Search Results (48,259)

Love wave surface acoustic wave (LSAW) sensors are crystal resonators known for their high potential for biosensing applications due to their high sensitivity, real-time detection, and compatibility with microfluidic systems. Commercial LSAW devices are costly, and manufacturing them is even more expensive, making accessibility a significant challenge. Additionally, their use requires specialized systems, and with only a few manufacturers dominating the market, most available solutions are proprietary, limiting customization and adaptability for specific research needs. In this work, a low-cost open-source LSAW biosensing system prototype was developed based on a commercially acquired resonator. The development integrates microfluidics through a polydimethylsiloxane (PDMS) chip, low-cost electronics, and both 3D printed ultraviolet (UV) resin and polylactic acid (PLA) parts. The instrument used for measurements was a vector network analyzer (VNA) that features open-source software. The code was customized for this study to enable real-time, label-free biosensing. Experimental validation consisted of evaluating the sensitivity and repeatability of the system, from the setup to its use with different fluids. Results demonstrated that the development is able to advance to more complex applications. Full article

The precipitation of minerals, in particular carbonates, is a widespread phenomenon in all ecosystems, where it assumes a high relevance both from a geological and biogeochemical standpoint. Most carbonate rocks are of biological origin and made in an aquatic environment. In particular, bioprecipitation of carbonates is believed to have started in the Mesoproterozoic Era, thanks to a process often driven by photosynthetic microorganisms. Nevertheless, an important contribution to carbonate precipitation is also due to the metabolic activity of heterotrophic bacteria, which is not restricted to specific taxonomic groups or to specific environments, making this process a ubiquitous phenomenon. In this framework, the relationship between carbonatogenic microorganisms and other living organisms assumes a particular interest. This study aims to isolate and identify the culturable heterotrophic bacterial component associated with the coenosarc of Corallium rubrum in order to evaluate the occurrence of strains able to precipitate carbonates. In particular, the study was focused on the identification and characterisation of bacterial strains isolated from a coral coenosarc showing a high carbonatogenic capacity under laboratory conditions. Samples of C. rubrum were taken in the coastal waters of three Italian regions. The concentration of the aerobic heterotrophic microflora colonising C. rubrum coenosarc samples spanned from 3 to 6∙10⁶ CFU/cm². This variation in microbial populations colonising the C. rubrum coenosarc, spanning over 6 orders of magnitude, is not mirrored by a corresponding variability in the colony morphotypes recorded, with the mean being 5.1 (±2.1 sd). Among these bacteria, the carbonatogenic predominant species was Staphylococcus equorum (93% of the isolates), whereas Staphylococcus xylosus and Shewanella sp. accounted only for 3% of isolates each. All these strains showed a remarkable capacity of precipitating calcium carbonate, in the form of calcite crystals organised radially as well crystalised spherulites (S. equorum) or coalescing spherulites (Shewanella sp.). S. xylosus only produced amorphous precipitates of calcium carbonate. All bacterial strains identified were positive both for the production of urease and carbon anhydrase in vitro at 30 °C. It seems that they potentially possess the major biochemical abilities conducive to Ca²⁺ precipitation, as they showed in vitro. In addition, all our carbonatogenic isolates were able to hydrolyse the phytic acid calcium salt and then were potentially able to induce precipitation of calcium phosphates also through such a mechanism. Full article

The development of compact high-speed low-pressure turbines with high efficiencies requires the characterization of the secondary flow structures and the interaction of cavity purge and leakage flows with the mainstream. During the SPLEEN project funded by the European Union’s Horizon 2020, the von Karman Institute and Safran Aircraft Engines performed detailed measurements of low-pressure turbines in engine-realistic conditions (i.e., low Reynolds and high exit Mach numbers considering background turbulence, wakes, row interactions, and leakages). The SPLEEN project is thus a fundamental contribution to the progress of high-speed low-pressure turbines by delivering unique experimental databases, essential to characterize the time-resolved 3D turbine flow, and new critical knowledge to mature the design of 3D technological effects. Being able to simulate the flow and associated losses in such a configuration is both challenging and of paramount importance to help the understanding of the flow physics complementing experimental measurements. This paper focuses on the high-fidelity numerical simulation of one of the SPLEEN configuration consisting of a linear blade cascade. The objective is to provide a validated numerical setup in terms of computational domain, boundary conditions, mesh resolution and numerical scheme to reproduce the experimental results. By mean of wall-resolved large-eddy simulations, the design point characterized by an exit Mach number of 0.9 and an exit Reynolds number of 70,000 with a turbulence level of 2.4% is investigated for the baseline configuration without purge and without wake generator. The results show that the considered computational domain and the associated inlet total pressure profile play a critical role on the development of secondary flows. The isentropic Mach number distribution around the blade is shown to be robust to the mesh and numerical scheme. The development of the wake and secondary flow fields are drastically influenced by the mesh resolution and numerical scheme, impacting the resulting losses. Full article

