Search Results (660)

Computed tomography (CT) at the third lumbar vertebra (L3) is widely used for muscle quantification, but manual segmentation is labor intensive. This study externally validates an AI model, trained on a public dataset, for automated L3 muscle segmentation using an independent cohort, including a subgroup analysis of subject characteristics (e.g., age and a history of cancer). The AI model was trained on 900 CT scans with expert annotations from a publicly available repository. Validation was performed on 232 PET CT scans from the University Hospital Brussels, each manually segmented by an expert. Segmentation post-processing employed a density-based clustering algorithm to discard arm muscles and Hounsfield unit (HU) thresholding to refine the muscle segmentation. Performance was assessed using the Dice Similarity Coefficient (DSC) and Segmentation Surface Error (SSE). The model achieved a median DSC of 0.978 and a median SSE of 3.863 cm² across the validation set. At lower BMI values, the model was more prone to overestimation of muscle surface area. Most segmentation errors occurred in the abdominal wall muscles. Analysis showed no significant difference between arm positioning above the head and alongside the body, indicating robustness to minor artifacts from arm positioning. The AI model delivers accurate, automated L3 muscle segmentation, supporting larger-scale body composition studies. However, diminished accuracy at low BMI values and limited demographic diversity of the data highlight the need for broader validation. Full article

The purpose of this study is to examine whether analyst-level credit can be assigned quantitatively in a lightweight human-feedback decision-support pipeline. In intelligence and national security workflows, analysts often provide edits, comments, and evaluative feedback during the production of analytic products, yet these intermediate contributions are usually discarded, leaving no auditable record of how individual feedback shaped the final output. To address this problem, this study proposes a proof-of-concept Analyst-of-Record framework that combines synthetic analyst feedback, a linear ridge reward model, first-order influence functions, and additive Shapley aggregation to estimate both feedback-item and analyst-level contribution scores. The research design uses the Fact Extraction and VERification (FEVER) fact-verification dataset under controlled experimental settings. The pipeline retrieves evidence with Best Matching 25 (BM25), generates a grounded template-based response, derives three synthetic analyst feedback channels from FEVER annotations, trains a reward model on simple claim–answer and analyst-identity features, and aggregates per-feedback influence scores into an Analyst Contribution Index (ACI). The main experiments are conducted on a 500-claim subset across five random seeds, with additional ablation and bootstrap analyses used to assess sensitivity and stability. The findings show that the reward model achieves a mean validation $R^2$ of $0.801\pm 0.037$, indicating that the synthetic feedback signals are learnable under the selected featureization. The analyst-level contribution scores remain stable across random seeds, with approximately half of the total influence magnitude attributed to the explanation-quality channel and the remainder split across the other two channels. Ablation results further show that removing the explanation-quality channel collapses validation fit, while bootstrap resampling demonstrates tight concentration of absolute ACI magnitudes. Theoretically, this study extends attribution research beyond document-only grounding by showing how analyst feedback itself can be modeled as an object of contribution analysis. It also demonstrates that influence functions and Shapley-style aggregation can be adapted into a tractable framework for estimating interpretable analyst-level credit in a reproducible experimental setting. Practically, the proposed framework offers an initial foundation for more traceable and accountable decision-support workflows in which intermediate analyst contributions can be preserved rather than lost. The results also provide a feasible implementation path for future systems that incorporate stronger generators, richer evidence representations, and real analyst annotations. Full article

Household food waste constitutes a major sustainability challenge with worldwide implications. In the current study, an online survey (N = 252) was developed to explore how routine food management practices in Greek households influence food waste. The survey was combined with a Social Practice Theory (SPT) framework and an exploratory high-school educational intervention (N = 27) with app-based diary tracking. Under the context of SPT, indices for meanings (MNG), competencies (CPT) and materiality (MAT) were constructed, along with a Food Waste Frequency (FWF) index. Respondents were highly willing to follow educational advice (87.3%) but more moderately open to using a food-tracking app (48.1%). FWF index results (M = 2.01, SD = 0.64) suggested that household food waste was present but not established as a daily routine of the participants. Pearson’s correlations and hierarchical regression of SPT indices and FWF showed that MNG were the main predictor of household FWF (R² = 0.38), with CPT providing a marginal contribution. Also, a positive correlation (r = 0.619, p < 0.01) was observed between FWF and MNG. The results from the food waste tracking app showed that legumes (14.6%), vegetable peel (14.6%), and meat (12.5%) were the most frequently discarded food categories and that 56.3% of the discarded food was used for animal feed. However, a decrease in the use of the application was observed after the second week. The results highlight the need for prevention policies that focus on practice configurations (i.e., MNG, CPT, and MAT). The current study operationalizes key SPT elements as measurable indices for quantitative analysis on these practice-based aspects. Full article

