Search Results (11,113)

Biomass pyrolysis has emerged as a flexible platform for converting low-value residues into higher-value energy carriers (bio-oil, biochar and gas) and carbon-rich materials, with realistic potential for negative emissions when biochar is deployed in long-lived sinks. Over the last decade, three developments have driven the field forward: first, a finer mechanistic understanding of devolatilization and secondary reactions; second, major improvements in analytical techniques for characterising feedstocks and products; and third, more rigorous techno-economic and life-cycle assessments that place pyrolysis in a broader energy-system context. Recent experimental work on forestry and agro-industrial residues has clarified how biomass composition, ash chemistry and operating conditions jointly govern product yields, energy content and stability. Parallel advances in GC×GC–MS, high-resolution mass spectrometry, NMR and thermogravimetric methods have shifted the discussion from bulk “bio-oil” and “char” to families of molecules and well-defined structural domains, which can be deliberately targeted by reactor and catalyst design. Data-driven models, ranging from support vector machines applied to TGA curves to ANFIS and random forests for yield prediction, are now accurate enough to support process screening and multi-objective optimisation. At the system level, commercial fast pyrolysis biorefineries report overall useful energy efficiencies on the order of 80–86%, while slow pyrolysis configurations centred on biochar can be economically viable when carbon storage and co-products are appropriately valued. Thermodynamic analyses confirm that indirect gasification via fast-pyrolysis oil sacrifices some energy and exergy efficiency relative to direct solid-biomass gasification but may offer logistical and integration advantages. This review synthesises recent work on (i) feedstock and process characterisation; (ii) state-of-the-art analytical methods for bio-oil, biochar and gas; (iii) modelling and machine-learning tools; and (iv) energy-system deployment of pyrolysis products. Throughout, the emphasis is on how characterisation and modelling inform concrete design choices and on the trade-offs that arise when pyrolysis is considered as part of a wider decarbonisation portfolio. By integrating laboratory-scale characterisation with system-level modelling, this review aligns biomass pyrolysis with several United Nations Sustainable Development Goals (SDGs). The optimisation of thermochemical conversion pathways for forestry and agro-industrial residues directly supports SDG 7 (Affordable and Clean Energy) by enhancing the efficiency of bio-oil and syngas production. Furthermore, the deployment of biochar as a stable carbon sink for negative emissions and soil amendment addresses SDG 13 (Climate Action) and SDG 15 (Life on Land). By converting low-value waste streams into high-value energy carriers and chemicals within a circular bioeconomy framework, the research further contributes to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). Full article

Infrared focal plane arrays (IRFPAs) often suffer from spatiotemporal nonuniformity that persists after conventional two-point nonuniformity correction (NUC), especially under temperature drift and time-varying readout conditions. These residuals are typically structured, including column-group striping caused by shared column-end circuits and row-wise baseline/common-mode drift induced by row-scanning paths. We propose a structured, digital-twin-inspired detector-side refinement of two-point NUC that augments the bias term with interpretable low-dimensional components: a static column bias vector capturing group-correlated residuals and a row-related structured term consisting of a static row baseline and a frame-synchronous common-mode component with row-dependent sensitivity, while keeping the two-point gain/offset backbone unchanged. Rather than representing a full system-level digital twin of the infrared payload, the proposed framework serves as a detector-side virtual representation of dominant readout-induced structured residual states that can be estimated and updated from calibration data. Experiments on blackbody calibration data across multiple temperature points demonstrate that the column-related structured component significantly reduces group-wise column residuals, the row-related structured component suppresses time-varying row striping, and the combined method improves both column- and row-direction metrics consistently across temperatures. Full article

