Target Location Research Articles

Installation of Public Access Defibrillators for Out-of-Hospital Cardiac Arrests: Identifying Suitable Locations by Using a Geographic Information System.

Survival following an out-of-hospital cardiac arrest depends on prompt defibrillation. Despite the efforts made to install automated external defibrillators (AEDs) in crowded areas, their usage rate remains suboptimal. This study evaluated the efficiency of installing AEDs at key landmarks in Taoyuan City to enhance accessibility and usage. This retrospective cohort study analyzed nontraumatic public out-of-hospital cardiac arrest cases in Taoyuan City from 2017 to 2021, using data from the Taoyuan Fire Department and a regional registry. AED data were collected for 1163 devices. A geographic information system mapped target locations within the city, and real-world walking routes were examined to assess coverage. The primary outcome was actual coverage and the coverage efficiency ratio, calculated as the actual coverage divided by the number of facilities at a location. The coverage efficiency ratio compared the coverage efficiency of target locations with existing public access defibrillators (PADs). Top locations for superior coverage in both downtown and outside downtown areas were bus stops and convenience stores (7-Eleven and FamilyMart), which outperformed existing PADs. Convenience stores had a higher coverage efficiency ratio than the public service sector. Bus stops showed high AED usage rates before ambulance arrival. The current PAD locations in Taoyuan City offer limited coverage, which highlights the need for strategically installed AEDs, particularly in convenience stores. Policymakers should consider using the cultural relevance and accessibility of convenience stores, particularly 7-Eleven branches, to enhance AED usage rates.

Investigation of pelvic floor influence on prostate displacement in image-guided radiotherapy.

The uncertainty of target location during prostate cancer radiotherapy plays an important role in accurate dose delivery and radiation toxicity in adjacent organs. This study analyzed displacement correlations between the prostate and pelvic floor. We retrospectively analyzed registration results from 467 daily cone-beam computed tomography (CT) in 12 patients with prostate cancer who received radiation therapy. We analyzed prostate displacement and the pelvic floor relative to the pelvic bone's anatomy in the translational and rotational directions and identified statistical correlations. The systematic (Σ) and random (σ) displacements of the prostate in the three translational directions, anterior-posterior (AP), superior-inferior (SI), and right-left (RL), were 1.49 ± 1.45, 2.10 ± 1.40, and 0.24 ± 0.53 mm, respectively, and in the rotational directions of the pitch, roll, and yaw were 2.10 ± 2.02°, 0.42 ± 0.74°, and 0.42 ± 0.64°, respectively. The pelvic floor displacements were 2.37 ± 1.96, 2.71 ± 2.28, and 0.47 ± 0.84 mm in the AP, SI, and RL directions, respectively, and 0.93 ± 1.49°, 0.98 ± 1.28 °, and 0.87 ± 0.94° in the pitch, roll, and yaw directions, respectively. Additionally, there were statistically significant correlations between the displacement of the prostate and pelvic floor in the AP and SI directions, with correlation coefficients (r) of 0.74 (p < 0.001) and 0.69 (p < 0.001), respectively. The movement of the pelvic floor may be an important factor that causes prostate displacement, affecting the accuracy of radiotherapy. Therefore, it is necessary to take appropriate measures to ensure that the pelvic floor muscle tension is as consistent as possible in the treatment' CT scan and daily treatment.

Anisotropies related to representational gravity.

