Pyramid Approach Research Articles

Convolutional laplacian gaussian pyramid approach multimodal medical image fusion

Convolutional laplacian gaussian pyramid approach multimodal medical image fusion

Konsep Arsitektur Organik pada Perancangan Politeknik Pertanian dengan Pendekatan Piramida Efektivitas Belajar

This architectural design explores the application of the millennial concept in organic architecture at the Indramayu Agricultural Polytechnic with a learning effectiveness pyramid approach. Focusing on millennial values ​​such as collaboration, sustainability, and technology integration, the learning space design is designed to maximize learning effectiveness. By integrating natural beauty, sustainability and technology, this research aims to create an innovative and sustainable learning environment at the Indramayu Agricultural Polytechnic, encouraging student interaction and critical thinking according to the pyramid concept of learning effectiveness.

Exploring Ethnomathematics at South Kalimantan NU University's As-Su'ada Campus Mosque, based on Geometry Perspective and Its Relationship to Learning Activity

This research aims to explore the cultural site of the Masjid Kampus As-Su’ada Universitas NU Kalimantan Selatan in terms of geometric aspects and its relationship to mathematics learning, especially geometry. The research method used was qualitative with a realist ethnographic approach. The data collection technique is exploration through observation and interviews. The analysis technique in this research is through source collation, namely clarifying the results of observations by interviewing informants. The research results show that the Masjid Kampus As-Su'ada has many geometric shapes on the exterior and interior of the mosque. The geometric shapes contained in it include plane geometry (circle, square, rectangle, triangle and ellipse), solid geometry (pyramid, block, prism) and modified geometry (half tube, truncated pyramid and elliptical approach). Lessons that can be carried out include case studies of mosque roofs, study tours identifying mosque geometric shapes, and determining the area of the building.

NARRATIVE RECONSTRUCTION OF TRAUMATIC MEMORY IN THE NOVEL OF REDEMPTION AT HACKSAW RIDGE

This research discusses the traumatic memory of Desmond Doss and analyzes Herndon’s narrative reconstruction of Desmond’s traumatic memory in his novel. This research uses the post-memory theory by Hirsch, through Freytag’s pyramid approach. The qualitative method and literary memory are use in this research. Data were acquired from the documentary interview, and the paragraphs that existed on every page of the novel. The researchers collected the date through four steps: exploration, reduction, abstraction, and read intensively. This research reveals that Desmond Doss experienced seven traumatic memories; he was frightened of the gun, intimidated, humiliated, lost his friend, Japanese Army strategies, and the aftermath. To make it entertaining to read, Herndon caught Desmond’s traumatic memory and reconstructed the narratives into his novel. To fulfill Herndon's and the reader’s satisfaction, he exaggerates and lessens some of the actual events. The purpose of narrative reconstruction in this novel was to modify what the author could not do in the past and fix it in the novel through his imaginative investment, along with highlighting the heroic story of Desmond Doss.

Rolling bearing fault diagnosis model based on DSCB-NFAM

Machine learning techniques have had great success in fault diagnosis. However, the traditional machine learning methods rely heavily on manual priori knowledge leading to poor fault diagnosis results in rolling bearing fault diagnosis. Deep learning techniques can improve the accuracy of fault intelligent diagnosis with the help of automatic extraction of fault features. In this article, a method of smart fault diagnosis for rolling bearings based on depth-separable convolutional block (DSCB)-non-local feature awareness module (NFAM) is proposed. Firstly, a DSCB is constructed, which can reduce the number of parameters and effectively alleviate the gradient disappearance phenomenon. Then a NFAM is constructed to enhance the global sensing ability while adaptively adjusting the weight of each channel. Further, a feature extraction layer is constructed to extend the fault feature of the input information. Finally, a feature pyramid approach is used to fuse the extracted information for output. The validity of the suggested method is tested on both of the bearing datasets. By comparing with several mainstream intelligent diagnosis algorithms, the results of the experiments show that the proposed method has better advancement under complex conditions such as variable load and variable noise.

Multi-scale feature pyramid approach for melt track classification in laser powder bed fusion via coaxial high-speed imaging

The randomness and low frequency of laser powder bed fusion defects are two important characteristics that can impact the quality and reliability of parts. Therefore, effectively detecting the forming quality of parts during the manufacturing process has become an important research problem in the field of intelligent additive manufacturing technology. In this study, the use of multi-scale and multi-feature manifold learning methods first demonstrated that the global optimal solution for predicting the forming morphology of the melt track cannot be obtained when the number of process phenomenon features in the laser powder bed fusion process is unknown. As an alternative, a multi-scale feature pyramid network is used for processing long sequence high-speed videos and predicting the forming morphology. Specifically, to address the randomness issue, this study used a coaxial high-speed imaging system to monitor the entire forming process and designed a 2D Transformer-based video understanding model to process high-speed video data and recognize key process phenomena. To solve the low frequency issue, physics-based simulation can quickly understand how process parameters affect the forming quality of parts to provide guidance for constructing multi-mode category datasets. The experimental results indicate that the model can accurately predict the forming morphology of the melt track, better control the entire forming process, and thus improve manufacturing quality and efficiency.