Leaching carbon residue (LCR) is a carbonaceous solid waste generated during the hydrometallurgical recycling of spent lithium-ion batteries. Although its high graphite content offers substantial potential for resource recovery, the residual heavy metals and fluorides present in LCR pose considerable environmental risks. Currently, LCR has not garnered sufficient attention within the industry, and the lack of recycling technologies suitable for large-scale disposal results in resource wastage and environmental pollution. To address these challenges, this study proposes an innovative strategy based on the concept of multi-field synergistic enhancement. The proposed approach involves recovering and regenerating graphite (RG) from LCR via low-temperature molten salt roasting assisted by high-pressure and mechanical activation. A combination of advanced characterization techniques was employed to compare the physicochemical properties of RG and commercial graphite (CG) and to systematically evaluate the technical feasibility of using regenerated graphite as an anode material for lithium-ion batteries. The results demonstrate that, under optimized molten salt roasting and aqueous leaching conditions, the carbon content of RG reaches 99.94 wt%, indicating the efficient removal of non-carbon impurities from the graphite matrix. Compared to CG, RG retains a typical layered structure; however, a lower carbon content (99.94 wt%) and poorer structural order (I_D/I_G = 0.30) are observed. In terms of electrochemical performance, RG delivers a discharge specific capacity of 394.64 mAh/g during the first cycle and exhibits excellent cycling stability, with a capacity retention of 86.50% after 100 cycles. This electrochemical performance is comparable to that of commercial graphite. The proposed multi-field-assisted low-temperature molten salt roasting technique enables the efficient recovery of high-value graphite resources from LCR, establishing a full-lifecycle recycling strategy tailored for lithium-ion battery applications. Full article

The craters on the moon hold important clues about the history of impacts in our solar system. To address the limitation of traditional intelligent methods in detecting buried craters, this study proposes a novel intelligent detection approach based on DEM and gravity data. We designed a hybrid network architecture (ResNet + ViT) that combines the local feature extraction strengths of Convolutional Neural Networks with the global context modeling capabilities of Vision Transformer. By combining the complementary information from DEM and gravity anomaly data, it achieves comprehensive detection of lunar craters—from those visible on the surface to buried subsurface structures. To mitigate the inherent sample imbalance in both gravity anomaly and DEM training data, we employ a U-Net architecture augmented with residual blocks and train it using a Focal Loss function with dynamic focusing parameters. Experimental results show that: (1) The proposed method attains high segmentation accuracy, achieving a mean Intersection over Union of 81.3% on the DEM test set and 82.6% on the gravity anomaly test set, respectively. (2) Our method outperforms U-Net and its mainstream variants, achieving a precision of 89.48% and superior detection completeness. (3) Application to representative geological units, including the Wugang Basin, Archimedes Crater, and Mare Moscoviense, validates the robustness and practical utility of our method. This study, thus, provides a novel technical framework for global-scale mapping of lunar impact craters and yields new insights into the evolutionary history of the lunar surface. Full article

This paper presents a unified analytical and computational framework for the study of typhoid fever transmission dynamics governed by a Caputo fractional-order compartmental model of order $\kappa \in (0,1]$. The population is stratified into five epidemiological classes, namely susceptible $\left(\mathcal{S}\right)$, asymptomatic $\left(\mathcal{A}\right)$, symptomatic $\left(\mathcal{I}\right)$, hospitalised $\left(\mathcal{H}\right)$, and recovered $\left(\mathcal{R}\right)$, and the governing system explicitly incorporates asymptomatic transmission, treatment dynamics, and temporary immunity with waning. The use of the Caputo fractional derivative is motivated by the well-documented existence of chronic asymptomatic Salmonella Typhi carriers, whose heavy-tailed sojourn times in the carrier state are naturally encoded by the Mittag–Leffler waiting-time distribution arising from the fractional operator. A complete qualitative analysis of the fractional system is carried out: the basic reproduction number ${\mathcal{R}}_0$ is derived via the next-generation matrix method; local and global asymptotic stability of both the disease-free equilibrium ${\mathcal{E}}_0$ (when ${\mathcal{R}}_0\le 1$) and the endemic equilibrium ${\mathcal{E}}^{*}$ (when ${\mathcal{R}}_0>1$) are established using fractional Lyapunov theory and the LaSalle invariance principle; and the normalised sensitivity indices of ${\mathcal{R}}_0$ are computed to identify transmission-amplifying and transmission-suppressing parameters. Existence, uniqueness, and Ulam–Hyers stability of solutions are established via Banach and Leray–Schauder fixed-point arguments. To complement the analytical results, a fractional physics-informed neural network ($\mathcal{PINN}$) framework is developed to simultaneously reconstruct compartmental trajectories and identify unknown biological parameters from sparse synthetic observations. $\mathcal{PINN}$ embeds the L1-Caputo discretisation directly into the training residuals and employs a four-stage Adam–L-BFGS optimisation strategy to recover five trainable parameters $\Theta$ = $\{\varphi ,\mu ,\sigma ,\psi ,\beta \}$ across three fractional orders $\kappa \in \{1.0,0.95,0.9\}$. The estimated parameters show strong agreement with the true values at the classical limit $\kappa =1.0$ ($\mathrm{MAPE}=2.27\%$), with the natural mortality rate $\mu$ recovered with $\mathrm{APE}\le 0.51\%$ and the transmission rate $\beta$ with $\mathrm{APE}\le 3.63\%$ across all fractional orders, confirming the structural identifiability of the model. Pairwise correlation analysis of the learned parameters establishes the absence of equifinality, validating that $\beta$ can be reliably included in the trainable set. Noise robustness experiments under Gaussian perturbations of $1\%$, $3\%$, and $5\%$ demonstrate graceful degradation ($\mathrm{MAPE}$: $0.82\%\to 3.10\%\to 7.31\%$), confirming the reliability of the proposed framework under realistic observational conditions. Full article

