Search Results (58)

Background/Objectives: The development of effective oncologic therapies with fewer adverse effects is often limited by the intrinsic and acquired resistance of tumor cells. Hybrid molecules, rationally designed to combine different pharmacophores, represent a promising strategy by providing synergistic effects, dose reduction, and a lower risk of resistance. In this study, the antitumor potential and mechanisms of action of 22 novel hybrid compounds derived from xanthene and pyran scaffolds (SJ022–SJ103) were investigated. The hybrids were initially evaluated through in vitro screening in four breast, three ovarian, and two prostate cancer cell lines, followed by the selection of T-47D, OVCAR-3, and LNCaP cells for detailed assays assessing cytotoxicity, apoptosis, cell cycle distribution, DNA damage, caspase-3/7 activity, morphology, and PI3K/AKT/mTOR pathway modulation. Methods: Cytotoxicity assays were performed in the selected cell lines, while mechanistic studies included apoptosis and cell cycle analysis by flow cytometry, γH2AX detection, Western blotting for PI3K/AKT/mTOR pathway proteins, and 3D spheroid assays. Combinatorial effects with hormone therapies (tamoxifen, fulvestrant, and letrozole) and the AKT inhibitor MK2206 were evaluated. AKT silencing by esiRNA and molecular docking was performed to confirm target engagement. Results: SJ028 demonstrated broad activity across all tested cell lines, whereas SJ064 and SJ078 exhibited higher selectivity. Treatments induced apoptosis, S/G2-M arrest, and DNA damage, accompanied by decreased phospho-AKT levels and stable PI3K and mTOR expression. In 3D models, the hybrids increased caspase-3/7 activity and necrotic core expansion. Co-administration with hormone therapies resulted in synergistic effects in breast and ovarian cancer cells, reducing IC₅₀ values by more than 50% in both parental and resistant models, while combinations with MK2206 were antagonistic across all tumor subtypes. AKT silencing abrogated cytotoxicity, and docking confirmed SJ028 binding to AKT. Conclusions: Xanthene- and pyran-based hybrids—particularly SJ028, SJ064, and SJ078—showed strong antitumor activity through apoptosis induction, cell cycle arrest, and PI3K/AKT pathway modulation. Their preserved efficacy in resistant models and synergistic interactions with hormone therapies contrasted with the antagonism observed with AKT inhibition, highlighting their potential as promising candidates for the treatment of hormone-responsive and -resistant cancers. Full article

While recent advances have successfully integrated neural networks with physical models to derive numerical solutions, there remains a compelling need to obtain exact analytical solutions. The ability to extract closed-form expressions from these models would provide deeper theoretical insights and enhanced predictive capabilities, complementing existing computational techniques. In this paper, we study the nonlinear Gardner equation and the (2+1)-dimensional Zabolotskaya–Khokhlov model, both of which are fundamental nonlinear wave equations with broad applications in various physical contexts. The proposed models have applications in fluid dynamics, describing shallow water waves, internal waves in stratified fluids, and the propagation of nonlinear acoustic beams. This study integrates a modified generalized Riccati equation mapping approach and a novel generalized $\left({\displaystyle \frac{G^{\prime }}{G}}\right)$-expansion method with neural networks for obtaining exact solutions for the suggested nonlinear models. Researchers are currently investigating potential applications of these neural networks to enhance our understanding of complex physical processes and to develop new analytical techniques. The proposed strategies incorporate the solutions of the Riccati problem into neural networks. Neural networks are multi-layer computing approaches including activation and weight functions among neurons in input, hidden, and output layers. Here, the solutions of the Riccati equation are allocated to each neuron in the first hidden layer; thus, new trial functions are established. We evaluate the suggested models, which lead to the construction of exact solutions in different forms, such as kink, dark, bright, singular, and combined solitons, as well as hyperbolic and periodic solutions, in order to verify the mathematical framework of the applied methods. The dynamic properties of certain wave-related solutions have been shown using various three-dimensional, two-dimensional, and contour visualizations. This paper introduces a novel framework for addressing nonlinear partial differential equations, with significant potential applications in various scientific and engineering domains. Full article

