Search Results (143)

Deposit return systems (DRS) can increase the capture of beverage packaging, but material circularity is not determined by return rate alone. A returned container contributes to high-value circularity only if it passes recognition, sorting, pre-processing, and material-specific quality gates. This article evaluates the material-quality performance of three returned beverage-packaging materials—polyethylene terephthalate (PET), aluminium and reusable glass—using a quality-gated high-value recovery framework. The model defines a high-quality recovery index, HQR = R × Q × Y, where R is the return rate, Q is the quality factor of the returned stream, and Y is the reprocessing or reuse yield. The HQR indicator describes the quality of the entire DRS process. The core purpose of the HQR model is to distinguish nominal packaging return from high-quality material recovery and to show whether returned PET, aluminium and reusable glass streams remain suitable for high-value circular pathways. A survey-supported early-stage return scenario (R = 0.50) is compared with the 77% and 90% separate-collection targets used in European policy. To strengthen the PET branch of the model, a pilot PET stream-quality and processing-yield dataset was incorporated, including PET purity, colour composition, non-PET impurities, residual moisture, organic residues, intrinsic viscosity, washed PET flake or pellet yield, and sorting/washing rejection. The pilot data indicate that Lithuania had higher PET quality (98.2% PET purity, 80% clear PET, 1.8% non-PET impurities, IV = 0.74 dL/g, and 84.5% washed PET yield) than the Polish regional average (94.3% PET purity, 72.7% clear PET, 5.7% non-PET impurities, IV = 0.721 dL/g, and 80.3% washed PET yield). At R = 0.50, the pilot-derived PET HQR is approximately 37.8% for Lithuania and 31.6% for the Polish regional average. The results indicate that the same nominal return rate can lead to substantially different high-quality recovery outcomes because PET is constrained by stream purity, colour, contamination, and processing yield; aluminium by alloy and remelting control; and reusable glass by inspection, breakage, and refill compatibility. The proposed framework can support structured DRS operator reporting by identifying the material-quality and yield variables that should be measured alongside mass collection; however, operator-level validation is required before the model can be used as a predictive performance tool. Full article

Background/Objectives: Accurate verification of single-isocenter multiple-target (SIMT) stereotactic radiosurgery is challenging owing to the complexity of multi-lesion delivery and the sensitivity of stereotactic dose gradients. We aimed to evaluate the efficacy of electronic portal imaging device (EPID)-based multileaf collimator (MLC) transmission and dosimetric leaf gap (DLG) correction for improving portal-dose prediction agreement in SIMT stereotactic radiosurgery (SRS) and to propose an exploratory target count-based institutional action level. Methods: This retrospective analysis included 112 consecutive patients treated with HyperArc™-based SRS (1–13 targets). Treatment plans were calculated using the Acuros XB algorithm for 6 MV flattening filter-free (FFF) beams. Two sets of MLC parameters were compared for portal-dose image prediction (PDIP): (1) standard parameters measured using an ion chamber; (2) EPID-derived corrected parameters. Portal-dose accuracy was evaluated using gamma index analysis with a 95% pass rate threshold. Results: The uncorrected method showed a strong negative correlation between target number and gamma passing rates, with complex plans (≥9 targets) dropping as low as 70%. EPID-based correction substantially improved dose agreement, yielding consistent passing rates above 98%, regardless of target number. Although uncorrected parameters remained within tolerance for plans with one to two targets, accuracy declined markedly starting at three targets. Conclusions: Our findings indicate that the EPID-based correction of MLC transmission and DLG mitigated cumulative modeling discrepancies in complex SIMT SRS. Thus, we propose the exploratory institutional observation of three targets, beyond which EPID-based correction may be considered to achieve optimal portal-dose prediction agreement in HyperArc-based stereotactic radiosurgery. Full article