Waste is increasingly recognized as misplaced biomass, underscoring its potential for reintegration into sustainable environmental management strategies. Biomass pyrolysis has emerged as a promising value-adding process capable of enhancing material properties for diverse applications. In this study, discarded Pili (Canarium ovatum Engl.) nutshells (PS) were utilized as a pyrolysis feedstock to upgrade their fuel characteristics. Pyrolysis conditions were optimized using response surface methodology (RSM) based on a central composite design (CCD) to maximize fixed-carbon content and higher heating value (HHV). The optimized biochar achieved a maximum fixed-carbon content of 86.15% and an HHV of 32.10 MJ/kg at a pyrolysis temperature of 600 °C and a residence time of 60 min, values comparable to those of conventional coal. Under these optimized conditions, the fixed-carbon content and HHV of the precursor biomass were enhanced by up to 254.7% and 58.4%, respectively. Statistical analysis indicated that pyrolysis temperature was the most significant factor influencing both fixed-carbon content and HHV (p < 0.05). The optimized biochar exhibited low volatile matter (8.88%), low ash content (4.97%), and low atomic ratios (H:C = 0.291; O:C = 0.077), indicating a high degree of carbonization and thermal stability. Energy-dispersive X-ray (EDX) analysis identified alkali and alkaline earth metals (Ca, Mg, Na), which contributed to the ash fraction, with minor heavy metals present, predominantly Pb. Hence, these findings enhance understanding of how pyrolysis conditions affect PS–biochar properties, improving fuel quality indicators. Full article

Agricultural credit rationing remains a persistent systemic friction driven by information opacity and limited collateral. This study develops a credit risk early-warning system by fusing multi-source institutional digital footprints (tax compliance signals, judicial enforcement records, and credit history indicators) for 1021 agricultural enterprises in China. Methodologically, we propose a Default Event Isolation protocol to enforce strict ex ante validity by discarding observations at and after the event month, and implement a two-step feature optimization pipeline that reduces 138 predictors to a parsimonious set of 50 features. Empirically, the optimized LightGBM (version 4.6.0) model achieves an AUC = 0.9345 (95% bootstrap CI: 0.8745–0.9563) and PR-AUC = 0.4421, representing a 47× lift over the random baseline under extreme class imbalance (0.94% event rate), and captures 87.4% of early-warning events by monitoring only the top 10% highest-risk firms. The interpretability analysis consistently highlights judicial boundary constraints and tax stability signals as dominant predictors, forming a “judicial baseline + tax stability” dual-core structure. A strict credit-only robustness check using bank-recorded NPL labels maintains strong predictive performance (AUC = 0.9089, 95% bootstrap CI: 0.8255–0.9591), mitigating concerns that the model’s signal is driven by label overlap. These findings suggest that integrating institutional records into automated screening pipelines can enable the earlier and more targeted identification of distressed borrowers in rural lending, even when traditional financial statements are unavailable. Full article

Time-series interferometric synthetic aperture radar (TS-InSAR) has become a widely used technique for monitoring surface deformation with high spatial and temporal resolution. The recent rise in cloud-based InSAR platforms has significantly accelerated the production of interferograms. However, the accuracy of deformation inversion remains limited by fundamental issues affecting interferogram quality, including temporal and spatial decorrelation and phase unwrapping errors. These degrading effects are most pronounced in vegetated, desert, and snow-covered terrains, which are common in active tectonic zones and thereby exert a major impact on the quality of the unwrapped phase. Traditional quality control methods are inefficient or inadequate for large-scale analysis, and discarding low-quality data reduces the inversion accuracy. To address these limitations, we developed a deep learning-based approach to automatically assess interferogram quality and integrate it into the time-series InSAR inversion workflow. We utilized Sentinel-1 interferograms generated by the COMET-LiCSAR system as the primary data source. Based on this dataset, we developed a multi-stage selection strategy for interferogram quality control, integrating loop phase closure analysis, statistical indicators (including coherence and phase standard deviation), and manual verification. As a result, we constructed a high-quality labeled dataset comprising approximately 20,000 samples. An improved ConvNeXt-InSAR model was designed and trained to automatically quantify the quality of each pixel in individual interferograms. The model generates pixel-wise quality maps, which are then incorporated as weight constraints in the time-series InSAR network inversion. The proposed method was applied to the interseismic deformation reconstruction in the central-southern Tibetan Plateau region. This study highlights the potential of deep learning-based interferogram quality assessment in facilitating large-scale, automated time-series InSAR processing. Full article

