Search Results (1,916)

Malachite green (MG), a triarylmethane dye extensively used in the textile and aquaculture industries, is a persistent aquatic contaminant with documented carcinogenic, mutagenic, and teratogenic effects even at trace concentrations. In the present work, a natural, low-cost Serpentine clay was surface-modified with copper oxide nanoparticles generated in situ through a green, lemon-extract-mediated reduction of copper nitrate, yielding a novel SER/CuO nanocomposite. The as-prepared adsorbent was characterized by FTIR, XRD, BET, and SEM to confirm the successful anchoring of CuO nanoparticles onto the Serpentine lattice. Batch adsorption experiments demonstrated that the removal of MG was governed by solution pH, adsorbent dose, contact time, and initial dye concentration, with maximum uptake obtained close to neutral pH, consistent with the point of zero charge (pH_pzc = 7.6) of the composite relative to the pKa (6.9) of the dye. Equilibrium data were described comparably well by the Langmuir and Freundlich isotherms at 25 and 55 °C, with a maximum monolayer capacity of 279.06 mg g⁻¹ at 25 °C, while kinetic analysis showed the closest statistical agreement with the Elovich model, pointing to an energetically heterogeneous, chemisorption-assisted process supported by a three-stage intraparticle-diffusion profile. The individual and combined effects of solution pH, adsorbent dose, and contact time on removal efficiency were systematically evaluated using a one-factor-at-a-time approach. Thermodynamic estimation from the two-temperature Langmuir constants indicated a spontaneous and exothermic, and entropy-favored adsorption process. The spent adsorbent was regenerated using dilute hydrochloric acid and retained appreciable efficiency over successive cycles. Comparison to previously reported adsorbents supported the competitiveness of SER/CuO in terms of capacity, cost, and simplicity of preparation, and a techno-economic appraisal supported the feasibility of scale-up. As a waste-valorization step, both the bare SER/CuO adsorbent and its MG-loaded form were evaluated as electrode materials for the urea oxidation reaction: MG loading raised the anodic current density from 143.10 to 176.46 mA cm⁻² at 1.0 M urea, nearly doubled the electrochemically active surface area (7.34 to 14.41 cm²), and lowered the charge-transfer resistance, while sustaining a higher stable current density (111 vs. 81 mA cm⁻²) over 3600 s of continuous operation demonstrating a promising route for coupling water remediation with energy recovery. Full article

In this study, a silica-based adsorbent was synthesized via a two-step sol–gel route. A branch of ora-pro-nobis (Pereskia aculeata Miller) was used as a porogenic template. The biomass was incorporated into the silica matrix and subsequently removed by calcination. The resulting material was characterized by nitrogen adsorption–desorption analysis, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and differential scanning calorimetry (DSC), and its performance for erythrosine red adsorption from aqueous solution was evaluated. The incorporation of the biotemplate modified the pore structure of the silica, decreasing the BET specific surface area from 297.33 to 249.10 m² g⁻¹ while increasing the average BJH pore diameter from 2.26 to 3.66 nm. Adsorption was favored at pH 6 and rapidly approached equilibrium within approximately 5–7 min. The equilibrium data were satisfactorily described by the Langmuir model, with a maximum adsorption capacity of 108.81 mg g⁻¹ at 25 °C. Adsorption capacity decreased with increasing temperature, and thermodynamic analysis indicated a spontaneous and exothermic process (ΔH° = −8.07 kJ mol⁻¹). Overall, ora-pro-nóbis biomass acted as a renewable porogenic biotemplate, modifying the pore architecture of silica. The resulting TSOPN material exhibited competitive adsorption performance for erythrosine removal. Full article

