Search Results (4,499)

Domestic wastewater discharge is the major source of pollution in Malaysia. Phytoremediation under hydroponic conditions was initiated to treat domestic wastewater and, at the same time, to resolve the space limitation issue by installing a hydroponic system in vertical space at the site. Water hyacinth (WH) was selected in this study to identify its performance of water hyacinth in removing nutrients in raw sewage under batch operation. In the batch experiment, the ratio of COD_initial/plant_initial was identified, and SPSS ANOVA analysis shows that the number of plant size factors was not statistically different in this study. Therefore, four WH, each with an initial weight of 60 ± 20 g, were recommended for this study. Throughout the 10 days of the batch experiment, the average of COD, BOD, TSS, TP, NH4, and color removal was 73%, 73%, 86%, 79%, 77%, and 54%, respectively. The WH biomass weight increased by an average of 78%. The plants have also improved the DO level from 0.24 mg/L to 4.88 mg/L. However, the pH of effluent decreased from pH 7.05 to pH 4.88 below the sewage Standard B discharge limit of pH 9–pH 5.50. Four WH plant groups were recommended for future study, as the COD removal among the other plant groups is not a statistically significant difference (p < 0.05). Furthermore, the lower plant biomass is preferable for the high pollutant removal performance due to the fact that it can reduce the maintenance and operating costs. Full article

Feline superficial non-healing corneal ulcers are persistent lesions requiring individualized treatment to reduce recurrence. This retrospective study evaluated 136 affected eyes (113 nonrecurrent; 23 recurrent) to identify clinical and treatment-related factors associated with recurrence. Recurrent ulcers were more common in older cats (7.2 ± 4.3 vs. 5.1 ± 4.6 years; p = 0.026). Domestic Shorthairs were the most frequently affected breed (50%), and central ulcer location predominated in both groups. Recurrent cases required more intensive management, with 16.9% needing ≥ 2 treatment courses, compared to 83% of nonrecurrent cases resolving after a single course. Healing time following corneal debridement was longer in recurrent cases (32.3 ± 34.4 vs. 25.5 ± 23.1 days; p = 0.272), and corneal sequestrum occurred more frequently (13.0% vs. 10.6%; p = 0.735). Corneal debridement was the primary treatment modality. Systemic medications were more often used in recurrent cases, notably oral lysine (47.8% vs. 26.5%; p = 0.049) and famciclovir (17.4% vs. 2.6%; p = 0.016). Recurrent cases also showed significantly higher rates of concurrent viral (p < 0.001) and bacterial/fungal infections (p = 0.027). In conclusion, recurrent superficial non-healing corneal ulcers were associated with age and systemic illness, emphasizing the need for early diagnosis and management of underlying conditions. Full article

The study’s aim was to evaluate electroretinographic (ERG) alterations in Fragile X syndrome (FXS), FMR1 premutation carriers, and controls, and to explore correlations with peripheral blood FMRP expression levels and behavioral outcomes. ERG recordings were obtained using a handheld device across three stimulus protocols in 43 premutation carriers, 39 individuals with FXS, and 23 controls. Peripheral blood FMRP expression levels were quantified using TR-FRET (Time-Resolved Fluorescence Resonance Energy Transfer). Correlations were assessed with cognitive and behavioral measures including IQ (Intelligence Quotient), ABC_FX (Aberrant Behavior Checklist for Fragile X Syndrome), SNAP-IV (Swanson, Nolan, and Pelham Teacher and Parent Rating Scale), SEQ (Sensory Experiences Questionnaire), ADAMS (Anxiety, Depression, and Mood Scale), and the Vineland III Adaptive Behavior Scale standard score. Significant group differences were observed in multiple ERG parameters, particularly in 2 Hz b-wave amplitude (p = 0.0081), 2 Hz b-wave time to peak (p = 0.0164), 28.3 Hz flash combined amplitude (p = 0.0139), 3.4 Hz red/blue flash b-wave amplitude (p = 0.0026), and PhNR amplitude (p = 0.0026), indicating both outer and inner retinal dysfunction in FXS and premutation groups. Despite high test–retest reliability for ERG (ICC range = 0.71–0.92) and FMRP (ICC = 0.70), no correlation was found between ERG metrics and FMRP or behavioral measures. However, FMRP levels strongly correlated with IQ (ρ = 0.69, p < 0.0001) and inversely with behavioral impairment [ABC_FX (ρ = −0.47, p = 0.0041), SNAP-IV (ρ = −0.48, p = 0.0039), SEQ (ρ = −0.43, p = 0.0146), and the Vineland III standard score (ρ = 0.56, p = 0.0019)]. ERG reveals distinct retinal functional abnormalities in FMR1-related conditions but does not correlate with peripheral FMRP expression levels, highlighting the need for multimodal biomarkers integrating radiological, physiological, behavioral, and molecular measures. Full article