In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no guarantee that the patient is satisfied by the action of the effectors. Hence, the need for the classification of brain signals as satisfactory/unsatisfactory is obvious. Based on previous work, we upgrade our neuromorphic MCD with a Neural Response Decoder (NRD) that is intended to predict whether ECoG data are satisfactory or not in order to improve MCD accuracy. The main aim is to design an actor–critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. For this aim, NRD was trained using not only an ECoG signal but also the MCD prediction or prescribed intended movement of the patient. The achieved accuracy of the trained NRD is satisfactory and contributes to improved MCD performance. However, further work has to be carried out to fully utilize the NRD for MCD performance optimization in an on-line manner. Possibility to include feedback from the patient would allow for further improvement of MCD-NRD accuracy. Full article

A single-station lightning location has the obvious advantages of low cost and convenience in lightning monitoring and warning. To address the critical challenge of distance estimation accuracy in this technology, we propose a novel physical time-reversal (PTR) method to utilize the full wave information of both the ground wave and the sky wave in the detected signal. First, we improved the numerical model for accurately calculating the lightning sferics signals in the complex propagation environment of the Earth–ionosphere waveguide using the measured International Reference Ionosphere 2020. Subsequently, the sferics signal with multipath effect is transformed by time reversal and back propagated in the numerical model. Furthermore, a broadening factor reflecting the waveform dispersion in the back propagation is defined as the single-station focusing criterion to determine the optimal lightning propagation distance, considering the multipath effect and the focus of the PTR process. The experimental results demonstrate that the average root mean square error (RMSE) and the mean relative error (MRE) of the PTR method for the lightning distance estimation in the numerical simulation within the range of 100–1200 km are 5.517 km and 1.21%, respectively, and the average RMSE and the MRE for the natural lightning strikes to the Canton Tower from the measured data in the range of 181.643–1152.834 km are 9.251 km and 2.07%, respectively. Moreover, the correlation coefficients of the detection results are all as high as 0.999. These results indicate that the PTR method significantly outperforms the traditional ionospheric reflection method, demonstrating that it is able to perform a more accurate single-station lightning distance estimation by utilizing the compensation mechanism of the multipath effect on the sferics. The implementation of the proposed method has significant application value for improving the accuracy of single-station lightning location. Full article

Background: Radiation dose escalation for locally advanced pancreatic cancer (LAPC) using stereotactic magnetic resonance (MR)-guided online adaptive radiation therapy (SMART) or computed tomography (CT)-guided moderately hypofractionated ablative radiation therapy (HART) can achieve favorable outcomes although have not previously been compared. Methods: We performed a multi-center retrospective analysis of SMART (50 Gy/5 fractions) vs. HART (75 Gy/25 fractions or 67.5 Gy/15 fractions with concurrent capecitabine) for LAPC. Gray’s test and Cox proportional regression analyses were performed to identify factors associated with local failure (LF) and overall survival (OS). Results: A total of 211 patients (SMART, n = 91; HART, n = 120) were evaluated, and none had surgery. Median follow-up after SMART and HART was 27.0 and 40.0 months, respectively (p < 0.0002). SMART achieved higher gross tumor volume (GTV) coverage and greater hotspots. Two-year LF after SMART and HART was 6.5% and 32.9% (p < 0.001), while two-year OS was 31.0% vs. 35.3% (p = 0.056), respectively. LF was associated with SMART vs. HART (HR 5.389, 95% CI: 1.298–21.975; p = 0.021) and induction mFOLFIRINOX vs. non-mFOLFIRINOX (HR 2.067, 95% CI 1.038–4.052; p = 0.047), while OS was associated with CA19-9 decrease > 40% (HR 0.725, 95% CI 0.515–0.996; p = 0.046) and GTV V120% (HR 1.022, 95% CI 1.006–1.037; p = 0.015). Acute grade > 3 toxicity was similar (3.3% vs. 5.8%; p = 0.390), while late grade > 3 toxicity was less common after SMART (2.2% vs. 9.2%; p = 0.037). Conclusions: Ablative SMART and HART both achieve favorable oncologic outcomes for LAPC with minimal toxicity. We did not observe an OS difference, although technical advantages of SMART might improve target coverage and reduce LF. Full article

