Search Results (263)

Infrastructure expansion in Indonesia has increased demand for paving blocks, raising concerns over cement production costs and environmental impact. This study investigates the comparative effectiveness of pineapple leaf fiber (PALF, Ananas comosus) and sisal fiber (Agave sisalana) as reinforcements in paving blocks, evaluating water absorption and 28-day compressive strength at fiber contents of 0%, 1%, 3%, 5%, and 7% by cement volume. A full-factorial two-way ANOVA with post-hoc Tukey HSD was employed. A dosage of 3% for both fiber types resulted in compressive strengths of 14.5 MPa (PALF, +59% vs. control) and 15.2 MPa (sisal, +67% vs. control), both of which met the requirements of SNI 03-0691-1996 Class B. Sisal fiber demonstrated superior compressive performance, consistent with its higher stiffness and tensile strength as reported in the literature. Water absorption increased monotonically with fiber content for both types, with SNI Class D compliance (≤10%) maintained only at 0% for PALF and 0–1% for sisal, a known consequence of the inherently hydrophilic nature of plant-based natural fibers. A statistically significant interaction term (F = 3.697, p = 0.012) confirmed that the two fibers respond differently to dosage increases, providing nuanced practical guidance beyond what single-factor studies can offer. These findings demonstrate the promising compressive strength of agricultural waste fiber-reinforced paving blocks, warranting further investigation of abrasion resistance, flexural strength, and long-term durability before practical deployment. Such utilization supports circular economy principles in the construction industry. Full article

This investigation addressed challenges in the delivery of poorly soluble drugs, and the colloidal processing of polymer–ceramic composites by fabrication of advanced supramolecular hydrogels. Polygalacturonic acid (PGA) polymer and 18β-glycyrrhetinic acid (GA) drug, both characterized by poor aqueous solubility, were selected as model building blocks for supramolecular hydrogels. Meglumine (MG) served as a multifunctional component in the gels, acting as a building block as well as an alkalizing and solubilizing agent for PGA and GA. Investigations revealed gel formation mechanisms, which were based on the electrostatic interactions of deprotonated anionic carboxylic groups of PGA and GA with protonated amino groups of MG and the hydrogen bonding of PGA polymer and GA molecules. The feasibility of the fabrication of PGA-MG and GA-MG gels opened an avenue for the fabrication of PGA-GA-MG gels. The composite gels provided a platform for drug delivery, and the kinetics of drug release from the composite gels containing MG excipient were investigated. Composite gels were obtained from colloidal dispersions, containing bioceramics, such as hydroxyapatite, silica, and titania, and bioglass in the PGA solutions in the presence of MG. The results of this investigation pave the way for the fabrication of novel supramolecular and composite gels loaded with various functional materials. Full article

The large-scale generation of asphalt dust waste (ADW) has raised increasing environmental concerns, while its high-value utilization in cementitious materials remains insufficiently explored, particularly in terms of mechanical performance, durability-related properties, and integrated sustainability evaluation. In this study, a graded utilization strategy based on particle size was proposed to incorporate ADW into alkali-activated concrete paving blocks, in which fine ADW fraction (<0.075 mm) was used as a partial replacement of blast furnace slag (BFS), while the coarser ADW fraction was used as a partial replacement of river sand, aiming at sustainable pavement applications. In addition, two types of ADW with different lithologies, namely limestone ADW and basalt ADW, along with their combined system, were investigated. The results show that the incorporation of ADW effectively enhances the engineering performance of paving blocks. The compressive strength increased from 45.3 MPa to 56.6 MPa, while water absorption decreased from 5.3% to 4.1%. All mixtures satisfied the requirements for abrasion resistance and slip resistance, demonstrating their compliance with the performance criteria for pedestrian pavement applications. Among all mixtures, the combined use of limestone ADW and basalt ADW exhibited the best overall performance. The improved performance may be attributed to the combined effects of graded particle utilization and the potential compositional complementarity between calcium-rich limestone ADW and silica–alumina-rich basalt ADW, which is consistent with the denser microstructure observed in SEM images. In addition, the proposed strategy contributes to improved solid waste utilization and reduced consumption of natural resources, as reflected in the quantitative sustainability assessment. Overall, this study demonstrates that graded utilization of ADW is a feasible approach for developing alkali-activated paving materials, with promising performance and sustainability potential. Full article