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

Background/Objective: Neuronal intranuclear inclusion disease (NIID) is characterized by widespread deposition of eosinophilic intranuclear inclusions in multiple systems throughout the body. The aim of this study was to investigate the clinical and phenotypic features of NIID, with a focus on the potential association between the morphological features of fibrils formed by polyG (polyglycine) proteins and cognitive dysfunction in patients with NIID. Methods: This study involved a retrospective collection of clinical data from 15 patients with NIID harboring GGC repeat expansions in the NOTCH2NLC (Notch 2 N-Terminal Like C) gene (including symptoms, signs, biochemical markers, cranial MRI, MMSE, and MoCA cognitive scores). All patients underwent skin biopsy, with one additional autopsy of brain tissue. Some skin samples were stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining with anti-p62 antibody. The remaining skin samples and brain tissue samples obtained from autopsies were analyzed using anti-p62 antibody immunofluorescence (IF) staining and transmission electron microscopy (TEM). The number of GGC repeats was quantified using repeat primer PCR (RP-PCR). Based on ultrastructural characteristics (morphology and diameter), inclusion fibers were classified into two subtypes, and differences in the severity of cognitive impairment between subtypes were compared. Results: The majority of patients in this cohort with NIID were female (73.3%), with an average age of onset of 61.06 ± 7.67 years. The core clinical manifestations were cognitive decline (93.3%) and autonomic dysfunction (93.3%). Cranial MRI revealed characteristic DWI “ribbon sign” in 93.3% of patients, accompanied by lateral ventricle enlargement (93.3%), cerebellar atrophy (86.6%), and high T2-FLAIR signal in the corpus callosum (93.3%). All patients were found to have pathogenic GGC amplification in the NOTCH2NLC gene (median 115, range 88–210). Skin/brain tissue pathology confirmed p62-positive nuclear inclusions, and transmission electron microscopy revealed two fiber subtypes for the first time: type A (Long, thin filamentous, 202.38 ± 42.35 nm) and type B (short rod-shaped, 73.08 ± 11.56 nm). Group analysis indicated that the diameter of fibers was significantly larger in the cognitive impairment group (p < 0.05), and the type B fiber group had lower cognitive levels (p < 0.05) and larger diameters (p < 0.05), suggesting a strong association between type B fibers and severe cognitive impairment and poor prognosis. Conclusions: The presence of two different forms of fibrils, type A and type B, in the inclusion bodies of NIID patients, and the poorer cognitive level of NIID patients in the type B group than that of type A suggest that type B fibrils can be used as a novel pathological marker of severe cognitive impairment and poor prognosis in NIID. Full article

Background/Objectives: The TOPLESS (TPL) and TOPLESS-related (TPR) proteins represent a highly conserved class of transcriptional co-repressors in plants, playing pivotal roles in modulating growth, development, and stress responses through the repression of key transcriptional regulators. However, a comprehensive genome-wide analysis of the TPL/TPR gene family and its involvement in stress responses remains unexplored in cotton. Methods: In this study, 60 TPL/TPR genes were identified from the genomes of ten Gossypium species via bioinformatics approaches, and their protein physicochemical properties, gene structures, phylogenetic relationships, cis-regulatory elements, and expression profiles were characterized. Results: Chromosomal localization and collinearity analyses revealed that segmental duplication events have contributed to the expansion of the TPL/TPR gene family. Further examination of exon–intron architectures and conserved motifs highlighted strong evolutionary conservation within each TPL/TPR subgroup. Expression profiling demonstrated that TPL/TPR genes exhibit tissue-specific expression patterns, with particularly high transcript abundance in floral organs (e.g., petals and stigmas). Cis-element analysis suggested their potential involvement in multiple stress-responsive pathways. Notably, GhTPL3 showed high constitutive expression across various tissues and under stress conditions, with the most pronounced up-regulation under salt stress. Functional validation via Virus-Induced Gene Silencing (VIGS) confirmed that GhTPL3 silencing significantly impairs cotton salt stress tolerance, underscoring its critical role in abiotic stress adaptation. Conclusions: Our findings provide novel insights into the functional diversification and regulatory mechanisms of the TPL/TPR family in cotton, offering a valuable genetic resource for breeding stress-resilient cotton varieties. Full article