Gender-neutral terms such as “sex work” and “sex worker” have gained prominence in academic, activist, and policy discourses. Unlike terms such as ‘prostitutes’ and ‘prostitution,’ these terms serve to reduce stigma and emphasize the labor involved in sex work. However, numerous studies suggest that these terms carry different meanings across cultural contexts, and their impact is experienced differently by various sub-groups of sex workers. Although originally coined by American activist Carol Leigh to unify sex workers across genders and sectors, these terms are, in some settings—including Kolkata, India—met with silence, coded language, or secrecy, particularly among transient sex workers, including cisgender and transgender women. As researchers with two decades of ethnographic research with 46 cisgender and 37 transgender women engaged in sex work in Kolkata and Eastern India, the authors argue that such silence and non-verbal forms of communication should not be interpreted merely as ignorance or disengagement but rather as strategic/willful acts of agency or resistance. These practices enable sex workers situated in different contexts to navigate unequal power dynamics within NGOs, manage respectability, mitigate structural violence, and foster communal identity. By examining how diverse groups of sex workers employ silence and secrecy, this study contributes to a more nuanced and empathetic understanding of the complexities surrounding the former’s lives and their agency. The study results indicate that it is essential for scholars, advocates and activists to employ strengths-based, context-specific language and research practices to be able to hear the stories of subaltern groups like commercial sex workers. It is also suggested that training of service providers and helping professionals focus on accepting and honoring the language people use to identify themselves in varied spaces and contexts. Full article

Spain is among the European countries with the greatest number of preserved castles and defensive structures—some estimates place the total at around 10,000, the majority of which date back to the medieval period. Yet, surprisingly, many of these fortifications remain uncatalogued and in an advanced state of ruin. This study focuses on a small fortress that has been overlooked by historiography and neglected by public authorities, yet which still stands after seven centuries: the Tower of Lopera, a castle belonging to the so-called Banda Morisca (the frontier of Al-Andalus in the 14th century). Using a combination of digital documentation techniques—namely, portable laser scanning, photogrammetry (via drone and camera), and digital image processing software—we have been able to digitize, geometrize, and document both the surviving architectural remains and their immediate physical environment. Rather than pursuing the latest technological innovations, this methodology prioritizes practical and realistic solutions based on the resources typically available to cultural heritage administrations. Our work serves two main objectives: to demonstrate the viability of applying such tools to this typology of architectural heritage and to conduct a detailed graphic and geometric analysis of the structure. Given the abundance of similar abandoned fortresses in Spain, the findings presented here could inform future heritage documentation strategies on a broader, potentially national, scale. Full article

Objective: With a global prevalence ranging from 50% to 100%, gingivitis is considered the most common oral disease in adults worldwide. It is characterized by clinical signs of inflammation, such as redness, swelling and bleeding, on gentle probing. Although it is considered a milder form of periodontal disease, gingivitis plays an important role in overall oral health. Early detection and treatment are essential to prevent progression to more severe conditions. Typically, diagnosis is performed by dental professionals, as individuals are often unable to accurately assess whether they are affected. Therefore, the aim of the present study was to determine to what degree gingivitis is visually detectable by an easy-to-use camera-based application. Materials and methods: Standardized intraoral photographs were taken using a specialized intraoral camera and processed using a custom-developed filter based on a machine-learning algorithm. The latter was trained to highlight areas suggestive of gingivitis. A total of 110 participants were enrolled through ad hoc sampling, resulting in 320 assessable test sites. A dentist provided two reference standards: the clinical diagnosis based on bleeding on probing of the periodontal sulcus (BOP) and an independent visual assessment of the same images. Agreement between diagnostic methods was measured using Cohen’s kappa statistic. Results: The agreement between the application’s output and the BOP-based clinical diagnosis was low, with a kappa value of 0.055 [p = 0.010]. Similarly, the dentist’s visual assessment of clinical photos showed low agreement with BOP, with a kappa value of 0.087 [p < 0.001]. In contrast, the agreement between the application and the dentist’s photo-based evaluations was higher, with a kappa value of 0.280 [p < 0.001]. Conclusions: In its current form, the camera-based application is not able to reliably detect gingivitis. The low level of agreement between dentists’ visual assessments and the clinical gold standard highlights that gingivitis is difficult to identify merely visually. These results underscore the need to refine visual diagnostic approaches further, which could support future self-assessment or remote screening applications. Full article

