Search Results (57,011)

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

This paper investigates how hybrid temples are created and transformed into tourist attractions, focusing on Wat Phothikyan Phutthathum—a Thai–Chinese temple located in Kelantan, close to Malaysia’s border with Thailand. This study aims to understand how both the local Thai minority and Chinese majority contribute to temple hybridisation, examine the influence of such temples on community identity, and explore their growing importance as tourist attractions. It highlights the negotiation and cultural exchange that shape new heritage spaces for borderland communities. Using visual analysis and interviews, the research shows that, since there are no Chinese temples nearby, Chinese Buddhists and Taoists adapt Thai temples by incorporating their own rituals and art. This results in blended iconography and practices, guided by an open-minded Thai monk. Features like large Buddha statues, staircases featuring naga-dragon designs, and murals combining different traditions reveal this fusion. The temple’s unique artwork and spiritual environment attract visitors from Muslim-majority areas and various countries like Thailand, Taiwan, and Singapore. As tourism becomes central to the temple’s role, the local authorities emphasise its religious significance and multicultural symbolism, aligning with economic interests and daily interactions among minority groups. This study offers valuable empirical and theoretical perspectives on the blending of religious heritage sites in border regions. Full article

Water pollution is one of the most critical societal and environmental challenges and remains a persisting problem worldwide. The origin of this pollution is diverse, while organic matter occupies a significant portion, originating from different sources. This creates major environmental and health risks, requiring reliable and sensitive analytical tools for effective monitoring. The permanganate index stands as a conventional assessment method for organic pollution, but it demonstrates compound non-specificity toward compounds and limited sensitivity to various contaminant structures. This research introduces cyclic voltammetry as a standalone electrochemical method that provides sensitive detection and characterization of organic oxidizing compounds. Six organic compounds, including gallic acid, phenol, oxalic acid, ascorbic acid, salicylic acid and p-benzoquinone, were used as model compounds and studied in aqueous media. These compounds were analyzed individually, in single-compound mode, to characterize their redox behavior and to identify the voltammetric peaks. Subsequently, a multi-compound analysis was studied to check for the validity of the concept in a more complex matrix. Notably, a strong linear correlation was observed between the measured charge and the theoretical permanganate index, highlighting the quantitative reliability of the electrochemical method. Comparing the obtained results with the permanganate index method confirmed the superiority of cyclic voltammetry in terms of response time and detection capability. The outcomes demonstrate that cyclic voltammetry functions as a robust alternative to the classical chemical oxidation method for environmental water assessment. Full article

After vessel preparation, using different strategies such as balloon angioplasty, specialty balloons, atherectomy or intravascular lithotripsy, definitive treatment has emerged as a key feature in endovascular treatment strategies. Based on current guidelines, endovascular treatment is the most common treatment option in patients with claudication. In patients with chronic limb-threatening ischemia (CLTI), on the other hand, the best treatment modality, including bypass surgery and endovascular revascularization, needs to be selected by an interdisciplinary team, focusing on individual anatomic and patient-specific characteristics, on the availability of a vein graft and on cardiovascular and other comorbidities of the patients. With endovascular therapy, currently, a plethora of options are available for the treatment of femoropopliteal lesions, which are increasingly gaining in complexity. Therefore, a practical systematic case-based approach, entailing contemporary treatment options, like drug-coated balloon (DCB) angioplasty tools, self-expanding bare-metal stents (BMSs), drug-eluting stents (DESs), interwoven stents and covered stents, is crucial. Generally, most endovascular operators adhere to the ‘leave nothing behind’ concept, meaning that, after proper lesion preparation, lesions can be treated with DCBs, avoiding the implantation of permanent metallic implants. However, in the case of severe dissections or significant recoil, stent implantation becomes necessary to achieve adequate limb perfusion. The selection between long versus spot stenting and the different stent options depends on the current scientific evidence, guidelines and expert opinion statements. An interdisciplinary expert consensus was recently compiled on how these modalities should be used in specific lesions and patients in the femoropopliteal segment. Herein we present a practical case-based approach, which is based on this algorithm and aims at harmonization of endovascular treatment strategies in daily practice and ultimately at further improvements in limb and patient outcomes. Full article

