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4,578 results for “Assistance”
GR-TR, Extended Sensors, Extended sensors for assisted border-crossing
<p>Use Case Category: <strong>Extended Sensors</strong><br> User Story: <strong>Extended sensors for assisted border-crossing</strong><br> Location: Greece - Turkey (GR-TR) cross-border corridor</p> <p>According to 3GPP TS 22.186 R16, Extended Sensors “enable the exchange of raw or processed data gathered through local sensors or live video data among vehicles, RSUs, devices of pedestrians and V2X application servers. The vehicles can enhance the perception of their environment beyond what their own sensors can detect and have a more holistic view of the local situation”.</p> <p>User Story: <strong>Extended sensors for assisted border-crossing</strong></p> <p>By utilizing the detailed data provided by the CCAM enabled truck’s sensors (Lidar, radar, GPS, etc.) as well as the data from surrounding heterogeneous information sources such as traffic cameras, road side sensors, smart phones, wearables and more, increased intelligence can be created based on a cooperative awareness of the borders’ environment. The transmission of these data over reliable, ultra-fast and ultra-low latency 5G network connection combined with modern AI and predictive analytics techniques (at the edge) allows for the creation of a virtual environment of the driver enabling various added-value functionalities. As part of this use case the functionalities that will be showcased at the Greek / Turkish borders are:</p> <p>· Border inspection preparation based on predictive CCAM truck routing</p> <p>· Secure CCAM truck border crossing with increased inspection confidence</p> <p>· Increased border cooperative environment awareness for incoming vehicles</p> <p>· Increased border personnel safety</p> <p>The above functionalities will showcase a significant minimization of inspection times at all European “hard” borders through the collaboration feasible of different 5G network operators which could even offer “zero touch” inspection (no human intervention needed) in optimal cases. The same solution offers increased cooperative awareness for passing vehicles at the chaotic border-crossing environment and taking advantage of the CCAM functionalities of vehicles, such as automated braking, to prevent accidents involving border personnel (customs agents, police officers).</p> <p>This intelligent border control functionality may be realized through the following trial set-up. Data originating from the truck sensors in areas around the borders are transmitted over 5G networks and analysed in a cloud-based AI platform after fusion. Once a trajectory towards the border crossing is predicted, special measures may be taken to facilitate further exchange of information and immediate response to predicted events (e.g. the assisted driving application may be downloaded from the Cloud to the edge server to minimize latency, a slice may be provisioned towards a cloud server on the neighbouring county’s PLMN, etc.). An exchange of available information is commencing towards the border authorities via 5G network (mMTC type of communication from the truck OBU itself or even from the cargo which may be equipped with relevant sensors / transmitters (e.g. NB-IoT)) which will facilitate the border inspection and prepare the customs agents for the appropriate checks. All relevant information is transmitted to the edge / MEC servers available at the trial site where they are processed by the downloaded AI/ML platform instantiating this functionality.</p> <p>Additional information can be exchanged over the 5G networks of the neighbouring countries facilitating the acquisition of relevant information about the specific truck (e.g. driver’s information, travel history, cargo inventory, etc.) which could speed-up the control process. Extra security and control measures can be deployed which are controlled and managed through 5G networks such as drones, street cameras, thermal or x-ray cameras, etc. and which can feed large amounts of data (eMBB functionality) in a very short amount of time. In the case that all the acquired data from on-board as well as surrounding sensors / devices agree with the information that is fetched by national archives regarding this truck (and potentially its driver) and provided material (video, thermal imaging, x-ray imaging) clears the truck of any suspicion, then a case of “zero touch” inspection may be realized in which case the truck may be allowed to cross-the border without any manual inspection performed on it.</p> <p>Additionally, the data originating from other vehicles, road side infrastructure, smart phones and wearables may also be fused and analysed at the edge generating a “live” cooperative update of the surrounding environment which can be fed on to the vehicles navigation system, thus increasing the environmental awareness of the vehicle (covering blind spots, pedestrian locations and trajectories, assigned inspection lane by the authorities, etc.) and actively contributing to the safety of the border ground personnel (i.e. automated trajectory alignment or braking upon detection of a potential incident).</p> <p>In all cases, the same services continue being provided as the truck passes the border from the neighbouring country’s network, based on exchanged information in such inter-PLMN scenarios. Service continuity during the inter-PLMN HO is of utmost importance in such cases, and the existence of such intelligence deployed at the edge close to the border greatly facilitates continuous service by identifying imminent HO’s and helping the MNOs prepare for it based on the available information. This could lead to the provisioning of a roaming slice before the HO even takes place.