature-responsive drug delivery study results reveal that the pNIPAm-co-pGMA-Mela hydrogel system is responsive to both pH and temperature stimuli and exhibits about ~100% of Ibu and 5-Fu, respectively, released at pH 4.0/45 °C. Moreover, the MTT assay and hemocompatibility analysis results proved that the pNIPAm-co-pGMA-Mela hydrogel system is biocompatible and hemocompatible, suggesting that that it could be used for drug delivery applications. The experimental results suggest that the proposed pNIPAm-co-pGMA-Mela hydrogel system is responsive to dual pH and temperature stimuli, and could be a promising drug carrier system for both hydrophilic and hydrophobic drug delivery applications. [ABSTRACT FROM AUTHOR]

Polybenzoxazines (Pbzs) are advanced forms of phenolic resins that possess many attractive properties, including thermal-induced self-curing polymerization, void-free polymeric products and absence of by-product formation. They also possess high Tg (glass transition temperature) and thermal stability. But the produced materials are brittle in nature. In this paper, we present our attempt to decrease the brittleness of Pbz by blending it with polyvinylalcohol (PVA). Benzoxazine monomer (Eu-Ed-Bzo) was synthesized by following a simple Mannich condensation reaction. The formation of a benzoxazine ring was confirmed by FT-IR and NMR spectroscopic analyses. The synthesized benzoxazine monomer was blended with PVA in order to produce composite films, PVA/Pbz, by varying the amount of benzoxazine monomer (1, 3 and 5 wt. % of PVA). The property of the composite films was studied using various characterization techniques, including DSC, TGA, water contact angle analysis (WCA) and SEM. WCA analysis proved that the hydrophobic nature of Pbz (value) was transformed to hydrophilic (WCA of PVA/Pbz5 is 35.5°). These composite films could play the same role as flexible electrolytes in supercapacitor applications. For this purpose, the composite films were immersed in a 1 M KOH solution for 12 h in order to analyze their swelling properties. Moreover, by using this swelled gel, a symmetric supercapacitor, AC//PVA/Pbz5//AC, was constructed, exhibiting a specific capacitance of 170 F g−1. [ABSTRACT FROM AUTHOR]

Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for machine learning. This paper proposes an early fire detection system optimized for certain spaces using a digital-twin-based automatic fire learning data generation model for each space. The proposed method first automatically generates realistic particle-simulation-based synthetic fire data on an RGB-D image matched to the view angle of a monitoring camera to build a digital twin environment of the real space. In other words, our method generates synthetic fire data according to various fire situations in each specific space and then performs transfer learning using a state-of-the-art detection model with these datasets and distributes them to AIoT devices in the real space. Synthetic fire data generation optimized for a space can increase the accuracy and reduce the false detection rate of existing fire detection models that are not adaptive to space. [ABSTRACT FROM AUTHOR]

A novel porous carbon, derived from polybenzoxazine and subjected to hydrogen peroxide treatment, has been meticulously crafted to serve dual functions as a supercapacitor and a CO2 capture material. While supercapacitors offer a promising avenue for electrochemical energy storage, their widespread application is hampered by relatively low energy density. Addressing this limitation, our innovative approach introduces a three-dimensional holey carbon ball framework boasting a hierarchical porous structure, thereby elevating its performance as a metal-free supercapacitor electrode. The key to its superior performance lies in the intricate design, featuring a substantial ion-accessible surface area, well-established electron and ion transport pathways, and a remarkable packing density. This unique configuration endows the holey carbon ball framework electrode with an impressive capacitance of 274 F g−1. Notably, the electrode exhibits outstanding rate capability and remarkable longevity, maintaining a capacitance retention of 82% even after undergoing 5000 cycles in an aqueous electrolyte. Beyond its prowess as a supercapacitor, the hydrogen peroxide-treated porous carbon component reveals an additional facet, showcasing an exceptional CO2 adsorption capacity. At temperatures of 0 and 25 °C, the carbon material displays a CO2 adsorption capacity of 4.4 and 4.2 mmol/g, respectively, corresponding to equilibrium pressures of 1 bar. This dual functionality renders the porous carbon material a versatile and efficient candidate for addressing the energy storage and environmental challenges of our time. [ABSTRACT FROM AUTHOR]