Under ongoing climate change and rapid urbanization, urban hydrothermal regimes are being reshaped, intensifying drought hazards and increasing stress on urban forests. Yet, systematic assessments of drought-induced stability dynamics of urban vegetation remain limited. We identified drought events across 330 Chinese cities during 2000–2022 and quantified vegetation resistance and resilience using multi-source remote sensing data. Pronounced latitudinal divergence emerged: high-latitude cities showed lower resistance but higher resilience, whereas low-latitude cities exhibited stronger resistance but weaker recovery. Across climatic zones, resistance was greater in humid and arid cities, whereas resilience was stronger in sub-humid and semi-arid cities, indicating a climate-dependent trade-off between disturbance buffering and recovery capacity. From 2000–2011 to 2012–2022, resistance increased significantly, whereas resilience declined. Seasonally, resistance was lowest and resilience highest in summer. Drought severity and climatic background—especially drought intensity and duration—primarily governed stability patterns: stronger droughts reduced resistance but enhanced recovery. Anthropogenic factors, including population density, economic development, and CO₂ emissions, also played a significant role in shaping vegetation stability. These findings highlight the need for long-term drought monitoring and climate-specific urban forest management to strengthen ecosystem stability in rapidly urbanizing regions. Full article

This paper develops a structured analytical framework and a robust numerical methodology for the spectral stability of time-varying bilateral quaternion differential equations of the form $\dot{q}=A\left(t\right)q+qB\left(t\right)$. By systematically extending classical real matrix theory to non-commutative dynamical systems via exact isometric real representations, this study utilizes the Kronecker product of real adjoint matrices to rigorously elucidate the underlying tensor structure of the bilateral evolution operator. This tensor-based reformulation proves that the Floquet multipliers of the bilaterally coupled system can be strictly decoupled into the product of the spectra corresponding to the left and right unilateral subsystems. Second, a “Scalar-Vector Stability Separation Principle” based on logarithmic norms is proposed, demonstrating that the transient energy evolution of the system is governed exclusively by the Hermitian real parts of the coefficient matrices, remaining entirely independent of the anti-Hermitian imaginary parts (rotation terms). Furthermore, for constant-coefficient and slowly varying systems, the Riesz projection from holomorphic functional calculus is introduced to establish algebraic criteria for exponential dichotomies, thereby revealing a cubic scaling law that relates the robustness threshold to the spectral gap (${\epsilon }_0\propto {\beta }^3$). Numerically, a Quaternion Chebyshev Spectral Collocation Method (Q-CSCM) is embedded within this exact vectorization framework to ensure that the algebraic symmetries of the bilateral system are strictly preserved through the isomorphic mapping. By explicitly constructing the fully discrete Kronecker product matrix via the exact real vectorization isomorphism, discrete energy estimates are utilized to rigorously prove that the numerical scheme successfully inherits the intrinsic spectral accuracy of the Chebyshev approximation. Comprehensive numerical experiments demonstrate that, within the low-dimensional regime, this methodology exhibits substantial temporal approximation efficiency advantages and superior numerical robustness compared to an alternative Legendre spectral baseline, as well as traditional explicit and state-of-the-art implicit symplectic Runge–Kutta methods, particularly when solving stiff and critically stable problems such as nonlinear Riccati oscillators. Full article

Huber regression serves as a prominent robust alternative to ordinary least squares (OLS), particularly in the presence of heavy-tailed error distributions. While the asymptotic relative efficiency (ARE) of Huber regression is well documented for the standard normal distribution, its worst-case efficiency across the class of all continuous and symmetric error distributions remains an important theoretical question. In this paper, we establish positive lower bounds for the ARE of Huber regression relative to OLS. By strategically selecting the robustification parameter based on the moments or quantiles of the error distribution, we first prove that the ARE is uniformly bounded away from zero across all continuous and symmetric error distributions. This result guarantees a baseline level of efficiency for Huber regression, sharing a similar theoretical spirit with the celebrated lower bound of the Wilcoxon rank estimator. Utilizing the empirical process theory, we further establish that the relative efficiency of Huber regression remains unchanged if the theoretical tuning parameter is replaced by an estimator with a suitable convergence rate. Simulation studies are conducted to examine the performance of Huber regression under the proposed tuning strategies. Full article

