Search Results (3,419)

The Citarum River Basin (DAS Citarum) in Indonesia faces significant challenges in waste management, necessitating a circular economy-based approach to reduce land-based pollution, which is critical for achieving the sustainability goals of the blue economy in the basin. This study addresses the complexity and inherent uncertainty in decision-making processes related to this challenge by developing a novel hybrid model, namely the Weighted Fuzzy N-Soft Set combined with the COmbinative Distance-based Assessment (CODAS) method. The model synergistically integrates the weighted 10R strategies in the circular economy, obtained via the Analytical Hierarchy Process (AHP), the capability of Fuzzy N-Soft Sets to represent uncertainty granularly, and the robust ranking mechanism of CODAS. Applied to a case study covering 16 types of waste in the Citarum River Basin, the model effectively processes expert assessments that are ambiguous regarding the 10R criteria. The results indicate that single-use plastics, particularly plastic bags (HDPE), styrofoam, transparent plastic sheets (PP), and plastic cups (PP), are the top priorities for intervention, in line with the high AHP weights for upstream strategies such as Refuse (0.2664) and Rethink (0.2361). Comparative analysis with alternative models, namely Fuzzy N-Soft Set-CODAS, Weighted Fuzzy N-Soft Set with row-column sum ranking, and Weighted Fuzzy N-Soft Set-TOPSIS, confirms the superiority of the proposed hybrid model in producing ecologically rational priorities, free from purely economic value biases. Further sensitivity analysis shows that the model remains highly robust across various weighting scenarios. This study concludes that the WFN-SS-CODAS framework provides a rigorous, data-driven, and reliable decision support tool for translating circular economy principles into actionable waste management priorities, directly supporting the restoration and sustainability goals of the blue economy in river basins. The findings suggest that targeting the high-priority waste types identified by the model addresses the dominant fraction of riverine pollution, indicating the potential for significant waste volume reduction. This research was conducted to directly contribute to achieving multiple targets under SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

Objective: Pain is among the most common and debilitating symptoms experienced by oncology patients and has been associated with adverse mental health outcomes, including depression and suicide. Nevertheless, the relationship between pain and suicide in oncology populations remains insufficiently characterized. A clearer understanding of this interplay is essential to guide personalized approaches aimed at reducing cancer-related burden and improving quality of life. Methods: We searched PubMed and PsycInfo without imposing limits regarding publication date using pain* AND (suicid* OR “self-harm” OR “self-injurious behavior” OR “self-inflicted injury” or “self-killing”) AND (cancer* OR oncolog* OR tumor* OR neoplasm* OR metasta*). A total of 832 articles were identified, and 15 of them were included in our review. Results: Inadequately managed pain in cancer patients is associated with a significantly elevated risk of suicidal ideation. This association is further exacerbated in individuals presenting with depressive symptoms, advanced-stage disease, or limited access to timely psychological support. These factors may interact synergistically, intensifying the emotional and cognitive burden of pain, thereby increasing vulnerability in cancer patients. Conclusions: Cancer-related pain should be conceptualized as a highly variable indicator of psychological vulnerability. Factors influencing this variability include cancer type and severity, as well as the presence of past psychopathology. These findings support the need for a personalized medicine approach, whereby pain management and psychosocial interventions are tailored to patient-specific factors such as disease stage, psychological comorbidity, and access to supportive care. Full article

Electrically conductive cement-based composites exhibit significant potential for a range of multifunctional applications. Nonetheless, the electrical and mechanical performance of ductile cement-based composites incorporating compound conductive additives has not been sufficiently explored. This study examines the effects of two distinct carbon-based fillers, namely carbon black and chopped carbon fibers, on strain-hardening cementitious composites (SHCC), and elucidates the synergistic mechanism of hybrid conductive fibers and fillers within SHCC. The findings indicate that a sufficiently high electrical conductivity can be achieved by incorporating 5 wt.% carbon black and 0.2–0.4 vol.% carbon fibers. The introduction of hybrid carbon-based fillers reduces the resistivity of SHCC by three orders of magnitude to less than 150 Ω∙cm, surpassing the performance of composites with a single carbon-based filler. Furthermore, the incorporation of hybrid carbon-based fillers and fibers enhances the compressive and flexural strength of cementitious composites. Compared to the referenced PE-SHCC, the tensile strength and strain of SHCC with 5 wt.% carbon black and 0.4 vol.% carbon fibers increased by 37.3% and 82.6%, respectively. A hybrid efficiency index (HEI) is proposed to assess both electrical conductivity and mechanical properties of SHCC incorporating with carbon-based fillers. The study’s findings offer an effective approach for utilizing hybrid carbon-based conductive fillers in the multifunctional applications of SHCC. Full article