Background: regular intravitreal anti-vascular endothelial growth factor (VEGF) treatment is crucial for patients with neovascular age-related macular degeneration (nAMD), and delayed treatment can exacerbate disease progression. Methods: we compared the outcomes of on-time versus delayed intravitreal anti-VEGF treatment for patients with nAMD. This study was conducted during the coronavirus disease 2019 (COVID-19) pandemic with a 2-year follow-up period. The best-corrected visual acuity (BCVA) and anatomical findings were evaluated before the pandemic, during the pandemic, and at 6-, 12-, 18-, and 24-months post-pandemic. Results: The delayed and on-time groups comprised 54 and 72 patients, respectively. After the pandemic, the injection interval increased by 0.65 ± 1.51 months (p = 0.003), with 22.2% of the patients in the delayed group switching to the treat-and-extended regimen (p < 0.001). The delayed group showed greater mean BCVA deterioration (p = 0.027) and central subfield thickness (p = 0.037) at 6 months and worse maximum subretinal fluid height (p = 0.022) at 18 months than the on-time group. No difference was observed between the groups in the second year. Conclusion: the negative effects of delaying anti-VEGF treatment because of the COVID-19 pandemic can be ameliorated by changing the treatment regimen and shortening treatment intervals. [ABSTRACT FROM AUTHOR]

This review explores the field of retreatment strategies for cases filled with calcium silicate-based root canal sealers. Since the introduction of calcium silicate-based materials in dentistry, calcium silicate-based root canal sealers have become popular among dentists because of their biocompatibility, bioactivity, and sealing ability. Therefore, effective retreatment strategies are indispensable. This article aims to identify the challenges associated with the removal of calcium silicate-based sealers themselves and removal of gutta-percha with the sealers during retreatment, evaluate current techniques and materials, and provide future directions for research in this field. Regarding the strategies of removal of root canal sealers, calcium silicate-based sealers are still relatively new materials for clinicians compared with traditional sealers such as epoxy- or eugenol-based sealers. First, no clinically established solvents have been reported. Second, calcium silicate-based sealers are currently utilized by clinicians in either the cold sealer-based technique or the warm vertical condensation technique. Third, the setting process of calcium silicate-based sealers generates byproducts, primarily calcium hydroxide and secondarily hydroxyapatite, that could interact with dentine. Lastly, there is a lack of clinical studies evaluating the efficacy of retreatment protocols for teeth filled with calcium silicate-based sealers. Therefore, it is important to investigate the chemo-mechanical properties of calcium silicate-based sealers themselves and their reactions to solvents and/or mechanical instruments and identify the interfacial properties of calcium silicate-based sealers with respect to dentine and gutta-percha. In addition, researchers in the clinical field need to actively gather and report data on retreatments of teeth filled with calcium silicate-based sealers. [ABSTRACT FROM AUTHOR]

Supercapacitors (SCs) are considered as emerging energy storage devices that bridge the gap between electrolytic capacitors and rechargeable batteries. However, due to their low energy density, their real-time usage is restricted. Hence, to enhance the energy density of SCs, we prepared hetero-atom-doped carbon along with bimetallic oxides at different calcination temperatures, viz., HC/NiCo@600, HC/NiCo@700, HC/NiCo@800 and HC/NiCo@900. The material produced at 800 °C (HC/NiCo@800) exhibits a hierarchical 3D flower-like morphology. The electrochemical measurement of the prepared materials was performed in a three-electrode system showing an enhanced specific capacitance for HC/NiCo@600 (Cs = 1515 F g−1) in 1 M KOH, at a current density of 1 A g−1, among others. An asymmetric SC device was also fabricated using HC/NiCo@800 as anode and HC as cathode (HC/NiCo@600//HC). The fabricated device had the ability to operate at a high voltage window (~1.6 V), exhibiting a specific capacitance of 142 F g−1 at a current density of 1 A g−1; power density of 743.11 W kg−1 and energy density of 49.93 Wh kg−1. Altogether, a simple strategy of hetero-atom doping and bimetallic inclusion into the carbon framework enhances the energy density of SCs. [ABSTRACT FROM AUTHOR]