Detailed Evidence Based Study on Cockroach Theory of Organizational Sustainability and Scalability of Bottom of the Pyramid Approach

Detailed Evidence Based Study on Cockroach Theory of Organizational Sustainability and Scalability of Bottom of the Pyramid Approach

Detailed Evidence Based Study on Cockroach Theory of Organizational Sustainability and Scalability of Bottom of the Pyramid Approach

Detailed Evidence Based Study on Cockroach Theory of Organizational Sustainability and Scalability of Bottom of the Pyramid Approach

A Novel Approach to Maritime Image Dehazing Based on a Large Kernel Encoder–Decoder Network with Multihead Pyramids

With the continuous increase in human–robot integration, battlefield formation is experiencing a revolutionary change. Unmanned aerial vehicles, unmanned surface vessels, combat robots, and other new intelligent weapons and equipment will play an essential role on future battlefields by performing various tasks, including situational reconnaissance, monitoring, attack, and communication relay. Real-time monitoring of maritime scenes is the basis of battle-situation and threat estimation in naval battlegrounds. However, images of maritime scenes are usually accompanied by haze, clouds, and other disturbances, which blur the images and diminish the validity of their contents. This will have a severe adverse impact on many downstream tasks. A novel large kernel encoder–decoder network with multihead pyramids (LKEDN-MHP) is proposed to address some maritime image dehazing-related issues. The LKEDN-MHP adopts a multihead pyramid approach to form a hybrid representation space comprising reflection, shading, and semanteme. Unlike standard convolutional neural networks (CNNs), the LKEDN-MHP uses many kernels with a 7 × 7 or larger scale to extract features. To reduce the computational burden, depthwise (DW) convolution combined with re-parameterization is adopted to form a hybrid model stacked by a large number of different receptive fields, further enhancing the hybrid receptive fields. To restore the natural hazy maritime scenes as much as possible, we apply digital twin technology to build a simulation system in virtual space. The final experimental results based on the evaluation metrics of the peak signal-to-noise ratio, structural similarity index measure, Jaccard index, and Dice coefficient show that our LKEDN-MHP significantly enhances dehazing and real-time performance compared with those of state-of-the-art approaches based on vision transformers (ViTs) and generative adversarial networks (GANs).

Soil spectroscopy with the Gaussian pyramid scale space

Soil visible-near infrared (vis–NIR) spectra are complex and modeling soil properties can be challenging. They can suffer from additive and multiplicative noise, they are hyper-dimensional and highly collinear, making their analyses and interpretation sometimes difficult. Here, we introduce the Gaussian pyramid scale space as a multi-resolution approach for denoising spectra, reducing dimensionality, and improving the interpretability and accuracy of spectroscopic machine learning. We also used the approach to analyse contextual interactions between different resolutions and the stability of feature importance across different resolutions. Using an Australian data set and the German data in the LUCAS spectral database we found that with a single Gaussian scale that represents a relatively coarse spectral resolution, we could estimate organic carbon, clay and pH as accurately as with multiple Gaussian scales, or using all resolutions. This indicates that, in the vis–NIR range, there are no relevant interactions between resolutions, which simplifies interpretations. We conclude that the Gaussian pyramid scale space can help to model soil properties with spectral machine learning, improving both accuracy and interpretability. Because the Gaussian pyramid approach is computationally efficient, it can also be used for preprocessing and knowledge discovery before more elaborate modeling is applied.

Advancing Nursing Science Through Site-Based Clinical Inquiry: Designing Problem Pyramids.

Advancing Nursing Science Through Site-Based Clinical Inquiry: Designing Problem Pyramids.

