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Mechanic is the branch of physics that is responsible for the study of the movement and balance of bodies in a system, within this can also be considered the bodies at rest. For the first case the scientist Isaac Newton considered the study of the movement of bodies, where he defines that any physical system which is subjected to different actions, as long as such actions are not nullified, will result in a change in the state of the body. The force is the magnitude that quantitatively measures the intensity, direction and direction of that interactions, having said that, if the summation of all forces of a system is different from zero, it can be concluded that the system will be in motion and therefore have an associated acceleration. Based on the above definition, was executed by the research group Design and Construction of Prototypes for Demonstration Experiments (DICOPED) in the design and construction of a prototype that allows to verify the Newton’s Second Law, looking to develop an autonomous, robust and low-cost prototype, that allows the use of the technology and tools present in the means, encourage and facilitate the teaching of basic concepts but no less important of physics to students of basic and higher education.

子午工程二期电离层TEC与闪烁接收机布网采用国产设备. 经过连续3天的运行测试，本文以同台站同天线的国外对比设备PolaRx5数据为参考，对样机的数据进行质量评估. 对于科学研究，数据的连续性和有效性，VTEC、幅度闪烁指数、相位闪烁指数这三项数据的精度是最重要的指标. 本文根据科研场景设计了这些方面的数据比较标准，评估样机的数据质量，并对两种设备探测结果特征的原因进行了分析，为数据准确性的评估提供参考和借鉴.

Background The Guideline Elements Model (GEM) has been widely used to translate natural language clinical practice guidelines (CPGs) into clinical decision support (CDS) using a highly replicable, guideline-centric approach. A CPG recommendation-to-CDS translation process, which uses GEM-processed content to support an oncology rapid learning system (RLS) prototype, is examined here. Objectives To develop rules for a breast cancer-specific CDS prototype using GEM-processed guideline content. Methods We created five breast cancer patient scenarios with expert input from oncologists based on nine published CPGs. Using the Yale Center for Medical Informatics-developed GEM Cutter III editor, we parsed the narrative CPG recommendations into an XML-based, machine-readable format. GEM-processed content was then encoded into a Drools business rule management system to develop an integrated platform prototype for rules, workflow, and event processing. We used meta-tags to create value sets for key components of each recommendation by selecting terms from UMLS vocabularies, including SNOMED CT and LOINC. Results Forty-five recommendations spanning nine CPGs were processed and converted into Drools rules. We identified 138 decision variables and 91 actions within the selected recommendations. From these, we encoded 148 concepts associated with value set meta-tags and 238 decision rules. Discussion The level of difficulty required to encode the recommendations was directly related to the specificity, complexity, and decidability of each recommendation; there was significant variability among the recommendations. Implications for Guideline Developers/Users CPG developers may need new processes in order to optimise recommendations for incorporation into CDS systems.

The task of few-shot action recognition aims to recognize novel action classes using only a small number of labeled training samples. How to better describe the action in each video and how to compare the similarity between videos are two of the most critical factors in this task. Directly describing the video globally or by its individual frames cannot well represent the spatiotemporal dependencies within an action. On the other hand, naively matching the global representations of two videos is also not optimal since action can happen at different locations in a video with different speeds. In this work, we propose a novel approach that describes each video using multiple types of prototypes and then computes the video similarity with a particular matching strategy for each type of prototypes. To better model the spatiotemporal dependency, we describe the video by generating prototypes that model the multi-level spatiotemporal relations via transformers. There are a total of three types of prototypes. The first type of prototypes are trained to describe specific aspects of the action in the video e.g., the start of the action, regardless of its timestamp. These prototypes are directly matched one-to-one between two videos to compare their similarity. The second type of prototypes are the timestamp-centered prototypes that are trained to focus on specific timestamps of the video. To deal with the temporal variation of actions in a video, we apply bipartite matching to allow the matching of prototypes of different timestamps. The third type of prototypes are generated from the timestamp-centered prototypes, which regularize their temporal consistency while serving as an auxiliary summarization of the whole video. Experiments demonstrate that our proposed method achieves state-of-the-art results on multiple benchmarks.