To achieve the commercialization of electric vertical takeoff and landing (eVTOL) aircrafts, which have recently garnered attention as the next-generation means of transportation, objective certification based on rigorous procedures is essential. With the advancement of structural analysis technology, aircraft airworthiness standards recommend a combination of testing and analytical methods to demonstrate structural integrity. In this study, we propose analytical techniques for demonstrating the structural integrity of components for eVTOL aircrafts in accordance with airworthiness standards. We evaluated the static structural integrity and fatigue safety of an electro-mechanical actuator. Multibody dynamics analysis was performed to calculate the loads for application in finite element analysis. Subsequently, static analysis and fatigue analysis based on finite element analysis were conducted to calculate the safety margin and fatigue life of all key components. Therefore, we have confirmed the feasibility of utilizing analytical methods for the structural integrity assessment of aircraft components. We propose the utilization of the technique introduced in this study as one of the approaches for demonstrating compliance with airworthiness standards for eVTOL aircrafts through the application of analytical methods. [ABSTRACT FROM AUTHOR]

Edge computing can provide core functions such as data collection and analysis without connecting to a centralized server. The convergence of edge computing and IoT devices has enabled medical institutions to collect patient data in real time, improving the efficiency of short- and long-term patient management. Medical equipment measures a large amount of biosignal data for analyzing diseases and patient health conditions. However, analyzing and monitoring biosignal data using a centralized server or cloud limit the medical institutions' ability to analyze patients' conditions in real time, preventing prompt treatment. Therefore, edge computing can enhance the efficiency of patient biosignal data collection and analysis for patient management systems. Analyzing biosignals using edge computing can eliminate the wait time present in cloud computing. Hence, this study aims to develop an IoT pulse oximeter using edge computing for medical institutions and proposes an architecture for providing a real-time monitoring service. The proposed system utilizes five types of raw (IR AC, IR DC, red AC, red DC, AMB), pulse, and SpO2 data measured using IoT pulse oximeters. Edge nodes are installed in every hospital ward to collect, analyze, and monitor patient biosignal data through a wireless network. The collected biosignal data are transmitted to the cloud for managing and monitoring the data of all patients. This system enables medical institutions to collect and analyze raw biosignal data in real time, by which an integrated management system can be established by connecting various types of IoT-based medical equipment. [ABSTRACT FROM AUTHOR]

Identifying individual pigs is crucial for efficient breeding, health management, and disease control in modern farming. Traditional animal face identification methods are labor-intensive and prone to inaccuracies, while existing CNN-based pig face recognition models often struggle with high computational demands, large sizes, and reliance on extensive labeled data, which limit their practical application. This paper addresses these challenges by proposing a novel, decoupled approach to pig face recognition that separates detection from identification. This strategy employs a detection model as a pre-processing step, significantly reducing the need for extensive re-annotation for new datasets. Additionally, the paper introduces a method that integrates offline knowledge distillation with a lightweight pig face recognition model, aiming to build an efficient and embedding-friendly system. To achieve these objectives, the study constructs a small-scale, high-quality pig face detection dataset consisting of 1500 annotated images from a selection of 20 pigs. An independent detection model, trained on this dataset, then autonomously generates a large-scale pig face recognition dataset with 56 pig classes. In the face recognition stage, a robust teacher model guides the student model through a distillation process informed by a knowledge distillation loss, enabling the student model to learn relational features from the teacher. Experimental results confirm the high accuracy of the pig face detection model on the small-scale detection dataset and the ability to generate a large-scale dataset for pig face recognition on unlabeled data. The recognition experiments further verify that the distilled lightweight model outperforms its non-distilled counterparts and approaches the performance of the teacher model. This scalable, cost-effective solution shows significant promise for broader computer vision applications beyond agriculture. [ABSTRACT FROM AUTHOR]

This study was undertaken to synthesize and characterize PVDF/CB (polyvinylidene fluoride/carbon block) nanofiber composites for flexible, wearable electronic applications. Nanofibers were produced by electrospinning method and used to produce thin films. Fiber surface morphologies were investigated by FE-SEM and HR-TEM, crystalline structures by FT-IR and P-XRD, and thermal characteristics by TGA and DSC. The prepared materials are thermally stable up to 390 °C. Mechanical properties were ascertained using tensile characteristics, and results showed that the addition of carbon black (CB) powder to PVDF polymer solution decreased Young's modulus values and reduced the dielectric constant of PVDF nanofiber films. The obtained dielectric constants of nanofibers loaded with various concentrations of CB were found from 1.4 to 2.0. Flexible electronics materials are essential for the production of wearable electronics and various biomedical engineering applications. The PVDF/CB nanofibers containing 1% showed maximum Young's moduli of 101.29 ± 15.94. Nanofiber thin films offer various advantages, including simplicity of manufacture, low power consumption, flexibility, and exceptional stability, all of which are crucial for flexible, wearable device applications. [ABSTRACT FROM AUTHOR]