Homomorphic encryption (HE) secure aggregation requires coordinate-aligned updates, whereas personalized federated learning (PFL) lets each client share a different parameter subset. Top-k sparsification widens this gap: per-client supports must be unioned and re-packed, exposing a data-dependent cleartext index. We propose Clustered-Support HE-PFL (CSS-FL), a two-phase protocol that clusters clients by their already-exposed Top-k support via k-means on binary indicator vectors. In an index phase, each cluster agrees on one shared sparse support by majority vote; in a value phase, clients encrypt exactly the agreed coordinates, which are aligned by construction and sum directly under CKKS, while non-shared coordinates remain local. Relative to full-union HE, the aggregation server gains no new information (clustering consumes only the routing indices Top-k already exposes), the only values ever decrypted are multi-client cluster means, and every party downstream of the server observes a coarse cluster-level index rather than per-client indices. On MNIST, Fashion-MNIST, and CIFAR-10 with $K\in \{2,4,8\}$ clusters, LeNet (three seeds; five at the most heterogeneous setting) and ResNet-18 (three seeds), CSS-FL reduce CKKS encryption cost by $5.3$–$7.3\times$ relative to a constructed Full-Union baseline at $0.25$–$0.5$ the downstreamcleartext index footprint for $K\le 4$ (the server-visible index footprint is unchanged), with personalized accuracy comparable to established PFL baselines (though below Ditto on CIFAR-10), and transmits 5–$7\times$ fewer end-to-end bytes including ciphertext expansion. An aggregation-width sensitivity study shows that averaging $\ge 4$ updates, a width the protocol enforces via a minimum-cluster-size merge floor, collapses single-sample DLG reconstruction from $34.5$ dB to $\approx 6$ dB. Full article

Underground distribution networks increasingly rely on aging cable assets whose parameter drift modifies propagation velocity, attenuation, and fault-initiated transient signatures, thereby reducing the reliability of conventional traveling-wave (TW) fault location and purely data-driven diagnosis. This paper proposes a physics-regularized hybrid learning framework for joint fault-type classification, feeder-area identification, and continuous fault localization in aging underground distribution feeders. The methodology integrates (i) an aging-aware simulation pipeline driven by a normalized aging-stress index $\alpha \in [0,1]$ that perturbs the per-unit-length cable matrices within a controlled domain; (ii) synchronized multi-sensor time–frequency representations of three-phase voltage and current transients; (iii) an area-aware multi-task architecture with fault-type and area-classification heads and area-specific local regression heads; and (iv) a propagation-consistency loss that depends explicitly on the model-predicted fault position and therefore contributes gradients during training. A branched underground feeder is evaluated using five synchronized sensing locations and a stratified dataset of $N_{\mathrm{tot}}=\mathrm{36,000}$ simulated fault events covering 11 fault classes (SLG-A/B/C, LL-AB/BC/CA, DLG-ABG/BCG/CAG, LLL, and LLLG), six non-overlapping feeder areas, fault resistance $R_f\in [0.1,50]\Omega$, measurement noise $\mathrm{SNR}\in [20,40]\mathrm{dB}$, and a $20\mathrm{ms}$ transient window sampled at $200\mathrm{kHz}$. On a held-out test set of 7200 previously unseen event records drawn from the same simulation domain, the Hybrid model achieves a fault-type accuracy of $0.93$, an area-identification accuracy of $0.96$, a localization MAE of $0.011$ p.u., and a 95th-percentile absolute error of $0.027$ p.u. The proposed configuration outperforms the TW-TOA, purely data-driven Baseline, and physics-regularized PINN references across the reported diagnostic tasks within the prescribed simulator and parameter ranges. Time–frequency attribution is included only as a qualitative interpretability illustration and is not treated as quantitative evidence of explanation faithfulness. Accordingly, the results demonstrate comparative in-domain simulation performance rather than field or cross-simulator deployment readiness. Full article

