Intersection Of Objects Research Articles

Exploring ethical implications in hospital management: Aligning business objectives with patient care ethics

Hospital management is a complex process that involves balancing operational efficiency, financial sustainability, and patient care. As healthcare institutions operate within commercial frameworks, the intersection of business objectives with patient care ethics becomes a significant concern. Ethical considerations in hospital management include resource allocation, stakeholder relationships, conflicts of interest management, and decision-making processes. Balancing profitability demands with ethical obligations poses challenges for administrators, necessitating a comprehensive understanding of managerial practices. Hospitals must balance business objectives with patient care ethics, ensuring ethical practices in resource allocation, managing relationships with multiple stakeholders, and ensuring patient autonomy, beneficence, nonmaleficence, and justice. As hospitals operate within commercial entities, it is crucial to ensure that profitability does not compromise patient well-being or erode trust in healthcare institutions. Healthcare professionals must be aware of the ethical implications of dual relationships with clients, resource allocation, and other ethical decisions. Collaborating scholars, policymakers, and healthcare practitioners can promote ethical excellence in hospital management, enhance patient care outcomes and advancing healthcare ethics.

O2SAT: Object-Oriented-Segmentation-Guided Spatial-Attention Network for 3D Object Detection in Autonomous Vehicles

Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. Consequently, current methods experience performance degradation when targeting only local features and overlooking the intersection of objects and road features, especially in uneven road conditions. This study proposes a 3D Object-Oriented-Segmentation Spatial-Attention (O2SAT) approach to distinguish object points from road points and enhance the keypoint feature learning by a channel-wise spatial attention mechanism. O2SAT consists of three modules: Object-Oriented Segmentation (OOS), Spatial-Attention Feature Reweighting (SFR), and Road-Aware 3D Detection Head (R3D). OOS distinguishes object and road points and performs object-aware downsampling to augment data by learning to identify the hidden connection between landscape and object; SFR performs weight augmentation to learn crucial neighboring relationships and dynamically adjust feature weights through spatial attention mechanisms, which enhances the long-range interactions and contextual feature discrimination for noise suppression, improving overall detection performance; and R3D utilizes refined object segmentation and optimized feature representations. Our system forecasts prediction confidence into existing point-backbones. Our method’s effectiveness and robustness across diverse datasets (KITTI) has been demonstrated through vast experiments. The proposed modules seamlessly integrate into existing point-based frameworks, following a plug-and-play approach.

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control.

The Sword of Roland in La Serenissima: Materiality and the Occult in Late Medieval Venice

Among the many recipes contained within a fifteenth-century book of secrets housed in Venice’s Biblioteca Marciana, there is a singularly unique one that offers to create the ‘Sword of Roland the Paladin’. The recipe, supposedly learned from a necromancer from Bologna, would create a solution from a mélange of herbs and alchemical salts and would purportedly invest the blade with occult powers. This recipe for creating the ‘Sword of Roland’ promises the potential of an object, rather than an actual physical thing. Nonetheless, this specific recipe offers an exceptional lens through which to investigate the intersection of material objects and magic in the late medieval Mediterranean Basin. It does so in four ways: in the physical space of the Biblioteca Marciana, in the physical codex in which the recipe is found, in the actual objects required to make the solution and in the very urban space of Venice itself.

Object feedback and feature information retention for small object detection in intelligent transportation scenes

Object detection is an important task in intelligent transportation scenes, and the performance of small object detection is directly related to its practical application. Small objects typically have poor detection performance due to reasons such as inadequate feature information and localization challenges. This paper proposes object feedback and feature retention, which can effectively improve the performance of small object detection. Firstly, we provide an in-depth analysis of popular methods in small object detection. In order to address the issue that location loss functions are mainly designed for general object detection, this paper proposes the Small Object Intersection over Union (SOIoU) loss function based on object feedback. This function can be adaptively optimized for small objects according to their size, thereby making the model more focused on small objects. In addition, for the issue of insufficient detail information in the output layer, this paper proposes the Small Object Path Aggregation Network (SOPANet) based on feature information retention, which can effectively enhance the information conditions for detecting small objects. Based on the above, the Object Feedback and Feature Retention-You Only Look Once (OFFR-YOLO) model is obtained in this paper. The proposed method improved the small object detection performance by 2.5 % and 4.8 % on the COCO (Common Objects in Context) and BDD100K (Diverse Driving Dataset for Heterogeneous Multitask Learning) datasets, respectively. The experimental results show that the proposed method can effectively improve the detection performance of small objects.