Pyrolysis presents a promising approach to harness the bioenergy potential of biomass and promote waste valorisation within a circular economy framework. This study offers a detailed analysis of the thermal characteristics, reaction kinetics, and thermodynamic behaviour of two common waste biomasses in Nigeria, sawdust (SD) and sugarcane bagasse (SB), to assess their bioenergy potential using Thermogravimetric Analysis and the Coats-Redfern integral method. Proximate analyses revealed a high volatile matter content (over 82%) in both feedstocks. The pyrolysis parameters indicated a higher comprehensive pyrolysis (3.486 × 10⁻⁶, %³/°C³) and pyrolysis stability (1002.04%/C²) indices for SD, confirming its superior overall thermal reactivity. In contrast, SB demonstrated a superior devolatilisation index (5.0621 × 10⁻⁷%/C³) and a lower stability index (869.27%/C²), signifying an intense, concentrated eruption of volatile matter over a narrow temperature window. A multi-stage, segmented approach produced more accurate results than the general single-step model. The diffusion-based D6 model was most suitable for describing the drying and devolatilisation of SD, while the reaction-order R2 model best explained the devolatilisation process of SB. The thermodynamic analysis revealed that formation of the activated complexes for both SD and SB across all stages is strictly non-spontaneous and endothermic, with SD encountering a higher thermal barrier (∆H = 57.57 kJ/mol) than SB (∆H = 37.03 kJ/mol) during the process. These findings indicate that SD possesses properties favourable for fast pyrolysis processes aimed at bio-oil production, whereas SB exhibits characteristics that make it a promising feedstock for biochar-orientated pyrolysis. Further studies involving product yield and quality analyses are recommended to validate these potential applications. These findings provide essential information for designing effective, locally adapted bioenergy systems in Sub-Saharan Africa. Full article

Drug–target interaction (DTI) prediction is a critical step in drug discovery, and accurate prediction of potential interactions can significantly accelerate the drug-development process. Although deep-learning approaches have achieved promising performance in DTI prediction, two challenges remain: single models often fail to comprehensively capture heterogeneous sequence information, resulting in limited stability and generalization, while insufficient integration of local and global features restricts interaction representation. To address these limitations, we propose HFEDTI, a DTI prediction model that integrates hierarchical feature fusion and weighted ensemble learning. Specifically, a residual convolutional neural network (ResCNN) is employed to extract local structural features of drugs and targets, while a self-attention-based hierarchical bidirectional long short-term memory network (SAHBiLSTM) captures global contextual dependencies. Furthermore, a hierarchical heterogeneous attention mechanism is introduced to align and fuse multi-level cross-modal representations, and a weighted ensemble strategy based on validation performance ranking is developed to enhance model robustness and generalization. Experimental results on three benchmark datasets demonstrate the effectiveness of HFEDTI. On the DrugBank dataset, HFEDTI achieves an AUC of 0.9238 and an AUPR of 0.9327, improving the best-performing baseline by 0.90 and 1.40 percentage points, respectively. Moreover, HFEDTI consistently achieves strong performance on the C. elegans and Human datasets, further validating its effectiveness and generalization capability for DTI prediction. Full article

The resilience of electrical grid infrastructures is increasingly challenged by high penetration of renewables, climate-induced stress events, and complex interdependencies between assets and control systems. This paper proposes a structured Resilience Assessment Cycle (RAC) and operationalizes it through a novel LLM-orchestrated Agentic Resilience Assessment System (A-RAS) for quantitative assessment of resilience to extreme weather events. RAC defines a structured assessment process linking disturbance characterization, operational-state evaluation, resilience quantification, and interpretation of the resulting system response. A-RAS implements this process through coordinated numerical engines and agentic components. First, an anomaly detection engine applies a residual-based Exponentially Weighted Moving Average scheme (OpS-EWMA) to identify incipient operational shifts in heterogeneous equipment from SCADA time series. Second, a labeling and diagnostic engine employs a retrieval-augmented RAG-LLM pipeline to generate structured diagnostic explanations and, in a subsequent step, assign operational state labels using a dual-scoring mechanism that combines two independent “votes”: a quantitative score derived from data-driven anomaly severity and a qualitative score derived from LLM-based semantic assessment. Third, a resilience assessment agent integrates (i) a module that detect extreme weather event windows and (ii) a module that computes a dual-output resilience vector—a service-performance deficit and a residual health-state deficit—derived from the temporal evolution of system performance and asset condition over the defined assessment horizon. Finally, a core orchestration agent manages data flow and task delegation, enabling automated, end-to-end resilience assessment. In contrast to existing approaches that represent equipment condition as a binary—functional or failed—the proposed methodology explicitly integrates the heterogeneity and temporal evolution of operating states. By accounting for intermediate health conditions, it addresses a key limitation of prevailing metrics: their limited ability to explain observed system behavior during stress events. The feasibility and practical value of the approach are demonstrated on a real operational wind power plant, showing that resilience trajectories can be traced to residual health deficits and the contributing equipment. As an initial implementation validated on a single site and hazard class, RAC and A-RAS provide a structured and extensible foundation intended to be generalized across additional assets, hazards, and operational contexts in future work. Full article