Sodium alginate was extracted from beach-cast Sargassum spp. collected along the coast of Puerto Progreso, Yucatán, Mexico, using two established pretreatment routes based on formaldehyde and ethanol. This study evaluates how extraction methodology controls alginate recovery, molecular structure, hydrogel rheology, macroscopic integrity, swelling behavior, and preliminary inorganic contaminant profiles. The ethanol-based route provided the highest extraction yield, reaching 19.87 ± 0.79% w/w for AE-5, whereas the formaldehyde route reached a maximum of 15.60 ± 0.62% w/w for AF-12; statistical analysis confirmed significant differences among extraction conditions (ANOVA, p < 0.05). Despite its lower yield, the formaldehyde route produced alginate with higher intrinsic viscosity (2.13 dL/g) and viscosity-average molecular weight (1.00 × 105 g/mol) than the ethanol-derived sample (1.33 dL/g and 0.62 × 105 g/mol), indicating better preservation of polymer chain length. ¹H NMR analysis showed that AE-5 had higher guluronic acid content (F_G = 0.60), lower M/G ratio (0.67), and higher G-block fraction (F_GG = 0.54), favoring Ca²⁺-mediated junction zone formation. Consequently, AE-5-derived hydrogels exhibited the highest storage modulus at 1 Hz (G′ = 23,650 Pa), compared with AF-12-derived hydrogels (13,160 Pa) and the commercial reference (14,480 Pa). However, visual inspection and swelling analysis showed that the higher small-amplitude stiffness of AE-5 did not translate into superior macroscopic integrity; these hydrogels showed greater fragmentation during handling and higher long-term swelling. In contrast, AF-12-derived hydrogels showed lower stiffness but better apparent cohesion and a more restricted swelling profile, consistent with enhanced long-range network connectivity derived from higher molecular weight. FTIR confirmed preservation of the characteristic functional groups of sodium alginate, whereas XRD provided qualitative evidence of residual crystalline inorganic phases. Selected-metal analysis by MP-AES detected Cu in both extracted alginates, while As was detected but not quantified only in AF-12; Cd and Pb were not detected under the analytical conditions employed. Overall, the results establish a route-dependent structure-property relationship in which extraction conditions govern yield, chain preservation, block architecture, viscoelastic response, swelling behavior, and preliminary contaminant profile. These findings support beach-cast Sargassum as a promising source of research-grade sodium alginate, while emphasizing that further purification, expanded contaminant profiling, arsenic speciation, biological evaluation, and direct mechanical testing are required before any food, biomedical, pharmaceutical, or environmental application can be proposed. Full article

This paper presents a comparative analysis of Flywheel Energy Storage Systems (FESS) employing Permanent Magnet Synchronous Machines (PMSMs) and AC Brushless DC (AC-BLDC) machines for fast and efficient frequency regulation. The study examines their electromechanical behavior during the key operational stages of charging, standby, and discharging, with a focus on mitigating inrush current and enhancing overall system efficiency. MATLAB/Simulink models were developed to evaluate machine dynamics, electromagnetic behavior, and harmonic distortion during their operation. The results show that electromagnetic effects, particularly inrush current, commutation harmonics, and inverter limitations, significantly influence torque smoothness, efficiency, and overall system performance. PMSMs demonstrate superior torque quality, lower Total Harmonic Distortion (THD), and more stable energy conversion under Field-oriented Control (FOC), making it well suited for high-performance FESS applications. In contrast, the AC-BLDC machine exhibits higher torque ripple and elevated THD due to six-step commutation but offers a simpler drive topology and cost advantages. The findings offer practical insights for selecting machines and controllers in high-speed FESS designs and emphasize the importance of mitigating transient electromagnetic effects to enhance efficiency and reliability in modern grid support applications. Improved modeling incorporating magnetic saturation, frequency-dependent iron losses, and inverter constraints is essential for accurate performance prediction. Future work includes Hardware-In-the-Loop (HIL), Power-HIL validation, and DlgSILENT PowerFactory co-simulation to confirm dynamic performance under grid-connected operation. Full article

In the realm of public safety, the automated identification of potential threats from voluminous surveillance streams is pivotal for developing intelligent security systems. Manual monitoring of such massive video feeds is highly inefficient, prone to human fatigue, and often leads to missed detections or false alarms. Leveraging deep learning for automatic anomaly detection is therefore essential to improve response efficiency and mitigate security risks. Weakly supervised video anomaly detection (WS-VAD) has emerged as a critical yet challenging task in this domain. In this study, we propose the Temporal-Enhanced and Visual-Text Adaptive Fusion (TE-VTAF) model for robust WS-VAD. Specifically, a Dynamic Local–Global Temporal Adaptive Module (DLG-TAM) is designed to capture multi-scale temporal dependencies and extract high-level video semantics. Concurrently, a Visual-Text Adaptive Fusion Module (VTAFM) is introduced to aggregate complementary cross-modal features, utilizing a competitive activation mechanism to suppress redundant information and enhance the discriminative power between normal and anomalous events. To further refine the learning process within the Multiple Instance Learning (MIL) framework, we incorporate a Top-K outer bag loss and a K-maxmin inner bag loss. These constraints effectively maximize the inter-class separability while suppressing label noise from normal instances within positive bags, thereby bolstering the detector’s robustness. Extensive experiments demonstrate that the proposed TE-VTAF consistently outperforms state-of-the-art methods on two large-scale benchmarks, achieving an AUC of 88.93% on UCF-Crime and an AP of 85.62% on XD-Violence. Full article