Lithium-ion batteries (LIBs) have become the dominant energy storage technology due to their versatility and superior performance across diverse applications. Silicon (Si) stands out as a particularly promising high-capacity anode material for next-generation LIBs, offering a theoretical capacity nearly ten times greater than conventional graphite anodes. However, its practical implementation faces a critical challenge: the material undergoes a ~300% volume expansion during lithiation/delithiation, which causes severe mechanical stress, electrode pulverization, and rapid capacity decay. In addressing these limitations, advanced polymer binders serve as essential components for preserving the structural integrity of Si-based anodes. Notably, self-healing polymeric binders have emerged as a groundbreaking solution, capable of autonomously repairing cycle-induced damage and significantly enhancing electrode durability. The evaluation of self-healing performance is generally based on mechanical characterization methods while morphological observations by scanning electron microscopy provide direct evidence of crack closure; for electrochemically active materials, electrochemical techniques including GCD, EIS, and CV are employed to monitor recovery of functionality. In this study, a novel self-healing copolymer (PHX-23) was synthesized for Si anodes using a combination of octadecyl acrylate (ODA), methacrylic acid (MA), 2-hydroxyethyl methacrylate (HEMA), and polyethylene glycol methyl ether methacrylate (PEGMA). The copolymer was thoroughly characterized using NMR, FTIR, TGA, SEM, and EDX to confirm its chemical structure, thermal stability, and morphology. Electrochemical evaluation revealed that the PHX-23 binder markedly improves cycling stability, sustaining a reversible capacity of 427 mAh g⁻¹ after 1000 cycles at 1C. During long-term cycling, the Coulombic efficiency of the PHX-23 polymer is 99.7%, and similar functional binders in the literature have shown similar results at lower C-rates. Comparative analysis with conventional binders (e.g., PVDF and CMC/SBR) demonstrated PHX-23’s exceptional performance, exhibiting higher capacity retention and improved rate capability. These results position PHX-23 as a transformative binder for silicon anodes in next-generation lithium-ion batteries. Full article

This study demonstrates a novel route to synthesize gold-decorated exfoliated graphite (EG) through graphite intercalation compounds (GICs) with tetrachloroauric acid (HAuCl₄). We aimed to develop a scalable method for producing EG/Au composites with controlled nanoparticle morphology by investigating the effects of precursor chemistry and thermal expansion conditions. II-stage GIC–HAuCl₄ (average gross-composition: C₂₃HAuCl₄; intercalate layer thickness d_i = 6.85 Å) was prepared via an exchange reaction of HAuCl₄ with graphite nitrate. Interaction of this GIC with liquid methylamine yielded an occlusive complex, where methylamine-bound HAuCl₄ occupies both interlayer and intercrystalline spaces in the graphite matrix. Methylamine treatment of GIC reduces the onset temperature of exfoliation by ≈100 °C and enhances the expansion efficiency, yielding EG with a low bulk density range of 4–6 g/L when processed at 900 °C in air or nitrogen. Thermal exfoliation of these GICs yielded EG decorated with gold nanoparticles, exhibiting a broad size distribution from a few nanometers to several hundred nanometers, as confirmed by electron microscopy. An X-ray diffraction analysis identified the coexistence of crystalline gold and hexagonal graphite phases, with no detectable impurity phases. Full article

The widespread expansion of Internet of Things devices has ushered in an era of unprecedented connectivity. However, it has simultaneously exposed these resource-constrained systems to novel and advanced cyber threats. Among the most impressive and complex attacks are those leveraging in-memory shellcode runners (malware), which perform malicious payloads directly in memory, circumventing conventional disk-based detection security mechanisms. This paper presents a comprehensive framework, both academic and technical, for detecting in-memory shellcode runners, particularly tailored to the unique characteristics of these networks. We analyze and review the limitations of existing security parameters in this area, highlight the different challenges posed by those constraints, and propose a multi-layered approach that combines entropy-based anomaly scoring, lightweight behavioral monitoring, and novel Graph Neural Network methods for System Call Semantic Graph Analysis. Our proposal focuses on runtime analysis of process memory, system call patterns (e.g., Syscall ID, Process ID, Hooking, Win32 application programming interface), and network behavior to identify the subtle indicators of compromise that portray in-memory attacks, even in the absence of conventional file-system artifacts. Through meticulous empirical evaluation against simulated and real-world Internet of Things attacks (red team engagements, penetration testing), we demonstrate the efficiency and a few challenges of our approach, providing a crucial step towards enhancing the security posture of these critical environments. Full article