Sequential decision-making requires animals to flexibly balance model-based (MB) and model-free (MF) strategies to adapt to changing environments. The hippocampus (Hp) and striatum (ST) are two important components of the broader neural networks supporting these processes; however, how their dynamic interactions reorganize during learning-dependent strategy transitions remains poorly understood. Here, we trained pigeons on a two-step sequential decision-making task while simultaneously recording local field potentials (LFPs) from the Hp and ST. A dynamic reinforcement learning framework combined with a sliding-window approach was used to characterize temporal changes in behavioral strategies, and phase transfer entropy (PTE) was applied to estimate directed information flow between the Hp and ST across theta, beta, and broad gamma (30–80 Hz) frequency bands. Behavioral modeling revealed a gradual transition from early MB-like, task-structure-sensitive control toward later MF-like value-guided behavior as learning progressed. PTE analysis demonstrated a consistent Hp-to-ST directional bias across all analyzed frequency bands during task acquisition. Notably, gamma-band Hp-to-ST information flow exhibited a consistent decline over training, whereas theta- and beta-band interactions showed less consistent changes across individuals. Additional analyses showed that relative MB model evidence and gamma-band Hp-to-ST information flow covaried across learning, but this association was no longer significant after controlling for learning progression, indicating parallel rather than independently coupled changes. These preliminary findings indicate that hippocampal–striatal communication undergoes frequency-specific reorganization during sequential learning. The reduction in gamma-band Hp-to-ST information flow accompanies, rather than independently predicts, the behavioral strategy transition, suggesting learning-related modulation of interregional coordination as task demands change. Full article

GNSS coordinate series noise modeling is essential for reliable geophysical signal estimation and uncertainty assessment. The generalized Gauss–Markov (GGM) noise model and the Matérn process (MP) noise model are widely used to describe low-frequency spectral flattening in GNSS coordinate series, but they differ in their definition domains, parameterizations and autocovariance function (ACF) structures. These differences may lead to misconceptions, complicate noise model comparison and practical implementation. Building on a systematic review of the theories of GGM and MP, this study proposes a generalized Matérn process (GMP) noise model. By introducing a fractional step size hyperparameter μ into the differencing operator, GMP provides a unified framework that continuously connects the two models: when μ = 1, GMP reduces to GGM; as μ → 0, the spectrum of GMP approaches that of MP. The preferred range of μ is investigated using 420 GNSS coordinate series from 140 global GNSS sites, considering differences in geographical region, coordinate component and length of observations. The results show that the preferred values of μ are robustly concentrated within the interval [0.7, 1]. Large-scale validation is then conducted using 846 GNSS coordinate series from 282 global GNSS sites. The experimental results show that under AIC, BIC and BIC_tp, the proposed WN + GMP family consistently accounts for a large proportion of the optimal models. These results demonstrate that GMP provides a more general noise model family for GNSS coordinate series noise modeling and can improve the fidelity and flexibility of stochastic noise modeling. Full article

Natural rubber (NR) block production from cup lump conventionally requires multiple mechanical processing stages, resulting in high energy consumption and complex manufacturing operations. This study presents a modern process approach by using a Rotary Disc Granulator (RDG) to integrate size reduction and cleaning into a simplified two-step processing. The modern process was evaluated in terms of energy consumption, productivity, drying performance, molecular characteristics, rheological behavior, and final product properties, with comparison to the conventional process. The modern process reduced Specific Energy Consumption (SEC) from 89.64 ± 9.52 to 62.56 ± 4.40 kWh/ton, corresponding to an energy saving of approximately 30%, while maintaining comparable productivity and meeting Standard Thai Rubber (STR 20) quality requirements. Improved drying efficiency was achieved through enhanced heat transfer associated with the more uniform pellet morphology produced by the RDG. Molecular and rheological analyses further demonstrated higher molecular weight, lower long-chain branching, and improved thermal stability, indicating reduced mechanical degradation during processing. Despite the simplified process, the cured rubber exhibited comparable curing characteristics and mechanical properties to those produced by the conventional process. These findings demonstrate that process intensification using an RDG provides a practical and sustainable strategy for improving the energy efficiency of industrial natural rubber manufacturing while preserving product quality. Full article