With an increase in local precipitation caused by extreme climatic phenomena, the frequency of landslides and associated damage has also increased. Therefore, compiling fine-scale landslide susceptibility assessment maps based on data from landslide-affected areas is essential. Deep neural network (DNN) and kernel-based DNN(DNNK) models were used to prepare landslide susceptibility maps of the mountainous Pyeongchang-gun region (South Korea) within a geographic information system framework. To map landslide susceptibility, datasets of landslide occurrence areas, topography, land use, forest, and soil were collected and entered into spatial databases, and 18 factors were then selected from the databases and used as model inputs. The training and test datasets consisted of 1600 and 400 landslide locations, respectively. The test accuracies of the DNN and DNNK models were 98.19% and 97.53% and 94.11% and 92.22% for the area under the receiver operating characteristic curve and the average precision value of the precision-recall curve, respectively. The location of future landslides can now be quickly and efficiently predicted using remote sensing data at a lower cost and with less labor. The landslide susceptibility maps produced in this study can play a role in sustainability and serve as references for establishing policies for landslide prevention and mitigation. [ABSTRACT FROM AUTHOR]

This paper presents a fractional linear reservoir model as the elementary response function of hydrologic systems corresponding to the classical linear reservoir model and tests its applicability to rainfall–runoff modeling. To this end, we formulate a fractional linear reservoir model in terms of fractional calculus following the same procedure as the classical linear reservoir model and, at the simplest level, compare its performance of rainfall–runoff modeling with the linear and nonlinear reservoir models. The impulse response function of a fractional linear reservoir model, a probability density function (PDF) following the Mittag–Leffler distribution, shows nonlinearity due to its time-variant behavior compared to that of a linear reservoir model. In traditional linear hydrologic system theory, the lag and route version of a fractional linear reservoir model produces the fast-rising and slow-recession of runoff hydrographs, implying the mixed response of linear and nonlinear reservoir models to rainfall. So, a fractional linear reservoir model could be considered a fundamental tool to effectively reflect the nonlinearity of rainfall–runoff phenomena within the framework of the linear hydrologic system theory. In this respect, the fractional order of the storage relationship specifying a fractional linear reservoir model can be viewed as a kind of parameter to quantify the heterogeneity of runoff generation within a river basin. [ABSTRACT FROM AUTHOR]

Cannabidiol (CBD) is a chemical obtained from Cannabis sativa; it has therapeutic effects on anxiety and cognition and anti-inflammatory properties. Although pharmacological applications of CBD in many types of tumors have recently been reported, the mechanism of action of CBD is not yet fully understood. In this study, we perform an mRNA-seq analysis to identify the target genes of CBD after determining the cytotoxic concentrations of CBD using an MTT assay. CBD treatment regulated the expression of genes related to DNA repair and cell division, with metallothionein (MT) family genes being identified as having highly increased expression levels induced by CBD. It was also found that the expression levels of MT family genes were decreased in colorectal cancer tissues compared to those in normal tissues, indicating that the downregulation of MT family genes might be highly associated with colorectal tumor progression. A qPCR experiment revealed that the expression levels of MT family genes were increased by CBD. Moreover, MT family genes were regulated by CBD or crude extract but not by other cannabinoids, suggesting that the expression of MT family genes was specifically induced by CBD. A synergistic effect between CBD and MT gene transfection or zinc ion treatment was found. In conclusion, MT family genes as novel target genes could synergistically increase the anticancer activity of CBD by regulating the zinc ions in human colorectal cancer cells. [ABSTRACT FROM AUTHOR]