What Got You through Lockdown?

What Got You through Lockdown?

Roadway construction noise model progression and integration with federal highway administration's traffic noise model

In 2006, the Federal Highway Administration (FHWA) published the Roadway Construction Noise Model (RCNM 1.0) to predict noise levels from construction activities. RCNM 1.0 included basic acoustical data for a variety of construction equipment. In 2018, RCNM 2.0 was developed using a similar user interface to FHWA's Traffic Noise Model (TNM) version 3.0. RCNM 2.0 improved prediction of construction noise propagation and mitigation. RCNM 2.0 also expanded the source database and allowed users to analyze the project site conditions using mapping. TNM 3.2 includes integration of the TNM 3.1 software structure with RCNM 2.0. Equipment source data in the RCNM database were calibrated to account for the excess attenuation from each new source height. The RCNM database consists of moving and stationary equipment sources, each with spectra for two-four cardinal directions relative to the equipment, which may not always operate constantly throughout the day. Modeling these novel source features in TNM required new logic to handle stationary point sources, partially inactive sources, and source directionality. The goal of TNM 3.2 was for analysts to combine roadway and construction noise analyses within one file. Doing so required changes to the TNM object intersection rules, as well as all aspects of the user interface including map displays, input commands, and output data tables.

Panoptic One-Click Segmentation: Applied to Agricultural Data

In weed control, precision agriculture can help to greatly reduce the use of herbicides, resulting in both economical and ecological benefits. A key element is the ability to locate and segment all the plants from image data. Modern instance segmentation techniques can achieve this, however, training such systems requires large amounts of hand-labelled data which is expensive and laborious to obtain. Weakly supervised training can help to greatly reduce labelling efforts and costs. We propose panoptic one-click segmentation, an efficient and accurate offline tool to produce pseudo-labels from click inputs which reduces labelling effort. Our approach jointly estimates the pixel-wise location of all N objects in the scene, compared to traditional approaches which iterate independently through all N objects; this greatly reduces training time. Using just 10% of the data to train our panoptic one-click segmentation approach yields 68.1% and 68.8% mean object intersection over union (IoU) on challenging sugar beet and corn image data respectively, providing comparable performance to traditional one-click approaches while being approximately 12 times faster to train. We demonstrate the applicability of our system by generating pseudo-labels from clicks on the remaining 90% of the data. These pseudo-labels are then used to train Mask R-CNN, in a semi-supervised manner, improving the absolute performance (of mean foreground IoU) by 9.4 and 7.9 points for sugar beet and corn data respectively. Finally, we show that our technique can recover missed clicks during annotation outlining a further benefit over traditional approaches.

Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction

Objective. Monte-Carlo simulation studies have been essential for advancing various developments in single photon emission computed tomography (SPECT) imaging, such as system design and accurate image reconstruction. Among the simulation software available, Geant4 application for tomographic emission (GATE) is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes. Approach. In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system. Main results. We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of 123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like 123I-IMP brain perfusion source distributions. Significance. This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.

Multi-objective Optimization of Multi-cells Foam Mattress

Body pressure dispersion mattresses are useful tools for preventing pressure ulcers in patients with limited mobility who experience prolonged body weight-related compression loads at their body contact areas over time. The objective of this study is to propose and optimize a multicell finite element (FE) model of foam mattress to prevent patients from developing pressure ulcers (bed sores), by improving the contact pressure distribution on the upper mattress surface and immersion in the mattress. The NSGA-II multi-objective genetic algorithm was used to predict different configurations of cell materials to provide a more comfortable sleep. Our mattress model contains many cells (50 × 50 × 50), each of which can contain one of the nine different foam firmnesses. The NSGA-II algorithm attempts to combine the properties of soft and firm foams into a single mattress. however, the complexity and intersection of the fitness function objectives and the high number of possible chances forced the optimal solutions set to extend into the area under the result of foams that have a compressive strength between soft and firm. Based on the overall optimization results, the standard deviation ranged from 0.00325 to 0.00175 MPa and the maximum mattress immersion ranged from 50 mm to less than 20 mm. Mattresses with optimal configurations disperse body pressure smoothly to fit the patient's body shape.

