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Search engines play a crucial role in today's Internet landscape, especially with the exponential increase in data storage. Ranking models are used in search engines to locate relevant pages and rank them in decreasing order of relevance. They are an integral component of a search engine. The offline gathering of the document is crucial for providing the user with more accurate and pertinent findings. With the web’s ongoing expansions, the number of documents that need to be crawled has grown enormously. It is crucial to wisely prioritize the documents that need to be crawled in each iteration for any academic or mid-level organization because the resources for continuous crawling are fixed. The advantages of prioritization are implemented by algorithms designed to operate with the existing crawling pipeline. To avoid becoming the bottleneck in pipeline, these algorithms must be fast and efficient. A highly efficient and intelligent web crawler has been developed, which employs the hamming distance method for prioritizing the pages to be downloaded in each iteration. This cutting-edge search engine is specifically designed to make the crawling process more streamlined and effective. When compared with other existing methods, the implemented hamming distance method achieves a high value of 99.8% accuracy.

An exploration of just how fast Daddy can crawl with a daughter on his back.

High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public's views on medical AI. Currently, the opinions of the general public on this matter remain unclear. The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients' emotional needs instead of focusing merely on technical issues.

Social loneliness is a prevalent issue in industrialized countries that can lead to adverse health outcomes, including a 26% increased risk of premature mortality, coronary heart disease, stroke, depression, cognitive impairment, and Alzheimer disease. The United Kingdom has implemented a strategy to address loneliness, including social prescribing-a health care model where physicians prescribe nonpharmacological interventions to tackle social loneliness. However, there is a need for evidence-based plans for global social prescribing dissemination. This study aims to identify global trends in social prescribing from 2018. To this end, we intend to collect and analyze words related to social prescribing worldwide and evaluate various trends of related words by classifying the core areas of social prescribing. Google's searchable data were collected to analyze web-based data related to social prescribing. With the help of web crawling, 3796 news items were collected for the 5-year period from 2018 to 2022. Key topics were selected to identify keywords for each major topic related to social prescribing. The topics were grouped into 4 categories, namely Healthy, Program, Governance, and Target, and keywords for each topic were selected thereafter. Text mining was used to determine the importance of words collected from new data. Word clouds were generated for words related to social prescribing, which collected 3796 words from Google News databases, including 128 in 2018, 432 in 2019, 566 in 2020, 748 in 2021, and 1922 in 2022, increasing nearly 15-fold between 2018 and 2022 (5 years). Words such as health, prescribing, and GPs (general practitioners) were the highest in terms of frequency in the list for all the years. Between 2020 and 2021, COVID, gardening, and UK were found to be highly related words. In 2022, NHS (National Health Service) and UK ranked high. This dissertation examines social prescribing-related term frequency and classification (2018-2022) in Healthy, Program, Governance, and Target categories. Key findings include increased \"Healthy\" terms from 2020, \"gardening\" prominence in \"Program,\" \"community\" growth across categories, and \"Target\" term spikes in 2021. This study's discussion highlights four key aspects: (1) the \"Healthy\" category trends emphasize mental health, cancer, and sleep; (2) the \"Program\" category prioritizes gardening, community, home-schooling, and digital initiatives; (3) \"Governance\" underscores the significance of community resources in social prescribing implementation; and (4) \"Target\" focuses on 4 main groups: individuals with long-term conditions, low-level mental health issues, social isolation, or complex social needs impacting well-being. Social prescribing is gaining global acceptance and is becoming a global national policy, as the world is witnessing a sharp rise in the aging population, noncontagious diseases, and mental health problems. A successful and sustainable model of social prescribing can be achieved by introducing social prescribing schemes based on the understanding of roles and the impact of multisectoral partnerships.