Four experiments examined whether representational gravity, in which memory for the location of a previously-viewed target is displaced in the direction of implied gravitational attraction, occurs uniformly across a target. Participants viewed stationary, vertically-moving, or horizontally-moving targets of different sizes and at different heights within the picture plane. After a target vanished, participants indicated the remembered location of the top edge or bottom edge of that target. Significant anisotropies were found, as the remembered location of the top edge was displaced downward, whereas the remembered location of the bottom edge was not displaced or was displaced upward. Anisotropies along the vertical axis were not influenced by whether participants knew prior to target presentation which edge to remember or by whether targets were stationary or moved vertically, although there was a trend foranisotropies along the vertical axis to be reduced when targets moved horizontally. Larger targets and targets higher in the picture plane resulted in larger displacement when targets were stationary, although effects of size and height were diminished when targets were moving. If the top edge and bottom edge of a target are considered analogous to the trailing edge and leading edge of a moving target, respectively, then anisotropies related to representational gravity are similar to anisotropies previously reported for representational momentum for horizontally-moving targets (as direction of implied gravitational attraction is downward). The existence of such anisotropies has implications for the representation of space and for the localization of and interaction with stimuli in the environment.

Recruitment and retention of new doctors in remote and Aboriginal medical services through the Remote Vocational Training Scheme's Targeted Recruitment Strategy: a focus group study.

To explore the results of a targeted recruitment strategy designed to attract and retain new doctors in remote and Aboriginal medical services where they can access Remote Vocational Training Scheme (RVTS) training and support to qualify as general practitioners. Two 2-hour purposeful online focus groups on the RVTS' Targeted Recruitment Strategy. Five participants and nine stakeholders with knowledge and experience of the strategy. Data were transcribed and deductively and inductively coded for themes including insights from separate project reference and stakeholder advisory groups. Perspectives of the strategy. The overarching theme was that the strategy is useful for attracting and retaining new general practitioners in areas of high need and is potentially scalable. Since 2018, 20 new doctors were recruited via the Targeted Recruitment Strategy and six of them completed the RVTS program. The strategy could better differentiate target locations because increasing communities are experiencing major general practice workforce shortages. The package of employment and training could also be more clearly defined for participants, nationally marketed and collaboratively implemented. Further, more site accreditation and ongoing risk and quality review is needed, along with intense early supports for participants who are new to both the community and general practice work. The Targeted Recruitment Strategy is still maturing but the early results suggest it is a unique and proactive model for attracting and improving access to general practitioners in places with high needs. It could be strengthened through formal agreements between communities and agencies, ensuring coordinated implementation, clarifying roles and responsibilities, and developing clear pathways for risk and quality management.

Rapid-Learning Collaborative Pushing and Grasping via Deep Reinforcement Learning and Image Masking

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

Wideband beam domain sparse Bayesian learning passive focusing localisation algorithm

AbstractTo address the challenges of large‐aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the issues of massive computational requirements for two‐dimensional SBL scanning and increased localisation errors due to interference energy leakage. The wideband beam domain SBL focusing localisation algorithm is developed by constructing an azimuth‐range two‐dimensional transformation matrix to preprocess array data, which effectively reduces the computational load of SBL processing while suppressing strong interference energy leakage in passive sonar operating environments, thus improving the range resolution and parameter estimation accuracy of focusing localisation. Simulation and sea trial data analyses demonstrate the feasibility of the proposed algorithm, with results indicating its superior performance compared to existing algorithms.

Infants Produce Optimally Informative Points to Satisfy the Epistemic Needs of Their Communicative Partner.

Pragmatic theories assume that during communicative exchanges humans strive to be optimally informative and spontaneously adjust their communicative signals to satisfy their addressee's inferred epistemic needs. For instance, when necessary, adults flexibly and appropriately modify their communicative gestures to provide their partner the relevant information she lacks about the situation. To investigate this ability in infants, we designed a cooperative task in which 18-month-olds were asked to point at the target object they wanted to receive. In Experiment 1, we found that when their desired object was placed behind a distractor object, infants appropriately modified their prototypical pointing to avoid mistakenly indicating the distractor to their partner. When the objects were covered, and their cooperative partner had no information (Experiment 2) or incorrect information (Experiment 3) about the target's location - as opposed to being knowledgeable about it - infants pointed differentially more often at the target and employed modified pointing gestures more frequently as a function of the amount of relevant information that their partner needed to retrieve their desired object from its correct location. These findings demonstrate that when responding to a verbal request in a cooperative task 18-month-old infants can take into account their communicative partner's epistemic states and when necessary provide her with the relevant information she lacks through sufficiently informative deictic gestures. Our results indicate that infants possess an early emerging, species-unique cognitive adaptation specialized for communicative mindreading and pragmatic inferential communication which enable the efficient exchange of relevant information between communicating social partners in cooperative contexts.