</p> <p>To implement this use case a laptop onboard the truck will be acting as the UE/gateway that will connect truck and/or cargo devices/systems (e.g. additional sensors deployed in the cargo hold of the truck) to the rest of the system via 5G connectivity (and 4G / NB-IoT during testing & development). These additional sensors are crucial in this case since they have the capability of raising alarms by cross-checking their data with nominal values. For instance, a thermal camera (or even CO2 sensor) installed in the cargo hold of the truck may provide indications of a human presence in the cargo hold (smuggling / human trafficking attempt) which will enable alerted reaction by the border officers upon the arrival of the truck at the border.</p> <p>Additional measures may take place in case contradicting information is gathered regarding a truck, in which case drones equipped with cameras for live feed may be deployed or thermal or x-ray imaging may be requested to rule out the possibility of smuggling goods and people. The AI based inspection functionality residing in the edge platform will fuse all available information from these heterogeneous sources (potentially originating from different 5G networks in the case of a cross-border scenario) and will locate potential inconsistencies, assigning a certain risk factor to each truck which will affect the degree (and thoroughness) to which border agents will perform a manual inspection. For the realization of this trial a single autonomous truck is needed equipped with additional sensors.</p>
Solar-assisted approach for the synthesis of nanoadsorbents for biogas desulfurization using wastes
<p>Raw data of scanning electron images obtained within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID262</p>
Data for: Knowledge, attitudes, and practices of food handlers in food assistance programs: A district study
<p>Objective: This study aimed to assess food handlers' knowledge, attitudes, and self-reported practices towards the safer donated foods.</p> <p>Design: This cross-sectional study was conducted within a 5-month consecutive time period in 2021.</p> <p>Setting: This study was conducted in the eThekwini District, South Africa.</p> <p>Participants: A convenience sampling-based cross-sectional survey of food handlers (n=252) served as the study's main source of data. A total of eight study sites across five municipal planning regions (MPRs) of eThekwini District were visited. The principal investigator administered the validated structured standardised questionnaire, using participant's language of choice, which was either English or isiZulu. All the questions in the questionnaire were asked in exactly the same manner, following the same flow, in order to avoid bias and ensure consistency. Furthermore, the close-ended nature of questions in the questionnaire mitigated the risks of question manipulation. The questionnaire was adapted from the World Health Organization's (WHO) five keys to a safer food manual.</p> <p>Primary and secondary outcome measures: The primary outcome was the knowledge, attitudes, and self-reported practices towards the safer donated foods. The secondary outcomes were the sanitary conditions of infrastructure and facilities, food handlers' observed food handling behaviour, and nutritional information/labeling.</p> <p>Results: The results show that the food handlers had good knowledge, positive attitude, and acceptable behaviours towards safe food handling and general hygiene, with mean scores (SD) of 62.8 (14.6), 92.5 (14.1), and 80.4 (13.3), respectively. Significant correlations were found between knowledge and attitudes (p=0.001), knowledge and practices (p<0.001), and attitudes and practices (p=0.02). However, the correlations between knowledge versus attitude and attitude versus practice were poor (spearman's rho<0.3), and the association between knowledge versus practice was moderate (0.3–0.7). The majority of food handlers (92.5%) did not understand the value of thorough cooking and temperature control. About 53% of respondents acknowledged to never adequately reheating cooked meals, 5% did not see the significance of preventing cross-contamination, and 5% were unsure.</p> <p>Conclusions: Despite the relatively positive KAP levels of the food handlers, safe food handling and hygiene practices, such as thorough cooking and temperature control, properly reheating cooked meals, and taking precautions to prevent cross-contamination, require some emphasis.</p>
Supplementary material for "How does AI assist scientific research domains?"
<p>Supplementary material for "How does AI assist scientific research domains?" </p>
Supplementary material 1 from: Seropian A, Arsenashvili E, Bulbulashvili N, Shubashishvili A, Iankoshvili G, Todua M, Ananiashvili A, Japarashvili S, Chkhartisvhili T, Memishishi A, Balkhamishvili S, Chitadze B, Karalashvili E, Mumladze L, Hein N, Rulik B (2023) Into the unknown: the first barcode-assisted checklist of Psocoptera (Insecta, Psocodea) of Georgia with a census on country species richness. ZooKeys 1168: 77-105. https://doi.org/10.3897/zookeys.1168.103666
Details for sampling locations
Assisted Extraction with Cyclodextrins as a Way of Improving the Antidiabetic Activity of Actinidia Leaves
<p>Data includes source data and the publication to which it is related</p>
Fig. 3 in Visualizing the spatial distribution of metabolites in Clausena lansium (Lour.) skeels using matrix-assisted laser desorption/ionization mass spectrometry imaging
Fig. 3. MALDI-MS spectrum of C. lansium fruit extract in positive ion mode.