This systematic review critically evaluates and synthesizes current evidence on the efficacy of biostimulants in enhancing soybean seed yield and quality. A comprehensive literature search was conducted following the PRISMA approach using the Web of Science (WoS) database, focusing on peer-reviewed studies from 2014 to 2025 reporting on the effects of biostimulants applied alone or in combination with other agro-inputs on soybean performance. Over 500 publications were retrieved from the database, of which 72 were included in this review. Extracted data were used to calculate changes in yield (kg ha⁻¹), percentage yield increase (%), oil content (%), and protein concentration (%). Our synthesis demonstrated that the sole application of biostimulants, including seaweed extracts, humic acids, amino acids, and beneficial microbes (Bradyrhizobium, PGPR, AMF), consistently enhanced soybean yield by 4% to 65%, while their interaction with other agro-inputs was shown to be capable of increasing yield by more than 150% under abiotic stress conditions, indicating strong synergistic effects. These improvements are mediated through various physiological mechanisms such as enhanced nutrient uptake, improved root growth, increased photosynthetic efficiency, and elevated stress tolerance. Furthermore, biostimulant application positively affects seed quality, increasing oil and protein content by 0.4–5.5% and 0.5–7.3%, respectively, by optimizing source–sink relationships and metabolic pathways. Overall, the greatest benefits are frequently observed through synergistic combinations of biostimulants with one another or with reduced rates of mineral fertilizers, highlighting a promising pathway toward sustainable crop intensification in soybean systems. Full article

Few-Shot Text Classification (FSTC) aims to classify text accurately into predefined categories using minimal training samples. Recently, prompt-tuning-based methods have achieved promising results by constructing verbalizers that map input data to the label space, thereby maximizing the utilization of pre-trained model features. However, existing verbalizer construction methods often rely on external knowledge bases, which require complex noise filtering and manual refinement, making the process time-consuming and labor-intensive, while approaches based on pre-trained language models (PLMs) frequently overlook inherent prediction biases. Furthermore, conventional data augmentation methods focus on modifying input instances while overlooking the integral role of label semantics in prompt tuning. This disconnection often leads to a trade-off where increased sample diversity comes at the cost of semantic consistency, resulting in marginal improvements. To address these limitations, this paper first proposes a novel Bayesian Mutual Information-based method that optimizes label mapping to retain general PLM features while reducing reliance on irrelevant or unfair attributes to mitigate latent biases. Based on this method, we propose two synergistic generators that synthesize semantically consistent samples by integrating label word information from the verbalizer to effectively enrich data distribution and alleviate sparsity. To guarantee the reliability of the augmented set, we propose a Low-Entropy Selector that serves as a semantic filter, retaining only high-confidence samples to safeguard the model against ambiguous supervision signals. Furthermore, we propose a Difficulty-Aware Adversarial Training framework that fosters generalized feature learning, enabling the model to withstand subtle input perturbations. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on most few-shot and full-data splits, with F1 score improvements of up to +2.8% on the standard AG’s News benchmark and +1.0% on the challenging DBPedia benchmark. Full article

Background: In the last decade, infections caused by Candida species have increased. Although C. albicans remains the most predominant species, fungal infections caused by non-albicans Candida (NAC) species have also been rising. This study aimed to determine which Candida spp. are most frequently associated with oral candidiasis. Methods: In accordance with PRISMA guidelines, a literature search was conducted in the PubMed, Cochrane Library, and ScienceDirect databases. The search used the keyword combination “candida spp” AND “oral candidiasis” AND “oral isolates” and included articles published between 2013 and 31 October 2025. Results: A total of 658 articles were identified, of which 24 met the inclusion criteria. Across these studies, 12,750 isolates were reported. C. albicans was the most prevalent species, accounting for 81.7% of all isolates. NAC species were detected at lower frequencies, including C. tropicalis (7.2%), C. glabrata (4.5%), C. krusei (4.1%), C. parapsilosis (1.0%), C. dubliniensis (0.8%), C. kefyr (0.2%), C. guilliermondii (0.1%), C. lusitaniae (0.1%), and other rare or unidentified species (0.2%). The increasing prevalence of Candida infections is associated with a growing population of immunocompromised individuals, and treatment remains challenging due to rising antifungal resistance. Conclusions: Although C. albicans remains the most prevalent, the appearance of NAC species is gradually increasing. With the increase of Candida spp. resistant to conventional antifungal agents and with the competitive or synergistic interaction between Candida spp., it is necessary to develop new therapeutic approaches. Full article