Preselection of potential target spaces based on partial information by the supplementary eye field

Before selecting a saccadic target, we often acquire partial information about the location of the forthcoming target and preselect a region of visual space even before the target becomes visible. To determine whether the supplementary eye field (SEF) represents information signifying the potential target space, we examined neuronal activity in the SEF of monkeys performing a behavioral task designed to isolate the process of visuospatial preselection under uncertainty from the process of selecting a specified location. Our data showed that the activity of SEF neurons represented information about the potential target space instructed by symbolic cues. Increased activity of visuospatially selective SEF neurons encoded the potential target space, which could be a mechanism facilitating subsequent selection of an appropriate target. Furthermore, electrical stimulation of the SEF during the preselection period disrupted subsequent target selection. These results demonstrate that the SEF contributes to the preselection of potential target spaces based on partial information.

Trajectory mapping through channel state information by triangulation method and fine-tuning

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping.

Evaluation of Autonomous Navigation and Path Accuracy for Unmanned Surface Vehicles Using IMU Sensor and GPS Data

The development of autonomous systems for environmental monitoring is becoming increasingly important. This project focuses on creating a robotic marine vessel, Aqua Sense, for efficient water quality monitoring. Existing methods are often labour-intensive, time-consuming, and lack real-time data capability. The objective is to develop a robotic vessel with autonomous navigation for water quality applications. A comprehensive design approach, including a literature review and careful component selection, was employed. Key components include the Arduino Mega, Blue Robotics T200 thruster, GlobalSat BU-353N5 GPS sensor, BNO055 Inertial Measurement Unit (IMU), and a Li-po 11.1v battery. In this paper, several field trials data collection is presented to evaluate the navigation algorithm. These findings validate the feasibility of Aqua Sense for real-world applications. The varying distance errors at points B and C demonstrate an error range, with a maximum error of approximately 1.130m at point B (about 0.11% of the target location) and a significant variation at point C, reaching up to 1.660m (approximately 0.15%).

A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data

Location data is frequently collected from populations and shared in aggregate form to guide policy and decision making. However, the prevalence of aggregated data also raises the privacy concern of membership inference attacks (MIAs). MIAs infer whether an individual's data contributed to the aggregate release. Although effective MIAs have been developed for aggregate location data, these require access to an extensive auxiliary dataset of individual traces over the same locations, which are collected from a similar population. This assumption is often impractical given common privacy practices surrounding location data. To measure the risk of an MIA performed by a realistic adversary, we develop the first Zero Auxiliary Knowledge (ZK) MIA on aggregate location data, which eliminates the need for an auxiliary dataset of real individual traces. Instead, we develop a novel synthetic approach, such that suitable synthetic traces are generated from the released aggregate. We also develop methods to correct for bias and noise, to show that our synthetic-based attack is still applicable when privacy mechanisms are applied prior to release. Using two large-scale location datasets, we demonstrate that our ZK MIA matches the state-of-the-art Knock-Knock (KK) MIA across a wide range of settings, including popular implementations of differential privacy (DP) and suppression of small counts. Furthermore, we show that ZK MIA remains highly effective even when the adversary only knows a small fraction (10%) of their target's location history. This demonstrates that effective MIAs can be performed by realistic adversaries, highlighting the need for strong DP protection.

