Search Results (6,854)

Introduction: Although lipomas are common benign tumors found in adults, lipomas of the bladder are extremely rare. Bladder lipomas are infrequently reported in the urologic literature, with only 19 cases published worldwide. These can present as a mass on cystoscopy and cause irritative voiding symptoms, depending on their location. Upon transurethral resection, seeing fat can be concerning for a perforation, as lipoma can be mistaken for extravesical fat. Hence, familiarity with this rare entity is of paramount importance for urologists to prevent unnecessary investigations and interventions that are needed in case of a true bladder perforation. Case presentation: This study presents a case of bladder lipoma in a 73-year-old male with end-stage renal disease who presented for pretransplant urologic evaluation due to microscopic hematuria and irritative lower urinary tract symptoms (LUTS). During cystoscopy, a bladder mass was seen, and a transurethral resection of the bladder tumor (TURBT) revealed bright yellow adipose tissue immediately underneath the bladder mucosa. Concerns about perforation were obviated when seeing intact detrusor muscle underneath, visually confirming the integrity of the bladder wall. The resection was completed, and the CT scan was re-read with the radiologist, which confirmed the presence of a lipoma that was missed pre-operatively due to patient’s oliguria and collapsed bladder. No catheter drainage or cystogram was performed based on these findings. Outcome: The patient healed without any complications. Histopathology confirmed the diagnosis of a mature lipoma. The patient was cleared for transplant from a urologic standpoint and had a successful renal transplantation without delay. Discussion: This case documents the anomalous occurrence of a lipoma within the bladder and supports maintaining a broad differential, including liposarcoma, angiomyolipoma, and other non-malignant fatty tumors during the evaluation of a bladder mass. Full article

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

The double wishbone suspension (DWS) system is widely used in automotive engineering because of its favorable kinematic properties, which affect vehicle dynamics, handling, and ride comfort; hence, it is important to have an accurate 3D model, simulation, and analysis of the system in order to optimize its design. This requires efficient computational tools for parametric study. The development of effective computational tools that support parametric exploration stands as an essential requirement. Our research demonstrates a complete Wolfram Mathematica system that creates parametric 3D kinematic models and conducts simulations, performs analyses, and generates interactive visualizations of DWS systems. The system uses homogeneous transformation matrices to establish the spatial relationships between components relative to a global coordinate system. The symbolic geometric parameters allow designers to perform flexible design exploration and the kinematic constraints create an algebraic equation system. The numerical solution function NSolve computes linkage positions from input data, which enables fast evaluation of different design parameters. The integrated 3D visualization module based on Mathematica’s manipulate function enables users to see immediate results of geometric configurations and parameter effects while calculating exact 3D coordinates. The resulting robust, systematic, and flexible computational environment integrates parametric 3D design, kinematic simulation, analysis, and dynamic visualization for DWS, serving as a valuable and efficient tool for engineers during the design, development, assessment, and optimization phases of these complex automotive systems. Full article

This study dives into what drives people to use AR filters in the catering industry, focusing on the Hong Kong market. The main idea is to determine how “perceived value” shapes users’ intentions to engage with these filters. To do this, the research combines concepts from two popular models—the extended Technology Acceptance Model (TAM) and the Satisfaction–Loyalty Model (SLM)—to understand what influences perceived value. The survey data were then analyzed with Structural Equation Modeling (SEM) to see how perceived usefulness, enjoyment, satisfaction, and value connect to users’ intentions. The results showed that “perceived value” is a big deal—the main factor driving whether people want to use AR filters. Things like how useful or enjoyable the filters are and how satisfied users feel all play a role in shaping this perceived value. These findings are gold for marketing teams and AR developers, especially in the catering world. Combining TAM and the Satisfaction–Loyalty Model offers a fresh perspective on how AR technology influences consumer behavior. On top of that, it gives practical advice for businesses looking to make the most of AR filters in their marketing and customer experience strategies. Full article

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article

Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn. Full article

This article examines two Caribbean texts which use 20th-century journeys on passenger ships as opportunities to investigate ways in which colonial anxieties of race and gender are worked out through nautical desires. Mayra Montero’s erotic novel La última noche que pasé contigo (1991) and Claude McKay’s Romance in Marseille (2020) both wrestle with the imagined and material consequences of pervasive anti-blackness. They also raise crucial questions about embodied practices of struggle for survival. My analysis seeks to answer the following questions. What happens when anti-blackness masquerades as desire? How do we read and represent an anti-blackness that seeks to consume parts of the Caribbean and then dispense as refuse with what it sees as superfluous? What reading practices might we adopt in order to make sense of Caribbean bodies dehumanized on their own shores, and what narrative solutions might Caribbean fiction propose that might begin to restore humanity and value to these bodies? Full article