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

Background: Accurate differentiation between Mycobacterium tuberculosis (MTB) and nontuberculous mycobacteria (NTM) is essential for proper diagnosis and treatment. This study compares the diagnostic performance of two commercial real-time PCR kits, AdvanSure^TM TB/NTM and Kogene PowerChek^TM MTB/NTM, for detecting MTB, NTM, and negative (no growth, NG) clinical specimens. Methods: A total of 390 clinical residual specimens were collected from patients between December 2022 and June 2023. The samples, including sputum, bronchoalveolar lavage, tracheal aspirate and body fluid, were initially tested with MGIT culture and then analyzed using both PCR kits. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were evaluated. Discrepant results between the two PCR assays were further investigated using sequencing to identify the detected mycobacterial species, and final diagnoses were verified by culture results and review of electronic medical records. Results: Of the 390 specimens, both AdvanSure^TM and PowerChek^TM real-time PCR assays demonstrated 100% sensitivity for both MTB and NTM detection. For MTB detection, AdvanSure^TM demonstrated a specificity of 100%, with a PPV, NPV, and overall accuracy all reaching 100%. In comparison, PowerChek^TM showed a specificity of 98.62%, a PPV of 96.15%, an NPV of 100%, and an overall accuracy of 98.97%. For NTM detection, both AdvanSure^TM and PowerChek^TM exhibited identical performance metrics. The specificity was 99.58% for both assays, with a PPV of 99.34%, NPV of 100%, and an overall accuracy of 99.74%. Five discrepant results were finally confirmed as four NTM detection cases and one negative case by culture and clinical diagnosis which showed four cases of PowerChek^TM MTB+NTM detection and one case of NTM detection, respectively. Conclusions: The PowerChek^TM MTB/NTM real-time PCR kit demonstrated excellent diagnostic performance for the detection of MTB and NTM, with high sensitivity, specificity, and accuracy. Minor discrepancies, particularly in detecting MTB+NTM mixed infections, highlight the importance of complementary sequencing analysis for resolving uncertain results. These findings support the clinical utility of both PCR assays as reliable tools for rapid diagnosis of mycobacterial infections. PowerChek^TM showed occasional false positives, suggesting that optimizing the assay’s cutoff threshold or amplification parameters could enhance its specificity and reduce false-positive results in clinically ambiguous cases. Full article

This study investigates whether combining singular value decomposition with wavelet analysis can provide new insights into the spatiotemporal complementarity between wind turbine sites, surpassing previous findings. Earlier studies predominantly relied on various forms of correlation analysis to quantify complementarity. While correlation analysis offers a way to compute global metrics summarizing the relationship between entire time series, it inherently overlooks localized and time-specific patterns. The proposed approach overcomes these limitations by enabling the identification of spatially explicit and temporally resolved complementarity patterns across a large number of wind turbine sites in the study area. Because complementarity information is derived from orthogonal components obtained through singular value decomposition of a wind power density matrix, there is no need to adjust for phase shifts between sites. Moreover, the complementary contributions of these components to overall wind power density are expressed in watts per square meter, directly reflecting the magnitude of the analyzed data. This facilitates a site-specific, complementarity-optimized strategy for further wind energy expansion. Full article

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min⁻¹. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article