Polytetrafluoroethylene (PTFE), commercially known as Teflon, is a fluoropolymer with a structure containing (CF2–CF2)n. It has high resistance to acids, alkalis and corrosive chemicals. PTFE is hydrophobic in nature with a water contact angle of 140°. Being hydrophobic in nature is a knotty problem, particularly in electrical applications, as it may lead to short circuits and result in reducing the lifetime of electrical equipment. Herein we describe the surface modification of PTFE from hydrophobic to hydrophilic without altering its bulk property. The surface hydrophilicity is achieved by two different techniques, viz., polymer coating (aquivion and nafion) and plasma treatment. Several characterization techniques including FTIR, Raman, XPS, WCA and SEM were used to analyze the surface of PTFE. It was found that 5% of the polymer solution and N2 plasma treatment for 2 min can produce huge differences in the surface property, as evidenced by the reduction in water contact angle from 140° (neat Teflon) to 80° (surface-modified Teflon). The surface morphology of neat PTFE is completely changed and collapsed as evidenced by the SEM images. The FTIR, Raman and XPS analyses confirm the presence of additional hydrophilic functional groups after the polymer coating and plasma treatment. Hence, this method represents a unique approach to modifying the surface property of Teflon, while maintaining its bulk property. [ABSTRACT FROM AUTHOR]

Pig husbandry constitutes a significant segment within the broader framework of livestock farming, with porcine well-being emerging as a paramount concern due to its direct implications on pig breeding and production. An easily observable proxy for assessing the health of pigs lies in their daily patterns of movement. The daily movement patterns of pigs can be used as an indicator of their health, in which more active pigs are usually healthier than those who are not active, providing farmers with knowledge of identifying pigs' health state before they become sick or their condition becomes life-threatening. However, the conventional means of estimating pig mobility largely rely on manual observations by farmers, which is impractical in the context of contemporary centralized and extensive pig farming operations. In response to these challenges, multi-object tracking and pig behavior methods are adopted to monitor pig health and welfare closely. Regrettably, these existing methods frequently fall short of providing precise and quantified measurements of movement distance, thereby yielding a rudimentary metric for assessing pig health. This paper proposes a novel approach that integrates optical flow and a multi-object tracking algorithm to more accurately gauge pig movement based on both qualitative and quantitative analyses of the shortcomings of solely relying on tracking algorithms. The optical flow records accurate movement between two consecutive frames and the multi-object tracking algorithm offers individual tracks for each pig. By combining optical flow and the tracking algorithm, our approach can accurately estimate each pig's movement. Moreover, the incorporation of optical flow affords the capacity to discern partial movements, such as instances where only the pig's head is in motion while the remainder of its body remains stationary. The experimental results show that the proposed method has superiority over the method of solely using tracking results, i.e., bounding boxes. The reason is that the movement calculated based on bounding boxes is easily affected by the size fluctuation while the optical flow data can avoid these drawbacks and even provide more fine-grained motion information. The virtues inherent in the proposed method culminate in the provision of more accurate and comprehensive information, thus enhancing the efficacy of decision-making and management processes within the realm of pig farming. [ABSTRACT FROM AUTHOR]

Radix Asteris, the root of Aster tataricus L. f., is historically significant in East Asian medicine for treating respiratory conditions. Yet, its implications on bone health remain uncharted. This research investigated the impact of an aqueous ethanol extract of Radix Asteris (EERA) on osteoclast differentiation and its prospective contribution to osteoporosis management. We discerned that EERA retards osteoclast differentiation by inhibiting receptor activator of nuclear factor kappa-B ligand (RANKL) expression and obstructing RANKL-induced osteoclastogenesis. EERA markedly suppressed RANKL-induced expression of NFATc1, a pivotal osteoclastogenic factor, via modulating early RANK signaling. EERA's therapeutic potential was underscored by its defense against trabecular bone degradation and its counteraction to increased body and perigonadal fat in ovariectomized mice, mirroring postmenopausal physiological changes. In the phytochemical analysis of EERA, we identified several constituents recognized for their roles in regulating bone and fat metabolism. Collectively, our findings emphasize the potential of EERA in osteoclast differentiation modulation and in the management of osteoporosis and associated metabolic changes following estrogen depletion, suggesting its suitability as an alternative therapeutic strategy for postmenopausal osteoporosis intertwined with metabolic imbalances. [ABSTRACT FROM AUTHOR]