Silicon carbide (SiC) has emerged as a highly attractive material for microelectromechanical systems (MEMS) operating in harsh environments, owing to its outstanding mechanical, thermal, and chemical properties. This review provides a comprehensive overview of the advantages and limitations of SiC-based MEMS, with particular emphasis on the strong interdependence between material structure, mechanical properties, and epitaxial growth processes. The role of defects, residual stress, and crystal quality is discussed in relation to device performance and reliability. Special attention is devoted to cubic SiC grown on silicon substrates, highlighting how growth-induced features influence the mechanical response of micromachined structures. Furthermore, a detailed analysis of the quality factor (Q-factor) is presented for 3C-SiC (111)/Si resonators, including the development of analytical models and their validation through numerical simulations performed using COMSOL Multiphysics (Version 6.1). The necessity of incorporating anisotropic loss factors in numerical modeling is demonstrated to be essential for accurately describing the experimentally observed behavior. This review aims to provide design guidelines and modeling strategies for the optimization of SiC MEMS, supporting their further development for high-performance and extreme-environment applications, including pressure sensors, mechanical resonators and high-stress-tolerant sensors. Full article

Exopolysaccharides (EPS) represent an important tool for application in bio- and phytoremediation technologies due to their ability to enhance water and nutrient retention, support microclimate stability, and protect plants from environmental stress. In the present study, xanthan-like EPS produced by Xanthomonas translucens TRK8 was precipitated by ethanol and isopropanol, with the former yielding 9.2 g L⁻¹ compared with 6.7 g L⁻¹ obtained with the latter. The monosaccharide profile of the TRK8-derived EPS indicated a branched structure composed of rhamnose, mannose, glucose, and galactose residues, containing both α- and β-type pyranose units. The rheological properties of the studied EPS were compared with those of commercial xanthan at concentrations of 1–3 wt.%. Fitting the obtained data to the Ostwald–de Waele power-law model revealed that the flow behaviour index (n) values were below 1 (−0.338, −0.499, and −0.647, respectively), indicating shear-thinning behaviour (i.e., pseudoplasticity). The potential of the TRK8-derived EPS as a plant protection agent was validated by coating barley seeds with 2 wt.% EPS, resulting in a 28.6% increase in shoot length and a 64.7% increase in root length relative to the oil-stressed control. Full article