The internet has become an increasingly important resource for health information. However, with a growing amount of web pages, it is nearly impossible for humans to manually keep track of evolving and continuously changing content in the health domain. To better understand the nature of all web-based health information as given in a specific language, it is important to identify (1) information hubs for the health domain, (2) content providers of high prestige, and (3) important topics and trends in the health-related web. In this context, an automatic web crawling approach can provide the necessary data for a computational and statistical analysis to answer (1) to (3). This study demonstrates the suitability of a focused crawler for the acquisition of the German Health Web (GHW) which includes all health-related web content of the three mostly German speaking countries Germany, Austria and Switzerland. Based on the gathered data, we provide a preliminary analysis of the GHW's graph structure covering its size, most important content providers and a ratio of public to private stakeholders. In addition, we provide our experiences in building and operating such a highly scalable crawler. A support vector machine classifier was trained on a large data set acquired from various German content providers to distinguish between health-related and non-health-related web pages. The classifier was evaluated using accuracy, recall and precision on an 80/20 training/test split (TD1) and against a crowd-validated data set (TD2). To implement the crawler, we extended the open-source framework StormCrawler. The actual crawl was conducted for 227 days. The crawler was evaluated by using harvest rate and its recall was estimated using a seed-target approach. In total, n=22,405 seed URLs with country-code top level domains .de: 85.36% (19,126/22,405), .at: 6.83% (1530/22,405), .ch: 7.81% (1749/22,405), were collected from Curlie and a previous crawl. The text classifier achieved an accuracy on TD1 of 0.937 (TD2=0.966), a precision on TD1 of 0.934 (TD2=0.954) and a recall on TD1 of 0.944 (TD2=0.989). The crawl yields 13.5 million presumably relevant and 119.5 million nonrelevant web pages. The average harvest rate was 19.76%; recall was 0.821 (4105/5000 targets found). The resulting host-aggregated graph contains 215,372 nodes and 403,175 edges (network diameter=25; average path length=6.466; average degree=1.872; average in-degree=1.892; average out-degree=1.845; modularity=0.723). Among the 25 top-ranked pages for each country (according to PageRank), 40% (30/75) were web sites published by public institutions. 25% (19/75) were published by nonprofit organizations and 35% (26/75) by private organizations or individuals. The results indicate, that the presented crawler is a suitable method for acquiring a large fraction of the GHW. As desired, the computed statistical data allows for determining major information hubs and important content providers on the GHW. In the future, the acquired data may be used to assess important topics and trends but also to build health-specific search engines.

Self-motion can be perceived via podokinetic information, that is, based upon the movements of the legs during legged locomotion. This information can be integrated in order to perceive a path of travel through the environment (i.e., via podokinetic path integration). Two types of podokinetic information have been distinguished by analyzing the patterns of bias that result from manipulating the gait patterns used in direct-route homing tasks. Each type of podokinetic information has been associated specific groupings of gaits that support equivalent perceptual measurements of self-motion. Specifically, gaits are grouped if they can be varied across the outbound and inbound phases of a homing task (e.g., walking outbound and jogging inbound) and the accuracy of homing task performances does not differ from matched-gait control conditions. Recently, it was theorized that different types of podokinetic information are related to the differences in the kinematic form of limb motions in these groupings of gaits. Here we test an alternative hypothesis, namely that attention plays a role in selecting the type of podokinetic information. In three experiments, we manipulated the crawling gait patterns used in direct-route homing tasks. Consistent with our hypotheses, we observe that self-motion is equivalently measured via crawling movement patterns that (1) have distinct kinematic forms, but that similarly direct participants’ attention onto controlling the swing phase trajectories of their arms, and (2) have distinct inter-limb coordination patterns (i.e., pace vs. trot), but do not require attention to be specifically focused upon swing phase arm trajectories.