The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. This study leverages the symmetry characteristics in the vibration signals of pumped storage units to enhance fault diagnosis accuracy. To address the challenges of selecting the key parameters (e.g., decomposition level and penalty factor) of the variational mode decomposition (VMD) algorithm during vibration signal analysis, this paper proposes an algorithm for an improved subtraction-average-based optimizer (ISABO). By incorporating piecewise linear mapping, the ISABO enhances parameter initialization and, combined with a balanced pool method, mitigates the algorithm’s tendency to converge to local optima. This improvement enables more effective vibration signal denoising and feature extraction. Furthermore, to optimize hyperparameter selection in the bidirectional long short-term memory (BILSTM) network—such as the number of hidden layer units, maximum training epochs, and learning rate—we introduce an ISABO-BILSTM classification model. This approach ensures robust fault diagnosis by fine-tuning the neural network’s critical parameters. The proposed method is validated using vibration data from an operational PSPS. Experimental results demonstrate that the ISABO-BILSTM model achieves an overall fault recognition accuracy of 97.96%, with the following breakdown: normal operation: 96.29%, thrust block loosening: 98.60%, rotor-stator rubbing: 97.34%, and rotor misalignment: 99.59%. These results confirm that the proposed framework significantly improves fault identification accuracy, offering a novel and reliable approach for PSPS unit diagnostics. Full article

The component characteristics of an aeroengine below idle speed are fundamental for start-up process simulations. However, due to experimental limitations, these characteristics must be extrapolated from data above idle speed. Existing extrapolation methods often suffer from insufficient utilization of available data, reliance on specific prior conditions, and an inability to capture unique operating modes (e.g., the stirring mode and turbine mode of compressor). To address these limitations, this study proposes a novel curve-and-surface fitting-based extrapolation method. The key innovations include: (1) extrapolating sub-idle characteristics through constrained curve/surface fitting of limited above-idle data, preserving their continuous and smooth nature; (2) transforming discontinuous isentropic efficiency into a continuous specific enthalpy change coefficient (SECC), ensuring physically meaningful extrapolation across all operating modes; (3) applying constraints during fitting to guarantee reasonable and smooth extrapolation results. Validation on a micro-turbojet engine demonstrates that the proposed method requires only conventional performance parameters (corrected flow, pressure/expansion ratio, and isentropic efficiency) above idle speed, yet successfully supports ground-starting simulations under varying inlet conditions. The results confirm that the proposed method not only overcomes the limitations of existing approaches but also demonstrates broader applicability in practical aeroengine simulations. Full article

This paper presents a novel approach to synthesizing phased antenna arrays (PAAs) by combining Taylor-series expansion with neural networks (NNs), enhancing the PAA synthesis process for modern communication and radar systems. Synthesizing PAAs is crucial for these systems, offering versatile beamforming capabilities. Traditional methods often rely on complex analytical formulations or numerical optimizations, leading to suboptimal solutions or high computational costs. The proposed method uses Taylor-series expansion to derive analytical expressions for PAA radiation patterns and beamforming characteristics, simplifying the optimization process. Additionally, neural networks are employed to model the intricate relationships between PAA parameters and desired performance metrics, providing adaptive learning and real-time adjustments. A validation of the proposed method is performed on a dual-band 5G antenna, which exhibits marked resonances at 28.14 GHz and 37.88 GHz, with reflection coefficients of $S_{11}$ = −19 dB and $S_{11}$ = −19.33 dB, respectively. The integration of Taylor expansion with NNs offers improved efficiency, reduced computational complexity, and the ability to explore a broader design space. Simulation results and case studies demonstrate the effectiveness and applicability of the approach in practical scenarios. This work represents a significant advancement in PAA synthesis, showcasing the synergistic integration of mathematical modeling and artificial intelligence for optimized antenna design in modern communication and radar systems. Full article