The optimal timing and therapeutic role of dexamethasone for neuroprotection in neonatal hypoxic–ischemic (HI) brain injury remain unclear. We investigated whether dexamethasone-mediated neuroprotection is time-dependent and explored its underlying molecular mechanisms in a neonatal HI mouse model. The Rice–Vannucci model (unilateral carotid artery ligation followed by 8% O₂ for 90 min) was constructed utilizing postnatal day 7 mice who received vehicle (n = 5), dexamethasone pre-treatment (0.5 mg/kg, 6 h before HI; n = 6), or dexamethasone post-treatment (0.5 mg/kg, 6 h after HI; n = 6). Brain injury severity was evaluated by two blinded investigators 72 h after HI, who measured the whitish discoloration in the ipsilateral hemisphere. Transcriptomic analysis was performed using five representative brain samples from each group. Dexamethasone pre-treatment significantly reduced the area of whitish discoloration compared with the vehicle (p < 0.001); dexamethasone post-treatment exerted no significant protective effect. Transcriptomic profiling identified 962 (407 upregulated and 555 downregulated) differentially expressed genes. Genes with upregulated expressions were enriched in pathways related to central nervous system development, synaptic signaling, and calcium homeostasis; those with downregulated expressions were associated with cellular metabolic processes. Protein–protein interaction network analysis identified Dlg4, Calm1, and Grin1 as hub genes. qRT-PCR validation confirmed significant upregulation of Grin1 and Calm1, whereas Dlg4 showed a concordant but non-significant trend. These findings suggest that dexamethasone pre-treatment may be associated with time-dependent changes in synaptic- and calcium-related gene expression following neonatal HI injury, providing insight into the optimal therapeutic window for neonatal HI brain injury. Full article

Sheep litter size is of major economic importance, yet its polygenic nature and low heritability limit the effectiveness of traditional selection methods. Following PRISMA guidelines, this systematic review integrated genome-wide association study (GWAS) evidence for sheep litter size and prioritized candidate genes. Four databases were searched, yielding 24 eligible studies comprising a total effective analytical sample of 7618 animals from 98 breeds/populations across nine countries. Following standardized re-annotation of genomic coordinates, 245 significant variations and 316 candidate genes were extracted. Gene prioritization using ToppGene identified 96 high-priority genes, including 10 core genes: GRIN2A, DLG2, FLT4, ESR2, AMH, ALK, INHBB, NF1, CAMK2D, and ERCC2. These genes collectively operate through four functional axes: the gonadal axis regulation, follicular development, hormonal signal transduction, and DNA damage repair. Functional enrichment analysis revealed significant involvement in protein binding and metal ion binding. Marked breed-specificity was observed, with only the BMPR1B (FecB) locus replicated across three studies. Key limitations include restriction to English-language publications, small median sample sizes in the included studies, and the inherent bias of ToppGene training genes toward previously reported loci. These findings clarify the molecular genetic architecture of sheep litter size and provide a validated candidate gene framework to support precision genomic breeding strategies. Full article