Research regarding airborne laser scanning (ALS) point cloud semantic segmentation typically revolves around supervised machine learning, which requires time-consuming generation of training data. Therefore, the models are usually trained using one of the benchmarking datasets that cover a small area. Recently, many European countries published classified ALS data, which can be potentially used for training models. However, a review of the classification schemes of these datasets revealed that these schemes vary substantially, therefore limiting their applicability. Thus, our goal was three-fold. First, to develop a common classification scheme that can be applied for the semantic segmentation of various ALS datasets. Second, to unify the classification scheme of existing ALS datasets. Third, to employ them for the training of a classifier that will be able to classify data from different sources and will not require additional training. We propose a classification scheme of four classes: ground and water, vegetation, buildings and bridges, and ‘other’. The developed classifier is trained jointly using ALS data from Austria, Switzerland, and Poland. A test on unseen datasets demonstrates that the achieved intersection over union accuracy varies between 90.0–97.3% for ground and water, 68.0–95.9% for vegetation, 77.6–94.8% for buildings and bridges, and 13.5–52.7% for ‘other’. As a result, we conclude that the developed method generalizes well to previously unseen data. Full article

Intraosseous hemangiomas (IH) are rare intrabony lesions that represent less than 1% of intraosseous tumors. IH are mostly seen in the axial skeleton and skull. Most commonly, the frontal bone, zygomatic, sphenoid, maxilla, ethmoid, and lacrimal bone can manifest IH. Currently, IH is classified as a developmental condition of endothelial origin. According to WHO, the five histological types of IH are cavernous, capillary, epithelioid, histiocytoid, and sclerosing. IH of the zygoma is an extremely rare condition with female predominance. A systematic review recently estimated that there were 78 cases published in the literature until 2023. The lesion is usually asymptomatic and presents with a gradually deteriorating deformity of the malar area, and the patient might be able to recall a history of trauma. Numbness due to involvement of the infraorbital nerve might also be present; however, atypical skin and bone sensations might also occur. Other symptoms include painful swelling, bone asymmetry, skin irritation, sinus pressure, paresthesia, diplopia, enophthalmos, or atypical neuralgia. A bony lesion with a trabecular pattern in a radiating formation (sunburst pattern) or a multilocal lytic lesion pattern created by the multiple cavernous spaces (honeycomb pattern) is commonly observed during radiologic evaluation. We present a rare case of IH of the zygoma in a 65-year-old generally healthy woman. A cyst-like bone tumor was revealed from the CT scan, which made preoperative biopsy of the lesion problematic. A careful radiological diagnostic differentiation of the lesion should always be conducted in such cases to outline a safe surgical plan and possible alternatives if needed. The patient underwent total tumor resection in the operating room, and the defect was reconstructed with the use of a titanium mesh and a synthetic hydroxyapatite bone graft based on a 3D surgical guide printed model. Full article

The shrubland ecosystems in arid areas are highly sensitive to global climate change and human activities. Accurate extraction of shrubs using computer vision techniques plays an essential role in monitoring ecological balance and desertification. However, shrub extraction from high-resolution GF-2 satellite images remains challenging due to their dense distribution and small size, along with complex background. Therefore, this study introduces a Feature Enhancement and Transformer Network (FETNet) by integrating the Feature Enhancement Module (FEM) and Transformer module (EdgeViT). Correspondently, they can strengthen both global and local features and enable accurate segmentation of small shrubs in complex backgrounds. The ablation experiments demonstrated that incorporation of FEM and EdgeViT can improve the overall segmentation accuracy, with 1.19% improvement of the Mean Intersection Over Union (MIOU). Comparison experiments show that FETNet outperforms the two leading models of FCN8s and SegNet, with the MIOU improvements of 7.2% and 0.96%, respectively. The spatial details of the extracted results indicated that FETNet is able to accurately extract dense, small shrubs while effectively suppressing interference from roads and building shadows in spatial details. The proposed FETNet enables precise shrub extraction in arid areas and can support ecological assessment and land management. Full article

