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The Influence of the 9E Learning Cycle with A STEM Approach on Students' Science Process Skills in Static Fluid Topics

This study investigated the effectiveness of integrating the 9E Learning Cycle Model with STEM approaches to enhance students' Science Process Skills (SPS) in static fluid concepts. The research employed a mixed-methods embedded experimental design with 32 senior high school students. Quantitative data were collected through pre- and post-tests, while qualitative data were gathered via observations and interviews. Results showed significant improvements across all SPS components, with N-gain values ranging from 0.63 to 0.64, indicating medium gains. Students demonstrated enhanced abilities in observing, classifying, measuring, inferring, predicting, and communicating scientific concepts. The integrated approach facilitated practical application of knowledge, as evidenced by students' work on flood-resistant garage prototypes and wave-powered electricity generators. Qualitative findings revealed increased student engagement and improved problem-solving skills. The study concludes that the integration of the 9E Learning Cycle Model with STEM approaches offers an effective framework for enhancing students' science process skills and their ability to apply these skills to real-world problems. These recommendations aim to provide a more comprehensive understanding of the approach's potential in science education across various domains and student populations

An Evolving Multivariate Time Series Compression Algorithm for IoT Applications.

The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach's potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications.

Revolutionizing X-ray Imaging: A Leap toward Ultra-Low-Dose Detection with a Cascade-Engineered Approach.

X-ray detection technology is essential in various fields, including medical imaging and security checks. However, exposure to large doses of X-rays poses considerable health risks. Therefore, it is crucial to reduce the radiation dosage without compromising detection efficiency. To address this concern, we propose an innovative cascade-engineered approach that uses two interconnected single-crystal devices to mitigate dark current and enhance the detection limit. Using laboratory-grown methylammonium lead bromide (MAPbBr3) perovskite single crystals, we engineered devices that significantly reduced detection thresholds and improved signal-to-noise ratios (SNRs). The detection threshold dropped from 590 nGy·s-1 with the conventional method to 100 nGy·s-1 using the cascade approach, surpassing the most recent record of 500 nGy·s-1 achieved for MAPbBr3 devices under nearly identical conditions. The dark current was halved compared to that of conventional devices, and spatial resolution improved from 5.6 to 8.5 lp·mm-1. Imaging trials confirmed improved resolution and effectiveness at low doses, highlighting the approach's potential for medical diagnostics that prioritizes reducing radiation exposure without compromising image quality. The groundbreaking nature of this approach is highlighted by its adaptability across diverse electrical environments and crystal types, as evident in CdTe crystals, indicating its potential for widespread utilization in low-dose leakage monitoring and commercial X-ray devices.

Fabrication of endothelialized capillary-like microchannel networks using sacrificial thermoresponsive microfibers.

In the body, capillary beds fulfill the metabolic needs of cells by acting as the sites of diffusive transport for vital gasses and nutrients. In artificial tissues, replicating the scale and complexity of these capillary vessels has proved challenging, especially in a three-dimensional context. In order to better develop thick artificial tissues, it will be necessary to recreate both the form and function of capillaries. Here we demonstrate a top-down method of patterning hydrogels using sacrificial templates formed from thermoresponsive microfibers whose size and architecture approach those of natural capillaries. Within the resulting microchannels, we cultured endothelial monolayers that remain viable for over three weeks and exhibited functional barrier properties. Additionally, we cultured endothelialized microchannels within hydrogels containing fibroblasts and characterized the viability of the co-cultures to demonstrate this approach's potential to when applied to cell-laden hydrogels. This method represents a step forward in the evolution of artificial tissues and a path towards producing viable capillary-scale microvasculature for engineered organs.

Under what conditions? A scenario‐based approach for exploring the prerequisites of future events

AbstractIn an increasingly complex world, futures thinking can be used to understand the conditions that define future events' realization. This paper presents a novel approach to explore under what conditions some chosen future event would occur. The approach can be seen as a fusion of exploratory scenarios with the backward‐looking perspective of backcasting while resembling cross‐impact methods in how future events' realization arises from the interaction of several uncertain assumptions. The approach maps the causal mechanisms and assumptions that lead to the investigated event's realization, and results in a subset of assumption combinations that lead to the event being realized or not, and scenario sets where the event is either realized or not. This analysis provides insights beyond “Is the event likely to occur?”, revealing the various circumstances under which it can occur. We also provide ways for considering probabilities and deliberate decisions within the approach. We illustrate the approach with three example cases from different problem domains, such as reaching long‐term climate targets, and employing various methods, such as causal mapping, influence diagrams, and optimization. We conclude with a discussion on the approach's potential to enhance foresight practice, emphasizing its synergistic relation to existing methods and its contribution to a richer, more nuanced anticipation of the future.

