Note:  Silicone wafers  For biological applications, silicon is similar to glass, which makes it a suitable support for the growth and/or nanoparticle mounting of nanowires.  For nanoelectronics applications, it is an ideal sample substrate for small nanoscale particles due to its low background signal and highly polished surface.Silicon wafer substrates can be used for sample substrates, microfabrication, substrates for nanowires or biological substrates.  A useful flat bed of  silicon wafer particles linked with nanowires  and  for biological applications, Si (silicon wafer) has properties similar to glass and can be used to mount or grow nanowire particles.  It can be easily wiped off or used as a whole wafer for the propagation of nanowires.Silicon nanowire arrays or SiNWs are vertical arrays of silicon nanowires on a flat crystalline silicon wafer substrate.  These nanowires are made by a catalytic oxidation and dissolution of Si in the presence of metal catalyst nanoparticles - a self-organized process commonly  known as silicon wafer-  assisted metal-chemical enhanced process.

This article develops a duality principle applicable to originally non-convex primal variational formulations. More specifically, as a first application, we establish a convex dual variational formulation for a non-linear model in elasticity. The results are obtained through basic tools of functional analysis, calculus of variations, duality and optimization theory in infinite dimensional spaces. We emphasize such a convex dual formulation obtained may be applied to a large class of similar models in the calculus of variations. Finally, in the last section, we present a global existence result for such a concerning model.

Ransomware has become an increasingly pervasive threat to cybersecurity, capable of causing severe operational disruptions and financial losses across various sectors. A novel approach is introduced through opcode analysis and a hybrid combination of machine learning algorithms, offering a significant improvement in detecting ransomware over traditional methods. By utilizing the low-level opcode sequences extracted from binaries, the system captures resilient features that are not easily manipulated through obfuscation techniques, making it more robust against evolving ransomware strains. The proposed framework integrates Random Forest, XGBoost, and Support Vector Machines (SVM), each contributing distinct strengths to the overall detection process. Experimental results highlight that the system achieved high accuracy, precision, and recall, outperforming existing detection methods. Furthermore, the use of opcode-based features enables the models to generalize well across various ransomware families, ensuring reliability in realworld cybersecurity applications. The research demonstrates the effectiveness of machine learning in addressing the rapidly evolving nature of ransomware, providing an efficient, scalable, and highly accurate detection mechanism.

The review paper by Bai et al. "Translating Earth system boundaries for cities and businesses" elucidates "the steps and choices involved in a scientifically rigorous translation of Earth System Boundaries (ESB) for businesses and cities".[1] It is hoped that such purportedly scientifically rigorous approaches will enable translation of global frameworks, such as planetary boundaries and carbon budgets to smaller scales for local decisions. Ten principles for translating ESBs to businesses and cities are proposed to establish a fully coherent and transparent procedure. Similar approaches have also been used by others in recent publications [2,3]

Introduction: Right bundle branch block (RBBB) following cardiac device extraction has not been previously reported but may have catastrophic consequences. Methods and Results: We present two cases of young male patients who developed right bundle branch block following the extraction of single chamber TV ICD systems where the coil was adherent close to the superior tricuspid valve annulus. Both patients had a subcutaneous ICD (SICD) implanted but suffered an inappropriate shock due to T-wave oversensing, requiring very early SICD removal for one patient. Conclusion: The development of RBBB following the extraction of a TV ICD is a previously unreported complication and may cause significant sensing problems if an SICD is implanted subsequently. Placement of the ICD lead tip in the right ventricular outflow tract or high on the intraventricular septum may predispose to this complication.

In the evolving landscape of financial security, a new battlefront has emerged: synthetic identity fraud powered by Generative Artificial Intelligence (GAI). This paper examines the high-stakes digital duel between fraudsters wielding GAI and the adaptive defense mechanisms of financial institutions. The paper explores how GAI-created synthetic identities challenge traditional fraud detection paradigms with convincing backstories, digital footprints, and AI-generated images. These artificial personas’ unprecedented scale and sophistication threaten to overwhelm existing security infrastructures, potentially compromising the integrity of financial systems and identity verification frameworks. Our analysis reveals large-scale synthetic identity campaigns’ far-reaching economic implications and disruptive potential across multiple sectors. It also investigates cutting-edge countermeasures, including adversarial machine learning, real-time anomaly detection, and multi-modal data analysis techniques. As this technological arms race intensifies, the paper concludes by proposing future research directions and emphasizing the critical need for collaborative initiatives to stay ahead in this ever-evolving digital battlefield.