The growing volume of waste electrical and electronic equipment presents both an environmental challenge and an opportunity for recovering critical raw materials embedded in discarded products. While recycling technologies are advancing, effective recovery remains strongly constrained by upstream collection systems, particularly in urban contexts subject to uncertainty, capacity limits, and regulatory constraints. This paper examines WEEE collection as a key lever for supporting sustainable critical-metal recovery in Europe. Methodologically, the study combines a Scopus-based bibliometric mapping and an institutional analysis of EU collection arrangements with the development of a robust multi-period mixed-integer linear programming model. After analysing organisational and regulatory arrangements in Poland and Portugal as illustrative cases, the paper introduces the Robust Multi-Period WEEE Allocation and Rare Metal Accumulation Problem (MP-WARMAP). The model integrates uncertain WEEE availability, intertemporal logistics planning, threshold-based rare-metal accumulation with endogenous sale timing, and a binding transport-related emission cap. Computational experiments show that robustness against inflow uncertainty can be achieved at a relatively low economic cost, that emission regulation exhibits a feasibility-threshold effect, and that capacity constraints may dominate price signals in determining recovery timing. The results highlight the importance of collection-system design and operational feasibility for improving the recovery of critical materials from urban WEEE streams. Full article

Rotor-induced micro-Doppler signatures are essential for radar-based characterization of rotary-wing UAVs, but practical echoes are often dominated by a strong quasi-static body return concentrated near zero Doppler. In hovering or low-speed scenarios, rotor-induced components may intermittently overlap this near-zero region, where hard DC suppression discards informative rotor content and fragments micro-Doppler structures. Data-driven decompositions such as EMD and VMD avoid fixed cutoffs, yet without explicit constraints on rotor periodicity they are vulnerable to mode mixing and residual leakage under low-SNR conditions. This paper proposes a Cyclic-Power-Guided Soft Mask (CPGSM) framework that exploits cyclostationary periodicity as a physically grounded prior for rotor–body separation. A CPS-guided soft masking procedure consisting of a DC-dominant overlap band is first identified from quasi-static dominance; within this band, cyclic power spectrum analysis yields a continuous rotor-consistency score that guides smooth time–frequency soft allocation, while deterministic assignment is applied elsewhere. Simulations demonstrate improved micro-Doppler continuity, reduced body leakage, and more stable performance from 5–30 dB SNR compared with hard DC isolation and EMD/VMD, together with consistent rotor-speed estimates across sensing configurations. Full article

The bearded fireworm Hermodice carunculata (Polychaeta) has become increasingly problematic in Mediterranean artisanal fisheries, yet remains understudied. This study provides a detailed analysis of interactions between H. carunculata and artisanal fishers in Marsaxlokk, Malta’s largest fishing village. Combining fisher interviews (local ecological knowledge) and field data, the study reveals that fishing practices inadvertently sustain and amplify local fireworm populations by discarding worms and by-catch nearshore, thereby providing abundant food sources. The highest fisher activity correlated with significantly denser fireworm populations and smaller-sized individuals, indicating population growth driven by fisher practices. Fireworm predation significantly impacts fishers economically, causing an estimated direct loss of 52.5% of the expected profit across the five onboard sessions, due to damaged catch, along with additional indirect losses from reduced gear efficiency and increased labor. The worm’s painful sting adds further nuisance and discomfort for fishers who frequently handle infested gear. Despite awareness of fireworm behavior, fishers largely did not recognize their practices as exacerbating the issue, creating a feedback loop perpetuating the problem. Behavioral experiments suggested that modifying fishing practices and gear design might mitigate fireworm impacts. Addressing this socio-ecological challenge requires further targeted research, education, and policy support to break the cycle that benefits fireworm proliferation in the region to the detriment of fishers. Full article

Long-term multivariate time series forecasting serves as a fundamental analytical tool across diverse domains, such as energy management, transportation analysis, and meteorology. However, conventional modeling paradigms often yield suboptimal results as they fail to adequately capture non-stationarity and multi-scale temporal correlations. While frequency-domain methods offer theoretical clarity, representative efficient spectral-domain architectures often rely on magnitude-based spectral pruning to ensure efficiency, inadvertently discarding high-frequency transient signals essential for non-stationary forecasting. To address these limitations, we propose the Structural Component-based Temporal Wavelet-Refine Network (SC-TWRNet), a framework that orchestrates adaptive wavelet filtering with explicit structural temporal decomposition. The architecture is anchored by the Adaptive Multi-Resolution Wavelet (AMRW) filter, designed to generate time-frequency representations while maintaining linear computational complexity. Concurrently, a structural temporal decomposition module decouples the input stream into distinct trend, seasonal, and residual components for targeted modeling. Extensive experiments on eight standard datasets demonstrate that SC-TWRNet achieves superior predictive accuracy compared to state-of-the-art baselines while maintaining linear computational complexity for efficient high-dimensional modeling. Full article