Few-Shot segmentation (FSS) is a marginally explored but challenging task that aims to identify unseen classes of objects with only a handful of densely annotated samples. By and large, current FSS approaches perform meta-inference based on the prototype learning paradigm, which fails to fully exploit the underlying information from support image-mask pairs, resulting in multiple segmentation failures, such as incomplete objects, ambiguous boundaries, and distractor activation. For this purpose, a flexible and generic framework is developed in the spirit of divide-and-conquer. We first implement a novel self-reasoning scheme on the labeled support image, and then divide the coarse segmentation mask into several regions with different properties. By employing effective masked average pooling techniques, a series of support-induced proxies are generated on the fly, each performing a specific role in conquering the above challenges. Furthermore, we meticulously devise the parallel decoder structure and semantic consistency regularization to eliminate confusion and enhance discrimination. In stark contrast to conventional prototype-based approaches, our proposed divide-and-conquer proxies (DCP) can provide “episode” level guidelines that go well beyond the object cues themselves. Extensive experiments are conducted on FSS benchmarks to verify the effectiveness, including standard settings as well as cross-domain settings. In particular, we propose a temporal DCP and successfully extend it to video object segmentation via memory repository and progressive propagation, illustrating the high scalability. The source codes are available at https://github.com/chunbolang/DCP.

The present work introduces a modern cost-effective data glove prototype designed for versatility, particularly in the fields of virtual reality, remote control or medical rehabilitation. Using an array of flex sensors, the glove measures finger flexion and extension angles in real-time. Data is processed through a microcontroller, translating sensor readings into accurate angular measurements, while Bluetooth communication ensures low-latency and stable transmission. A Butterworth filter is applied to minimize noise. The presented device shows potential for further improvements through automatic calibration and user-specific customization.

Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo labels, and source transferability discrimination, potentially resulting in suboptimal adaption performance. Therefore, we address these issues by proposing a prototype aggregation method that models the discrepancy between source and target domains at the class and domain levels. Our method achieves domain adaptation based on a group of prototypes (i.e., representative feature embeddings). A similarity score-based strategy is designed to quantify the transferability of each domain. At the class level, our method quantifies class-specific cross-domain discrepancy according to reliable target pseudo labels. At the domain level, our method establishes distributional alignment between noisy pseudo-labeled target samples and the source domain prototypes. Therefore, adaptation at the class and domain levels establishes a complementary mechanism to obtain accurate predictions. The results on three standard benchmarks demonstrate that our method outperforms most state-of-the-art methods. In addition, we provide further elaboration of the proposed method in light of the interpretable results obtained from the analysis experiments.

The main objective of this research was to design a WSD (word sense disambiguation) prototype model for Amharic words using semi-supervised learning method to extract training sets which minimizes the amount of the required human intervention and it can produce considerable improvement in learning accuracy. Due to the unavailability of Amharic word net, only five words were selected. These words were atena (...), derese (...), tenesa (...), bela (...) and ale (...). A separate data sets using five ambiguous words were prepared for the development of this Amharic WSD prototype. The final classification task was done on fully labelled training set using Adaboost, bagging, and AD tree classification algorithms on WEKA package.

•We propose XADLiME as a novel explainable predictive framework to tackle ADPM by estimating an explainable AD likelihood map aided by a set of clinically-guided prototypes, offering succinct interpretation while maintaining the diagnostic performance for downstream tasks.•Through the proposed ADPEN, we simultaneously discover a hypothetical AD progression manifold portrayed with a set of topological-aware prototypes in an unsupervised manner, eliminating the necessity to predetermine the number of class-specific prototypes.•We promote the framework’s interpretability from two viewpoints: (i) A clinical viewpoint where we merge decoded clinical values of each prototype with the estimated AD likelihood map over a brain sMRI to highlight the current clinical states of a subject. (ii) A morphological viewpoint where we organize a set of prototypical brains to investigate the brain changes of an unseen sMRI scan.•We demonstrate extensive interpretability analysis for the potential utilization of XADLiME in terms of longitudinal clinical applications for the estimated AD likelihood map on several progression study cases. Identifying Alzheimer’s disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.

A developing trend in stormwater treatment and management is the use of green technologies. Plant biofilters have been gaining increasing use in support of green technology objectives. This technical note reports on the development and preliminary testing of a laboratory-scale plant biofilter prototype for ammonia removal using a South African native plant species (Juncus efusus ). The prototype design was based on a conceptual model for nitrogen fixation, plant uptake, bacterial nitrification and soil sorption. Additionally, a plug compartment was incorporated into the design to simulate plug flow as part of the conceptual model. Biofilter models with and without inoculated bacteria were compared. Ammonia reduction, nitrite and nitrate formation were observed. Results showed that the inoculated plant biofilter performed best, with an average of 61% reduction in ammonia within the filter compared to 15% in the normal plant biofilter. The incorporation of a plug compartment aided in slowing down the ammonia infiltration rate, increasing the retention time, and allowing for nitrification to occur.