A document by Kamal Acharya. Click on the document to view its contents.

Changes in land use land cover includes residential development, farmland expansion and exploration of a terrestrial ecosystem that have detrimental effect on the natural environment. In order to detect spatiotemporal land use changes of the OGRB, this study looked into Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) machine learning algorithms. Among these optimal algorithm was assigned to further classification of all available datasets. For this purpose, cloud free Thematic mapper (TM) 1994 data, Enhanced Thematic Mapper Plus (ETM+) and Operational Landsat Imagery (OLI) 2014 and February month 2024 images were freely obtained from Google Earth Engine (GEE) platform. Classification was performed in open source QGIS 3.36.3 machine leaning Dzetsaka Classification Plugins (DCP). Ground truth, Google Earth, and supplementary data were used to assess each period classification through the analysis of confusion matrices in ArcGIS Pro 2.8 to determine Kappa Statistics and Overall accuracy. RF was shown to be the most effective and precise classifier with an overall accuracy of 0.91, outperforming SVM (0.88) and K-Nearest Neighbor (0.75) throughout the 2014 timeframe. Therefore, it was adopted to classify all the remaining 1994, 2004 and 2024 years datasets. In average over 0.91% accuracy was achieved in all dataset classification. The results of this study can be mostly attributed to the increases in agricultural land from 0.8% in 1994 to 30.8% in 2024, built up area from 0.08 in 1994 to 5.6% in 2024 and water body from 0.62 in 1994 to 1.3% in 2024. Mainly in expense of forest from 51% in 1994 to 29% in 2024 and shrub/scrub 37% in 1994 to 33% in 2024. The outcomes of this research can contribute to improving land policy, management and public understanding of land use dynamics in the study area.

In anthropized environments such as agricultural zones, soil degradation significantly impacts crop productivity, environmental and economic Sustainability. This degradation is often accelerated by man with inadequate management. The risks associated with soil degradation are particularly pronounced in tropical regions, where extremely weathered soils and rainfall dynamics exacerbate erosion. Soil loss due to erosion is a subject already known by the scientific community and producers with global estimates, but its characterization in the tropical environment is still a dimension that is poorly integrated on a local scale. In this context, this study aimed to validate the use of a simple RGB camera on a Unmanned Aerial Vehicle (UAV) to quantify small-scale erosion in Curral de Cima municipality, Paraíba, Brazil. To this end, a monitoring strategy with biweekly observations over a course of a year was implemented. The results indicate. In this context, and based on this validation, this work proposes to discuss the interest of PRAs in the integrated management of soil, water and economic resources in a tropical environment, demonstrating their interest in integrating the panel of precision agriculture tools by also supporting the “conservation” dimension,thus being able to value soil degradation remotely.

The realistic images generated by GANs(Generative Adversarial Networks) enrich people’s lives, but they also pose serious threats to personal privacy and society, and it has become essential to study algorithms that can accurately detect GAN-generated images. Existing studies use artifacts to detect GAN-generated images, but the artifacts present in different GAN-generated images vary widely, and thus the cross-model generalization performance of such algorithms is weak. In this thesis, we propose the MaxPix, a new algorithm based on the combination of statistical features and deep learning techniques, for generating image detection. Firstly, MaxPix obtains the filter map of the image by designing the MaxSel filtering algorithm and then designs MA Block embedded in ResNet (Residual Network) to obtain MResNet. MaxPix finally utilizes MResNet to extract features from the filter map to detect GAN-generated images. Experimental results on publicly available datasets such as Wang and Faces-HQ show that the detection accuracy of MaxPix reaches 85.9% and 99.6% on average, which improves 7.6% and 10.2% relative to state-of-the-art algorithms such as the NAFID and the GocNet. Thus MaxPix has strong cross-model generalization performance.