Early diagnosis of ovarian cancer remains one of the most important unmet needs in gynecologic oncology because survival is strongly stage-dependent and most patients still present with disseminated disease. Conventional non-invasive tools, particularly CA-125, transvaginal ultrasound, and composite triage algorithms, remain clinically useful but are limited by suboptimal sensitivity for stage I disease and by reduced specificity in premenopausal women and in benign inflammatory or endometriosis-associated conditions. Circulating tumor DNA (ctDNA) has therefore emerged as a candidate biomarker capable of extending liquid biopsy beyond conventional serology. In ovarian cancer, however, ctDNA implementation is constrained by low tumor shedding in early-stage disease, marked biologic heterogeneity across histotypes, clonal hematopoiesis-related background noise, and major pre-analytical and analytical sources of variability. This narrative review, informed by structured searches of PubMed, Scopus, and Web of Science, examines the evolving evidence for ctDNA mutations, methylation-based assays, multi-omic platforms, and machine-learning models across three distinct clinical contexts: population screening, preoperative triage of adnexal masses, and post-treatment assessment of molecular residual disease. We also discuss positive predictive value, false-positive harms, health-economic implications, standardization initiatives, and ongoing prospective studies. Overall, current evidence suggests that the most plausible near-term role for liquid biopsy in ovarian cancer is not as a universal stand-alone screening test, but as an integrated component of risk stratification and disease-monitoring frameworks that combine molecular signals with clinicopathologic and imaging data. Full article

Reinforced concrete structures must balance immediate structural performance with long-term durability against environmental degradation, particularly carbonation-induced corrosion. While traditional cast-in-place concrete covers serve as the primary barrier, their substitution with prefabricated permanent formworks made of fiber-reinforced cementitious composites often fails to provide the necessary protective qualities required for aggressive environments. This study evaluates the durability and mechanical behavior of sisal-fiber-reinforced cementitious composites specifically engineered for use as permanent formwork. Short sisal fibers, treated by hornification to enhance dimensional stability and fiber–matrix adhesion, were incorporated at dosages of 2%, 4%, and 6% by weight. The experimental program included tests for water absorption, ultrasonic pulse velocity, axial compression, three-point flexural strength, and accelerated carbonation. The results indicated that composites with 2% and 4% of fibers exhibited reduced water absorption, sorptivity, compressive strength, and modulus of elasticity compared to the reference cement matrix. Residual stress values further demonstrated that the composites maintain significant post-cracking strength and stress transfer capacity, confirming their viability for structural elements. Although sisal-fiber-reinforced cementitious composites exhibit higher porosity and water absorption than conventional concrete used as reinforcement cover, they show sufficient resistance to carbonation to ensure a service life exceeding 50 years for reinforced concrete elements. Full article

Customer churn refers to the termination of a customer’s business relationship with a bank, representing a direct loss of future revenue. Product downsell manifests as a reduction in the number of financial products held or a downgrade in service tier, often signaling early customer disengagement. Accurately identifying customers at risk of these two behaviors has become a cornerstone of profitable growth in the competitive retail banking industry as downsell frequently serves as a precursor to total churn. However, the existing research typically treats these highly correlated behaviors as independent prediction tasks, overlooking their intrinsic link and failing to address the critical challenges of class imbalance and regulatory demands for model interpretability. To tackle these problems, we propose an enhanced multi-task learning network (EMTL-Net), a deep learning framework specifically designed to capture the nuanced interplay between churn and downsell behaviors. EMTL-Net introduces an explicit feature interaction module to enhance the modeling of high-order feature relationships and utilizes a shared representation layer to extract universal customer risk patterns, enabling the joint prediction of churn and downsell. Furthermore, we employ Focal Loss as the training objective to dynamically adjust sample weights, effectively mitigating the class imbalance problem. Critically, to meet financial compliance requirements, we implement a SHAP-based interpretation mechanism that is compatible with multi-task outputs, providing preliminary insights into feature importance. Formal validation of interpretability claims remains an important direction for future research. The experimental results on a publicly available pedagogical bank customer benchmark dataset demonstrate that EMTL-Net achieves excellent performance on both tasks. For churn prediction, the model achieves an AUC of 0.8259, an accuracy of 0.8361, and an F1-score of 0.6235, significantly outperforming the existing baseline models. For downsell prediction (noting that the downsell label is rule-derived from the number of products held), the model achieves an AUC of 0.8932, an accuracy of 0.8571, and an F1-score of 0.7504. Ablation studies confirm the critical contributions of the explicit feature interaction module, Focal Loss, and the residual structure to model performance. Crucially, the interpretability analysis corroborates business intuition by identifying customer age, account balance, and product holdings as dominant churn drivers—a consistency that reinforces the model’s credibility and practical utility in high-stakes financial environments. Full article