The improper disposal of domestic medical waste (DMW) constitutes a significant public health and environmental concern; however, limited studies exist concerning DMW disposal practices in South Africa. This study evaluated the knowledge and practices involving the disposal of domestic medical waste (DMW) in the City of Tshwane Metropolitan Municipality. The study investigated common disposal methods, levels of awareness of appropriate techniques, and associated health risks. Data were collected using structured questionnaires (Annexure A) with closed-ended questions, administered both physically at shopping complexes and electronically via LinkedIn, WhatsApp, and email to eligible participants. Data analysis was conducted using the Statistical Package for the Social Sciences (SPSS) Version 29 and Microsoft Excel, with results presented in graphical form. Findings revealed that 78.3% of residents disposed of DMW in general waste bins, while 85.8% reported discarding medicine bottles in the same manner, and only 5.2% returned unused medications to pharmacies. The findings highlight gaps in awareness, infrastructure, and policy, necessitating comprehensive education programs, improved waste management services, and policy revisions to include DMW. A proposed model emphasizes education, community involvement, infrastructure enhancement, and ongoing policy evaluation to address these challenges. These efforts aim to reduce health risks, mitigate environmental impacts, and promote safe DMW disposal practices, safeguarding public health and creating a sustainable environment. Full article

Food waste remains a global challenge, particularly among younger generations. This study examines the attitudes and behaviours of 330 Generation Z individuals (aged 18–24 years) from Italy, Estonia, Croatia, Romania, and Serbia using an extended Theory of Planned Behaviour (TPB). The TPB model was expanded to include moral social values, awareness of health risks, and good provider identity. A mixed-methods approach was applied, combining 7-day food waste diaries, visual plate-waste analysis, and self-administered questionnaires. Food recognition analysis showed that Estonian participants wasted less food per meal (3.43%) than those from Italy, Serbia, Croatia, and Romania (12.53%, 12.57%, 14.53%, and 17.18%). Nationality-specific patterns emerged: Romanians mainly discarded meat and potatoes, while participants from Estonia, Croatia, and Serbia wasted fruit and vegetables; Italians most frequently wasted fish and dairy. The extended TPB effectively predicted intentions to reduce food waste, identifying key behavioural determinants that can inform targeted interventions for young consumers. Full article

Objective: The purpose of this study was to assess the outcome of sinus grafting with a beta-tricalcium phosphate/hydroxyapatite (ß-TCP/HA) alloplast particulate biofunctionalized with cross-linked hyaluronic acid (xHya), comparing two surgical access techniques. Clinical, histological, histochemical, immunohistochemical and histomorphometrical parameters were used to characterize the tissue samples, which were retrieved at the second surgery for implant placement five months after sinus floor elevation (SFE). Materials and Methods: Twenty patients with a residual bone height ≤ 4 mm, estimated by a Cone Beam Computed Tomography (CBCT), were randomly allocated either to an innovative transcrestal sinus floor elevation (tSFE = tests) approach or a conventional lateral window approach (lSFE = controls) using piezoelectric preparation. The tSFE was carried out using the hydraulic Jeder^®-System. Grafting in both groups was performed using a ß-TCP–HA combination, which was biofunctionalized with a cross-linked hyaluronic acid. For both access techniques, a cross-linked collagen membrane covered either the bone window or transcrestal osteotomy. For second-stage surgery, a second CBCT was used to assess the bone volume and possible implant positioning to compare it with the baseline CBCT. Bone cores were harvested at implant placement and evaluated histomorphometrically. Patients were followed for 1-year post-op for survival rate estimation. Non-superiority was hypothesized for both surgical methods; thus, the primary outcome measure assessed different discomfort levels using patient-reported outcome measures (PROMs) for each therapeutic approach. Secondary outcomes were the volume change in subantral bone after sinus floor elevation, the chance of placing a 10 mm long implant with no need for additional augmentation, histological evaluation of the newly gained tissue, and implant integration and one-year survival. Results: Eighteen patients (n = 18/20) qualified for implant placement at five months, and ten donated tissue biopsies for microscopic analysis. Primary outcome reporting using PROMs was discarded due to truncated patient enrollment. The secondary parameter, placement of a ≥10 mm long implant without additional augmentation, was achieved for nine sites/patients from the lSFE control group. All patients from the tSFE test group received an implant that was positioned alongside additional augmentation. In both groups, all implants integrated and were functionally loaded. A total of 10 core samples (3 from the tSFE group and 7 from the lSFE group) were obtained and analyzed. Microscopically, new bone formation appeared consistent in all obtained samples. Specimens revealed advanced and ongoing osteogenesis, with most histological markers reacting positively in the immunohistochemical (IHC) staining. The histomorphometric calculation revealed that a mean of 61.17 ± 16.55% of the total area was occupied by newly formed bone, 30.43 ± 10.09% by connective tissue and 8.92 ± 15.29% by residual graft substitute. One-year follow-up of the loaded implants showed a 100% implant survival rate. Conclusions: Biofunctionalizing ß-TCP + HA particulate with cross-linked hyaluronic acid in sinus floor elevation procedures appears to be a safe and beneficial approach, resulting in satisfactory clinical, radiographic and histological parameters. In our study population, which presented with very atrophic residual subantral bone conditions, the hydrodynamic transcrestal sinus floor elevation method required a back-up treatment by the conventional lateral approach. Full article

