Search Results (273)

Aryl diazonium salt chemistry offers a robust and versatile approach for the modification of material surfaces via the covalent immobilization of reactive functional groups under mild conditions. In this study, this strategy was successfully applied to graft the chelating agent 4-(2-pyridylazo)resorcinol (PAR) onto Amberlite XAD-4 resin. Initially, 4-nitrobenzenediazonium tetrafluoroborate (NBDT) was covalently anchored onto the resin surface using hypophosphorous acid as a reducing catalyst to introduce aryl nitro groups. These nitro groups were subsequently reduced to aniline functionalities, enabling diazo coupling with PAR. The successful modification of the resin was confirmed by ATR-FTIR spectroscopy, thermogravimetric analysis (TGA), and X-ray photoelectron spectroscopy (XPS). The synthesized chelating resin exhibited sorption capacities of 0.152, 0.167, and 0.172 mM g⁻¹ for Co(II), Ni(II), and Cu(II), respectively. The functionalized resin was packed into standard SPE cartridges and employed as a selective sorbent for the extraction and preconcentration of trace metals from groundwater samples collected from Dhalamah Valley, Al-Madinah Al-Munawwarah, prior to quantification by inductively coupled plasma mass spectrometry (ICP-MS). These results demonstrate the effectiveness of rapid diazonium-based surface functionalization for the preparation of selective polymeric metal chelators suitable for the extraction of trace metals from complex groundwater matrices. Full article

Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm’s global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bézier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 × 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D*, A*, and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 × 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm’s runtime is optimal on the 20 × 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 × 40 map, it is on par with A*, and outperforms WSO, D*, and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. Full article

Background: A precise radiographic evaluation of adolescent idiopathic scoliosis (AIS) is essential for effective treatment planning and follow-up. The pelvic axial rotation (PAR) and horizontal balance of the pelvis are critical factors to consider throughout the treatment and monitoring of AIS. While some previous studies have examined spinal curvature in relation to PAR direction and the direction of the major curve (DMC) in AIS patients, this study aims to explore the relationship between scoliosis morphology, pelvic axial rotation (PAR), and the direction of the major curve in patients with adolescent idiopathic scoliosis. Methods: Radiographic images of 397 patients diagnosed with AIS between 2023 and 2024 at a Tertiary Referral Hospital were retrospectively evaluated. Morphological and morphometric measurements, including sex, Lenke and Risser classifications, lower leg discrepancy, Cobb angle, PAR direction, and major curvature direction, were performed. Results: The mean age of the 397 patients (246 female, 151 male) was 14.47 ± 2.29. There is no significant correlation between PAR and DMC (p = 0.919). No significant differences were found in terms of sex (p = 0.603). Regardless of the PAR direction, major curvature was more common on the left side (57.7%). Furthermore, a positive correlation was noted between the Cobb angle and LLD. Conclusions: Our study contributes to a growing body of literature questioning the deterministic role of PAR in AIS. While previous reports have emphasized the directional correlation between the pelvis and spinal curvature, our findings suggest that pelvic rotation may not be a reliable indicator of curve direction in all patients. This highlights the complexity of AIS biomechanics and underscores the need for individualized radiographic and clinical evaluation rather than a reliance on generalized compensatory models. Full article

Machine learning algorithms are commonly employed for classification and interpretation of high-dimensional data. The classification task is often broken down into two separate procedures, and different methods are applied to achieve accurate results and produce interpretable outcomes. First, an effective subset of high-dimensional features must be extracted and then the selected subset will be used to train a classifier. Gradient Boosted Trees (GBT) is an ensemble model and, particularly due to their robustness, ability to model complex nonlinear interactions, and feature interpretability, they are well suited for complex applications. XGBoost (eXtreme Gradient Boosting) is a high-performance implementation of GBT that incorporates regularization, parallel computation, and efficient tree pruning that makes it a suitable efficient, interpretable, and scalable classifier with potential applications to medical data analysis. In this study, a Monte Carlo Gradient Boosted Trees (MCGBT) model is proposed for both feature reduction and classification. The proposed MCGBT method was applied to a lung cancer dataset for feature identification and classification. The dataset contains 107 radiomics which are quantitative imaging biomarkers extracted from CT scans. A reduced set of 12 radiomics were identified, and patients were classified into different cancer stages. Cancer staging accuracy of 90.3% across 100 independent runs was achieved which was on par with that obtained using the full set of 107 radiomics, enabling lean and deployable classifiers. Full article