The purpose of this study was to construct a map of expected fertilization rates for nitrogen (N) and phosphorus (P2O5) based on measurements of components in soil samples and to identify the spatial variabilities of four lots of a salt-affected paddy field in Korea. Four salt-affected paddy field lots in Korea were divided into 30 sectors for collecting soil samples. They were then analyzed for soil organic matter (SOM), silicon dioxide (SiO2), total nitrogen (TN), and available phosphorus (Av.P2O5) in accordance with international standards. Expected fertilization rates of N and P2O5 were developed as prescription standards for the application of fertilizer to paddy fields. They were derived using a model of the fertilization rates of N and P2O5. To determine the presence of spatial correlation and continuity in the given fields, a spherical variogram was used. Based on the spherical model with the application of a regular kriging interpolation, maps of the contents of TN and Av.P2O5 as well as the expected fertilization rates of N and P2O5 at each sector of 1 × 1   m2 were developed. The expected fertilization rate of N at each sector appeared in the range of min. 10.0 g to max. 25.7 g, while that of P2O5 appeared in the range of min. 0.68 g to max. 8.46 g. [ABSTRACT FROM AUTHOR]

Flavonoids, known for their antioxidant properties, can prevent reactive oxygen species (ROS) and influence athletic performance through various physiological and metabolic mechanisms. However, there are conflicting results after summarizing and analyzing the relevant literature. Hence, it is warranted to evaluate the overall impact of flavonoids on athletic performance in healthy adults based on a comprehensive and systematic review and meta-analysis. After searching four databases for literature published since their respective establishments until February 2023 and conducting publication bias and quality assessments, a total of 22 studies were ultimately included. The names and doses of flavonoids, various outcome measurements, as well as types of training, were extracted from included studies. The athletic performance outcomes from the included studies were categorized into 'performance tests' and 'exercise tolerance,' depending on the type of training undertaken. Several statistical results, such as pooled effect size (ES), among others, were implemented by meta-analysis using the random effects model. The results of meta-analysis suggest that there is currently sufficient evidence (ES = −0.28; 95% confidence interval (CI): [−0.50, −0.07]; p = 0.01 and ES = 0.23; 95% CI: [0.07, 0.39]; p = 0.005) to support the notion that flavonoid supplementation enhanced athletic performance in performance tests and exercise tolerance. In addition, among the subgroups, nonsignificant results were observed for athletes (p = 0.28) and acute supplementation (p = 0.41) in performance tests, as well as athletes (p = 0.57) and acute supplementation (p = 0.44) in exercise tolerance. Meanwhile, significant results were found for non-athletes (p = 0.04) and long-term supplementation (p = 0.02) in performance tests, as well as non-athletes (p = 0.005) in performance tests and long-term supplementation (p = 0.006) in exercise tolerance. The nonsignificant results were likely due to the limitation in the number of related papers, sample sizes, optimal dosage, duration, type of flavonoids, and other factors. Therefore, future research should focus on further investigating these relationships with larger sample sizes, optimal dosage, duration, and type of flavonoids to provide more robust conclusions. [ABSTRACT FROM AUTHOR]

Neuroinflammation activated by microglia affects inflammatory pain development. This study aimed to explore the anti-inflammatory properties and mechanisms of 1,6,7-trihydroxy-2-(1,1-dimethyl-2-propenyl)-3-methoxyxanthone (THMX) from Cudrania tricuspidata in microglia activation-mediated inflammatory pain. In RAW 264.7 and BV2 cells, THMX has been shown to reduce lipopolysaccharide (LPS)-induced inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and pro-inflammatory mediators and cytokines, including nitric oxide (NO), prostaglandin (PG) E2, interleukin (IL)-6, and tumor necrosis factor alpha (TNF-α). THMX also decreased LPS-induced phosphorylation of mitogen-activated protein kinase (MAPK) and the activation of p65 nuclear factor kappa B (NF-κB). Interestingly, THMX also activated heme oxygenase (HO)-1 expression. These findings suggest that THMX is a promising biologically active compound against inflammation through preventing MAPKs and NF-ĸB and activating HO-1 signaling pathways. [ABSTRACT FROM AUTHOR]