Linear models are not always able to sufficiently capture the structure of a dataset. Sometimes, combining predictors in a non-parametric method, such as deep neural networks (DNNs), would yield a more flexible modeling of the response variables in the predictions. Furthermore, the standard statistical classification or regression approaches are inefficient when dealing with more complexity, such as a high-dimensional problem, which usually suffers from multicollinearity. For confronting these cases, penalized non-parametric methods are very useful. This paper proposes two heuristic approaches and implements new shrinkage penalized cost functions in the DNN, based on the elastic-net penalty function concept. In other words, some new methods via the development of shirnkaged penalized DNN, such as ${\mathrm{DNN}}_{\mathrm{elastic-net}}$ and ${\mathrm{DNN}}_{\mathrm{ridge}\&\mathrm{bridge}}$, are established, which are strong rivals for ${\mathrm{DNN}}_{\mathrm{Lasso}}$ and ${\mathrm{DNN}}_{\mathrm{ridge}}$. If there is any dataset grouping information in each layer of the DNN, it may be transferred using the derived penalized function of elastic-net; other penalized DNNs cannot provide this functionality. Regarding the outcomes in the tables, in the developed DNN, not only are there slight increases in the classification results, but there are also nullifying processes of some nodes in addition to a shrinkage property simultaneously in the structure of each layer. A simulated dataset was generated with the binary response variables, and the classic and heuristic shrinkage penalized DNN models were generated and tested. For comparison purposes, the DNN models were also compared to the classification tree using GUIDE and applied to a real microbiome dataset. Full article

The growth potential of Crinum bulbispermum was evaluated on gold mine tailings. The primary objectives were to model the species’ climatic niche in relation to gold mining regions, assess its germination success on tailings, and compare seedling survival and growth on tailings versus other soil types. Species distribution modelling identified the South African Grassland Biome on the Highveld (1000+ m above sea level), where the majority of gold mines are located, as highly suitable for the species. Pot trials demonstrated above 85% germination success across all soil treatments, including gold mine tailings, indicating its potential for restoration through direct seeding. An initial seedling establishment rate of 100% further demonstrated the species’ resilience to mine tailings, which are often seasonally dry, nutrient-poor, and may contain potentially toxic metals. However, while C. bulbispermum was able to germinate and establish in mine tailings, long-term growth potential (over 12 months) was constrained by low organic carbon content (0.11%) and high salinity (194.50 mS/m). These findings underscore the critical role of soil chemistry and organic matter in supporting long-term plant establishment and growth on gold tailings. Building on previous research, this study confirms the ability of this thick-rooted geophyte to tolerate chemically extreme soil conditions. Crinum bulbispermum shows promise for phytostabilization and as a potential medicinal plant crop on tailings. However, future research on microbial community interactions and soil amendment strategies is essential to ensure its long-term sustainability. Full article

In resistance spot welding (RSW), the total electrical resistance (dynamic resistance) as the sum of bulk and contact resistance is a key variable. Both of these respective resistances influence the welding result, but the exact ratio to the total resistance of a real existing sheet is not known. Due to the high scatter in the RSW of aluminum alloys compared to steel, it is of interest to be able to explicitly determine the individual resistance components in order to gain a better understanding of the relationship between the initial state and the lower reproducibility of aluminum welding in the future. So far, only the total resistance and the bulk resistance could be determined experimentally. Due to the different sample shapes, it was not possible to consistently determine the contact resistance from the measurements. In order to realize this, a method was developed that contains the following innovations with the aid of simulation: determination of the absolute bulk resistance at room temperature (RT), determination of the absolute contact resistance at RT and determination of the ratio of bulk and contact resistance. This method makes it possible to compare the resistances of the bulk material and the surface in the initial state quantitatively. This now allows the comparison of batches regarding the surface resistance, especially for welding processes. For the aluminum sheets (EN AW-5182-O, EN AW-6014-T4) investigated, the method showed that the contact resistance dominates and the bulk resistance is less than 20%. These data can also be used to make predictions about the weldability of the alloy using artificial intelligence (AI). If experimental data are available, the method can also be applied to higher temperatures. Full article