The use of plastic waste as a filler in concrete, particularly in paving block production, represents an approach aligned with circular economy principles. While previous studies have focused on mechanical properties, the effect of such materials on slip risk remains insufficiently investigated, especially for pedestrian applications. This study evaluates the influence of the volumetric content of recycled plastic waste from electrical cable insulation on slip resistance of concrete paving blocks. A series of specimens was prepared with 0–45% replacement of natural aggregate by granulated cable insulation (GCI). Slip resistance was measured using the British Pendulum Tester and expressed as Skid Resistance Value (SRV) after statistical processing. Two sliders were used, Mounted Shoe 55 and Mounted Shoe 96, corresponding to road and pedestrian conditions. The results show that increasing GCI content reduces mass by approximately 9.6 g per 1% GCI, reaching a reduction of about 20% at 50% GCI. For polished surfaces, SRV increased up to 77 (MS55) and 75 (MS96) at 40–45% GCI. For ground surfaces, optimal performance was observed at 10% GCI, while higher contents reduced SRV and caused mechanical degradation above 30–35% GCI. The results indicate that optimized GCI content can improve slip resistance while reducing material weight. Full article

The synergistic utilization of waste plastics and tires in asphalt modification is a highly promising sustainable strategy. However, the differential impacts of distinct plastic molecular architectures on the performance and network evolution of rubber-modified asphalt remain fundamentally unclear. This study systematically investigated the physical, rheological, and microstructural properties of composite asphalts modified with desulfurized rubber powder (DRP) and four representative plastics: polyethylene (PE), styrene–isoprene–styrene (SIS), styrene–ethylene–butylene–styrene (SEBS), and styrene–butadiene–styrene (SBS). Furthermore, the pavement performance of the asphalt mixtures prepared via dry and wet methods was comparatively evaluated. Microstructural and spectroscopic analyses revealed that the composite modification was primarily governed by physical blending and swelling. The non-polar, semi-crystalline PE resulted in severe phase separation and extreme low-temperature brittleness. Conversely, the saturated hydrogenated mid-blocks of SEBS endowed the asphalt with the highest high-temperature rutting resistance but severely compromised its low-temperature stress relaxation. Remarkably, SBS interacted synergistically with DRP to form a highly homogeneous and densely interwoven three-dimensional network, thereby achieving an optimal viscoelastic balance, outstanding storage stability, and superior low-temperature ductility. Pavement performance tests further demonstrated that the wet method significantly outperformed the dry method for block copolymers by facilitating sufficient pre-swelling. Overall, the SBS-DRP composite-modified asphalt prepared via the wet method exhibited the most exceptional and balanced comprehensive pavement performance, providing a robust theoretical foundation for the sustainable and high-value recycling of multi-source solid wastes in paving engineering. Full article

Dragon fruit (Hylocereus spp.) is an emerging crop in the tropics and subtropics, but its production is increasingly threatened by diseases that reduce yield and profitability. Early diagnosis of these diseases is crucial for timely intervention, yet visual symptoms often appear only after significant infection has occurred. The study aims to evaluate how optical spectral reflectance can detect dragon fruit diseases and identify the most responsive spectral regions. In this study, six major dragon fruit stem diseases: Neoscytalidium stem canker, stem sunburn, anthracnose, Botryosphaeria stem canker, Bipolaris stem rot, and bacterial soft rot were characterized by the goal of identifying unique spectral signatures for early detection and differentiation of each disease. Seventy-two potted dragon fruit plants of three distinct species were grown under four organic vermicompost treatments (0, 5, 10, 20 tons/acre) in both open-field and high-tunnel conditions together, in a randomized complete block design. A handheld spectroradiometer (350–2500 nm) was used to collect reflectance from the diseased and healthy cladodes (stem segment). Various spectral vegetative indices were computed to identify disease-specific features. The results revealed distinct spectral features for each disease. Infected cladodes consistently exhibited higher reflectance especially in the visible region (400–700 nm) and the near-infrared region (900–2500 nm) of the spectrum than healthy cladodes. The Normalized Difference Vegetative Index (NDVI), Green Normalized Difference Vegetative Index (GNDVI), and Spectral Ratio (SR) spectral indices were significantly higher in healthy plants than in diseased ones, reflecting higher chlorophyll concentration and plant biomass. Conversely, the 1110/810 ratio was lower in healthy plants than in diseased plants, suggesting a more compact internal plant structure. Statistical analysis revealed highly significant differences (p < 0.00001) between healthy and diseased spectra in the Red, Green and NIR regions. Linear Discriminant Analysis(LDA) achieved the highest classification accuracy (OA = 0.642, κ = 0.488), though performance was limited for minority classes. These findings demonstrate that targeted spectral sensing can identify dragon fruit diseases before obvious symptoms emerge. By pinpointing disease-specific spectral indices, our study paves the way for early-warning tools such as targeted multispectral sensors or drone-based imaging that would enable growers to intervene sooner and limit losses. These results highlight the potential for development of UAV-based or portable spectral sensors for large-scale, near real-time disease monitoring in dragon fruit production. Full article