TRIVIA’s social media effectiveness

Learning outcomes Besides the metrics developed to measure the impact of the video campaign run by the company, the case has serious practical implications for all companies in emerging markets selling budget/low-cost products. Marketing managers can take note of these key performance indicators in evaluating the success of their social media campaigns.More importantly, students need to understand how the campaign managed to integrate uninitiated and less-privileged consumers to the world of social media with aspirational sentiments. Case overview/synopsis The case deals with a digital media planning activity for a low-cost handset company based in India. TRIVIA International Ltd is a manufacturer and marketer of smart phones and feature phone in the price range of below US$70–100 and US$20, respectively. The phones cater to low-income consumer bracket, which forms the vast microcosm of India. The consumer base is huge, but the purchasing power is very low, so they are at times referred to as the micro-consumer in Bottom of Pyramid approach. To approach this consumer base, Trivia planned a very engaging campaign on social media that yielded positive results, contrary to popular belief that only high-income individuals explore the social media intensively through their smart phones. The chapter ends with a set of recommendations for all digital managers who want to analyse their campaigns effectively via awareness, sentiment and engagement metrics. Complexity academic level The case is meant for all post graduate programs in Business Management, which include the MBAs, Masters in Business Administration and the Executive Development program for managers. The case can also be used in Part Time Business Management programs held over weekends for working professionals. Most likely the students are going to handle this case in their second year of a full-time program or at an advanced level of their career development programs. The subjects so mentioned here were offered as electives or areas of specialization. It is presumed that the students undergoing these courses have basic understanding of marketing management. Supplementary materials Teaching notes are available for educators only. Subject code CSS 8: Marketing.

Out-of-plane modal property extraction based on multi-level image pyramid reconstruction using stereophotogrammetry

Understanding the dynamic behavior of as-built structures is important, because structural condition can often be inferred from changes in its dynamic response. To this end, modal properties such as natural frequencies, damping ratios, and mode shapes can be extracted from acceleration measurements. Associated installations require sensors, cabling, and data acquisition systems that can be expensive and time consuming. Alternatively, computer vision-based approaches have been proposed that offer non-contact measurements, as well as substantial cost savings. However, such approaches usually focus only on the structural component responses that are in the plane of the image; researchers seldom consider the structural response that is out-of-plane (i.e., perpendicular to the image plane). For complex structural component that possess asymmetries, the out-of-plane behavior is critical to understanding their dynamic response. Determination of dynamic response of the structure requires that multiple cameras are used and can be computationally demanding. In this study, imagery from a commercially available stereo camera is combined with a multi-level image pyramid approach and operational modal analysis (OMA) to extract out-of-plane modal properties. The measurements are first padded to reduce the distortion effect caused by the image pyramid. The responses are then compressed and decomposed into sub-bands using an image pyramid decomposition; followed by extraction of the modal properties using the OMA approach known as frequency-domain stochastic subspace identification. The advantages and limitations of the proposed approach are illustrated numerically using a physics-based graphics model (PBGM) of a continuous beam. Subsequently, experimental validation is conducted for a 3-story model building using the Intel® RealSenseTM D415 depth camera. Modal properties are shown to be determined quickly, with high accuracy and noise robustness. Moreover, detailed near-continuous mode shapes are obtained using multi-level image pyramid reconstruction. These results demonstrate that the proposed approach can give an accurate picture of the dynamic characteristics of a structure, offering the potential for effective long-term structural health monitoring for important structural component.

An Exploratory Evaluation of Multiscale Data Analysis for Landform Element Detection on High-Resolution DEM

The representation of landform element varies over multiple scales, or multiresolution digital elevation model (DEM) and its derivatives. When more details of land surfaces are available to be characterized based on the existing high spatial resolution elevation products, the influence of scale variation might become more significant. This poses a demand for determining a scale-independent approach being competent to support multiscale landform element detection on high-resolution DEMs. Although the practicability of the state-of-the-art scale-independent approaches have been reported on moderate-resolution DEMs, how these approaches perform based on the multiscale data including high and moderate spatial-resolution DEMs is still unexplored. This letter evaluates the performance of four scale-independent techniques including filtering, spatial pyramid, multiscale segmentation, and spatial-contextual approach in landform element detection on different spatial resolution DEMs. The experimental results show that spatial–contextual approach is more effective to support multiscale landform element detection than others.

Nursing and Maslow’s Hierarchy: A Health Care Pyramid Approach to Safety and Security During a Global Pandemic

The COVID-19 pandemic of 2020 tested health care organizations in ways many had never experienced. In response to new problems, nurses and other staff needed creative solutions that would allow them to quickly and safely provide care. Nurses concurrently experienced stressors to their personal needs. The authors collected examples of creative problem-solving through staff interviews and reflected on Maslow’s hierarchy of needs to consider how nurse’s needs were being met throughout the pandemic. Recognizing that basic needs must be addressed before an individual can ascend the levels to eventual self-realization, the authors share how one health care organization quickly and successfully met the patient care requirements while supporting nurses basic needs like safety and trust.