Soot and black carbon (BC) are typically regarded as troublesome products of incomplete combustion; however, growing interest in circular economy strategies and sustainable manufacturing highlights their potential as secondary functional carbon materials, including additive manufacturing (AM). This review synthesises the recovery, upgrading, and valorization pathways for soot/BC and recovered carbon black (rCB), with a particular focus on streams captured by mandatory emission-control systems (e.g., diesel/gasoline particulate filters, electrostatic precipitators, baghouse filters, and chimney soot) and the requirements for transforming these heterogeneous residues into reproducible AM feedstocks. A two-stage approach was applied, combining (i) an analysis of the European Union regulatory context (waste classification, end-of-waste routes, and chemical safety obligations, including REACH) with (ii) a structured literature review of studies published in 2017–2026 indexed in the Web of Science and Scopus, culminating in a qualitative synthesis of 152 papers. Evidence indicates that scale-up is primarily constrained by strong compositional variability and contaminant burdens (ash, metals, and PAHs), which affect dispersion, rheology, and property reproducibility, necessitating robust standardisation and risk assessment. This review maps key preparation and upgrading strategies (e.g., classification, ash/metal reduction, and control of organic fractions) and discusses their relevance across AM routes such as FDM/FFF, SLS, DLP, and DIW. Overall, realising credible waste-to-value pathways requires aligning technical performance targets with regulatory compliance and developing consistent characterisation protocols to enable the safe and predictable use of soot/rCB-derived fillers in AM. Full article

When ground transportation is disrupted by natural disasters, airdropped rescue vehicles require energy-absorbing cushioning devices to prevent landing impact damage. Thin-walled circular tubes are preferred for their high energy absorption capacity and structural efficiency. However, to reduce platform force fluctuations and decrease residual stroke after compression, thereby avoiding unbalanced loading and ensuring post-landing mobility, slots are introduced into the tube wall, which renders the mean crushing force (MCF) difficult to predict accurately using conventional methods. To address this issue, this paper proposes a physics–data-driven method for predicting the energy absorption characteristics of slotted thin-walled circular tubes. The engineering scenario is introduced, followed by comparative validation via drop weight tests and impact simulations to obtain a sample set via design of experiments (DOE). A multi-layer perceptron (MLP) neural network then augments the samples to generate a dataset. Dimensional analysis yields candidate MCF prediction equations, whose forms and coefficients are determined via a physics–data-driven approach. Weighted graph encoding transforms the equation-solving problem into a graph optimization problem to reduce the computational complexity, and an improved differential evolution (DE) algorithm with a dual-adaptive mutation operator (DSADE) adjusts the parameters and accelerates convergence. The resulting MCF prediction formula, combined with drop test requirements as the optimization objective, achieves a simulation relative error below 5%. These parameters also satisfy engineering requirements in actual airdrop tests, confirming the method’s effectiveness in predicting the energy absorption characteristics of slotted thin-walled tubes. Full article