Reliable detection of event-related potentials (ERPs) at the single-trial level remains a challenge due to low signal-to-noise ratio and high variability in electroencephalography (EEG) recordings. This work investigates the use of the Deep-Match framework (Deep-MF) for ERP detection. We examine whether incorporating prior knowledge of an ERP template into deep learning models improves detection performance. As a proof-of-concept study, the framework was evaluated on a single dataset with multi-channel EEG recordings during laser stimulation. The model was trained in two stages. First, an encoder–decoder architecture was trained to reconstruct input EEG signals in order to learn compact signal representations. In the second stage, the decoder was replaced with a detection module and the network was fine-tuned for ERP identification. Two model variants were evaluated: a standard model with randomly initialized filters and a Deep-MF model in which input kernels were initialized using ERP templates. Models performance was assessed on a single-trial ERP detection task during leave-one-out validation, and then compared with matched filter detector. The neural network models outperformed the matched filter detector and proposed that the Deep-MF model slightly outperformed the detector with standard kernel initialization for the majority of held-out subjects. Although both approaches exhibited substantial inter-subject variability, Deep-MF achieved a higher average F1-score (0.37) compared to the standard network (0.34), indicating improved robustness to cross-subject differences. Performance varied considerably across participants. The best performance obtained by Deep-MF reached an F1-score of 0.71, exceeding the maximum score achieved by the standard model (0.59). These results showed that ERP-informed kernel initialization provides improvements in single-trial ERP detection under subject-independent evaluation. These findings demonstrate that integrating domain knowledge with deep learning architectures can improve single-trial ERP detection. The proposed approach provides a step towards practical wearable EEG and passive brain–computer interface applications, as well as towards real-time monitoring of cognitive processes. Full article

In conservation work on historic built heritage, the transition from executive design to the worksite often requires the verification, integration and updating of information acquired during the design phase. This issue is particularly relevant for the documentation of the state of conservation since new accessibility conditions on site may allow previously unreadable areas to be observed and decay maps to be revised. This paper presents the preliminary development and testing of SMAT, an iPadOS application designed to support the in situ updating of decay maps and their reintegration within an HBIM workflow. The experimentation was carried out on the Oratory of San Giovanni Battista in Bussana Vecchia, starting from a previously developed HBIM model. The application allows operators to annotate decay phenomena on reference orthophotos using vector geometries, associate each annotation with semantic and temporal information, and store the data in JSON format. The workflow also includes a differential synchronization procedure based on Dynamo scripts, aimed at comparing successive annotation versions and updating the corresponding two-dimensional views in Autodesk Revit. The preliminary experimentation indicates the potential of the proposed procedure to support the traceable management of information updates between the design and worksite phases and to streamline the intermediate steps between direct observation, graphic restitution, and data reintegration, while maintaining an operational approach compatible with the requirements of the worksite phase in restoration processes. Full article

The accurate characterization of human immunodeficiency virus (HIV) genetic diversity and drug resistance is critical for effective surveillance and treatment, yet current sequencing technologies face limitations in sensitivity and scalability for community-level implementation. We present G-HIV, an integrated platform combining long-read sequencing (G-seq500) with an automated bioinformatics pipeline. G-HIV processes raw FastQ data to generate automated reports on point mutations, drug resistance predictions, viral quasispecies diversity, and haplotype networks via a two-step analytical approach. Applied to 44 HIV-1 plasma samples (42 used in the final comparison after excluding 2 samples with low-quality Sanger chromatograms), G-HIV detected 3–48 candidate minority variants per sample that were not observed by Sanger sequencing, identifying drug-resistant quasispecies in two samples with undetectable Sanger signals, and revealed mixed infection cases (e.g., inter-subtype CRF07_BC/CRF08_BC) through phylogenetic analysis. G-HIV addresses an integration of long-read sequencing with a fully automated, one-stop bioinformatics pipeline designed for frontline laboratories without specialized bioinformatics expertise—providing a scalable solution for community-based resistance surveillance and personalized therapy optimization in resource-limited settings. This research addresses an integrated long-read sequencing and automated bioinformatics platform for rapid and precise HIV-1 surveillance. G-HIV surpasses conventional approaches like Sanger sequencing in resolution, efficiency, and accessibility for community-level surveillance. By integrating long-read sequencing, streamlining workflows and eliminating the need for specialized bioinformatics expertise, G-HIV is positioned to become a new solution, providing more effective one-stop services for HIV-1 prevention and control. Full article

