In medical image analysis, accurate 3D brain tissue segmentation is crucial. This study undertakes a meticulous comparative evaluation of two 3D segmentation models: the established 3DUnet and the innovative 3DUnet Tr...
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Environmental impacts of small scale industries are still not well investigated, especially for estates where multiple small scale industries are clustered causing cumulative effects potentially impacting most vulnera...
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Environmental impacts of small scale industries are still not well investigated, especially for estates where multiple small scale industries are clustered causing cumulative effects potentially impacting most vulnerable groups. This study aimed to investigate the concentrations of heavy metals (Mn, Fe, Ni, Cu, Pb, Al, Zn, and Cr) and the metalloid (As) in surface soils near the intensely active small-scale industrial area in Thailand, assess the contamination levels in the co-located child care center, and estimate potential human health risks from exposure to the heavy metals. Surface soil heavy metal(loid) elements were evaluated across a background area, area near main roads, a small-scale industrial area, and a child care centre. The concentrations of Al were found be the highest at all investigation sites, with the most elevated concentrations observed in the small-scale industrial area. The concentration of Pb in the industrial area reached 1176 mg/kg, exceeding the national standard and acceptable limit. Moreover, the contamination level for Cu, Pb, and Zn in the industrial area imply a considerable risk with enrichment factor EF > 40, and geo-accumulation index Igeo ≥5. Heavy metal levels (Ni, Cu, Pb) in the soil near the road and the child care center were similar, suggesting shared contamination sources. Conversely, Al, Zn, As, and Cr in the child care center had slightly higher concentrations than near the road but lower than the small-scale industrial area, indicating potential industrial influence on metal levels within the child care center. The principal component analysis showed Mn and Fe distributed in the background, while As, Pb, Ni, Cu, Al, Zn, and Cr accumulated in the small-scale industrial area. The findings indicate that small-scale industrial activities contribute to elevated levels of heavy metals in the soil, particularly within the child care center, posing significant ingestion risks, especially for children (HI > 1). Implementing conta
This paper presents the context of the Ubiquitous Computing course carried out in an Industrial engineering undergraduateprogram throughout 2020 and the first semester of 2021. This course took advantage of the Insti...
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ISBN:
(纸本)9781665424899
This paper presents the context of the Ubiquitous Computing course carried out in an Industrial engineering undergraduateprogram throughout 2020 and the first semester of 2021. This course took advantage of the Institutional Modernization program led by the Control and Automation engineering Undergraduateprogram, which consists in the modernization of undergraduateengineeringprograms at the Pontifical Catholic University of Paraná. The course aimed to help develop competencies, i.e., a set of knowledge, skills, and attributes aligned with Student Outcomes suggested by ABET. Also, the course proposal brought the productive sector very close to the academic environment, proposing real engineering problems as challenges. However, what are the methods and assessment tools for practical learning in a course with modern elements? This paper proposed the flipped learning and the Challenge-Based Learning Framework with the support of the CDIO Framework as learning methodologies to answer such question. Additionally, quizzes, tests, presentations, rubrics, and peer evaluation were applied as assessment tools to measure the student's progress. Finally, students were listened to about their perceptions during the course. The results suggested that the learning methodologies and assessment tools are suitable for the course context, although some improvements are expected for the following course offers. Moreover, students approved the initiative to bring in real challenges proposed by companies, the mentoring hours, and the feedback about the projects.
The scope of this research is delimited by the study of not-for-profit organizations performance through operations management lens. According to a systematic literature review, the performance measurement frameworks ...
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Combining several low-calorie sweeteners in a food is a common strategy for reducing caloric sweeteners. The effects of binary sweetener mixtures on sweetness enhancement and sweet–bitter taste receptor interactions,...
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Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN arch...
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ISBN:
(数字)9798350378511
ISBN:
(纸本)9798350378528
Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning
Deep reinforcement learning (DRL) achieved significant progress in several areas enabling computers to perform complex decision-making tasks. Applied to quantitative trading, DRL trading agents can optimize their deci...
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Many rural communities lack adequate broadband infrastructure, which limits the economic development potential in these regions. They are not able to attract new businesses, and established businesses are unable to us...
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Modulating inflammatory cells in an implantation site leads to severe complications and still unsolved challenges for blood-contacting medical *** by the role of galectin-1(Gal-1)in selective functions on multiple cel...
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Modulating inflammatory cells in an implantation site leads to severe complications and still unsolved challenges for blood-contacting medical *** by the role of galectin-1(Gal-1)in selective functions on multiple cells and immunomodulatory processes,we prepared a biologically target-specific surface coated with the lipid bilayer containing Gal-1(Gal-1-SLB)and investigate the proof of the biological ***,lipoamido-dPEG-acid was deposited on a gold-coated substrate to form a self-assembled monolayer and then conjugated dioleoylphosphatidylethanolamine(DOPE)onto that to produce a lower leaflet of the supported lipid bilayer(SLB)before fusing membrane-derived vesicles extracted from B16-F10 *** Gal-1-SLB showed the expected anti-fouling activity by revealing the resistance to protein adsorption and bacterial *** vitro studies showed that the Gal-1-SLB can promote endothelial function and inhibit smooth muscle cell ***,Gal-1-SLB presents potential function for endothelial cell migration and angiogenic *** vitro macrophage culture studies showed that the Gal-1-SLB attenuated the LPS-induced inflammation and the production of macrophage-secreted inflammatory ***,the implanted Gal-1-SLB reduced the infiltration of immune cells at the tissue-implant interface and increased markers for M2 polarization and blood vessel formation in *** straightforward surface coating with Gal-1 can be a useful strategy for modulating the vascular and immune cells around a blood-contacting device.
Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic...
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Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject’s subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes a conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoor threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine’s behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern as the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability.
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