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Delayed feeding disrupts diurnal oscillations in the gut microbiome of a neotropical bat in captivity
Get PDFDiurnal rhythms of the gut microbiota are emerging as an important yet often overlooked facet of microbial ecology. Feeding is thought to stimulate gut microbial rhythmicity, but this has not been explicitly tested. Moreover, the role of the gut environment is entirely unexplored, with rhythmic changes to gut pH rather than feeding per se possibly affecting gut microbial fluctuations. In this study, we experimentally manipulated the feeding schedule of captive lesser long-nosed bats, Leptonycteris yerbabuenae, to dissociate photic and feeding cues, and measured the faecal microbiota and gut pH every 2 h. We detected strong diurnal rhythms in both microbial alpha diversity and beta diversity as well as in pH within the control group. However, a delay in feeding disrupted oscillations of gut microbial diversity and composition, but did not affect rhythms in gut pH. The oscillations of some genera, such as Streptococcus, which aid in metabolizing nutrients, shifted in accordance with the delayed-feeding cue and were correlated with pH. For other bacterial genera, oscillations were disturbed and no connection to pH was found. Our findings suggest that the rhythmic proliferation of bacteria matches peak feeding times, providing evidence that diurnal rhythms of the gut microbiota likely evolved to optimize their metabolic support to the host’s circadian phenotype
Capsule network with using shifted windows for 3D human pose estimation
No full text3D human pose estimation (HPE) is a vital technology with diverse applications, enhancing precision in tracking, analyzing, and understanding human movements. However, 3D HPE from monocular videos presents significant challenges, primarily due to self-occlusion, which can partially hinder traditional neural networks’ ability to accurately predict these positions. To address this challenge, we propose a novel approach using a capsule network integrated with the shifted windows attention model (SwinCAP). It improves prediction accuracy by effectively capturing the spatial hierarchical relationships between different parts and objects. A Parallel Double Attention mechanism is applied in SwinCAP enhances both computational efficiency and modeling capacity, and a Multi-Attention Collaborative module is introduced to capture a diverse range of information, including both coarse and fine details. Extensive experiments demonstrate that our SwinCAP achieves better or comparable results to state-of-the-art models in the challenging task of viewpoint transfer on two commonly used datasets: Human3.6M and MPI-INF-3DHP
Design Optimization of a 3-PUU Parallel Machine for Friction Stir Welding Robots
Get PDFFriction Stir Welding (FSW) has recently emerged as an effective solid-state joining technique for welding high-strength aluminum alloys and light metals, particularly in the railway industry. However, a limitation of using FSW in this industry is the restricted workspace available. To address this issue, a 3-PUU (Prismatic and Universal Joints) parallel machine with 3-DoF (Degrees of Freedom) translational movement is utilized to support the FSW robot’s tool head, allowing the robot to operate over a larger horizontal workspace. This paper focuses on the design and optimization of a 3-PUU parallel machine to achieve an optimal configuration based on performance indices, specifically the Local Conditioning Index (LCI) and the dynamic isotropy index ( d1 ). Initially, a design space atlas was created, mapping both LCI and d1 were identified. A Genetic Algorithm (GA) was then used to determine a single optimal design within this region. A cuboid workspace was defined for this optimal design, and two test trajectories were generated for the robot to follow. A prototype of the optimal design was built, and a series of experiments were conducted to verify and validate the kinematic and dynamic models within the defined cuboid workspace, which provides foundations for further industrial applications of the proposed machine in FSW
A New Genetic Algorithm‐Based Network for Text Localization in Degraded Social Media Images
Get PDFABSTRACTThis paper presents a novel model for understanding social image content through text localization. For text localization, we explore maximally stable extremal regions (MSER) for detecting components that work by clustering pixels with similar properties. The output of component detection includes several non‐text components due to the degradations of social media images. To select the best components among many, we explore the genetic algorithm by convolving different kernels with components, which results in a feature matrix that is further fed to EfficientNet for choosing actual text components. Therefore, the proposed model is called genetic algorithm based network for text localization in degraded social media images (TLDSMI). For evaluating text localization, we consider the images of the standard dataset of natural scenes by uploading and downloading from different social media platforms, namely, WhatsApp, Telegram, and Instagram. The effectiveness of our method is shown by testing on original and degraded standard datasets. For example, for the degraded images of different complexities including degradations caused by social media platforms, the proposed method performs well in almost all situations. In addition, the proposed model achieves the best F1‐Score, 0.76, 0.77, 0.70, and 0.78 for the degraded images of CUTE, ICDAR 2013, Total‐Text, and CTW1500, respectively, compared to the state‐of‐the‐art methods
Acoustic diffusion described using the largest Lyapunov exponents
No full textIn this study, we theoretically established a method to map ray movements in 3D enclosures and used a ray-tracing algorithm to describe the paths along which a ray propagates in space. Moreover, we used the time-series points extracted from these paths to calculate the largest Lyapunov exponents (LLEs). Two models, ray reflections in rectangular enclosure spaces assigned with 1–16 cylindrical and 1–8 spherical diffusers, were established. By investigating how the LLEs of ray systems vary with the diffuser parameters, we found that the LLEs can be used as a function of the number of diffusers in the spaces. The results showed that LLEs can be potentially implemented in diffusers to improve room acoustics