Stretching the limits of automated symbolic orienting

Arrows trigger reflexive shifts of attention and instantiate the prototypical example of automated symbolic orienting. We conducted four experiments to further test the boundary conditions of this phenomenon. Participants discriminated a peripheral target while spatially uninformative arrows, pointing leftwards or rightwards, appeared at fixation. In all experiments, arrow direction could either randomly vary (intermixed condition) or be kept constant within a block of trials (blocked condition). Moreover, in Experiments 3 and 4, a direction word presented at the beginning of the trial informed participants about the target location with 100% certainty. Overall, the results highlighted a significant arrow-driven orienting effect in both the blocked and the intermixed conditions. The present findings support the notion that automated symbolic orienting is resistant to suppression in that it endures even when the context should stress the uninformative nature of the arrows while also creating ideal conditions to boost participants’ tendency to ignore them.

Optimized interferential stimulation of human brains is sensitive to head anatomies and target locations

Optimized interferential stimulation of human brains is sensitive to head anatomies and target locations

Olfactory-based powered descent guidance for Mars methane plume exploration in long-time-average wind environment

This paper investigates an olfactory-based powered descent guidance method that enable a lander to autonomously locate and target the vent source of any methane plume in long-time-average wind environment on Mars. The episodic methane plumes emanating from the Martian surface invite the possibility of direct access to the subsurface methane reservoir to acquire pristine organic materials. Constrained by the insufficient knowledge of the accurate plume vent location, Mars landers must infer the target location — the plume source — autonomously during the powered descent. However, existing powered descent guidance methods require the target location as a terminal constraint, rendering them ineffective in plume exploration missions. We propose an olfactory-based powered descent guidance method that could steer a lander to target the methane vent source by tracking the gradient of methane concentration in long-time-average wind environment, imposing sub-optimal fuel consumption. A novel olfactory navigation method based on tracking the negative logarithmic concentration gradient, and the corresponding gradient measurement method, is proposed. By implementing the negative logarithmic gradient field, gradient-dependent dynamic equations for the powered descending lander are proposed. The gradient-dependent equations provide a way to transform the original optimal guidance problem, which is non-convex and terminal-free, into a convex and terminal-fixed problem. A suboptimal guidance law, similar to E-guidance, is then derived by solving this problem. The proposed guidance could target the vent source of any methane plume in long-time-average wind environment, imposing modest fuel consumption by feeding back the concentration gradient. Numerical simulations illustrate that, in comparison to the terminal-fixed E-guidance and the open-loop fuel-optimal guidance, the lander imposes additional fuel consumption of 2.57% and 12.15% respectively, using the proposed guidance law.

ISAC From the Sky: UAV Trajectory Design for Joint Communication and Target Localization

ISAC From the Sky: UAV Trajectory Design for Joint Communication and Target Localization

Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy

Purpose: We propose a tumor tracking framework for 2D cine MRI based on a pair of deep learning (DL) models relying on patient-specific (PS) training.Methods and materials: The chosen DL models are: 1) an image registration transformer and 2) an auto-segmentation convolutional neural network (CNN). We collected over 1,400,000 cine MRI frames from 219 patients treated on a 0.35 T MRI-linac plus 7,500 frames from additional 35 patients which were manually labelled and subdivided into fine-tuning, validation, and testing sets. The transformer was first trained on the unlabeled data (without segmentations). We then continued training (with segmentations) either on the fine-tuning set or for PS models based on eight randomly selected frames from the first 5 s of each patient's cine MRI. The PS auto-segmentation CNN was trained from scratch with the same eight frames for each patient, without pre-training. Furthermore, we implemented B-spline image registration as a conventional model, as well as different baselines. Output segmentations of all models were compared on the testing set using the Dice similarity coefficient (DSC), the 50% and 95% Hausdorff distance (HD50%/HD95%), and the root-mean-square-error of the target centroid in superior-inferior direction (RMSESI).Results: The PS transformer and CNN significantly outperformed all other models, achieving a median (inter-quartile range) DSC of 0.92 (0.03)/0.90 (0.04), HD50% of 1.0 (0.1)/1.0 (0.4) mm, HD95% of 3.1 (1.9)/3.8 (2.0) mm and RMSESI of 0.7 (0.4)/0.9 (1.0) mm on the testing set. Their inference time was about 36/8 ms per frame and PS fine-tuning required 3 min for labelling and 8/4 min for training. The transformer was better than the CNN in 9/12 patients, the CNN better in 1/12 patients and the two PS models achieved the same performance on the remaining 2/12 testing patients.Conclusion: For targets in the thorax, abdomen and pelvis, we found two PS DL models to provide accurate real-time target localization during MRI-guided radiotherapy.