Forest carbon sinks have faced significant challenges with the accelerating warming trend in the 21st century. Net primary productivity (NPP) serves as a critical indicator of the carbon cycle in forest ecosystems and is intricately influenced by both human activities and climate change. This study focuses on the subtropical Southern Forests of China as the research object, using the Wuyi Mountains as a representative study area. The positive and negative contributions of ecologically oriented human activities driven by China’s forestry construction over the past few decades were investigated along with potential extreme climate factors affecting the forest NPP from an altitude gradient perspective and regional-scale forest NPP changes from a novel viewpoint. MODIS NPP, climate, and land use data, along with a vegetation type transfer matrix and statistical methods, were utilized for this purpose. The results are summarized as follows. (1) From 2000 to 2022, NPP in the Wuyi Mountains exhibited a high distribution pattern in the northeastern and southern areas and a low distribution pattern in the central region, with a weak overall increase and an average annual growth increment of only 0.11 gC·m⁻²·year⁻¹. NPP increased with altitude, with a mean growth rate of 5.0 gC·m⁻²·hm⁻¹. Notably, the growth rate of NPP was most pronounced in the altitude range below 298 m in both temporal and vertical dimensions. (2) In the context of China’s long-term Forestry Ecological Engineering Projects and Natural Forest Protection Projects, as well as climate warming, the transformation of vegetation types from relatively low NPP types to high NPP types in the Wuyi Mountains has resulted in a total NPP increase of 211.58 GgC over the past 23 years. Specifically, only the altitude range below 298 m showed negative vegetation type transformation, leading to an NPP decrease of 119.44 GgC. The expansion of urban and built-up lands below 500 m over the 23-year period reduced NPP by 147.92 GgC. (3) The climatic factors inhibiting NPP in the Wuyi Mountains were extreme nighttime high temperatures from June to September, which significantly weakened the NPP of evergreen broadleaf forests above 500 m in elevation. This inhibitory effect still resulted in a reduction of 127.36 GgC in the NPP of evergreen broadleaf forests within this altitude range, despite a cumulative increment in the area of evergreen broadleaf forests above 500 m over the past 23 years. In conclusion, the growth in NPP in the southern inland subtropical regions of China slowed after 2000, primarily due to the significant rise in nighttime extreme high temperatures and the expansion of human-built areas in the region. This study provides valuable data support for the adaptation of subtropical forests to climate change. Full article

Gravity water waves in the shallow-ocean scenario described by generalized Boussinesq–Broer–Kaup–Whitham (gBBKW) equations are discussed. The residual symmetry and Bäcklund transformation associated with the gBBKW equations are systematically constructed. The time and space evolution of wave velocity and height are explored. Additionally, it is demonstrated that the gBBKW equations are solvable through the consistent Riccati expansion method. Leveraging this property, a novel Bäcklund transformation, solitary wave solution, and soliton–cnoidal wave solution are derived. Furthermore, miscellaneous novel solutions of gBBKW equations are obtained using the modified Sardar sub-equation method. The impact of variations in the diffusion power parameter on wave velocity and height is quantitatively analyzed. The exact solutions of gBBKW equations provide precise description of propagation characteristics for a deeper understanding and the prediction of some ocean wave phenomena. Full article

Wood cellulose is an abundant bio-based resource with diverse applications in construction, cosmetics, packaging, and the pulp and paper industries. Transparent wood (TW) is a novel, high-quality wood material with several advantages over traditional transparent materials (e.g., glass and plastic). These benefits include renewability, UV shielding, lightweight properties, low thermal expansion, reduced glare, and improved mechanical strength. TW has significant potential for various applications, including transparent roofs, windows, home lighting structures, electronic devices, home decoration, solar cells, packaging, smart packaging materials, and other high-value-added products. The mechanical properties of TW, such as tensile strength and optical transmittance, are typically up to 500 MPa (Young’s modulus of 50 GPa) and 10–90%, respectively. Fabrication methods, wood types, and processing conditions significantly influence the mechanical and optical properties of TW. In addition, recent research has highlighted the feasibility of TW and large-scale production, making it an emerging research topic for future exploration. This review attempted to provide recent and updated manufacturing methods of TW as well as current and future applications. In particular, the effects of structural modification through various chemical pretreatment methods and impregnation methods using various polymers on the properties of TW biocomposites were also reviewed. Full article

Magnetic sensors are highly relevant in clinical and industrial applications such as localization tasks and geological investigations. The spatial behavior of these sensors is of great interest for accurate forward modeling and the consequential possibilities for sophisticated applications, e.g., solutions to inverse problems. In this contribution, we present a novel characterization approach using adaptive system identification approaches. We utilize a gradient-based algorithm for estimating impulse and corresponding frequency responses for a directivity analysis in 1D, 2D, and 3D. For this, we built a triaxial Helmholtz coil setup to generate a 3D directive field. This is controlled by an algorithm that exploits similarities in sensor behavior with respect to small differences in excitation field angles. We found advantages for a controlled adaptation, with faster convergence and a smaller system distance between estimations and measurements with a proposed control based on the contraction–expansion approach (CEA). With runtimes averaging less than 1.5 s per direction for full impulse response estimation, this proof of concept shows the potential of the proposed algorithm for enabling a feasible frequency and directivity characterization method. Full article