Recent advances in 3D Gaussian splatting (3DGS) have enabled efficient image-based scene reconstruction, but existing methods that rely heavily on multi-view photometric consistency remain sensitive to dynamic illumination and weakly constrained regions. This issue is especially evident in tunnel scenes, where limited ambient light and localized active illumination cause strong appearance variation and shadowed regions that appear weakly textured in the captured images. As a result, existing methods often suffer from appearance inconsistency, floating artifacts, and unstable Gaussian distributions. To address these challenges, we present dynamic lighting-aware Gaussian splatting (DLG-GS), a real-time framework designed primarily for tunnel-oriented reconstruction under dynamic lighting. DLG-GS includes two complementary components: a dynamic lighting-adaptive appearance modeling strategy that reduces illumination-induced artifacts while preserving local texture details, and a voxel–depth joint constraint that uses monocular depth priors to regularize the spatial distribution of voxel anchors and neural Gaussians, thereby improving optimization stability and suppressing floating artifacts in shadow-induced weak-texture regions. By jointly optimizing appearance adaptation and depth-guided spatial regularization, DLG-GS improves reconstruction stability and rendering quality while maintaining real-time performance. Experiments on a self-collected tunnel dataset show clear improvements over selected baselines, and additional evaluations on public benchmarks indicate competitive performance beyond the target tunnel setting. Full article

Background/Objectives: Increasing evidence indicates that gliomas co-opt mechanisms of excitatory synaptic transmission and plasticity to support tumor progression, yet these processes remain poorly characterized in lower-grade gliomas (LGGs). Here, we investigated whether genes associated with excitatory synaptic function are linked to patient prognosis in LGG. Methods: A curated panel of 36 synaptic genes was analyzed in LGG using RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Results: Among the genes investigated, DLG2, DLG3, and DLG4, which encode the postsynaptic scaffolding proteins PSD-93, SAP-102, and PSD-95, respectively, showed strong associations with patient overall survival (OS). Higher expression of each gene was consistently associated with longer OS across both datasets. Expression of DLG2–DLG4 was higher in oligodendroglioma and IDH-mutant, 1p/19q co-deleted tumors, and lower in astrocytoma and IDH-wild-type tumors. Furthermore, expression of all three genes positively correlated with a broad gene signature associated with a synaptic gene program, including multiple components of glutamatergic signaling and postsynaptic organization. Conclusions: These findings suggest that elevated expression of DLG2–DLG4 is associated with a transcriptional program resembling differentiated neuron-like features and favorable clinical outcome in LGG. Full article

Background and Objectives: Gastric cancer remains a leading cause of cancer-related mortality worldwide and develops through complex interactions between environmental factors, microbial dysbiosis, and host molecular pathways. Although Helicobacter pylori infection is a well-established risk factor, emerging evidence suggests that broader alterations in the gastric microbiome may also contribute to carcinogenesis. However, the associations between gastric cancer-associated microbial taxa and host gene expression profiles remain insufficiently characterized. This study aimed to identify host gene signatures associated with gastric cancer-related microbial taxa through a descriptive analysis integrating microbiome-derived taxa with transcriptome data. Materials and Methods: Microbial taxa associated with gastric cancer were systematically retrieved from the Disbiome database. Taxon set enrichment analysis (TSEA) was performed using the MicrobiomeAnalyst platform to identify host genes associated with gastric cancer-associated taxa. Importantly, TSEA relies on healthy reference data from the Human Microbiome Project and does not establish gastric cancer-specific interactions or causal relationships. Gene expression levels were subsequently evaluated using The Cancer Genome Atlas (TCGA) PanCancer stomach adenocarcinoma (STAD) dataset by comparing tumor and matched normal gastric tissues. Gene interaction network and transcription factor (TF) enrichment analyses were conducted to explore predicted regulatory relationships. Results: Among 64 microbial taxa associated with gastric cancer, 43 were reported as elevated. After removing overlapping taxa across studies, 37 elevated and 21 reduced taxa were retained for analysis. TSEA identified 11 host genes associated with gastric cancer-related microbial taxa. Transcriptomic analysis demonstrated significant downregulation of DPP6 and DLG2, while KDM4D, USP34, and VDR were significantly upregulated in gastric cancer tissues compared with normal controls. Network and TF enrichment analyses revealed predicted co-expression and co-localization patterns among these genes, suggesting their potential involvement in immune-related processes, epigenetic regulation, and cellular organization. Conclusions: This descriptive study identifies distinct host gene expression signatures associated with gastric cancer-associated microbial dysbiosis. This study is purely associative and hypothesis-generating; no causal or mechanistic inferences are made. TSEA used healthy reference data and therefore does not reflect gastric cancer-specific host–microbe interactions. The findings provide a basis for future hypothesis-driven research but require validation in independent cohorts. Full article