In social networks like community Question-Answering (cQA) services, members interact with each other by asking and answering each other’s questions. This way they find counsel and solutions to very specific real-life situations. Thus, it is safe to say that community fellows log into this kind of social network with the goal of satisfying information needs that cannot be readily resolved via traditional web searches. And in order to expedite this process, these platforms also allow registered, and many times unregistered, internauts to browse their archives. As a means of encouraging fruitful interactions, these websites need to be efficient when displaying contextualized/personalized material and when connecting unresolved questions to people willing to help. Here, demographic factors (i.e., gender) together with frontier deep neural networks have proved to be instrumental in adequately overcoming these challenges. In fact, current approaches have demonstrated that it is perfectly plausible to achieve high gender classification rates by inspecting profile images or textual interactions. This work advances this body of knowledge by leveraging lexicalized dependency paths to control the level of abstraction across texts. Our qualitative results suggest that cost-efficient approaches exploit distilled frontier deep architectures (i.e., DistillRoBERTa) and coarse-grained semantic information embodied in the first three levels of the respective dependency tree. Our outcomes also indicate that relative/prepositional clauses conveying geographical locations, relationships, and finance yield a marginal contribution when they show up deep in dependency trees. Full article

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y⁻¹ to 3 mm y⁻¹ for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. Full article

Accurate monitoring of rice-planting areas underpins food security and evidence-based farm management. Recent work has advanced along three complementary lines—multi-source data fusion (to mitigate cloud and spectral confusion), temporal feature extraction (to exploit phenology), and deep-network architecture optimization. However, even the best fusion- and time-series-based approaches still struggle to preserve fine spatial details in sub-meter scenes. Targeting this gap, we propose an HRNet-CA-enhanced DeepLabV3+ that retains the original model’s strengths while resolving its two key weaknesses: (i) detail loss caused by repeated down-sampling and feature-pyramid compression and (ii) boundary blurring due to insufficient multi-scale information fusion. The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. A coordinate attention (CA) block is embedded in the decoder to strengthen spatially explicit context and sharpen class boundaries. The rice dataset consisted of 23,295 images (11,295 rice + 12,000 non-rice) via preprocessing and manual labeling and benchmarked the proposed model against classical segmentation networks. Our approach boosts boundary segmentation accuracy to 92.28% MIOU and raises texture-level discrimination to 95.93% F1, without extra inference latency. Although this study focuses on architecture optimization, the HRNet-CA backbone is readily compatible with future multi-source fusion and time-series modules, offering a unified path toward operational paddy mapping in fragmented sub-meter landscapes. Full article

The objective of this study is to investigate the degradation characteristics of oat grass in the rumen of Mindong goats and changes in microbial community attached to the grass surface. Four healthy male goats, aged 14 months, with permanent rumen fistula, in eastern Fujian, were selected as experimental animals. The rumen degradation rate of oat grass was measured at 4, 12, 24, 36, 48, and 72 h using the nylon bag method. Surface physical structure changes in oat grass were observed using scanning electron microscopy (SEM), cellulase activity was measured, and bacterial composition was analyzed using high-throughput 16S rRNA gene sequencing technology. The findings of this study indicate that oat grass had effective degradation rates (ED) of 47.94%, 48.69%, 38.41%, and 30.24% for dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), and acidic detergent fiber (ADF), respectively. The SEM was used to investigate the degradation process of oat grass in the rumen. After 24 h, extensive degradation of non-lignified tissue was observed, resulting in the formation of cavities. At 36 h, significant shedding was observed, and by 72 h, only the epidermis and thick-walled tissue, which exhibited resistance to degradation, remained intact. Surface-attached microorganisms produced β-GC, EG, CBH, and NEX enzymes. The activity of these enzymes exhibited a significant increase between 4 and 12 h and showed a positive correlation with the degradation rate of nutrients. However, the extent of correlation varied. Prevotella and Treponema were identified as key genera involved in the degradation of roughage, with their abundance decreasing over time. Principle Coordinate Analysis (PCOA) revealed no significant differences in the rumen microbial structure across different time points. However, Non-Metric Multidimensional Scaling (NMDS) indicated a discernible diversity order among the samples. According to the Spearman correlation coefficient test, Ruminococcus, Fibrobacter, and Saccharoferments exhibited the closest relationship with nutrient degradation rate and surface enzyme activity, displaying a significant positive correlation. In summary, this study delineates a time-resolved correlative framework linking microbial succession to structural and enzymatic dynamics during oat grass degradation. Full article