This research explores the use of industrial waste as an alternative to natural raw materials, promoting a circular economy in the construction sector. It specifically investigates the manufacturing of paving blocks using blast furnace slag and recycled aggregates. Paving blocks were produced without altering typical industry conditions, entirely replacing cement with alkaline-activated blast furnace slag. The study replaced natural aggregate in three proportions (20%, 50%, and 100%) with three types of recycled aggregates: concrete recycled aggregate (CA), masonry recycled aggregate (MA), and recycled mixed aggregate (RMA), in both coarse and fine fractions. The experimental procedure analysed the impact of recycled aggregates in an alkaline-activated slag matrix through three phases: characterising physical properties (mechanical properties, water absorption, density, abrasion resistance, and slip resistance), evaluating leaching behaviour, and conducting a life cycle analysis. The results of physical characterisation were statistically analysed using principal component analysis (PCA). The results obtained show the feasibility of manufacturing paving blocks with blast furnace slag by completely replacing the natural aggregate with the coarse fraction of the three recycled aggregates used and replacing up to 20% in the case of using the fine fraction. The properties of the paving blocks manufactured with slag depend mainly on the degree of substitution of natural aggregate with the recycled aggregate. All paving blocks can be considered environmentally safe from leaching according to the Dutch Soil Quality Decree. Paving blocks made from alkali-activated ground granulated blast furnace slag and recycled aggregates generate a lower carbon footprint compared to concrete paving blocks. Full article

The performance of recycled hot-mix asphalt mixtures (RHAM) is strongly governed by the extent and uniformity of interactions between the aged binder in reclaimed asphalt pavement (RAP) and the virgin binder. However, in current engineering practice, it remains difficult to accurately evaluate the blending degree of aged and virgin asphalt during RHAM production, where the blending degree refers to the extent and uniformity of binder interaction during hot mixing. Moreover, influenced by various construction-related factors, the uniformity of interfacial diffusion between the two asphalt layers is also hard to control, which compromises the durability of RHAM. To address these issues, fluorescence microscopy was used to quantitatively characterize the blending behavior of aged and virgin asphalt, and Fourier transform infrared spectroscopy (FTIR) was employed to investigate the interfacial diffusion process and its evolution under time-temperature coupling conditions from plant production to field paving. The results indicate that, owing to the fluorescent characteristics of the Styrene-butadiene-styrene block copolymer (SBS) modifier in polymer-modified asphalt, the blending behavior during hot mixing can be quantitatively characterized by the fluorescent area and its areal proportion, providing a rapid solution for quantitative evaluation during RHAM production. Increasing the preheating temperature of RAP, extending mixing time, raising mixing temperature, and adopting Mixing Sequence I reduced the proportion of fluorescent area, suggesting improved blending between aged and virgin asphalt. After blending, the interfacial diffusion between aged and virgin asphalt occurs within the RHAM; the uniformity of this diffusion becomes more pronounced as the elapsed duration from production to paving increases. Nevertheless, excessively long duration may induce secondary aging of the blended binder. Accordingly, the duration is recommended to be controlled at approximately 90 min and should not exceed 180 min. By elucidating the blending and diffusion behaviors of aged and virgin asphalt, this study provides practical guidance for contractors in controlling production-process parameters for RHAM. Full article