Effectiveness and Safety ofBlending Traditional Chinese Medicine with Western Medicine for Enhanced Secondary Stroke Prevention: AMeta-Analysis of RCTs

BackgroundTo conduct a comprehensive evaluation of the efficacy and safety of combining Traditional Chinese medication (TCM) with Western medication to improve secondary stroke prevention. Through meticulous analysis, we investigate the combined approach's potential to yield superior outcomes compared to stand-alone Western medical treatments. MethodsWe carefully searched for randomized controlled trials (RCTs) on secondary stroke prevention using TCM and Western medicine from database inception to October 2023. We found relevant studies using PubMed, EMBASE, Cochrane Library, CNKI, SinoMed, WanFang, and VIP. Comprehensive meta-analyses were conducted using Stata 16.0 software, with predetermined inclusion and exclusion criteria. ResultsOur comprehensive study included 40 RCTs with 3478 treatment and 3396 control individuals. Our meta-analysis shows that integrating TCM and Western medicine significantly improves secondary stroke prevention rates compared to Western medicine alone (OR=0.83, 95% CI: 0.55-1.12, p< 0.001). Significantly, the integrated strategy reduced the NIHSS score compared to Western medical treatment for secondary stroke prevention (OR=-0.62, 95% CI: -0.89, -0.35, p< 0.001). Additionally, this combined technique reduced stroke recurrence rates more effectively than Western medical care alone (OR=-0.94, 95% CI: -1.10, -0.76, p< 0.001). The rate of adverse reactions was not statistically different between Western medical treatment and integrated TCM (OR=0.01, 95% CI: -0.18, 0.05, p = 0.131). ConclusionThe evidence shows that combining TCM and Western therapy improves secondary stroke prevention. Besides improving clinical effectiveness, this integrated approach may reduce stroke recurrence. These findings strongly support the widespread use of this integrated approach in clinical practice.

A Study on Resolving Dynamic analysis using deep learning Framework Logical Methods

Deep learning technologies have gained a great deal of interest in recent months for demonstrating material removal capabilities in a variety of separation, categorization, as well as other machine vision activities. The majority of these deep networks are built employing convolution or completely convolution structures. Our suggested Dynamic and Static gestures System guided by a hand gesture high dimensional data methodology We use a snapshot of a human's thoracic spine to determine the person's depth and the size of the space around his or her hands. In this research, we provide a novel object-based deep-learning framework for semantic segmentation of extremely high-resolution satellite data. In specifically, we leverage object-based prior record by augmenting a fully convolutional neural network's training strategy with an anisotropic diffusion data pre-processing phase and an additional loss term. The goal of this restricted framework is to ensure that similar visual data is assigned to the same semantic class. Here, we employ intermediate steps based on traditional image processing techniques to aid in the resolution of the subsequent problem of classification, detection, or segmentation. Research shows that adding pre- and post-processing steps to a deep learning pipeline can boost model performance over using only the network. Recent advances in UQ algorithms used for deep learning are analysed, and their potential for use in relevance feedback is addressed. It also emphasizes basic research difficulties and directions linked with UQ. Quantifiably, man-made classes with more precise geometry, such as buildings, benefited the most from our strategy, particularly along object borders, indicating the developed approach's enormous potential. The purpose of this paper is to offer an overview of the approaches used inside deep learning frameworks to either appropriately prepare the input (pre-processing) or enhance the outcomes of the network output (post-processing), with an emphasis on digital pathology image analysis.

Accumulation analysis and overall measurement to represent airborne toxic metals with passive tree bark biomonitoring technique in urban areas.

Authorities have long proved the utility of bioindicators in monitoring the state of environmental pollution. Some biological indicators can measure environmental pollutant levels, and many tree species have been tested for suitability for monitoring purposes. The differences in morphological characteristics in the trees have demonstrated the effects of human activities on different materials. Measuring bark and wood biomass from contaminated sites was identified and directly compared with those from a clean site or areas characterized by distinct contamination sources. However, preliminary results demonstrate the approach's potential in the realization of strategies for disease control and promoting health to reduce environmental and health inequalities in at-risk urban areas. Picea orientalis L. and Cedrus atlantica Endl., especially their bark, can be regarded as a more robust storage of Cu (37.95 mg/kg) and Mn (188.25 mg/kg) than Pinus pinaster, Cupressus arizonica, and Pseudotsuga menziesii, which and is therefore a better bioindicator for Cu and Mn pollution. Considering the total concentrations as a result of the study, the pollution is thought to be caused by environmental problems and traffic in the region. The deposition of Cu, Mn, Ni, and Zn elements was found P. menziesii (60, 443, 58, and 258 mg/kg) and P. orientalis (76, 1684, 41, and 378 mg/kg) and seems to reflect atmospheric quite clearly compared to P. pinaster, C. arizonica, and C. atlantica. Ni and Zn concentrations have significantly increased since 1983, and P. menziesii and P. orientalis can be potentially valuable bioindicators for emphasizing polluted fields.