In this paper, we propose a novel Width-Adaptive Convolutional Autoencoder (WACAE) that automatically learns the optimal network width. The proposed approach assigns a relevance weight to each channel in the encoder’s hidden layers and leverages these weights to guide architectural adaptation. Based on the learned relevance, the model incrementally introduces new channels when needed and prunes irrelevant ones to achieve an optimal configuration. The WACAE simultaneously trains the network and learns its width in an unsupervised manner. Moreover, a novel cost function is devised to optimize channel relevance weights concurrently with model hyperparameters. Unlike conventional static or widening strategies, the proposed method adaptively enhances feature expressiveness within a single encoder–decoder framework. The model is evaluated on standard benchmark datasets (MNIST and CIFAR-10) and two real-world medical datasets (Brain Tumor MRI and Kvasir-Capsule). Experimental results demonstrate its effectiveness compared to state-of-the-art methods based on empirical tuning and network-width scaling. Furthermore, the proposed inner-product-based relevance weighting mechanism reduces model complexity while achieving high classification accuracy. Full article

With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R² exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts. Full article

The electrodeposition of rare earth metal alloys has attracted considerable interest, not only due to the challenges associated with the reduction in metal ions, but also because of their unique material properties and promising technological applications. This review presents a comprehensive analysis of the state-of-the-art in the electrochemical deposition of these alloys, focusing on various electrolytic systems, including aqueous solutions, organic molecular solvents, ionic liquids, and deep eutectic solvents. Despite inherent problematic factors such as low reduction potentials, competing hydrogen evolution reactions, and difficulties in controlling metal formation, recent advancements have enabled improved control over film formation, typically through the induced codeposition of lanthanides with iron-group metals. The influence of key factors, such as electrolyte composition and current/potential modes, on alloy codeposition, elemental and phase composition, structure, and deposition efficiency is discussed. The magnetic properties, electrocatalytic behavior, and corrosion resistance of the deposited films are also shown, highlighting their relevance for high-performance applications. Full article

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms ($FHT2DT$, $FHT2SP$, $ASD2$, $KHM$, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies $ASD2$ as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: $FHT2DT$ (1.1), $ASD2$ (15.3), and $KHM$ (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for $ASD2$, $KHM$, and Fast Slant Stack; lies between 0.5 and 1.5 for $FHT2SP$; and reaches 2.1 for $FHT2DT$. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by $FHT2DT$ (2.0), Fast Slant Stack (4.0, lower bound), and $ASD2$ (6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

Referring Remote Sensing Image Segmentation (RRSIS) is a representative multimodal understanding task for remote sensing, which segments designated targets from remote images according to free-form natural language descriptions. However, complex remote sensing characteristics, such as cluttered backgrounds, large-scale variations, small scattered targets and repetitive textures, lead to unstable visual grounding and further spatial grounding drift, resulting in inaccurate segmentation results. Existing approaches typically perform implicit visual–linguistic fusion across encoding and decoding stages, entangling spatial grounding with mask refinement. This tightly coupled formulation lacks explicit structural constraints and is prone to cross-modal ambiguity, especially in complex remote sensing layouts. To address these limitations, we propose a Structurally consistent and Grounding-aware Stagewise Reasoning Framework (SGSRF) that follows a grounding-first, segmentation-second paradigm. The framework decomposes inference into three cascaded stages with progressively imposed structural constraints. First, Cross-modal Consistency Refinement (CCR) lays the foundation for stable spatial grounding by enhancing visual–textual structural alignment via CLIP-based features and Structural Consistency Regularization (SCR), producing well-aligned multimodal representations and reliable grounding cues. Second, Grounding-aware Prompt (GPG) Generation bridges grounding and segmentation by converting aligned representations into complementary sparse and dense prompts, which serve as explicit grounding guidance for the segmentation model. Third, Grounding Modulated Segmentation (GMS) leverages the Segment Anything Model (SAM) to generate fine-grained mask prediction under the joint guidance of prompts and grounding cues, improving spatial grounding stability and robustness to background interference and scale variation. Extensive experiments on three remote sensing benchmarks , namely RefSegRS, RRSIS-D, and RISBench, demonstrate that SGSRF achieves state-of-the-art performance. The proposed stagewise paradigm integrates structural alignment, explicit grounding, and prompt-driven segmentation into a unified framework, providing a practical and robust solution for RRSIS in real-world Earth observation applications. Full article