Hyperspectral imaging (HSI) captures the same scene across multiple spectral bands, providing richer spectral characteristics of materials than conventional RGB images. The spectral reconstruction task seeks to map RGB images into hyperspectral images, enabling high-quality HSI data acquisition without additional hardware investment. Traditional methods based on linear models or sparse representations struggle to effectively model the nonlinear characteristics of hyperspectral data. Although deep learning approaches have made significant progress, issues such as detail loss and insufficient modeling of spatial–spectral relationships persist. To address these challenges, this paper proposes the Cascaded Multi-Attention Feature Recurrent Enhancement Network (CMFREN). This method achieves targeted breakthroughs over existing approaches through a cascaded architecture of feature purification, spectral balancing and progressive enhancement. This network comprises two core modules: (1) the Hierarchical Residual Attention (HRA) module, which suppresses artifacts in illumination transition regions through residual connections and multi-scale contextual feature fusion, and (2) the Cascaded Multi-Attention (CMA) module, which incorporates a Spatial–Spectral Balanced Feature Extraction (SSBFE) module and a Spectral Enhancement Module (SEM). The SSBFE combines Multi-Scale Residual Feature Enhancement (MSRFE) with Spectral-wise Multi-head Self-Attention (S-MSA) to achieve dynamic optimization of spatial–spectral features, while the SEM synergistically utilizes attention and convolution to progressively enhance spectral details and mitigate spectral aliasing in low-resolution scenes. Experiments across multiple public datasets demonstrate that CMFREN achieves state-of-the-art (SOTA) performance on metrics including RMSE, PSNR, SAM, and MRAE, validating its superiority under complex illumination conditions and detail-degraded scenarios. Full article

The development of efficient and stable oxygen evolution reaction (OER) electrocatalysts based on non-precious metals is pivotal for advancing sustainable energy conversion technologies. We present a facile and green strategy for synthesizing a high-performance HO-CDs-FeOOH/NF(D) composite catalyst by leveraging a synergistic system of FeCl₃/urea deep eutectic solvent (DES) and hydroxyl-functionalized carbon dots (HO-CDs). This system orchestrates the rapid, in situ growth of FeOOH on nickel foam (NF) via simple immersion, wherein the DES acts as both an etchant and an iron source, while the HO-CDs induce a morphological transformation from sheet-like to granular stacking, thereby constructing highly active interfaces and increasing the density of accessible catalytic sites. The optimized catalyst exhibits exceptional OER performance, requiring an overpotential of only 251 mV to achieve 50 mA cm⁻², with a Tafel slope of 55.4 mV dec⁻¹. Moreover, it demonstrates outstanding stability, maintaining 98% of its initial current density after 24 h of continuous operation and showing negligible performance decay after 3000 cycles. This work presents a straightforward approach for designing high-performance Fe-based electrocatalysts through carbon dot-mediated morphology control via a facile DES-based impregnation strategy. Full article

TS-1 zeolite and a series of Zr-modified samples (TS-1/xZr) were synthesized and systematically characterized to investigate the influence of zirconium incorporation on structural, textural, and photocatalytic properties. The structural and textural properties of the samples were examined by XRD and nitrogen adsorption isotherms. Elemental analysis (EDXRF, SEM/EDS) and FTIR confirmed successful incorporation of Zr into the TS-1 framework. Photocatalytic tests under white light irradiation using crystal violet (CV), methylene blue (MB), rhodamine B (RhB) and methyl orange (MO) dyes revealed enhanced degradation efficiency for the Zr-containing samples, particularly TS-1/10Zr. Kinetic modeling using pseudo-first-order (PFO) and pseudo-second-order (PSO) approaches indicated that dye degradation followed mainly PSO kinetics. Reusability studies demonstrated sustained stability and recyclability of the catalysts. The improved photocatalytic performance is attributed to synergistic electronic effects between Ti and Zr species, which enhance charge separation and light absorption. Full article