Fabrication of zeolite-supported cobalt ferrite (CoFe2O4/ H-zsm-5) catalyst for the synthesis of 4-(9-(4-substituted)-1,2,3,4,5,6,7,8-octahydro-3,3,6,6-tetramethyl-1,8-dioxoacridin-10(9H)-yl)benzoic acid derivatives in semi aqueous condition

A cobalt ferrite (CoFe2O4/H-ZSM-5) catalyst supported by zeolite has been studied as a multicomponent reaction for making dioxoacridin-10(9H)-ylbenzoic acid derivatives in semi-aqueous conditions. This reaction offers a cost-effective and simple operational strategy and provides different substrates. Scope and various functional groups are well tolerated under optimized reaction conditions. Firstly, the catalyst was characterized using FT-IR, XRD, BET, XPS, TGA, and SEM techniques. The study determined the optimal operating conditions for achieving an efficient synthesis of target organic compounds. The optimized conditions for the synthesis of model compounds (4a) with a yield of 96 % were achieved through a reaction time of 40 min, a catalyst amount of 10 mol%, a temperature of 90 °C, and a solvent of 6/4 of the ethanol and water mixture. All of the characterization measurements confirmed that the catalyst's stability remained unaltered even after four consecutive cycles of the synthesis of model compounds. Furthermore, the reusability feature cuts costs and lessens environmental effects, making it an appealing option for practical applications. The findings emphasize this approach's enormous potential for sustainable chemical synthesis, opening the path for future advances in the sector. In addition, an experiment was set up to look into the thermodynamic and kinetic properties of compound (4a) when it was heated to between 343.15 and 373.15 K with the help of a catalyst. The current investigation has shown that the reaction favors thermodynamics rather than kinetic control.

Diffusion Weighted Magnetic Resonance Imaging of Spinal Cord Injuries After Instrumented Fusion Stabilization.

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a promising technique for assessing spinal cord injury (SCI) that has historically been challenged by the presence of metallic stabilization hardware. This study leverages recent advances in metal-artifact resistant multi-spectral DW-MRI to enable diffusion quantification throughout the spinal cord even after fusion stabilization. Twelve participants with cervical spinal cord injuries treated with fusion stabilization and 49 asymptomatic able-bodied control participants underwent multi-spectral DW-MRI evaluation. Apparent diffusion coefficient (ADC) values were calculated in axial cord sections. Statistical modeling assessed ADC differences across cohorts and within distinct cord regions of the SCI participants (at, above, or below injured level). Computed models accounted for subject demographics and injury characteristics. ADC was found to be elevated at injured levels compared with non-injured levels (z = 3.2, p = 0.001), with ADC at injured levels decreasing over time since injury (z = -9.2, p < 0.001). Below the injury level, ADC was reduced relative to controls (z = -4.4, p < 0.001), with greater reductions after more severe injuries that correlated with lower extremity motor scores (z = 2.56, p = 0.012). No statistically significant differences in ADC above the level of injury were identified. By enabling diffusion analysis near fusion hardware, the multi-spectral DW-MRI technique allowed intuitive quantification of cord diffusion changes after SCI both at and away from injured levels. This demonstrates the approach's potential for assessing post-surgical spinal cord integrity throughout stabilized regions.

Relay-empowered beyond 5G radio access networks with edge computing capabilities

Relevant services envisaged for beyond 5G (B5G) systems, such as extended reality and holographic communications, have extremely demanding user experience requirements with significant computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers near the user, the communication will take advantage of the technologies developed for 5G New Radio jointly with an never-before-seen degree of network densification. This paper proposes the use of relays with edge computing capabilities. The approach's potential for B5G are identified, and a system model is defined to characterize both computational and communications viewpoints. Based on this, results are provided to highlight the gains and limitations of the proposed approach from a system-level perspective. Finally, the main challenges for enabling relays with computing capabilities in B5G deployments are discussed.