Cables are critical in engineering structures for load‐bearing, electronic connection, and mechanical transmission. Various cable‐crawling robots (CCRs) have been developed to perform scheduled inspection or convey supplies. Most existing CCRs are often actuated by motors and used in large‐scaled engineering structures. The heavy bodies of these CCRs can cause damage or even casualties once slippage or drop occurs. A small and lightweight CCR that can crawl on thin cables is highly demanded for safety inspection in narrow and confined inner spaces of engineering structures. Herein, a soft CCR (weight, 2.1 g; length, 43 mm) is developed by utilizing multilayered dielectric elastomer actuators. Compared with existing solutions, this CCR achieves crawling on thin cables (diameter: <1 mm) while crawling fastest (horizontal: 0.72 body length per second). The CCR is also capable of transporting objects (horizontal: 3.69 times its own weight; vertical: 0.76 times its own weight), climbing upward on a vertical cable, and locomoting across the water–air interface. The CCR is also demonstrated to crawl on a slack cable and circular/spiral cables. Finally, the soft robot, equipped with an endoscope, demonstrates inspections on a tensegrity structure as well as in an airplane wing model with a preplaced cable. A centimeter‐scaled cable‐crawling robot for thin cables is developed by utilizing dielectric elastomer actuators. This cable‐crawling robot can crawl bidirectionally at a speed of 0.72 body length per second, deliver objects up to 3.69 times its own weight, climb upward vertically, and locomote across the water‐air interface. The robot was demonstrated to perform inspections in two scenarios, open and enclosed.

Objective This study aims to evaluate the crawling motor function in children with cerebral palsy (CP) using a cluster-based muscle synergy analysis scheme. Methods Surface electromyography (sEMG) signals were recorded from 26 muscles across the body in 14 typically developing (TD) subjects and 10 children with CP while they performed eight prescribed crawling modes. The sEMG signals were preprocessed, and muscle synergies were extracted using a non-negative matrix factorization (NNMF) algorithm. A hierarchical clustering algorithm, incorporating synergy similarity constraints, was employed to cluster synergies from TD subjects performing the same crawling mode, identifying common synergies within each mode. A subsequent clustering process revealed common synergies across different modes. Using the common synergies of TD subjects as a benchmark, four evaluation metrics based on synergy similarity were developed to assess the crawling motor function in children with CP. Results The analysis successfully extracted common synergies within and across crawling modes in TD subjects. Under the condition of auditory cueing, children with CP showed a significantly lower number and similarity of common synergy structures while maintaining a comparable number of common recruitment curves relative to the TD subjects. Conclusion The cluster-based muscle synergy analysis scheme effectively assesses the crawling motor function in children with CP. Significance This research offers novel insights into the neuromuscular control mechanisms underlying crawling movements and has significant implications for understanding abnormal control mechanisms in children with CP.

Jointed exoskeletons permit rapid appendage-driven locomotion but retain the soft-bodied, shape-changing ability to explore confined environments. We challenged cockroaches with horizontal crevices smaller than a quarter of their standing body height. Cockroaches rapidly traversed crevices in 300–800 ms by compressing their body 40–60%. High-speed videography revealed crevice negotiation to be a complex, discontinuous maneuver. After traversing horizontal crevices to enter a vertically confined space, cockroaches crawled at velocities approaching 60 cm·s−1, despite body compression and postural changes. Running velocity, stride length, and stride period only decreased at the smallest crevice height (4 mm), whereas slipping and the probability of zigzag paths increased. To explain confined-space running performance limits, we altered ceiling and ground friction. Increased ceiling friction decreased velocity by decreasing stride length and increasing slipping. Increased ground friction resulted in velocity and stride length attaining a maximum at intermediate friction levels. These data support a model of an unexplored mode of locomotion—“body-friction legged crawling” with body drag, friction-dominated leg thrust, but no media flow as in air, water, or sand. To define the limits of body compression in confined spaces, we conducted dynamic compressive cycle tests on living animals. Exoskeletal strength allowed cockroaches to withstand forces 300 times body weight when traversing the smallest crevices and up to nearly 900 times body weight without injury. Cockroach exoskeletons provided biological inspiration for the manufacture of an origami-style, soft, legged robot that can locomote rapidly in both open and confined spaces.