Limbal dermoids are congenital, benign, choristomatous growths affecting the corneal-limbal junction. Conventional excision techniques often result in persistent epithelial defects, corneal thinning, and vascularization due to sectoral limbal stem cell deficiency. This study investigated a novel surgical approach for limbal dermoid excision, utilizing Bowman’s membrane lenticule and autologous limbal stem cell transplantation, aimed at improving epithelial healing and visual outcomes. Thirty-four subjects (24 females, 10 males; mean age 8.33 ± 6.47 years) with limbal dermoids underwent the procedure. After dermoid excision, a Bowman’s membrane lenticule was placed over the defect and tucked 1 mm beneath the surrounding tissue. Sectoral limbal reconstruction was then performed using the AutoSLET technique. Pre- and postoperative assessments included visual acuity, corneal thickness, and epithelialization time. Statistical analysis employed paired t-tests. The mean epithelialization time was 3.36 ± 0.74 weeks, indicating rapid healing. Best-corrected visual acuity (BCVA) significantly improved from a preoperative mean of 0.136 ± 0.121 decimal units to a postoperative mean of 0.336 ± 0.214 decimal units (p < 0.001). Corneal thickness also demonstrated a significant increase, rising from a preoperative mean of 294 ± 49.68 microns to a postoperative mean of 484 ± 5.037 microns (p < 0.001). There is a transient edema below the Bowman lenticule observed in many cases, which resolves with deposition of granulation tissue. The findings suggest that the combined use of Bowman’s membrane lenticule and autologous limbal stem cell transplantation offers a promising surgical strategy for limbal dermoid excision. This technique promotes rapid epithelialization and leads to significant improvements in visual acuity and corneal thickness compared to conventional methods. The utilization of Bowman’s membrane as a natural basement membrane and the direct application of limbal stem cells facilitate enhanced epithelial healing and visual rehabilitation. While the study is limited by its small sample size, the results demonstrate the potential of this novel approach in managing limbal dermoids effectively. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. Full article

Time-domain (TD) diffuse reflectance can be modeled using diffusion theory (DT) to non-invasively estimate optical transport coefficients of biological media, which serve as markers of tissue physiology. We employ an optimized N-layer DT solver in cylindrical geometry to reconstruct optical coefficients of bilayered media from TD reflectance generated via Monte Carlo (MC) simulations. Optical properties for 384 bilayered tissue models representing human head or limb tissues were obtained from the literature at three near-infrared wavelengths. MC data were fit using the layered DT model to simultaneously recover transport coefficients in both layers. Bottom-layer absorption was recovered with errors under 0.02 cm⁻¹, and top-layer scattering was retrieved within 3 cm⁻¹ of input values. In contrast, recovered bottom-layer scattering had mean errors exceeding 50%. Total hemoglobin concentration and oxygen saturation were reconstructed for the bottom layer to within 10 μM and 5%, respectively. Extracted transport coefficients were significantly more accurate when obtained using layered DT compared to the conventional, semi-infinite DT model. Our results suggest using improved theoretical modeling to analyze TD reflectance analysis significantly improves recovery of deep-layer absorption. Full article

This study presents a deep learning-based approach for forecasting Sentinel-1 displacement time series, with particular attention to irregular temporal patterns—an aspect often overlooked in previous works. Displacement data were generated using the Parallel Small BAseline Subset (P-SBAS) technique via the Geohazard Thematic Exploitation Platform (G-TEP). Initial experiments on a regular dataset from Lombardy employed Long Short-Term Memory (LSTM) models to forecast multiple future time steps. Empirical analysis determined that optimal forecasting is achieved with a 50-time-step input sequence, and that predicting 10% of the input sequence length strikes a balance between temporal coverage and accuracy. The investigation then extended to irregular datasets from Lisbon and Washington, comparing two preprocessing strategies: imputation and the inclusion of time intervals as a second feature. While imputation improved one-step predictions, it was inadequate for multi-step forecasting. To address this, a Time-Gated LSTM (TG-LSTM) was implemented. TG-LSTM outperformed standard LSTM for irregular data in one-step prediction but faced limitations in handling heteroscedasticity and computational cost during multi-step forecasting. These issues were effectively resolved using Temporal Fusion Transformers (TFT), which achieved the best performance, with RMSE values of 1.71 mm/year (Lisbon) and 1.26 mm/year (Washington). A key contribution of this work is the development of a GIS-integrated forecasting toolbox that incorporates LSTM models for regular sequences and TG-LSTM/TFT models for irregular ones. The toolbox enables both single- and multi-step displacement predictions, offering a scalable solution for geohazard monitoring and early warning applications. Full article