Sporadic Parkinson’s Disease (PD) affects 3% of people over 65 years of age. People are living longer, thanks in large part to improvements in global health technology and health access for non-neurological diseases. Consequently, neurological diseases of senescence, such as PD, are representing an ever-increasing share of global disease burden. There is an intensifying research focus on the processes that underlie these conditions in the hope that neurological decay may be arrested at the earliest time point. The concept of neuronal death linked to ageing- neural senescence- first emerged in the 1800s. By the late 20th century, it was recognized that neurodegeneration was common to all ageing human brains, but in most cases, this process did not lead to clinical disease during life. Conditions such as PD are the result of accelerated neurodegeneration in particular brain foci. In the case of PD, degeneration of the substantia nigra pars compacta (SNpc) is especially implicated. Why neural degeneration accelerates in these particular regions remains a point of contention, though current evidence implicates a complex interplay between a vast array of neuronal cell functions, bioenergetic failure, and a dysfunctional brain immunological response. Their complexity is a considerable barrier to disease modification trials, which seek to intercept these maladaptive cell processes. This paper reviews current evidence in the domain of neurodegeneration in Parkinson’s disease, focusing on alpha-synuclein accumulation and deposition and the role of oxidative stress and inflammation in progressive brain changes. Recent approaches to disease modification are discussed, including the prevention or reversal of alpha-synuclein accumulation and deposition, modification of oxidative stress, alteration of maladaptive innate immune processes and reactive cascades, and regeneration of lost neurons using stem cells and growth factors. The limitations of past research methodologies are interrogated, including the difficulty of recruiting patients in the clinically quiescent prodromal phase of sporadic Parkinson’s disease. Recommendations are provided for future studies seeking to identify novel therapeutics with disease-modifying properties. Full article

Background: Distalization is a fundamental orthodontic strategy for correcting Class II and Class III malocclusions, particularly in cases where specific dental or skeletal conditions favor its application. Recent technological advances have enabled complex dental movements to be performed using clear aligners, aided by digital planning platforms such as ClinCheck^®. Methods: This retrospective study aimed to evaluate the accuracy of ClinCheck^® in predicting molar and canine distalization outcomes with the Invisalign^® system and to identify clinical factors influencing treatment predictability. Thirty patients with complete permanent dentition and at least 2 mm of programmed distalization were selected. Planned movements were extracted from the Invisalign^® Doctor Site and compared to achieved outcomes using Geomagic^® Control X™ software. Occlusal improvements were assessed using the Peer Assessment Rating (PAR) index. Results: The results revealed significant discrepancies between the programmed and achieved distalization, with mean deviations greater than 1 mm in both arches. Skeletal anchorage with mini-screws significantly improved distalization outcomes in the maxillary arch; however, no significant effect was observed in the mandibular arch. Additionally, no significant associations were found between distalization outcomes and skeletal pattern (ANB angle) or facial biotype. Conclusions: Clear aligners are effective in achieving substantial occlusal improvements, particularly when combined with personalized digital planning and supplementary strategies such as skeletal anchorage. Mandibular cases demonstrated greater reductions in PAR scores, emphasizing the potential of aligners in complex distalization treatments. Full article

To explore how varying viewpoints influence the accuracy of distributed fusion in asynchronous, nonlinear visual-field systems, this study investigates fusion strategies for multi-target tracking. The primary focus is on how different sensor perspectives affect the fusion of nonlinear moving-target data and the spatial segmentation of such targets. We propose a differential-view nonlinear multi-target tracking approach that integrates the Gaussian mixture, jump Markov nonlinear system, and the cardinalized probability hypothesis density (GM-JMNS-CPHD). The method begins by partitioning the observation space based on the boundaries of distinct viewpoints. Next, it applies a combined technique—the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and SOS (stochastic outlier selection)—to identify outliers near these boundaries. To achieve accurate detection, the posterior intensity is split into several sub-intensities, followed by reconstructing the multi-Bernoulli cardinality distribution to model the target population in each subregion. The algorithm’s computational complexity remains on par with the standard GM-JMNS-CPHD filter. Simulation results confirm the proposed method’s robustness and accuracy, demonstrating a lower error rate compared to other benchmark algorithms. Full article