• Effective diapycnal mixing is quantified in realistic high-resolution simulations using passive tracer experiments and online diagnostics of effective diapycnal mixing • Effective diapycnal mixing is close to parameterized values over the abyssal plain but can be larger above steep ridge slopes • Numerical mixing is minimized by smoothing topography and effective mixing aligns closely with parameterized mixing

Title: Comment on: Retrospective study evaluating safety, clinical effect, and dosing of dalteparin for the treatment of venous thromboembolism in term neonates

30.0 Cervical cancer, like many other cancers, is most treatable when detected at an early stage. Using classification methods helps find early signs of cancer and small tumors. This allows doctors to act quickly and offer treatments that might cure the cancer. This study presents a comprehensive approach to the classification of squamous cell carcinoma (SCC) leveraging a dataset comprising 1140 single-cell images sourced from Herlev. A variety of deep learning models, including DenseNet121, DenseNet169, InceptionResNet, XceptionNet, ResNet50, and ResNet101, are employed both individually and in ensembles, demonstrating their efficacy in classifying diverse cellular features. To validate the robustness of results, k-fold cross-validation is conducted, further affirming the effectiveness of the proposed methodology. Thorough exploration produces a precise and effective model for SCC classification, providing detailed insights into both normal and abnormal cell types. These findings show that transfer learning-based deep neural networks and ensemble methods can improve the diagnostic capabilities by 98% accuracy, of SCC classification systems for different cell types.

This article explores the concept of home among 25 Bedouin children aged 4–6 from unrecognized villages in the Negev, southern Israel. These children live in harsh conditions due to ongoing socio-political conflict and the threat of house demolition, with limited access to infrastructure and basic services. The study uses drawing activities and interviews to reveal how children perceive home, showing it as both a source of protection and risk. Five key themes emerge: (1) the concept of home for Bedouin children, (2) perceptions of police and their roles, (3) experiences of home demolition by police, (4) the home as a source of emotional distress, and (5) children’s coping mechanisms. Family, rituals, and attachments play crucial roles in providing emotional security, while nature and community offer safety. The study underscores the need for culturally sensitive interventions and improved infrastructure, emphasizing the importance of considering children’s perspectives in policy-making that affects their living conditions.

XEC, a new sublineage of the KP3.3 and KS1.1 Omicron variants, has several mutations in the Spike protein (T22N, F59S, F456L, Q493E and V1104L). Due to the importance of the S protein as a mediator of SARS-CoV-2 entry into cells, in addition to being a potential drug target for COVID-19, XEC was included in the list of “Currently Circulating Variants Under Monitoring” on 24 September 2024. The XEC variant has been detected in at least 29 countries and 24 US states and its rapid spread demonstrates its high contagious potential, attracting the attention of health authorities. Maintaining genomic surveillance is essential for monitoring new mutations, in addition to supporting the continuous improvement of diagnostic kits and, eventually, the need to develop new vaccines.

Objective: This meta-analysis investigated parent-child discrepancies in reporting anxiety and depression symptoms during digital interventions for youth. Method: We analyzed 13 randomized controlled trials involving 2,022 children (mean age = 12.25 years, 52.76% female) and 2,069 parents. Studies included digital interventions for anxiety and/or depression, with both parent and child reports. Effect sizes (Hedge’s g) were calculated for intervention and control groups across four time points. Discrepancies were assessed using Bland-Altman plots, Kendall’s Tau, correlation analyses, and paired tests. Results: Small, nonsignificant effects were found for both parent (g = -0.02 to 0.71) and child (g = -0.14 to 0.22) reports across time points. Parent-child agreement varied, with Kendall’s Tau ranging from 0.33 to 1.00 for anxiety and -0.33 to 0.50 for depression. Parents reported larger intervention effects than children, particularly for anxiety at mid-term (parent g = 0.71, child g = 0.15) and depression at short-term (parent g = 1.36, child g = 0.03). However, these differences were not statistically significant. Conclusions: While not statistically significant, notable discrepancies exist between parent and child reports of anxiety and depression symptoms during digital interventions. These findings suggest the importance of considering both perspectives in clinical assessments and research, particularly at different stages of treatment for anxiety and depression.