Maximizing Date Palm Yield: Automatic Extension Sprinklers for Enhanced Aerial Pollination

Abstract Date palms require pollen transfer from male to female palms in order to yield a fruitful harvest. The traditional method of accomplishing this artificial cross-pollination involved climbing each palm tree and physically putting male flower spikelets on female inflorescences. This process required a great deal of labor and was time-consuming. Due to developments in farming practices during the modern age, farmers have experimented with mechanical pollination systems. Recently, to pollinate date palms very quickly, safely, affordably, and with a minimum of risk of injury, agricultural drones have been used. In this research, experiments on drone dispersal pollination are conducted on three Omani date palm cultivars—Al Naghal, Al Khanezi, and Al Kasab. The applicability of this method is evaluated by comparing its fruit yield properties to those of the conventional method, which is commonly employed, and the less popular machine/hand pollination methods. The main limitations of drone dispersal pollination are, its inability to disperse exact amount of pollen to the target location of female inflorescences, especially in shorter palms; wastage of pollens and environmental pollution. The study recommends the use of a novel design of extendable pollen sprinkler for target-specific pollination in order to increase pollination efficiency and decrease DPP waste. The designed system comprises of a robotic arm extension with a remotely controlled sprayer mounted on a computer vision android device; that can shoot themselves out to the female flowers and disperse the required amount of pollens in the specific location. This unique spraying mechanism can overcome the drawbacks of aerial pollination and increase its effectiveness.

Current opinion: optimize radiofrequency ablation through electrophysiological principles, modeling, and clinical recommendations.

This article aims to empower the interventional pain physician to utilize RFA effectively by explaining the technical and electrophysiological features of monopolar, bipolar, and internally cooled RFA. Scientific data are used to provide advice on the effective, well tolerated, and rational application of these techniques. Moreover, physicians need to know how to analyze and generalize ex-vivo and in-vivo models to the clinical setting to optimize clinical outcomes. Recent studies suggest that there are many ways to enhance the technical effectiveness of interventional pain medicine RFA through adjustments in the equipment selection and settings and the local tissue conditions specific to the targeted anatomical area. These modifications could assist in improving clinical and safety outcomes. To optimize both the efficacy and safety of RFA, physicians must understand, conceptualize, interpret, and clinically translate the basic science of RFA. This knowledge is crucial for optimizing equipment selection and settings based on target location to enhance clinical outcomes and limit technical failures.

Research on Factors Influencing the Production of Deep Shale Gas Wells in Weiyuan Block : A Case Study of X Well on Platform A

Abstract The practice of Weiyuan deep shale gas development shows that there are many factors that affect the gas production, resulting in significant differences in gas production between wells in the same region and on the same platform. In response to the phenomenon of low gas production, low wellhead pressure, and high flowback rate in Well X on Platform A, using data from drilling, fracturing, and gas production profile testing, this study examines the impact of geological and engineering factors on gas well production through gray correlation analysis and geological engineering integrated numerical simulation methods. The results show that: ① The production of adjacent wells reduces the formation energy, resulting in a large pressure drop rate after the pump is stopped in the corresponding fracturing section of the well, poor fracturing effect and low gas production contribution; ② The order of the influence of geological factors on the gas production rate of each section is: gas content &gt; Si content &gt; Al content &gt; GR value&gt; Ca content;③The segment with natural fractures and faults contributes significantly to gas production, but its long-term effectiveness needs further evaluation. This study will provide useful references for target location optimization, fracturing design optimization, and overall development deployment of deep shale gas horizontal wells in Weiyuan.

A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.