Food loss and waste from the European Union’s dairy supply chain, particularly in the management of fresh milk, imposes significant environmental burdens. This study demonstrates that implementing Radio Frequency Identification (RFID)-enabled digital decision-support tools can substantially reduce these impacts across the region. A cradle-to-grave life cycle assessment (LCA) was used to quantify both the additional environmental burdens from RFID (tag production, usage, and disposal) and the avoided burdens due to reduced milk losses in the farm, processing, and distribution stages. Within the EU’s fresh milk supply chain, the implementation of digital tools could result in annual net reductions of up to 80,000 tonnes of CO₂-equivalent greenhouse gas emissions, 81,083 tonnes of PM_2.5-equivalent particulate matter, 84,326 tonnes of land use–related carbon deficit, and 80,000 cubic meters of freshwater-equivalent consumption. Spatial analysis indicates that regions with historically high spoilage rates, particularly in Southern and Eastern Europe, see the greatest benefits from RFID enabled digital-decision support tools. These environmental savings are most pronounced during the peak months of milk production. Overall, the study demonstrates that despite the environmental footprint of RFID systems, their integration into the EU’S dairy supply chain enhances transparency, reduces waste, and improves resource efficiency—supporting their strategic value. Full article

The aim of this research is to identify policy and practice interventions that support a just transition towards resilient practices for resource-dependent communities. We focus on Thai Nguyen and Phu Tho, two provinces in the Northern Midlands and Mountains of Vietnam. The region is reliant on agriculture but is assessed as highly vulnerable to climate change. We surveyed 105 farming households. A Likert-type questionnaire asked respondents to self-assess their experiences of weather extremes and of changes they had made to their farming practices. Our results show that for both Thai Nguyen and Phu Tho, farmers see the effects of climate change on their crops. Respondents in Thai Nguyen were more likely to report technically driven adaptation and engagement with extension services. Respondents in Pho Tho were more likely to continue traditional practices. For both, use of traditional knowledge and practices was related to taking measures to adapt to climate change. Our main conclusion is that at least three actions could support a just transition to resilient livelihoods. First is incorporating natural science and traditional knowledge into decision-making for just transitions. Second is considering long-term implications of interventions that appear to support livelihoods in the short term. Third is tailoring messaging and engagement strategies to the requirements of the most vulnerable people. The main message of this study is that a just transition for resource-dependent communities will inevitably be context-specific. Even in centralized and authoritarian contexts, flexibility to adapt top-down policies to locals’ own experiences of changing climates is needed. Full article

As large language models (LLMs) and vision–language models (VLMs) become increasingly used in robotics area, a crucial question arises: to what extent do these models replicate human-like cognitive processes, particularly within socially interactive contexts? Whilst these models demonstrate impressive multimodal reasoning and perception capabilities, their cognitive plausibility remains underexplored. In this study, we address this gap by using human visual attention as a behavioural proxy for cognition in a naturalistic human-robot interaction (HRI) scenario. Eye-tracking data were previously collected from participants engaging in social human-human interactions, providing frame-level gaze fixations as a human attentional ground truth. We then prompted a state-of-the-art VLM (LLaVA) to generate scene descriptions, which were processed by four LLMs (DeepSeek-R1-Distill-Qwen-7B, Qwen1.5-7B-Chat, LLaMA-3.1-8b-instruct, and Gemma-7b-it) to infer saliency points. Critically, we evaluated each model in both stateless and memory-augmented (short-term memory, STM) modes to assess the influence of temporal context on saliency prediction. Our results presented that whilst stateless LLaVA most closely replicates human gaze patterns, STM confers measurable benefits only for DeepSeek, whose lexical anchoring mirrors human rehearsal mechanisms. Other models exhibited degraded performance with memory due to prompt interference or limited contextual integration. This work introduces a novel, empirically grounded framework for assessing cognitive plausibility in generative models and underscores the role of short-term memory in shaping human-like visual attention in robotic systems. Full article