To reveal the driving mechanisms of oxidative carbon components in urban forest soils in highly urbanized areas, this study collected 126 soil samples from the 0–30 cm layer of typical urban forests in Shenzhen, China. Soil organic carbon (SOC) was classified into four fractions based on oxidation stability: highly oxidizable organic carbon (VAC), moderately oxidizable organic carbon (AC), poorly oxidizable organic carbon (PAC), and inert oxidizable organic carbon (IAC). Integrating multi-source data on climate, topography, vegetation, soil, and urbanization, we adopted a synergistic multi-model approach to screen key drivers, identify nonlinear thresholds, and quantify pathway contributions, thereby systematically exploring the dominant characteristics and driving mechanisms of soil carbon components under urbanization. The results showed that (1) urban forest soils in Shenzhen were dominated by reactive carbon, with VAC accounting for the highest proportion of SOC, and the proportion of reactive organic carbon was significantly higher than that of recalcitrant organic carbon; (2) SOC and total nitrogen (TN) were the core driving factors of carbon fractions, and the number of regulatory factors increased with the enhancement of carbon fraction oxidation stability; (3) soil factors directly affected carbon fractions, while urbanization indirectly acted on inert carbon by altering vegetation characteristics. Based on the research results, urban soil and forest managers can implement zonal management for carbon fractions with different oxidation stabilities, which is expected to effectively enhance the carbon sink capacity and stability of urban forest soil carbon pools, providing practical support for ecological sustainable development. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

The widespread adoption of low-power devices and microelectronic systems has intensified the need for efficient energy harvesting solutions. While cantilever-beam piezoelectric energy harvesters (PEHs) are popular for their simplicity, their performance is often limited by conventional mass block designs. This study addresses this by proposing a comprehensive structural optimization framework for a triangular cantilever PEH to significantly enhance its electromechanical conversion efficiency. The methodology involved a multi-stage approach: first, an embedded coupling design was introduced to connect the mass block and cantilever beam, improving space utilization and strain distribution. Subsequently, the mass block’s shape was optimized. Furthermore, a negative Poisson’s ratio (NPR) honeycomb structure was integrated into the cantilever beam substrate to induce biaxial strain in the piezoelectric layer. Finally, a variable-density mass block was implemented. The synergistic combination of all optimizations—embedded coupling, NPR substrate, and variable-density mass block—culminated in a total performance enhancement of 69.07% (17.76 V) in voltage output and a 44.34% (28.01 Hz) reduction in resonant frequency. Through experimental testing, the output performance of the prototype machine showed good consistency with the simulation results, successfully verifying the effectiveness of the structural optimization method proposed in this study. These findings conclusively show that strategic morphological reconfiguration of key components is highly effective in developing high-performance, low-frequency adaptive piezoelectric energy harvesting systems. Full article

The growing environmental effect of asphalt pavements has fueled interest in sustainable alternatives including the application of recycled materials and self-healing systems. This research investigates the synergistic possibilities of steel slag aggregates and steel wool fibers in hot-mix asphalt compositions to increase sustainability and let crack healing via electromagnetic induction heating. Using either recycled steel slag or natural porphyritic aggregates, two kinds of AC16 Surf S mixtures with 35/50 bitumen were created incorporating two levels of steel fiber content (2% and 4%). Based on repeated semi-circular bending (SCB) testing following regulated induction heating and confinement, a committed self-healing evaluation plan was developed. The results verified that combinations including recycled steel slag met or outperformed traditional mixes in terms of mechanical behavior. Induction heating successfully set off partial recovery of fracture toughness, with more fiber content and repeated heating cycles producing better healing values. Recovery levels ran from 14.6% to 40%, therefore proving the practicality of this approach. These results encourage the creation of asphalt mixtures with improved endurance and environmental advantages. The research offers both an approved approach for assessing healing and real-world recommendations for the construction of low-maintenance, round pavements utilizing induction-based techniques. Full article