Business Model Involvement, Adaptive Capacity, and the Triple Bottom Line at the Base of the Pyramid

Almost two decades ago, Prahalad and Hammond [Harv Bus Rev, 80(9):48–59, 2002] introduced the base/bottom of the pyramid (BOP) approach to profitably serving the poor with business models adapted from developed markets while alleviating poverty. In response to disappointing results and ethical criticism, the BOP approach evolved from a just-for-profit approach with a passive role of the poor to an inclusive development approach that integrates the principles of the triple bottom line. A recent review of the BOP literature [Dembek et al., J Bus Ethics 165(3):365–382, 2020], however, reveals a lack of empirical evidence to support the sustainable BOP approach. In this paper, we specify the assumptions underlying the sustainable BOP approach and test them using structural equation modeling with clustered robust standard errors on a unique dataset of 212 firms. Our findings show that BOP business model involvement and adaptive capacity are significant drivers of the triple bottom line at the BOP; however, business model adaptive capacity does not guarantee an ecologically sustainable performance at the BOP. We find that there is a need for further extension of the ethical foundations of the sustainable BOP approach.

Revisiting Kaya Identity to define an Emissions Indicators Pyramid

The impact of energy use on the planet due to related CO2 emissions is continuously increasing, despite the adoption of efficiency and decarbonisation policies and widespread environmental awareness. Climate change mitigation will only succeed if the driving forces of consumption and emissions are deeply analysed, and effective means are provided to reverse their trends. To this aim, the Kaya Identity framework is revisited to classify indicators and decomposition studies in the literature. A comprehensive pyramid approach is proposed for the progressive disaggregation and discussion of energy and emissions changes. The approach is applied to the OECD and non-OECD to provide meaningful regional analysis of past trends and future projections according to stated policy intentions. Results show that a hopeful change has already begun in the developed region due to a sustained decrease of the energy intensity and a promising reduction of the carbon intensity. Emerging economies follow the performance of developed nations since 2013, held back by later economic development. Activity slowdown, energy conservation, renewable electrification, efficient power plants and coal phase out appear as the keystones for decarbonisation. As a result, emissions stabilisation could have already been achieved as rises in emerging countries are offset by drops in developed nations. However, more stringent climate policies, especially targeting carbon drivers, are urgently needed to enable emissions reductions compatible with a global temperature increase of 1.5°C.

A Hierarchical Pyramid for Food Waste Based on a Social Innovation Perspective

Food waste (FW) represents one of the greatest concerns facing mankind today; thus, the UN Agenda 2030 for Sustainable Development establishes that it must be halved by 2030. European Union legislators have taken part in this debate by publishing Directive 2018/851 to monitor the FW reduction goals, according to the waste hierarchical pyramid approach. At present, there are several proposed FW hierarchical pyramids, but these do not regard the associations between the level of waste-tackling strategies and social innovation (SI) models. Thus, the paper aims to build a hierarchical pyramid that considers, for each step of the food-supply chain and each level of the pyramid, all the FW social innovation models. A qualitative analysis of academic studies, institutional documents, and specific projects has been conducted. The results confirm the gap in the scientific literature and the lack of a systematic classification of SI activities to reduce FW. Furthermore, current SI practices are actually more focused on the human reuse of FW than on prevention, whereas SI models based on prevention might return the FWL issue to its systematic dimension. This information will help policymakers to reconsider the structural causes of FW inside the agro-food system, and not only its final consequences.

Lord of the Rings: Hanoi Pooling and Self-Knowledge Distillation for Fast and Accurate Vehicle Reidentification

Vehicle reidentification has seen increasing interest, thanks to its fundamental impact on intelligent surveillance systems and smart transportation. The visual data acquired from monitoring camera networks come with severe challenges, including occlusions, color and illumination changes, as well as orientation issues (a vehicle can be seen from the side/front/rear due to different camera viewpoints). To deal with such challenges, the community has spent much effort in learning robust feature representations that hinge on additional visual attributes and part-driven methods, but with the side effects of requiring extensive human annotation labor as well as increasing computational complexity. In this article, we propose an approach that learns a feature representation robust to vehicle orientation issues without the need for extra-labeled data and adding negligible computational overheads. The former objective is achieved through the introduction of a Hanoi pooling layer exploiting ring regions and the image pyramid approach yielding a multiscale representation of vehicle appearance. The latter is tackled by transferring the accuracy of a deep network to its first layers, thus reducing the inference effort by the early stop of a test example. This is obtained by means of a self-knowledge distillation framework encouraging multiexit network decisions to agree with each other. Results demonstrate that the proposed approach significantly improves the accuracy of early (i.e., very fast) exits while maintaining the same accuracy of a deep (slow) baseline. Moreover, our solution obtains the best existing performance on three benchmark datasets. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> [Online]. Available: <uri>https://github.com/iN1k1/</uri>. </fn>

Mending Broken Hearts

Mending Broken Hearts