On the use of operational transmissibilities for the predication & analysis of coupled assemblies
Get PDFAs is usually the case in many scientific disciplines, prediction and analysis techniques are firstly developed within laboratory conditions and subsequently extended to industrial settings through further studies. Within vibro-acoustics, we usually begin testing a method in a controlled environment via Experimental Modal Analysis (EMA). One example of an EMA-based method called the Round-trip (RT) method has gained traction within industry recently, a highly convenient and accurate driving-point mobility prediction technique which may be used in influence structural design. Often within various industry, the driving-point mobility may be needed at locations such as mounting points between two sub-structures coupled to one another. It is common that these areas are access limited, meaning conducting a measurement using a modal hammer can be impractical. The RT provides a solution to this by allowing the user to measure three transfer mobilities which uses force inputs on areas of the coupled structure that are much easier to apply. However, a limitation facing EMA-based methods is that it requires any sources within the coupled system to be shut down. Additionally, if the force and measured response locations are far away from each other, this will yield data with poor signal-to-noise (SNR). This often limits the RT to smaller scale structures which can be tested in laboratory conditions. There has been a lack of investigation into incorporating an Operational Modal Analysis (OMA) approach into the RT method to circumvent these limitations. OMA similarly allows the extraction of modal properties, but does so by using ambient, unmeasured, and stochastic excitations (such as wind) and output-only responses.In this thesis, we show that output-only/operational transmissibilities can be used to represent two of the three transfer mobilities in the point RT identity, allowing its extension to much larger structures and uncontrolled active environments. This novel approach to the RT, termed the Operational Round-trip (ORT) method, is analysed and compared to the original technique across three experimental examples. It is shown that it too can accurately predict driving-point mobilities while adding further convenience. Additionally, this thesis has investigated an OMA-based approach for analysing transmission paths using output-only transmissibilities. It is known that by using the ‘bottleneck’ effect and applying the SVD to a transfer mobility of a coupled system, the singular values can be analysed to detect unaccounted flanking and the number of transmission paths. Similarly to the RT method, this EMA-based method may present challenges on larger scale structures, and/or if an active component (such as a compressor) cannot be shutdown. Instead, it is shown that by identically analysing the singular values of output-only transmissibilities instead, this method can be extended to industrial applications. The findings in this thesis show that using output-only transmissibilities provides a more convenient means of using the vibro-acoustic prediction and analysis methods presented, while also allowing them to be used in a wider range of systems within industry. Finally, an output-only extension to these methods allows the potential to be developed into real-time monitoring tools
The wellbeing impacts of participation in civic environmental activities in an urban context: a mixed methods realist evaluation
Get PDFEnvironmental organisations are operating in the context of climate emergency and a biodiversity crisis, alongside a growing green health movement that seeks to understand better the links between the natural environment and human health. This study gained a closer understanding of how and why and in what context wellbeing impacts may be derived from participation in civic environmental activities to assess whether this might be an innovative solution to tackling health issues upstream whilst improving local greenspace provision. Civic environmental activity is facilitated by urban greening initiatives delivered by organisations that enable participants to improve or develop publicly accessible green infrastructure within the urban environment at a neighbourhood level. The research takes a realist approach to evaluating the context and mechanisms that may lead to wellbeing outcomes through participation in civic environmental activity and the implications for the implementation of delivery models. The study gathered perspectives from both participants engaged in civic environmental activities and practitioners facilitating activities, to better understand the Context – Mechanism- Outcome configuration conceptualised using a programme theory approach. Data were collected from individuals attending greening initiatives delivered by Greater Manchester based organisations. The Five Ways to Wellbeing (Aked et al., 2008) framework provided the starting point to guide a deeper understanding into causal mechanisms within the phenomenon that may lead to the intended outcome of increased wellbeing (or conversely unintended outcomes). Data were gathered from a bespoke Green Ways to Wellbeing (GWTWB) questionnaire (QUANT-QUAL) conducted pre and post activities. Semi-structured interviews and focus groups (QUAL) with participant and practitioners were subjected to a retroductive Reflexive Thematic Analysis approach. A triangulation strategy determined convergence, differences or a combination of the two between datasets. The findings suggest that participation in civic environmental activities leads to improved mood leading to short-term hedonistic wellbeing experiences, and in the longer term eudaimonic wellbeing. In the context of nature, the phenomenon facilitates physical activity, social connections, learning opportunities, a sense of citizenship, a connection to nature, and nurtures a sense of purpose and self-worth. Findings from the research present a deeper understanding and insight into the journey and contextual factors, e.g. participant reasoning and resources needed, to achieve the desired wellbeing outcomes. The study provides an engagement framework and tool (GWTWB questionnaire) for evaluating wellbeing impacts into the future. The discussion seeks to address conflicting paradigms regarding evaluation with an aim to help bridge an existing gap between the health sector and environmental third sector stakeholders. The findings aim to support policy and practitioners to deliver nature-based activities for health and wellbeing and support the emerging green social prescribing movement, by understanding better what works for whom and why and the implications for practice in the context of climate change and nature recovery agendas. The research provides a unique contribution to the evidence base in understanding wellbeing impacts of the phenomenon, and the implications for practice, using a realist evaluation approach