Control strategy for biopharmaceutical production by model predictive control.

The biopharmaceutical industry is rapidly advancing, driven by the need for cutting-edge technologies to meet the growing demand for life-saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real-time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

Multiscale Frozen Density Embedding/Molecular Mechanics Approach for Simulating Magnetic Response Properties of Solvated Systems.

We present a three-layer hybrid quantum mechanical/quantum embedding/molecular mechanics approach for calculating nuclear magnetic resonance (NMR) shieldings and J-couplings of molecular systems in solution. The model is based on the frozen density embedding (FDE) and polarizable fluctuating charges (FQ) and fluctuating dipoles (FQFμ) force fields and permits the accurate ab initio description of short-range nonelectrostatic interactions by means of the FDE shell and cost-effective treatment of long-range electrostatic interactions through the polarizable force field FQ(Fμ). Our approach's accuracy and potential are demonstrated by studying NMR spectra of Brooker's merocyanine in aqueous and nonaqueous solutions.

Major Depressive Disorder: Etiology, Impact Factors and the Effectiveness of Mindfulness-Based Intervention

This comprehensive review discusses the implications of mindfulness-based interventions in treating Major Depressive Disorder (MDD) in children, an increasingly recognized global mental health issue. MDD cast significant challenges to a child's social interaction, academic performance, and overall personal development, necessitating effective interventions. While traditional therapies, such as cognitive-behavioral therapy and interpersonal therapy, have proven their effectiveness, this paper explores the emerging field of mindfulness-based interventions. These interventions, encompassing training in mindfulness meditation, focus on enhancing present-moment awareness, emotional regulation, and a non-judgmental acceptance of one's feelings and thoughts. Several studies have reported a reduction in MDD symptoms and improvements in emotional regulation in children due to mindfulness-based interventions. The study acknowledges some potential limitations and challenges with this approach but advocates for its promise in treating childhood MDD. The paper strongly urges for further research in this area to fully exploit this innovative therapeutic approach's potential, fine-tune its application, and investigate its efficacy across different clinical contexts.

Community theory-based learning framework for Higher education

With the increased request for better student success, several methods investigate teachers' effectiveness undertaken with continued support from a related learning technology. With less durational experiences or extended education, programmers can have several ranges in theory-based universities. Teaching effectiveness in higher education strengthens students' impact and performance in the learning environment. The technological development in different platforms is completely understood and enhances education strategies. Community-based learning advantages for higher education are well recorded. The author concludes that educational institutions must make thoughtful choices about the parts they use, that they should strategically manage the network of internal and external stakeholders, including all communications and well-informed decisions, that they should recognize that shifts in student perceptions are common and encourage flexibility in system operations, and that it is normal for platforms to have specialized, high-performing departments/sections. The perspective of the group member is relatively less understood. Therefore, this paper proposes a community theory-based learning (CT-BL) framework to improve higher education learning technologies. CT-BL presents perspectives on how professors should contribute to strengthening partnerships between students and educators. The descriptive statistical model is used in a questionnaire session to recognize the central points in community participants' answers. Practical application of the framework within a variety of conservation groups illustrates the integrated approach's potential to serve as a portal through which practitioners can enter the realm of social science theory to better comprehend the current state and future directions of CT-BL interventions and activities. Students from several universities form collaborations to describe the way of theory-based learning. The community-based learning is evaluated based on the questionnaire raised by the learning participants. The community-based theory enhances higher education learning technologies. The overall participants of the University's higher education strategies are focused on community-based learning. Added teacher support for student awareness of information, experience, and competence and expanded academic participation in all cooperation elements involving advisor suggestions. The numerical results show that the proposed CT-BL model enhances the evaluation rate of 95.6%, reliability ratio of 94.2%, an efficiency ratio of 95.3%, the recognition accuracy of 90.3%, learning recognition rate of 92.2%, the impact of beliefs of 96.2%, dynamic nature rate of 93.4% when compared to other existing models.

Exploring the Transformed Landscape of Grammar Learning in Language Education: Unveiling Teaching Insights through Harnessing the Flipped Classroom Approach's Potential

The paper explores the potential of the flipped classroom model in language education, specifically for teaching grammar. The flipped model shifts traditional teaching by moving direct instruction outside class, allowing more engaging in-class activities. It also reverses teaching, putting direct instruction before class and interactive learning during class. It may stand language instructors in good stead, for it enhances engagement and comprehension. This experiment investigates its implementation, benefits, challenges, and impact on grammar learning outcomes. Notable among the challenges posed by this model, if instructors opt to avail themselves of its mechanisms, are technological barriers, including limited access to high-speed internet and a lack of compatible hardware. The study comprises an illustrative analysis that applies the model to instructing subject-verb agreement through pre-class activities, video content, quizzes, games, and essay assignments. The results palpably demonstrate an improved understanding and practical application of grammar. The flipped classroom offers transformative opportunities for language education, needing careful preparation and addressing tech challenges. It attempts to affect a qualitative leap in learning by using technology for content delivery and focusing on interactive in-class activities.