Background/Objectives: The clinical application of CAD/CAM restorative materials continues to evolve due to increasing demand for aesthetic, durable, and minimally invasive indirect restorations. Hybrid nanoceramics, such as Grandio disc (VOCO GmbH, Cuxhaven, Germany), are increasingly used in indirect restorative dentistry due to their favourable combination of mechanical strength, polishability, wear resistance, and bonding potential. One challenge associated with adhesive protocols for CAD/CAM materials lies in achieving durable bonds with resin cements. Extensive post-polymerization during fabrication reduces the number of unreacted monomers available for chemical interaction, thereby limiting the effectiveness of traditional adhesive strategies and necessitating specific surface conditioning approaches. This study aimed to evaluate, in a preliminary, non-inferential manner, the influence of several combined conditioning protocols on surface micromorphology, elemental composition, and descriptive SBS trends of a CAD/CAM hybrid nanoceramic. This work was designed as a preliminary pilot feasibility study. Due to the limited number of specimens (two discs per protocol, each providing two independent enamel bonding measurements), all bond strength outcomes were interpreted descriptively, without inferential statistical testing. This in vitro study investigated the effects of various surface conditioning protocols on the adhesive performance of CAD/CAM hybrid nanoceramics (Grandio disc, VOCO GmbH, Cuxhaven, Germany) to dental enamel. Hydrofluoric acid (HF) etching was performed to improve adhesion to indirect resin-based materials using two commercially available gels: 9.5% Porcelain Etchant (Bisco, Inc., Schaumburg, IL, USA) and 4.5% IPS Ceramic Etching Gel (Ivoclar Vivadent, Schaan, Liechtenstein), in combination with airborne-particle abrasion (APA), silanization, and universal adhesive application. HF may selectively dissolve the inorganic phase, while APA increases surface texture and micromechanical retention. However, existing literature reports inconsistent results regarding the optimal conditioning method for hybrid composites and nanoceramics, and the relationship between micromorphology, elemental surface changes, and adhesion remains insufficiently clarified. Methods: A total of ten composite specimens were subjected to five conditioning protocols combining airborne-particle abrasion with varying hydrofluoric acid (HF) concentrations and etching times. Bonding was performed using a dual-cure resin cement (BiFix QM) and evaluated by shear bond strength (SBS) testing. Surface morphology was examined through environmental scanning electron microscopy (ESEM), and elemental composition was analyzed via energy-dispersive X-ray spectroscopy (EDS). Results: indicated that dual treatment with HF and sandblasting showed descriptively higher SBS, with values ranging from 5.01 to 6.14 MPa, compared to 1.85 MPa in the sandblasting-only group. ESEM revealed that higher HF concentrations (10%) created more porous and irregular surfaces, while EDS indicated an increased fluorine presence trend and silicon reduction, indicating deeper chemical activation. However, extending HF exposure beyond 20 s did not further improve bonding, suggesting the importance of protocol optimization. Conclusions: The preliminary observations suggest a synergistic effect of mechanical and chemical conditioning on hybrid ceramic adhesion, but values should be interpreted qualitatively due to the pilot nature of the study. Manufacturer-recommended air abrasion alone may provide limited adhesion under high-stress conditions, although this requires confirmation in studies with larger sample sizes and ageing simulations. Future studies should address long-term durability and extend the comparison to other hybrid CAD/CAM materials and to other etching protocols. Full article

Mastitis remains a prevalent and economically detrimental disease within the dairy industry, profoundly affecting animal welfare, milk quality, and overall production output. Nowadays, Somatic Cell Count (SCC) is widely recognized as the gold-standard indicator for the detection of mastitis; however, its limitations in pathogens discrimination and the lack of early-stage characterization of mastitis highlight the need for complementary diagnostic approaches. This review synthesizes recent research into the development and validation of novel biomarkers for the early and accurate identification of mastitis in dairy cows. The investigation encompasses a range of biological molecules for improving mastitis diagnosis. Biomarkers such as lactoferrin (LTF), β-defensin 4 (DEFB4), vitronectin, paraoxonase 1 (PON1), and N-acetyl-β-D-glucosaminidase (NAGase) show promise in distinguishing between cows not susceptible and cows susceptible to mastitis. Concurrently, nucleic acid-based biomarkers are emerging as a particularly promising frontier. While mitochondrial DNA (mtDNA) has demonstrated insufficient specificity, microRNAs (miRNAs) are gaining attention as highly stable and sensitive indicators of intramammary inflammation, potentially enabling the detection of subclinical infections before they become clinically apparent. Despite these advances, significant challenges related to specificity, reliability, and cost-effectiveness currently hinder the widespread practical application of any single biomarker. Therefore, future research should be directed towards the validation of a synergistic panel of multiple biomarkers to improve mastitis management in dairy cow farms. Full article