Gender Prediction Using Real-time Convolutional Neural Network
Get PDFThis work presents real time gender prediction using Convolutional Neural Network. Automatic classification of gender has become an area that has garnered importance due to the emergence of breakthroughs in the world of computing particularly with the advent of machine learning and Artificial intelligence. The goal of gender prediction in computer vision involves accurate prediction of gender from visual data. Gender prediction is an indispensable biological metric as it plays a significant role in many human applications. The application varies from immigration, border access, law enforcement, defence and intelligence, citizen identification and banking. Image processing combined with machine learning algorithms have been employed to build solutions from image representation of biological attributes such as facial images, human skeletal radiographs (most notably skull and pelvic bones), gait, smiles and non-biological features such as social media activities, names and other means that could be employed to determine gender by extracting regions of interest, applying necessary filters and normalizing the matrix values obtained. For the purpose of this work, a total of 6760 hand-bone radiographs were acquired from the Radiological Society of North America Repository. The dataset was divided into 70% training datasets and 30% test dataset using Random Sampling Cross-Validation Method. The acquired data images were pre-processed using image cropping, histogram equalization and segmentation techniques. Performance evaluation was carried out on the developed system using the metrics: Accuracy, False Positive Rate (FPR), Recognition Time, Specificity and Sensitivity, these metrics were evaluated at threshold values of 0.25, 0.35, 0.5 and 0.75. Optimal values were gotten at optimum threshold value of 0.75, the values are; 4.79, 93.67, 95.21, 94.53 and 137.87 respectively for metrics (FPR, Sensitivity, Specificity, Accuracy and Time)
Modelling Joule Heating in Magnetized Porous Structures Using Statistical Techniques
No full textHybrid nanofluids have been utilized in various thermal engineering applications, including heat exchangers, materials science research, and industrial domains like solar trough collectors, food processing and aerospace engineering. This study's ultimate objective is to examine a Casson hybrid nanofluid's hydrodynamic and thermal behavior in a porous medium subjected to a bilinear stretching surface. The effects of thermal radiation, chemical reactions, volumetric heat source/sink, Joule heating, and viscous dissipation are all included in the mathematical model. When a magnetic field with inclination is present, the fluid is electrically conducting. By means of similarity transformations, the governing nonlinear coupled partial differential equations (PDEs) that characterize the flow phenomena are transformed into a system of coupled ordinary differential equations (ODEs). The MATLAB bvp4c solver in conjunction with a shooting technique yields numerical solutions. The outcomes, which show how different dimensionless parameters affect the flow field, temperature distribution, and concentration profiles, are displayed graphically and tabularly. The skin friction coefficient, Sherwood number, and Nusselt number at the stretching surface are among the derived quantities that are calculated and examined. As the Casson parameter rises, the momentum boundary layer becomes thinner. The Lorentz force causes the temperature to exhibit the inverse trend as the magnetic parameter increases, causing a drop in fluid velocity. The chemical reaction parameter and the Schmidt number tend to drop as the concentration profile rises, whereas the Soret effect demonstrates the exact reverse. According to statistical analysis using modified R-squared and R-squared metrics, this model matches the skin friction coefficient exceptionally well, with an average accuracy of 99.87%. The Nusselt number is noticeably more sensitive to thermal radiation and heat sources than the Dufour effect. Nomenclature: Subscripts hnf A combination of two or more distinct nanomaterials (hybrid nanofluid
How to undertake peripheral intravenous cannulation.
Get PDFPeripheral intravenous (IV) cannulation in adults is one of the most commonly performed healthcare procedures. It involves the insertion of a small tube into a vein using a needle, enabling the administration of fluids, blood products and nutrition, and the collection of blood samples. Healthcare professionals performing this procedure must undergo training to be able to undertake it effectively and safely. • Knowledge of vein anatomy and understanding the risks and benefits of the procedure supports safe practice, reduces errors, costs and infection risk, and improves the overall patient experience. • To provide holistic care, nurses should understand the indications for peripheral IV cannulation, which can be a short-term intervention for administering medicines, fluids and blood products, and for parenteral nutrition • Various pharmacological interventions and psychological techniques can be used to alleviate or minimise the pain and anxiety experienced by some patients during cannulation. • Following the successful insertion of a peripheral IV cannula, nurses must provide ongoing care to preserve the cannula's patency and safeguard the patient. REFLECTIVE ACTIVITY: 'How to' articles can help to update your practice and ensure it remains evidence-based. Apply this article to your practice. Reflect on and write a short account of: • How this article might improve your practice when undertaking peripheral IV cannulation. • How you could use this information to educate nursing students or your colleagues on the appropriate and safe methods for undertaking peripheral IV cannulation
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