The annual output of Moutan Cortex is significant, but the epidermis of the root bark of peony (EPRP), as a by-product of Moutan Cortex, is typically discarded. To achieve full use of resources, this study aimed to develop an HPLC fingerprint analysis method using HPLC-DAD-ESI-MS/MS on EPRP sourced from different regions to establish EPRP quality control standards. For chromatography, a ShimPack Scepter C₁₈ column (4.6 mm × 250 mm, 5 μm) was used. The mobile phase for HPLC consisted of acetonitrile (A) and a 0.1% aqueous formic acid solution (B). An HPLC fingerprint was established, featuring 30 characteristic peaks with a similarity of over 0.80. A total of 31 components were identified, with 22 chemical markers determined, including 1-galloylglucose, gallic acid, methyl gallate, oxypaeoniflora, paeonolide, apiopaeonoside, albiflorin, paeoniflorin, p-coumaric acid, ferulic acid, 1,2,3,6-tetragalloylglucose, ellagic acid, galloylpaeoniflorin, luteoloside, 1,2,3,4,6-O-penta-galloylglucose, diosmin, neodiosmin, resveratrol, mudanpioside C, benzoyloxypaeoniflorin, benzoylpaeoniflorin, and paeonol. These markers align with component structure theory, allowing for the analysis of the structural characteristics of EPRP from different regions. These findings provide a valuable reference for the future quality evaluation of EPRP, enhance the understanding of the components in EPRP from diverse sources, and lay a foundation for the development and greater utilization of EPRP. Full article

The transition to more sustainable models of production and consumption has encouraged the scientific community to seek innovative solutions that promote environmental responsibility and reduce waste. The cosmetic industry, in particular, has increasingly invested in natural and eco-friendly ingredients as alternatives to synthetic and environmentally harmful components. In this context, plant-derived bioactive compounds with antioxidant and anti-inflammatory potential have gained attention for their ability to enhance photoprotection and reduce the concentration of conventional ultraviolet (UV) filters in sunscreens. Humulus lupulus L. (hop), a plant traditionally used in the brewing industry, generates large amounts of organic waste after the beer production process, especially through the dry-hopping technique. Despite often being discarded, this residual biomass retains important secondary metabolites with high biological value. Our investigation researched the sustainable valorization of hop brewing residues as a source of bioactive compounds for the development of more natural photoprotective products. We performed HLPC-MS/MS analysis and confirmed the presence of α-acids in both pure and reused hop material extracts, while a xanthohumol-like prenylated flavonoid was tentatively detected exclusively in the extract obtained from reused hop extract. In vitro tests demonstrated that sunscreens containing extract obtained from reused material significantly increased the sun protection factor (SPF) without negatively altering the critical wavelength when water was used as the solvent. None of the samples developed higher UVAPF values compared to the control. Our investigation, to the best of our knowledge, constitutes the first successful proof of concept demonstrating the use of both pure (non-reused) and reused hop material extracts as functional photoprotective adjuvants in sunscreen formulations evaluated by a robust, standardized in vitro methodology. This work highlights the dual benefit of reducing industrial waste and developing more sustainable, consumer-friendly cosmetic products. Full article