Thyroid hormones (THs) and estrogen (E2) play essential roles in neuronal differentiation and plasticity during brain development. S-equol, a plant-derived isoflavone metabolite, is a selective E2 receptor (ER) ligand that exhibits neurotrophic effects; however, its interaction with TH receptor (TR) signaling remains unclear. In this study, we investigated the effects of S-equol on TRβ-associated transcriptional activity and neuronal morphogenesis in mouse neuroblastoma-derived Neuro-2a cells or rat C6 glioma cells. Luciferase reporter assays demonstrated that S-equol significantly enhanced T3-induced TRβ transcriptional activity in a concentration- and time-dependent manner. Additionally, exposure to S-equol or T3 alone promoted neurite outgrowth and wound closure, whereas co-exposure to both compounds resulted in a more significant enhancement of these processes. Furthermore, mRNA expression levels of synapse-related genes (Dlg4, Syn1, Syp, Camk2b, and Bdnf) were significantly increased by S-equol co-exposure in the presence of T3. In silico docking analysis revealed that S-equol exhibited moderate to high binding affinity for TRβ (−8.7 kcal/mol), ERα, and ERβ, suggesting a structural basis for TR–ER crosstalk. Collectively, these findings indicate that S-equol functions as a dual-acting modulator that may modulate T3 signaling involving TR–ER interaction. Although S-equol may exert beneficial effects on neurodevelopment, it may also act as an endogenous endocrine modulator that alters the fine regulation of TH action during development, warranting careful evaluation from physiological and toxicological perspectives. Full article

Polyethylene terephthalate (PET) plays a pivotal role in the chemical fiber industry, constituting over 50% of fiber consumption. However, the reduction of the recycled fiber-derived viscosity of the PET significantly impacts its spinning performance and restricts its closed-loop recycling to high-value regenerated fibers. To address these limitations, this study explored the viscosity improvement of black and white waste fiber-derived polyester particles through a two-step process involving micro-glycolysis and self-polycondensation. Initially, a continuous micro-glycolysis of fiber-derived PET was carried out in a twin-screw extruder with ethylene glycol (EG), which effectively cleaves the ester bonds in the PET chains, generating oligomers with reactive hydroxyl end groups. Subsequently, these oligomers were repolymerized without purification, and a higher molecular weight regenerated PET with enhanced intrinsic viscosity was obtained with antimony ethylene glycolate (Sb-EG) as a catalyst. The results revealed that the intrinsic viscosity decreased exponentially with increasing EG dosage during glycolysis, reaching approximately 50% of the initial value at 0.2–2 phr EG dosages. Optimal viscosity enhancement was achieved at a polycondensation time of 1–3 h, resulting in improved thermal stability and reduced crystallization temperatures. Importantly, regenerated PET samples with EG dosages of ≤2 phr demonstrated intrinsic viscosities of about 0.70 dL/g, meeting the standard for spin-grade polyester fiber, which is used to produce regenerated polyester fibers. This recycling process is low cost, environmentally friendly, and easy to scale-up, contributing significantly to the development of industrial recycling of waste polyester fabrics. Full article

With the aim of identifying key proteins that play a role in the disorder tinnitus, interactions between proteins involved in thalamo-amygdala signaling under conditions of normal hearing (NH), acoustic stimulation (AS), and tinnitus (Tin) were studied. Three gene lists compiled from the GeneCards database using keywords were characterized by analyses of overlap, protein–protein interaction (PPI) networks, and by protein-enrichment analysis. Key proteins were selected on the basis of the degree and combined score value of the corresponding PPI network. In the NH process, BDNF, CASP3, and PVALB were identified as high-degree proteins (HDPs). In the AS process, BDNF, PVALB, and DLG4 are the top three HDPs; in the Tin process, these are BDNF, APP, and TNF. In the Tin process, key proteins appear that differ pre- and postsynaptically from those detectable in NH or AS. The glucocorticoid receptor NR3C1 and its interaction with FKBP5, a glucocorticoid receptor-induced co-chaperone, appear to be of particular importance for the emotional aspects of tinnitus. In tinnitus, the HDPs, together with their high-score interaction proteins, indicate processes of chronic neurodegeneration and of changes in transcription, intercellular communication, and in the survival and growth of neurons. Full article