The use of cellulosic reinforcement fillers, including cellulose and holocellulose, in the development of sustainable biopolymer composites has become increasingly essential and continues to attract significant attention in the composite industry. This study aimed to improve the structural and morphological characteristics of the polyhydroxybutyrate (PHB) matrix by incorporating cellulosic fillers—namely, α-cellulose and holocellulose produced via a green processing method—and to evaluate the effect of hemicellulose, present in holocellulose and exhibiting compatibilizing capability, on the overall performance of PHB-based blends. For this, the PHB matrix was first dissolved in chloroform, after which the cellulosic fillers were incorporated into the PHB–chloroform mixtures at 1 wt.% to provide the best homogeneous fiber dispersion. The PHB and cellulosic filler mixtures were blended at 500 rpm with a magnetic mixer for 30 min, and the resulting composite was cast onto a Teflon plate. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy were used to characterize the morphological and structural analysis of the obtained biopolymer-based composites. Thermogravimetric analysis (TG-DTG) was used to determine the thermal properties. The results obtained confirmed the presence of cellulosic fillers in the PHB matrix using FTIR, XRD, and SEM. In contrast to holocellulose, α-cellulose in the PHB matrix was shown to create a more organized structure. Both α-cellulose and holocellulose reinforcements were found to have similar effects on the thermal properties of the PHB matrix. Compared with neat PHB, the amount of residual char was found to be more than 36-fold in the sample containing α-cellulose and more than 41-fold in the sample containing holocellulose. Full article

Lactase enzyme-based products experience challenges including residual lactose that result in lactose intolerance. The purpose of this study was to develop polyelectrolyte polysaccharide-enriched lactase-encapsulated liposomal hydrogel films as an edible coating of Perline Mozzarella cheese that delivers enzymes along with the product on the side of absorption in the small intestine. Coatings were investigated for shelf-life enhancement and in vitro enzyme release behaviour. Two different polymeric hydrogel film formulations were evaluated: lactase-encapsulated liposome-enriched chitosan (PCLLa) and lactase-encapsulated liposome-enriched polyelectrolyte chitosan and sodium alginate (CLLA). Lactase-encapsulated liposomes (mean particle size: 176 nm) were produced using 20% v/v lactase enzyme and 8% w/v lecithin using probe sonication. The edible hydrogel film coatings were applied on Perline Mozzarella cheese using the standard dip-coating method. Shelf-life characteristics of all samples were evaluated using pH, colour change, dry matter determination, microbial evaluation, and sensory analysis. CLLA coatings increased shelf life up to 60 days, displaying a pH of 5.48, continued normal colour, enhanced humidity balance, minimal bacterial growth, and the highest scores for sensory values when compared to both PCLLa (coatings) and the bare cheese substrate (control) samples. Furthermore, CLLA coatings provided greater stability for liposomes within the polyelectrolyte polymeric edible hydrogel film structure. Hence, the combination of liposomes with polyelectrolyte edible hydrogel films provides a novel strategy to enhance lactase enzyme encapsulation (for intolerance), stability, and delivering ability to the small intestine as well as improving the shelf life of coated cheese products. Full article

Massive pelagic Sargassum influxes along Caribbean coasts have created an urgent need for valorization routes for this biomass. Here, sodium alginate was extracted from Sargassum fluitans collected at Chuburná Beach, Yucatán, Mexico, using a multistep extraction involving 0.2% formaldehyde pretreatment at 4 °C and brief heating at 65–70 °C, and subsequently used to prepare calcium-crosslinked alginate nanoparticles by ionotropic gelation. To our knowledge, this is the first direct synthesis of alginate nanoparticles from non-commercial alginate extracted from pelagic S. fluitans. An extraction yield of 18.7 ± 0.05% (mean ± SD, n = 3) was obtained, and UV–Vis, FTIR, and NMR analyses confirmed the characteristic structural features of alginate. ¹H NMR revealed an M-rich composition (F_M = 0.61, F_G = 0.39; M/G = 1.54) with short guluronate blocks (N_G>1 = 2.42), whereas ¹³C NMR corroborated the presence of both β-D-mannuronic and α-L-guluronic acid residues. SEM images showed predominantly spherical-to-subspherical nanoparticles with representative dry diameters of 233–269 nm, whereas DLS measurements at 0, 24, and 72 h revealed a dominant volume-based nanoscale population with main peaks at 12.75–15.31 nm and PDI values of 0.229–0.291, indicating reasonable short-term colloidal stability at room temperature. These results demonstrate that pelagic S. fluitans can serve as a viable feedstock for the production of structurally preserved alginate and calcium-crosslinked alginate nanoparticles. The study supports converting recurrent Sargassum biomass into higher-value polysaccharide-based materials and provides a basis for future application-specific evaluation of these nanomaterials. Full article