Approaches for object detection and tracking through computer vision and machine learning techniques have become essential, particularly in the field of parasitology. This study focused on the parasites of the genera Henneguya and Myxobolus. Diverse sets of light microscopy images was collected and a practical and robust dataset was constructed. Four object detection models, Yolov5n, Yolov5s, Yolov5l, and Yolov5m, were tested and their performances compared using metrics such as precision, recall, F1_Score, and mean average precision. The results showed an average precision of 97.9%, recall of 96.7%, and F1_Score of 97%. Additionally, a WebApp MLens was implemented to visualize the detected objects, facilitating data management and analysis.

Introduction Diet diversity (DD) in infancy may be protective for early food allergy (FA) but there is limited knowledge about how DD incorporating consumption frequency influences FA risk. Methods Three measures of DD were investigated in 2060 infants at 6 and/or 9 months of age within the NorthPop Birth Cohort Study; a weighted DD score based on intake frequency, the number of introduced foods, and the number of introduced allergenic foods. In multivariable logistic regression models based on directed acyclic graphs, associations to parentally reported physician diagnosed FA at 9- and 18-months age were estimated, including sensitivity- and stratified analyses. Results High weighted DD scores (24-31p) at 9 months of age was associated with a 61% decreased FA risk at age 18 months [OR (95% CI) = 0.39 0.18-0.88] compared with infants with the lowest DD scores (0-17p). The association remained significant after exclusion of early FA cases. Having introduced 13-14 foods at age 9 months, independent of consumption frequency, was associated to a 45% decreased FA risk [OR (95% CI) = 0.55 (0.31-0.98)] compared to having introduced 0-10 foods. When stratifying, results remained only for children with no FA history in the family. No association was seen between reduced FA risk and DD measured at 6 months of age or having introduced more allergenic foods in infancy. Conclusion A diverse diet at 9 months of age may prevent FA at 18 months and results underscore the need for additional investigations on the impact of consumption frequency in infancy.

Bacterial infection and mismatched mechanical properties are important factors that increase the risk of dental implant failure. However, zirconium (Zr)-based bulk metallic glasses (BMG) can have both high strength and low modulus, as well as good biocompatibility, due to their unique atomic arrangement structure. Based on these common characteristics, different elemental compositions can endow zirconium-based amorphous alloys with different properties. Here we present a Zr-based BMG containing silver (Ag) with good amorphous process ability, exhibiting lasting antibacterial, pro-angiogenic and pro-osteogenic properties. This newly developed Zr61Cu23Al12Ti2Ag2 (at.%) BMG has higher strength and lower modulus than pure Titanium (Ti). Furthermore, it could exert antibacterial effects through both contact inhibition and metal ion sterilization. And this antibacterial property could last over 3 months. The systematically in vitro and in vivo results thus demonstrate the advantages and application potentials of Zr-based BMG as a highly promising oral implant material for dental implantation.

Ransomware has rapidly evolved into one of the most significant cybersecurity threats, with the ability to encrypt critical data and demand ransoms, causing substantial financial and operational damage to organizations worldwide. The novel approach presented in this paper addresses the limitations of traditional signature-based detection methods through a hybrid model that combines supervised and unsupervised machine learning techniques, offering enhanced accuracy in identifying both known and previously unseen ransomware variants. Through real-time monitoring of file system activities on Windows environments, the model utilizes XGBoost for classifying known ransomware behaviors while leveraging Isolation Forest to detect anomalous activities indicative of novel threats. The experimental results demonstrate that the hybrid model achieves high detection accuracy, reduces false positives, and scales efficiently in dynamic environments with varying system loads, making it a viable solution for proactive ransomware mitigation. Moreover, the ability to generalize across zero-day ransomware variants provides a robust defense mechanism against evolving cyber threats. Overall, the proposed hybrid model offers a significant advancement in the field of ransomware detection, bridging the gap between traditional and contemporary detection strategies.