Background/Objectives: The aim of this study was to evaluate the level of awareness among dental students and practitioners regarding the utilization of endocrowns in clinical settings, along with any differences in knowledge based on gender, educational level, and workplace. Methods: A cross-sectional online survey-based study was conducted, including 1154 participants from various dental institutions across Saudi Arabia. The questionnaire included demographic data and closed-ended questions focused on knowledge and awareness of endocrowns. Data was analyzed using statistical tests, including chi-square, to see any significant differences. Results: Most participants (81%) had knowledge about endocrowns, mainly from their colleagues. Knowledge levels and preferences (like the use of lithium disilicate and adhesive resin cement) differed based on gender, educational level, and workplace. Male participants and undergraduates showed better awareness in some areas. Faculty members mostly depend on college training, while private practitioners obtain most of their information from workshops. Conclusions: Males showed significant superiority in knowledge about endocrown usage. Colleges were the most prevalent source of information regarding endocrown restorations. Significant molar tooth loss and restricted inter-arch space were the most common reasons for utilizing endocrowns. Moreover, endocrowns were considered a viable alternative to traditional post and core. Full article

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

In this study, we investigate the learning opportunities offered in the enactment of a finger pattern activity with numbers exceeding 10 that shows how smaller units can be composed into larger units. Research on early arithmetic learning shows the importance of students understanding numbers as composed units and making use of arithmetic strategies that are based on unitizing rather than single-unit counting. The Many Hands activity was enacted in an intervention program focusing on 6-year-olds’ learning of structuring numbers and number relations during one school year, conducted in collaboration with teachers. The activity, with numbers exceeding 10, was enacted at the end of the program. Video observations of 19 teaching episodes in which the activity was used were analyzed using the variation theory of learning. The analysis focused on identifying which aspects of numbers were made visible for students to discern and how finger patterns became a tool for structuring numbers and number relations. Five aspects were made visible in the enactments of the Many Hands activity: (i) small numbers as composed units; (ii) units within units; (iii) units within units and new, larger units; (iv) relationships between units in the number system; and (v) place value. In 12 of the 19 episodes, the teacher or the students used their fingers to show and see the structure of numbers in relation to the identified aspects. Full article

Network Music Performance (NMP) refers to network-based remote collaboration when applied to music performances, such as musical education, music production and live music concerts. In NMP, the most important parameter for the Quality of Experience (QoE) of the participants is low end-to-end audio delay. Increasing delays prevent musicians’ synchronization and lead to a suboptimal musical experience. Visual contact between the participants is also crucial for their experience but highly demanding in terms of bandwidth. Since audio compression induces additional coding and decoding delays on the signal path, most NMP systems rely on audio quality reduction when bandwidth is limited to avoid violating the stringent delay limitations of NMP. To assess the delay and quality tolerance limits for NMP and see if they can be satisfied by emerging 5G networks, we asked eleven pairs of musicians to perform musical pieces of their choice in a carefully controlled laboratory environment, which allowed us to set different end-to-end delays or audio sampling rates. To assess the QoE of these NMP sessions, each musician responded to a set of questions after each performance. The analysis of the musicians’ responses revealed that actual musicians in delay-controlled NMP scenarios can synchronize at delays of up to 40 ms, compared to the 25–30 ms reported in rhythmic hand-clapping experiments. Our analysis also shows that audio quality can be considerably reduced by sub-sampling, so as to save bandwidth without significant QoE loss. Finally, we find that musicians rely more on audio and less on video to synchronize during an NMP session. These results indicate that NMP can become feasible in advanced 5G networks. Full article

This paper examines the philosophy of language in The Platform Sutra of the Sixth Patriarch (六祖壇經), demonstrating its centrality to Zen Buddhism and Buddhist sinicization. The sutra emphasizes the ineffability of ultimate truth (至道無言) and the principle that words falter in encapsulating the Dao (言語道斷), framing language as a provisional “raft” (筏) that must be instrumentalized yet transcended through a dialectic of employing and abandoning (用離辯證). It ontologically grounds this view in Buddha-nature’s (佛性) pre-linguistic essence, advocating transcending reliance on words and letters (不假文字) while strategically deploying language to dismantle its own authority. Historically, this constituted a revolt against Tang scholasticism’s textual fetishism. The text adopts a dynamic dialectic, neither clinging to nor rejecting language, exemplified by Huineng’s awakening through the Diamond Sutra, where recitation catalyzes internal insight. Operationally, it utilizes negational discourse, the “Two Paths Mutually Condition” method (二道相因) embedded in the “Twelve Pairs of Dharmic Forms” (法相語言十二對) in particular, to systematically deconstruct dualisms, while promoting embodied unity of speech, mind, and action (口念心行) to critique empty recitation. Ultimately, the sutra orchestrates language as a self-subverting medium: balancing acknowledgment of its limitations with pragmatic instrumentality, it presents an Eastern paradigm where language actively disrupts conceptual fetters to facilitate direct insight into Buddha-nature, reframing it as a dynamic catalyst for “illuminating the mind and seeing one’s nature” (明心見性). Full article