This study proposes the PHD-YOLO model as a means to enhance the precision of passion fruit detection in intricate orchard settings. The model has been meticulously engineered to circumvent salient challenges, including branch and leaf occlusion, variances in illumination, and fruit overlap. This study introduces a pioneering partial convolution module (ParConv), which employs a channel grouping and independent processing strategy to mitigate computational complexity. The module under consideration has been demonstrated to enhance the efficacy of local feature extraction in dense fruit regions by integrating sub-group feature-independent convolution and channel concatenation mechanisms. Secondly, deep separable convolution (DWConv) is adopted to replace standard convolution. The proposed method involves decoupling spatial convolution and channel convolution, a strategy that enables the retention of multi-scale feature expression capabilities while achieving a substantial reduction in model computation. The integration of the HSV Attentional Fusion (HSVAF) module within the backbone network facilitates the fusion of HSV color space characteristics with an adaptive attention mechanism, thereby enhancing feature discriminability under dynamic lighting conditions. The experiment was conducted on a dataset of 1212 original images collected from a planting base in Yunnan, China, covering multiple periods and angles. The dataset was constructed using enhancement strategies, including rotation and noise injection, and contains 2910 samples. The experimental results demonstrate that the improved model achieves a detection accuracy of 95.4%, a recall rate of 85.0%, mAP@0.5 of 91.5%, and an F1 score of 90.0% on the test set, which are 0.7%, 3.5%, 1.3%, and 2. The model demonstrated a 4% increase in accuracy compared to the baseline model YOLOv11n, with a single-frame inference time of 0.6 milliseconds. The model exhibited significant robustness in scenarios with dense fruits, leaf occlusion, and backlighting, validating the synergistic enhancement of staged convolution optimization and hybrid attention mechanisms. This solution offers a means to automate the monitoring of orchards, achieving a balance between accuracy and real-time performance. Full article

We aimed to examine the effect of zonulin and zonulin inhibition on gastrointestinal (GI) motility and the mRNA expression of zonulin and the protease-activated receptor 2 (par2), the primary receptor for zonulin, under conditions of inflammation by lipopolysaccharide (LPS) injection. The experimental models included zonulin transgenic mice (ztm), par2 knockout ztm (ztm-par2 −/−), ztm exposed to the zonulin inhibitor AT1001 (ztm-AT1001), and wildtype mouse controls. GI transit was measured by fluorescein isothiocyanate-dextran and mRNA expression by real-time quantitative polymerase chain reaction in whole, and in epithelial and non-epithelial tissues of all GI segments. There were no differences in the GI transit between mouse groups at baseline. After the LPS injection, ztm mice had an attenuated slowing of the GI transit compared to wildtype mice. The zonulin-inhibited mice had motility patterns similar to wildtype mice. zonulin upregulation was noted in GI segments of the ztm, ztm-par2 −/−, and ztm-AT1001 after the LPS injection. Differences in motility patterns between ztm and zonulin inhibition models despite zonulin expression in GI segments of all mouse groups supports that PAR2 is key for zonulin’s effect on motility under conditions of inflammation. However, the findings from the epithelial and non-epithelial compartments suggest that the pathway of activity is complex and likely indirect. Full article

This study addresses the growing concern of water contamination by pharmaceutical residues, focusing on the simultaneous removal of paracetamol (PAR) and metronidazole (MTZ). Batch and fixed-bed column adsorption processes were evaluated using activated carbon. In the batch experiments, the effects of pH (3, 7, and 11), adsorbent mass (0.5, 1.25, and 2 g), and contact time (10, 30, and 60 min) were evaluated, while the fixed-bed column was optimized considering initial pollutants concentration (30, 40, and 50 mg/L), adsorbent mass (0.5, 0.75, and 1 g), and flow rate (5, 10, and 15 mL/min) to improve the maximum adsorption capacity of the bed for both pollutants (q_maxPAR and q_maxMTZ). Parameter estimation and model selection were performed using a Bayesian Monte Carlo approach. Optimal conditions in the batch system (pH = 7, W = 2 g, and time = 60 min) led to high removal efficiencies for both compounds (≥98%), while in the column system, the initial pollutant concentration was the most significant parameter to improve the maximum adsorption capacity of the bed, resulting in values equal to 49.5 and 43.6 mg/g for PAR and MTZ, respectively. The multicomponent Gompertz model showed the best performance for representing the breakthrough curves and is suitable for scale-up (R² ≥ 0.75). These findings highlight the complexity of multicomponent adsorption and provide insights, contributing to the development of more efficient and sustainable water treatment technologies for pharmaceutical residues. Full article

Re-identification in automated surveillance systems is a challenging deep learning problem. Learning part-based features augmented with one or more global features is an efficient approach for enhancing the performance of re-identification networks. However, the latter may increase the number of trainable parameters, leading to unacceptable complexity. We propose a novel part-based model that unifies a global component by taking the distances of the parts from the global feature vector and using them as loss weights during the training of the individual parts, without increasing complexity. We conduct extensive experiments on two large-scale standard vehicle re-identification datasets to test, validate, and perform a comparative performance analysis of the proposed approach, which we named the global–local similarity-induced part-based network (GLSIPNet). The results show that our method outperforms the baseline by 2.5% (mAP) in the case of the VeRi dataset and by 2.4%, 3.3%, and 2.8% (mAP) for small, medium, and large variants of the VehicleId dataset, respectively. It also performs on par with state-of-the-art methods in the literature used for comparison. Full article