Background: Given the limited research on folate and ferritin status in children with cancer undergoing treatment, we investigated the prevalence of abnormalities and their impact on clinical outcomes and treatment complications. Methods: This prospective cohort study enrolled children <18 years diagnosed with cancer between August 2010-February 2014. Data collection occurred at diagnosis, 3, 6, 9, 12 and 18 months. Clinical outcomes were classified as event-free survival or events (relapse, death, the development of new metastasis, becoming palliative), and treatment complications. Micronutrient status was assessed through clinical and nutritional analyses. Binary logistic regression, multilevel model analysis explored relationships between micronutrient status and clinical outcomes. Results: Eighty-two patients [median (IQR) 3.9 (1.9-8.8) years, 56% males] were recruited. Excess ferritin (85%) and folate deficiency (25.5%) were prevalent micronutrient abnormalities throughout the study. Decreased ferritin levels reduced the odds of events by 83.9% (OR=0.161,95% CI=1.000–1.002, p=0.032). Higher ferritin was associated with increased number of treatment-related complications (B=7.3E-5,95% CI=1.5E-5–0.000, p=0.013). Folate status showed significant association with BMI category (χ 2=9.564, p=0.008), indicating that overweight and obese patients were more prone to deficiency. Conclusion: Paediatric cancer patients undergoing treatment exhibit high ferritin and reduced folate levels. Elevated ferritin is linked to increased toxicity and negative clinical outcomes, highlighting the importance of regular assessment and monitoring of both folate and ferritin. Implementing routine monitoring for these biomarkers could help mitigate adverse effects associated with treatment. Large-scale population-based studies and clinical trials are now warranted.

Plants frequently adapt to environmental changes by modifying hormone signal transduction and regulating the synthesis of secondary metabolites in response to stress. The APETALA2/ethylene‐responsive factor (AP2/ERF) domain transcription factors are important in regulating abiotic stress tolerance. The accumulation of asperosaponin Ⅵ in the root was significantly enhanced under low temperature stress, which exhibited a correlation with the AP2/ERF family. However, the involvement of AP2/ERF in regulating asperosaponin VI biosynthesis under cold stress remains ambiguous. Under cold stress conditions below 10°C, we observed the accumulation of asperosaponin VI and an increase in jasmonic acid (JA) levels. This response was attributed to the activation of the JA synthesis pathway induced by low temperatures. Additionally, a comprehensive analysis of the full-length transcriptome of Dipsacus asper identified a total of 80 DaAP2/ERF transcription factors, which exhibited significant homology with Arabidopsis thaliana and Citrus ERFs based on phylogenetic analysis. Furthermore, qRT-PCR analysis demonstrated that both cold stress and methyl jasmonate (MeJA) induction upregulated DaERF108 expression. The expression of DaERF108 is notably upregulated in the leaves and during the early stages of growth and development of D. asper, while subcellular localization analysis confirmed its presence in the nucleus. The overexpression of DaERF108 significantly enhanced the accumulation of oleanolic acid, a precursor of asperosaponin VI, and activated the triterpenoid biosynthesis pathway in Arabidopsis roots. Additionally, the overexpression of DaERF108 induced the activation of the terpenoid synthesis pathway under cold stress conditions. Notably, there was a positive correlation between DaERF108 expression and genes involved in asperosaponin VI biosynthesis, particularly with 3-hydroxy-3-methylglutaryl coenzyme A synthase ( DaHMGS). The interaction between DaERF108 and the GCC-box element in the DaHMGS promoter was demonstrated by LUC and Y1H assays, leading to enhanced activity. These findings suggest that DaERF108 specifically binds to the G-box element, thereby regulating DaHMGS gene expression, activating the JA signaling pathway, and promoting asperosaponin VI biosynthesis in response to cold stress.

This paper studies the practical fixed-time consensus (Fd-TC) for uncertain multi-agent systems (MASs) under strongly connected directed graphs utilizing adaptive event/self-triggered controllers, respectively. Considering the uncertainty in the agent’s nonlinear dynamic parameters, the adaptive event-triggered controller is designed by incorporating an event-triggered strategy with an adaptive control scheme, which achieves the practical Fd-TC for uncertain nonlinear MASs and the settling time remains constant irrespective of the initial conditions. Moreover, the designed controller is independent of any global information, including communication topology and network size. It also employs the hyperbolic tangent function to tackle Zeno and nondifferential issue. Following this, the adaptive event-triggered controller serves as the foundation for introducing a self-triggered controller, which eliminates the necessity for continuous monitoring and guarantees practical Fd-TC. Furthermore, the results are extended to a more generalized case with uncertain disturbances, highlighting the adaptability of the designed controller. Finally, two numerical simulation examples are conducted to validate the efficacy of the developed controllers.

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