165 research outputs found

    Food Factors: The Role of Nutritional Clarity in Enhancing Public Health Outcomes

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    In the milieu of modern food consumption, public health is beleaguered by the prevalence of processed foods and a dearth of clear nutritional information, factors that are intimately linked to the surge of global health issues. Our exploration into the interplay between food labeling, dietary education, regulatory policies, and community interventions reveals a complex landscape where each element is critical to steering public health toward a more nutritious future. Through a synthesis of multi-disciplinary research and pragmatic case studies, we dissect the efficacy of current nutritional transparency practices and propose a suite of actionable strategies aimed at catalyzing improvement. We argue for the necessity of a robust, unified approach where policy reform, comprehensive education, and grassroots empowerment converge to enact substantive change. This article posits that such a collaborative and systemic endeavor is not merely beneficial but imperative for engendering informed dietary choices, mitigating the tide of nutrition-related diseases, and ultimately sculpting a framework for sustained public health advancement. Our findings illuminate the potential for significant public health outcomes through concerted action and suggest that the path to a healthier society is paved by the collective efforts of policymakers, educators, and community advocates working in tandem to combat the complexities of the contemporary food environment

    Shift invariant sparse coding ensemble and its application in rolling bearing fault diagnosis

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    This paper proposes an automatic diagnostic scheme without manual feature extraction or signal pre-processing. It directly handles the original data from sensors and determines the condition of the rolling bearing. With proper application of the new technique of shift invariant sparse coding (SISC), it is much easier to recognize the fault. Yet, this SISC, though being a powerful machine learning algorithm to train and test the original signals, is quite demanding computationally. Therefore, this paper proposes a highly efficient SISC which has been proved by experiments to be capable of representing signals better and making converges faster. For better performance, the AdaBoost algorithm is also combined with SISC classifier. Validated by the fault diagnosis of bearings and compared with other methods, this algorithm has higher accuracy rate and is more robust to load as well as to certain variation of speed

    The seasonal cycle and break-up of landfast sea ice along the northwest coast of Kotelny Island, East Siberian Sea

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    Arctic landfast sea ice (LFSI) represents an important quasi-stationary coastal zone. Its evolution is determined by the regional climate and bathymetry. This study investigated the seasonal cycle and interannual variations of LFSI along the northwest coast of Kotelny Island. Initial freezing, rapid ice formation, stable and decay stages were identified in the seasonal cycle based on application of the visual inspection approach (VIA) to MODIS/Envisat imagery and results from a thermodynamic snow/ice model. The modeled annual maximum ice thickness in 1995-2014 was 2.02 +/- 0.12 m showing a trend of -0.13 m decade(-1). Shortened ice season length (-22 d decade(-1)) from model results associated with substantial spring (2.3 degrees C decade(-1)) and fall (1.9 degrees C decade(-1)) warming. LFSI break-up resulted from combined fracturing and melting, and the local spatiotemporal patterns of break-up were associated with the irregular bathymetry. Melting dominated the LFSI break-up in the nearshore sheltered area, and the ice thickness decreased to an average of 0.50 m before the LFSI disappeared. For the LFSI adjacent to drift ice, fracturing was the dominant process and the average ice thickness was 1.56 m at the occurrence of the fracturing. The LFSI stages detected by VIA were supported by the model results.Peer reviewe

    Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control

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    Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to comprehensively assess UAS-SfM survey capability in fast ice environments, and could serve as a reference for future improvements
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