Revitalizing Ming-Style on Contemporary Furniture Design For Sustainability

This study investigates how eco-friendly practices have contributed to the resurgence of Ming aesthetics in contemporary furniture design. The Ming dynasty left a legacy in the arts that continues to this day with its emphasis on minimalism, harmony, and refinement. As a way to accommodate progressive preferences and address the pressing demand for sustainability, these ideas are finding a fresh lease on life in the context of contemporary furniture design. This research looks into the feasibility of using eco-friendly materials and production methods to recreate Ming-era–inspired designs. Those with environmental values will appreciate the fresh take on design that emerges when traditional aesthetics are fused with eco-friendly processes. Through an examination of case studies and design prototypes, this study reveals how Ming-style concepts can be implemented into contemporary furniture without compromising sustainability. The findings demonstrate the blended approach's potential for producing eco-friendly, aesthetically beautiful furnishings. The report also highlights the significance of circular economies, waste reduction, and carbon footprint decrease as they relate to sustainable design concepts. More than just a gorgeous face, the fusion of Ming style and eco-friendliness demonstrates the power of bringing together elements from different eras. By reviving the Ming style from a sustainable perspective, furniture designers may pay homage to a fabled past while also contributing to the global imperative of sustainable design and consumption.

Real-Time Pedestrian Detection with YOLOv7 and Intel MiDaS: A Qualitative Study

Abstract: In the realm of Advanced Driving Assistance Systems (ADAS), the accurate assessment of pedestrian proximity is of paramount importance. This paper introduces a qualitative methodology that integrates YOLOv7-pose for object detection and pose estimation with the MiDaS (Monocular Depth Estimation in Real-Time with Deep Learning) model for monocular depth estimation. The main objective is to qualitatively assess pedestrian proximity to the camera within the ADAS framework. This procedure involves classifying pedestrians as "near" or "far" based on an inverse depth threshold that has been predetermined. In addition, the paper performs a qualitative comparative analysis of the results produced by the MiDaS Small, Hybrid, and Large variants to learn more about the performance of depth estimation in these contexts, particularly in relation to the presence of pedestrians. The evaluation emphasises this approach's qualitative potential for achieving situationally appropriate and context-aware pedestrian proximity assessment. The safety and adaptability of ADAS systems can be improved with the help of such insights, which have numerous applications in robotics, surveillance, and autonomous vehicles.

ESCAPE simplified approach designed to evaluate materials sustainability: The case of new adsorbent materials for activated carbon substitution

This paper discusses a novel simplified approach for assessing the sustainability of new material, specifically focusing on the synthesis technology used. The material can be obtained through emerging technologies, even if they are in the early laboratory development stage. The evaluation is conducted by comparing it to a reference material, which could be a natural resource, for instance. This approach can be applied in situations where complete life cycle assessment (LCA) data is unavailable or a comprehensive LCA evaluation is not required. ESCAPE (standing for Evaluation of Sustainability of material substitution using CArbon footPrint by a simplifiEd approach) involves the calculation of two parameters, the embodied energy, and the CO2 footprint, for all steps of the material synthesis procedure. The manuscript discusses in detail for the first time the approach's applicability and potentialities with some examples, devoted to new proposed adsorbents, derived from wastes. For this aim, 50 literature papers are considered and analysed. Surprisingly, the results show that only 28% of the 50 selected materials are in accord with the introduced sustainability index, showing that most of the new proposed adsorbents appear to be not environmentally sustainable.This study's findings highlight the ESCAPE approach's potential in facilitating materials eco-design by identifying the most resource-intensive steps in synthesis procedures. Notably, this approach provides the flexibility to incorporate the energy mixes of different Countries where the new processes may be deployed, enabling sustainability evaluations based on specific locations. This capability opens possibilities for a more comprehensive and context-specific assessment of sustainability in materials development. In essence, ESCAPE serves as a preliminary screening method before conducting an LCA, enabling rapid evaluations. It serves as a valuable tool for researchers and industries aiming to develop sustainable materials, thereby expediting the transition toward a circular economy.

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach's potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applications.