Photosynthetically Active Radiation (PAR) is critical for sustaining plant growth in the ground and on building surfaces, but how to accurately predict PAR availability in a complex urban environment can be a challenge. Using an advanced ray-tracing software (Radiance 4.0) and local weather data, this study presents a dynamic analysis of the effects of layout, façade orientation and height on PAR availability in four high density residential areas in Shanghai city, China. A metric system was also adopted using three light level requirements of outdoor plants (low, medium, high light levels). Key findings included: (1) the urban layout with the highest ratio of building height to north–south facing adjacent building separation achieved the higher levels of PAR availability for low/medium light level plants and the lower levels of PAR availability for high-light plants for middle and low façades and the ground, while high façades in all layouts could see similar PAR availability for all plants. (2) The PAR availability for low/medium-light plants decreased with the increasing façade height, while the PAR availability for high-light plants showed the opposite trend. (3) The north façade and its ground had higher levels of PAR availability for low/medium-light plants and lower levels of PAR availability for high-light plants than other façades. (4) All layouts offered more opportunities to apply high-light and medium-light plants at façades and the ground. Full article

Reverberation is the primary interference of active detection. Therefore, the effective suppression of reverberation is a prerequisite for reliable signal processing. Existing dereverberation methods have shown effectiveness in specific scenarios. However, they often struggle to exploit the distinction between target echo and reverberation, especially in complex, dynamically changing underwater environments. This paper proposes a novel dereverberation network, ERCL-AttentionNet (Echo–Reverberation Complementary Learning Attention Network). We use the Continuous Wavelet Transform (CWT) to extract time–frequency features from the received signal, effectively balancing the time and frequency resolution. The real and imaginary parts of the time–frequency matrix are combined to generate attention representations, which are processed by the network. The network architecture consists of two complementary UNet models sharing the same encoder. These models independently learn target echo and reverberation features to reconstruct the target echo. An attention mechanism further enhances performance by focusing on target information and suppressing irrelevant disturbances in complex environments. Experimental results demonstrate that our method achieves a higher Peak-to-Average Signal-to-Reverberation Ratio (PSRR), Structural Similarity Index (SSIM), and Peak-to-Average Ratio (PAR) of cross-correlation while effectively preserving key time–frequency features, compared to traditional methods such as autoregressive (AR) and singular value decomposition (SVD). Full article

This article offers a critical methodological reflection on the use of poetry workshops in migration research, positioning them as empowering, ethically complex, yet powerful research tools for studying migrant experience. While arts-based methods have gained momentum, their application often lacks critical reflexivity regarding their benefits, challenges, and interdisciplinary potential. Drawing on implementing and designing over 50 poetry workshops facilitated by the author across Bosnian/Yugoslav, U.S., and U.K. diaspora contexts, this paper employs an autoethnographic and participatory lens to explore the workshops’ dual role as sites of empowerment and tools for epistemic transformation. Beyond examining their use in participatory action research (PAR), the paper highlights how poetry workshops can serve as interdisciplinary research tools that capture not only emotional and narrative dimensions of displacement but also spatial and material aspects of migrant experience. In doing so, the paper contributes to a broader rethinking of qualitative migration research by integrating methods from the social-oriented to spatial-oriented disciplines. Ultimately, it calls for a shift from viewing poetry as an extractive technique to embracing it as a reflexive, practical research method, capable of producing richly layered, interdisciplinary knowledge about transnational migrant lives. Full article

Background: Strategies are needed to address the elevated prevalence of chronic kidney disease (CKD) in socioeconomically disadvantaged regions where obesity, smoking, and type 2 diabetes rates are high. Methods: Recognising the inadequacy of generic health approaches in complex contexts, this study employed a participatory action research (PAR) framework to design and deliver five co-design community workshops in two stages over one year. Stage one workshops identified key matters of concern and stage two focussed on problem solving and co-creating solutions. The goal was to inform health service delivery in a region with high CKD prevalence and explore strategies to overcome barriers to individualised, collaborative care, and promote self-management. Results: The workshops identified three themes: 1. achieving person/family-centred care; 2. multimorbidity and siloed care (stage one); and 3. a kidney wellness framework (stage two). Conclusions: The findings reinforce the need for enhanced care coordination, and highlight the importance of consistent information sources, clear referral pathways, and centralised data sharing among health professionals. The proposed kidney healthcare framework aims to support various professionals, fostering linkages between primary and tertiary care, with an emphasis on professional development, especially in communicating complex information to individuals with multimorbidities. While co-designed healthcare models show promise, challenges persist in effective self-management amidst complex disease information and multimorbidity. Full article