This LCA study addresses the research gap concerning the comprehensive environmental implications of using paving block aggregates (PBA), derived from crushed waste concrete paving blocks (CPB), as a sustainable replacement for natural aggregates in cementitious materials. While the concrete industry faces twin challenges—high CO₂ emissions from cement and the massive ecological toll of extracting 20 Gt/year of natural aggregates—a systematic life cycle assessment of this specific waste stream was necessary, especially one that considered potential material interaction trade-offs. The study’s conclusions offer critical insight into achieving genuine sustainability. Consistently, cement production was identified as the overwhelming environmental hotspot, contributing over 90% of the global warming potential (GWP) across all scenarios. This finding indicates that even substantial changes in aggregate sourcing can only deliver limited GWP reductions unless accompanied by strategies targeting cement-related emissions. While substituting natural aggregates with PBA generally provided environmental benefits, a crucial trade-off was identified: the significantly higher dosage of superplasticizer required to maintain the workability of the PBA mixes. For mortar, the burden from the increased plasticizer became a major secondary hotspot, occasionally offsetting the gains from aggregate replacement. In these scenarios, the contribution of admixtures to the total GWP was sufficiently high to reduce or negate the environmental benefits achieved through aggregate substitution. In contrast, aggregate replacement proved more favorable in concrete than in mortar, as the concrete scenarios showed a weaker correlation between environmental impact and plasticizer use. The authors conclude that future strategies must prioritize reducing cement content and, critically, systematically consider the necessary use of admixtures to ensure that the intended environmental improvements are genuine and not counteracted by the side effects of material substitution. The quantified LCA results demonstrate that cement reduction offers the highest mitigation potential, while admixture optimization is essential to prevent secondary environmental hotspots, particularly in mortar applications. Full article

With the increasing demand for perception in complex road environments in intelligent driving, rapid and accurate identification of paved-road obstacles has become a critical prerequisite for driving safety and comfort. Various types of road obstacles can significantly affect vehicle stability and ride quality. To address this challenge, a lightweight and efficient vision-based obstacle detection framework, termed GroupScale-YOLO, is proposed, in which detection accuracy and computational efficiency are jointly enhanced through the collaborative design of multiple novel modules. First, a dedicated dataset targeting common paved-road obstacles is constructed, and six data augmentation strategies are employed to mitigate the adverse effects of road surface undulations and illumination variations on visual perception. Second, to overcome the limitations of YOLOv11n in paved-road obstacle detection tasks, targeted optimizations are introduced to the backbone network, convolutional blocks, and detection head. Experimental results indicate that GroupScale-YOLO achieves a 29.95% reduction in model parameters while simultaneously increasing mAP@0.5 by 0.6% on the self-built dataset, demonstrating its suitability for deployment in resource-constrained scenarios. Furthermore, real-vehicle road tests confirm that the proposed method maintains stable and accurate obstacle detection performance under practical driving conditions, offering a reliable solution for intelligent vehicle environmental perception. Full article

Presently, the segmentation of brain tumors from magnetic resonance imaging (MRI) scans is a very important challenge in the medical area, and it has a huge impact on correct diagnosis, efficient treatment planning, and patient prognosis. We present here the Contextual Transformer U-Net (CT-UNet), a novel deep learning approach that can significantly increase the accuracy and speed of brain tumor segmentation. The CT-UNet method features Transformer blocks embedded in a U-Net layout that extracts the most important contextual information across different types of MRI sequences, thereby drastically refining the delineation of tumor regions. We have tested CT-UNet on the Brain Tumor Segmentation (BraTS) challenge dataset that includes a large variety of tumor types, localization, and progression stages. To check the model’s performance, we used the Dice coefficient, sensitivity, specificity, precision, and Hausdorff distance metrics. The findings from our experiments demonstrate that CT-UNet has a substantial advantage over the classical segmentation model, and the 0.92 Dice coefficient it has achieved testifies to its state-of-the-art tumor localization in terms of both extent and form. Besides that, CT-UNet has achieved a very high sensitivity (0.90) and specificity (0.94); thus, it has been perfectly capable of discriminating tumor from non-tumor tissues. Spatial accuracy has also been improved significantly, as can be seen from the 7.5 mm Hausdorff distance achieved by this model, which means it can closely replicate the given tumor boundaries. By employing dynamic modality fusion and incorporating the Transformer mechanism into the established U-Net architecture, we have raised the bar for brain tumor segmentation. Our solution paves the way for another breakthrough in medical imaging technologies. CT-UNet not only speeds up the workflow of radiologists but also facilitates more targeted therapeutic strategies that may result in better patient care and prognosis. Yet the main goal of this work is to provide a basis for future studies that can consider incorporating deep learning methods in a routine clinical setting, thus paving the way for healthcare providers to benefit from both technical and clinical advantages. Full article