Ultrasound-assisted vacuum drying (USVD) has emerged as an increasingly studied food drying approach to overcome mass and energy transfer limitations associated with conventional vacuum drying. This study aims to clarify the behavior of the USVD process by synthesizing findings from product- and condition-specific studies. This review critically examines 38 core USVD studies published between 2014 and 2025, complemented by related comparative research, to assess the effects of USVD on drying efficiency, product quality, and key process parameters across diverse food matrices. The reviewed literature consistently demonstrates that USVD enhances drying kinetics, with increases in drying rate reaching approximately 94%, depending on product characteristics and operating conditions. Due to shorter drying times, USVD also provides potential economic advantages through reduced energy costs, equipment utilization and overall process costs. Furthermore, research has found that USVD retains quality attributes including color and bioactivity of a wide range of foods. USVD-dried products commonly exhibit improved microstructural integrity and enhanced porosity, which imparts superior rehydration. In conclusion, this study highlights the strong potential of USVD to enhance drying efficiency while preserving product quality. Full article

The digital reconstruction of damaged archeological wall paintings is a challenging task due to severe material degradation, high fragmentation, and the lack of reference images. A crucial preliminary step is the separation and grouping of fragments originating from different wall paintings, which are often found mixed together at archeological sites. To address this issue, we explored the potential of multispectral imaging (MSI) for unsupervised fragment clustering, aiming to assess whether integrating multiple spectral bands can enhance fragment discrimination compared to using the visible band alone. As a test set, we examined five groups of wall painting fragments from a Roman domus (1st c. BC–1st c. AD) provided by the Archaeological Museum of Cremona (Italy). Images were acquired using the Hypercolorimetric Multispectral Imaging (HMI) system developed by Profilocolore^® Srl (Rome, Italy). Specifically, we considered visible reflectance (VIS), infrared reflectance (IR), infrared false color (IRFC), and Ultraviolet-induced Fluorescence (UVF) images. Through a systematic benchmarking study, we compared several state-of-the-art feature extraction and clustering methods across single- and multi-band configurations. Results show that combining MSI data can substantially enhance the system’s ability to correctly separate and group fragments, indicating a promising direction for future research. Full article

Spatially fractionated radiotherapy with X-ray minibeams (x-MBRT) aims to increase normal-tissue tolerance by delivering alternating high- and low-dose regions. We provide a Monte Carlo-based framework to design and optimize multi-slit collimators, quantifying how geometry and material govern peak–valley modulation. A validated digital twin of the SmART X-RAD225Cx irradiator was implemented in TOPAS/Geant4. Various x-MBRT collimators were simulated with parallel or divergent slits. The parameter space covered a slit width w (0.1–0.9 mm), center-to-center spacing CTC (1–3 mm), thickness T (1–5 mm), and acceptance angle θ. Dose was scored in a 2 × 2 × 2 cm³ water phantom at a 1 cm depth. For fixed w/CTC, peak-valley dose ratio PVDR increases with larger CTC via an increase in peak dose, with the valley dose nearly constant. Peak transmission saturated at θ ≈ 3°, indicating minimal benefit from larger acceptance. Divergent slits yielded flatter lateral profiles but higher valley doses than parallel slits, reducing PVDR around the central axis. This Monte Carlo study provides insights for optimizing collimator geometries in x-MBRT using small-animal irradiators, informing the design of more effective collimation systems to enhance treatment precision and normal-tissue sparing. Full article