Magma-assisted Continental Rifting: The Broadly Rifted Zone in SW Ethiopia, East Africa
<p>Supporting information accompanying a paper entitled "Magma-assisted Continental Rifting: The Broadly Rifted Zone in SW Ethiopia, East Africa" submitted to AGU publications, Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He data from the southern Main Ethiopian Rift, Gofa Province and Chew Bahir Basin, as well as additional QTQt thermal models that are discussed in the paper.</p> <p>Table 1 and 2 contain apatite (AHe) (U-Th-Sm)/He (Table 1) and zircon (ZHe) (U-Th-Sm)/He (Table 2) single grain ages. Sampling locations are indicated in figure 2a in the main text. Data table 1 and 2 are similar with Table 1 and 2 in the main text. </p> <p>Table 3: Show Onset of cooling and cooling rate estimated for each model.</p> <p>Supplementary information S1A and S1B show model input data and modeling strategy for QTQt (S1A1) and HeFTy (S1A2). </p> <p>Supplementary information S2A1–S2A4: Show AHe single grain ages plot against effective uranium concentration (eU) and grain size (ESR) colored with grain geometry (1, 2 or 0 terminations). Single grain ages excluded for QTQt modeling are denoted as asterisk. S2A5: AHe single grain ages plot against effective uranium concentration (eU) and grain size (ESR) colored with grain geometry (1, 2 or 0 terminations).</p> <p>Supplementary information S3A1 shows all QTQt thermal modeling results for individual and elevation profile samples. The expected thermal history represents the weighted mean of all the models sampled (solid lines). For elevation profile samples, thermal history for the topmost and lowermost samples plotted in blue and red, respectively. For all models, the 95% confidence interval drawn as stippled lines. For elevation profile samples, thermal histories for samples in between the top and lower are drawn in gray. </p> <p>Supplementary information S3A2: Shows individual HeFTy inverse and forward modeling results. Excluded single grain ages are denoted as transparent. Accepted thermal histories are indicated as green and the ''good'' fits are light purple. The orange boxes are time-temperature constraints. Note: M-1A and M-1B are model results for sample M-1. We rerun the same model to increase the number of "good" fit time temperature history paths for model M-1A..</p>
Adapting the Shed-MEDS Deprescribing Intervention to Dementia Care in Assisted Living
ClinicalTrials.gov study NCT05956665. IPD Sharing: YES. Countries: 1. Publications: 0.
Impact of Catheter Size on Peri-Operative Pain After Robotic Assisted Laparoscopic Prostatectomy
ClinicalTrials.gov study NCT04098809. IPD Sharing: NO. Countries: 1. Publications: 0.
Operator Radiation Exposure During Image-Guided Robotic-Assisted Bronchoscopy: A Multicenter Prospective Cohort Study
ClinicalTrials.gov study NCT07318402. IPD Sharing: YES. Countries: 1. Publications: 0.
Computer Assisted Volumetric Analysis of CT-Identified Metastatic Pulmonary Or Hepatic Lesions
ClinicalTrials.gov study NCT00676455. IPD Sharing: Not stated. Countries: 1. Publications: 0.
CorPath™ 200: Robotically-Assisted Percutaneous Coronary Intervention (PCI)
ClinicalTrials.gov study NCT01076036. IPD Sharing: Not stated. Countries: 1. Publications: 0.
AssocIation of PULSatility and Occurrence of Complications Related to Mechanically Assisted Circulatory Support
ClinicalTrials.gov study NCT04951999. IPD Sharing: YES. Countries: 1. Publications: 0.
Additional MDMA-assisted Therapy for People Who Relapsed After MDMA-assisted Therapy Trial
ClinicalTrials.gov study NCT01458327. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Conscious Dying/Conscious Living: Ketamine-Assisted Psychotherapy
ClinicalTrials.gov study NCT05214417. IPD Sharing: NO. Countries: 0. Publications: 73.
Diabetes Data-Assisted Remission Trial (DDART)
ClinicalTrials.gov study NCT04663061. IPD Sharing: YES. Countries: 1. Publications: 0.
Computerized Cognitive Behavioral Therapy Assisted Life Management for Pain in Sickle Cell Disease
ClinicalTrials.gov study NCT03029468. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Patient-Assisted Compression in 3D - Impact on Image Quality and Workflow
ClinicalTrials.gov study NCT03456427. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assist-Knee: Energy-Harvesting Knee Prosthesis
ClinicalTrials.gov study NCT04023045. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.