The Korean fir tree Abies koreana, an endangered species in Korea, faces threats primarily from climate change-induced stress and drought. This study proposed a sustainable method to enhance A. koreana drought tolerance using a black yeast-like fungus identified as Aureobasidium pullulans (AK10). The 16S/ITS metabarcoding analysis assessed the impact of drought and AK10 treatment on the seedlings' rhizosphere microbiome. Results revealed a profound drought influence on the microbiome, particularly affecting fungal mycobiota. Drought-stressed seedlings exhibited elevated Agaricaceae levels, opportunistic fungi generally associated with decomposition. AK10 treatment significantly mitigated this proliferation and increased the relative abundance of beneficial fungi like Cystofilobasidium and Mortierella, known biocontrol agents and phosphate solubilizers. A notable reduction in the phytopathogenic Fusarium levels was observed with AK10, alongside an increase in beneficial bacteria, including Azospirillum and Nitrospirillum. Furthermore, the conducted correlation analysis shed light on microbial interrelationships within the rhizosphere, elucidating potential co-associations and antagonisms. Taken together, the isolated A. pullulans AK10 identified in this study serves as a potential biostimulant, enhancing the drought tolerance in A. koreana through beneficial alterations in the rhizosphere microbiome. This approach presents a promising strategy for the conservation of this endangered species. [ABSTRACT FROM AUTHOR]

Multiple-instance learning has become popular over recent years due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. Many Algorithms based on MIL have been put forth over the years. In this paper, we will discuss MIL, the background of MIL and its application in multiple domains, some MIL-based methods, challenges, and lastly, the conclusions and prospects. [ABSTRACT FROM AUTHOR]

Melia toosendan fructus, traditionally employed in traditional Chinese and Korean herbal medicine, exhibits diverse biological properties encompassing anti-tumor, anti-inflammatory, and anti-viral effects. However, its influence on bone metabolism remains largely unexplored. In this study, we investigated the impact of an ethanolic extract of Melia toosendan fructus (MTE) on osteoclast differentiation and characterized its principal active constituent in osteoclast differentiation and function, as well as its effects on bone protection. Our findings demonstrate that MTE effectively inhibits the differentiation of osteoclast precursors induced by receptor activator of nuclear factor κB ligand (RANKL). Utilizing a bioassay-guided fractionation approach coupled with UHPLC-MS/MS analysis, we isolated and identified the triterpenoid compound toosendanin (TSN) as the active constituent responsible for MTE's anti-osteoclastogenic activity. TSN treatment downregulated the expression of nuclear factor of activated T cells c1, a pivotal osteoclastogenic transcription factor, along with molecules implicated in osteoclast-mediated bone resorption, including tumor necrosis factor receptor-associated factor 6, carbonic anhydrase II, integrin beta-3, and cathepsin K. Furthermore, treatment of mature osteoclasts with TSN impaired actin ring formation, acidification, and resorptive function. Consistent with our in vitro findings, TSN administration mitigated trabecular bone loss and reduced serum levels of the bone resorption marker, C-terminal cross-linked telopeptides of type I collagen, in a mouse bone loss model induced by intraperitoneal injections of RANKL. These results suggest that TSN, as the principal active constituent of MTE with inhibitory effects on osteoclastogenesis, exhibits bone-protective properties by suppressing both osteoclast differentiation and function. These findings imply the potential utility of TSN in the treatment of diseases characterized by excessive bone resorption. [ABSTRACT FROM AUTHOR]

This study aimed to compare the fatigue resistance of files made from different heat treatment methods and surface treatment. Four prototype files were created through heat treatment and titanium coating surface treatment (AT, DT, ER, EN; named arbitrarily by the manufacturer) at different times and temperatures. Artificial canals with curvatures of 45- and 90-degree were used for the fatigue testing. The files were operated at the speed of 500 rpm at 37 °C, and the time until fracture incurred by a 4-mm dynamic pecking motion at a speed of 8 mm/s was measured, and the number of cycles to failure (NCF) was calculated by applying rotation speed and time. The length of the fractured fragment was measured. The fractured specimens were observed under the SEM to compare the characteristics of fatigue fracture patterns. Differential scanning calorimetry analysis was performed to estimate the phase transformation temperature. One-way ANOVA with Duncan's post-hoc comparison, the Kruskal–Wallis test, and Mann–Whitney U were applied to compare the fatigue resistance among the prototypes at a significance level of 95%. Regardless of the canal angle, the EN showed the highest fatigue resistance (p < 0.05). AT had the lowest NCF at the 90-degree canal (p < 0.05). ER had a higher NCF than the DT at 45 degrees (p < 0.05), but there was no difference at 90 degrees. DSC analysis revealed that the ER and EN groups exhibited two austenite peaks above 40 °C. In conclusion, the file that underwent a specific temperature heat treatment with titanium coating surface treatment showed the highest fatigue resistance. [ABSTRACT FROM AUTHOR]