Negative refraction of nanolight (e.g., polaritons, hybrid light, and matter excitation) provides a promising building block for nanophotonics, as it paves the way for developing cutting-edge nanoscale applications, such as super-resolution and subwavelength imaging. In the visible regime, negative refraction of surface plasmon polaritons has been extensively studied in conventional plasmonic and metamaterial systems; however, the inherent metallic losses remain a challenge that hinders their practical applications. Herein, we demonstrate negative refraction of low-loss and highly confined hyperbolic plasmon polaritons (HPPs) in a lateral heterojunction of a natural hyperbolic van der Waals material, molybdenum dioxide chloride (MoOCl₂). Owing to the exotic and ray-like propagating properties of HPPs, the negative refraction-inspired superlens can easily reach into the deep subwavelength scale, with spatial confinement of 800 nm near-infrared light wavelengths to below 150 nm focal spots. By elaborately adjusting the orientation directions of two-sided MoOCl₂, the mirror-symmetric superlensing effect can be tilted, and therefore, the focal spots are tuned and steered to deviate from the vertical interfacial lines. Our results applying the concepts of in-plane negative refraction with vdW materials achieve deep subwavelength light confinement and manipulation, offering new possibilities for constructing efficient and compact nanophotonic and opto-electronic devices. Full article

This study presents AMUSE++, an advanced speech enhancement framework that extends the MUSE++ model by redesigning its core Mamba module with two major improvements. First, the originally unidirectional one-dimensional (1D) Mamba is transformed into a bi-directional architecture to capture temporal dependencies more effectively. Second, this module is extended to a two-dimensional (2D) structure that jointly models both time and frequency dimensions, capturing richer speech features essential for enhancement tasks. In addition to these structural changes, we propose a Preliminary Denoising Module (PDM) as an advanced front-end, which is composed of multiple cascaded 2D bi-directional Mamba Blocks designed to preprocess and denoise input speech features before the main enhancement stage. Extensive experiments on the VoiceBank+DEMAND dataset demonstrate that AMUSE++ significantly outperforms both the backbone MUSE++ across a variety of objective speech enhancement metrics, including improvements in perceptual quality and intelligibility. These results confirm that the combination of bi-directionality, two-dimensional modeling, and an enhanced denoising frontend provides a powerful approach for tackling challenging noisy speech scenarios. AMUSE++ thus represents a notable advancement in neural speech enhancement architectures, paving the way for more effective and robust speech enhancement systems in real-world applications. Full article

Metalloproteinases (MPs) are zinc-dependent endopeptidases, including matrix metalloproteinases (MMPs) and a disintegrin and metalloproteinases (ADAMs), implicated in various diseases such as cancer, neurodegenerative disorders, and cardiovascular conditions. Among MPs, ADAM-17, also known as tumor necrosis factor-α (TNF-α)-converting enzyme (TACE), plays a crucial role in extracellular matrix remodeling and cytokine release. Dysregulation of ADAM-17 contributes to inflammatory diseases, cancer progression, and immune modulation. While small-molecule inhibitors have been limited by off-target effects and instability, antibody-based approaches offer a more selective strategy. Monoclonal antibodies show promise in blocking ADAM-17 activity, but there are concerns about toxicity due to the lack of selectivity. Enhancing the binding affinity and selectivity of single-chain antibodies requires unraveling the structural details that drive MP targeting. This study uses yeast surface display (YSD) and fluorescence-activated cell sorting (FACS) to engineer single-chain variable fragment (scFv) antibodies with optimized complementarity-determining region 3 of the heavy chain (CDR-H3) conformations. Next-generation sequencing (NGS) was used to identify key residues contributing to high-affinity ADAM-17 binding. These findings offer a framework for designing monoclonal antibodies against ADAM-17 and other MPs, paving the way for novel antibody-based designer scaffolds with applications in developing therapeutics. Full article

The nano-technological approach and supramolecular chemistry principles relation to the encapsulation of enzymes pave the way for creating next-generation nano-system-functionalized nano-compartments. The most promising approach for prophylaxis and the treatment of organophosphate (OP) poisoning is the use of stable, bioavailable nano-compartments containing OP-scavenging enzymes. Such enzymes, like butyrylcholinesterase (BChE), wild type and mutants, could also be used for the detoxification of other poisonous esters. There are two types of IRD-labeled human BChE-containing nano-scavengers: PEGylated liposomes and polyethyleneglycol–polypropylenesulfide polymersomes, which were developed with diameter close to 100 nm. BChE-polymersomes have higher encapsulation efficiency (95%) and slower release rate of enzymes (more than 7 days) compared to BChE-liposomes. The catalytic properties of encapsulated enzymes were analyzed for nano-compartment formulations, lipophilicity, the structure of block copolymers, and for different ester substrate polarity: positively charged butyrylthiocholine iodide, neutral phenyl acetate, and negatively charged aspirin. The highest k_cat (more than three times) compared to non-encapsulated BChE was for polymersomes based on diblock PEG-PPS polymersomes towards the neutral phenyl acetate substrate. Full article