Phycobiliproteins are water-soluble photosynthetic pigments extracted mainly from microalgae and cyanobacteria with many potential biotechnological applications, such as healthy food colorants, nutraceuticals, fluorescent tags, or non-toxic therapeutic agents. We have recently devised a green innovative two-step ultrasound-based process (named UltraBlu) to obtain blue phycocyanin (a phycobiliprotein) with a high-purity grade from the cyanobacterium Limnospira platensis. To assess the possibility of a wider use of the method, this study evaluates the UltraBlu process on an organism of a different taxonomic domain, the red microalga Porphyridium cruentum, for extracts rich in B-phycoerythrin (B-PE), the main phycobiliprotein produced by this organism. The UltraBlu process is characterized by the extraction of phycobiliproteins decoupled from biomass cell lysis, although the process is entirely carried out in an aqueous medium. Conversely, cell lysis is integrated with the purification step and is carried out by ultrasonication in ammonium sulfate solution before the pigment extraction/recovery step. B-PE, significantly purer than that obtained via conventional one-step direct ultrasound-assisted extraction, was recovered within a few hours from fresh biomass, only isolating the B-PE extract from the leftover biomass by centrifugation. Yields generally exceeded 9%, approaching the highest pigment contents reported for P. cruentum in the literature. Full article

Music-to-dance generation requires precisely aligning movement dynamics with musical rhythm, yet existing methods rely on shallow conditioning or auxiliary beat-alignment objectives that fail to establish stable beat–motion correspondences. We present ChoreDiffusion, a diffusion-based framework that integrates explicit beat guidance directly into the denoising process. Central to our approach is a beat-enhanced cross-modal attention mechanism that injects beat-salience cues at every refinement step, promoting fine-grained synchronization beyond the reach of conventional conditioning pipelines. To support multiple dance styles within a unified model, we incorporate lightweight low-rank adaptation (LoRA) modules that encode style-specific motion signatures with only a small set of additional parameters per style, and a three-stage progressive curriculum stabilizes the joint learning of rhythmic alignment and stylistic expressivity. Experiments on two public multi-style dance benchmarks (AIST++ and FineDance) show that ChoreDiffusion achieves the lowest FID values among the compared generation methods on both benchmarks, while maintaining competitive rhythm alignment and multi-style controllability. These results indicate that embedding beat-aware guidance during generation, rather than applying it afterwards, is an effective route toward human-like musicality in music-driven choreography. Full article

Rapid urban population aging and increasing land constraints pose significant challenges for improving both service coverage and spatial equity in elderly care facility planning. This study develops an integrated optimization framework that simultaneously addresses accessibility, efficiency, and equity in the spatial allocation of elderly care facilities. First, an improved Gaussian Two-Step Floating Catchment Area (G2SFCA) method is employed to evaluate accessibility patterns across multiple facility types under both walking and driving scenarios. Second, resource allocation equity is quantified using Lorenz curves and spatial Gini coefficients to identify mismatches between elderly care supply and population demand. Building upon these analyses, a fairness-oriented maximum covering location model—termed the Equity Maximum Covering Location Problem (EMCLP)—is formulated and further transformed into a Markov Decision Process. A deep reinforcement learning-based algorithm is subsequently designed to solve the EMCLP under complex spatial constraints. Experiment results demonstrate that the proposed approach achieves improved computational efficiency while maintaining robust solution quality, and effectively enhances service provision in underserved areas through differentiated functional allocation strategies. Full article

Hot pressing is a promising technique for fabricating high-density molybdenum (Mo) alloys; however, obtaining low oxygen content remains a significant challenge. In this study, Mo10Cr alloy billets were prepared by hot pressing with nanocarbon addition and a two-step pressurization strategy to reduce the oxygen content. Results show that the relative density of the Mo10Cr alloy increases with hot-pressing temperature and eventually remains above 98%, accompanied by grain growth and an increased degree of solid solution. The incorporation of nanocarbon facilitates in situ oxygen reduction during hot pressing under one-step pressurization, but this deoxygenation process is impeded by the applied pressure. In contrast, the two-step pressurization method effectively reduces the oxygen content of the nanocarbon-added Mo10Cr alloy to 169 ppm while maintaining a low residual carbon level. Furthermore, this approach significantly improves the compositional uniformity of the alloy. It is also observed that increasing the applied pressure promotes densification alongside grain growth. Consequently, the combination of two-step pressurization and in situ nanocarbon deoxygenation presents an effective pathway for fabricating dense, low-oxygen Mo alloys. Full article