Ramsey properties of semilinear graphs

A graph G is semilinear of complexity t if the vertices of G are elements of ℝd for some d ∈ ℤ+, and the edges of G are defined by the sign patterns of t linear functions f1,…, ft: ℝd × ℝd → ℝ. We show that semilinear graphs of constant complexity have very tame Ramsey properties. More precisely, we prove that if G is a semilinear graph of complexity t which contains no clique of size s and no independent set of size n, then G has at most \({O_{s,t}}\left(n \right) \cdot {\left({\log \,n} \right)^{{O_t}\left(1 \right)}}\) vertices. We also show that the logarithmic term cannot be omitted. In particular, this implies that if G is a semilinear graph of constant complexity on n vertices, and G contains no clique of size s, then G can be properly colored with polylog(n) colors. In the past 60 years, this coloring question was extensively studied for several special instances of semilinear graphs, e.g. shift graphs, intersection and disjointness graphs of certain geometric objects, and overlap graphs. Our main result provides a general upper bound on the chromatic number of all such, seemingly unrelated, graphs.Furthermore, we consider the symmetric Ramsey problem for semilinear graphs as well. It is known that if there exists an intersection graph of N boxes in ℝd (such graphs are semilinear of complexity 2d) that contains no clique or independent set of size n, then N = Od(n2(log n)d−1). That is, the exponent of n does not grow with the dimension. We prove a result about the symmetric Ramsey properties of semilinear graphs, which puts this phenomenon in a more general context.

Object Intersection Captures on Interactive Apps to Drive a Crowd-sourced Replay-based Compiler Optimization

Traditional offline optimization frameworks rely on representative hardware, software, and inputs to compare different optimizations on. With application-specific optimization for mobile systems though, the idea of a representative testbench is unrealistic while creating offline inputs is non-trivial. Online approaches partially overcome these problems but they might expose users to suboptimal or even erroneous code. Therefore, our mobile code is poorly optimized, resulting in wasted performance and energy and user frustration. In this article, we introduce a novel compiler optimization approach designed for mobile applications. It requires no developer effort, it tunes applications for the user’s device and usage patterns, and it has no negative impact on the user experience. It is based on a lightweight capture and replay mechanism. Our previous work [ 46 ] captures the state accessed by any targeted code region during its online stage. By repurposing existing OS capabilities, it keeps the overhead low. In its offline stage, it replays the code region but under different optimization decisions to enable sound comparisons of different optimizations under realistic conditions. In this article, we propose a technique that further decreases the storage sizes without any additional overhead. It captures only the intersection of reachable objects and accessed heap pages. We compare this with another new approach that has minimal runtime overheads at the cost of higher capture sizes. Coupled with a search heuristic for the compiler optimization space, our capture and replay mechanism allows us to discover optimization decisions that improve performance without testing these decisions directly on the user. Finally, with crowd-sourcing we split this offline evaluation effort between several users, allowing us to discover better code in less time. We implemented a prototype system in Android based on LLVM combined with a genetic search engine and a crowd-sourcing architecture. We evaluated it on both benchmarks and real Android applications. Online captures are infrequent and introduce ~5 ms or 15 ms on average, depending on the approach used. For this negligible effect on user experience, we achieve speedups of 44% on average over the Android compiler and 35% over LLVM -O3. Our collaborative search is just 5% short of that speedup, which is impressive given the acceleration gains. The user with the highest workload concluded the search 7 \( \times \) faster.

THEME: DEVELOPMENT OF A METHODOLOGY CREATION OF DIGITAL TOPOGRAPHIC PLANS OF TERRAIN INCLINATION

Every year during the rainy season, large water flows accumulate on the streets of Samarkand city. This work is devoted to the methodology development for creating digital topographic plans of the terrain slope in order to identify the rainwater accumulation on the city When creating digital topographic plans of the terrain slope, stereo topographic methods were used using data from remote sensing of the Earth. The general technological scheme for creating plans based on Earth remote sensing data consists of the following stages: - editorial and preparatory work; - photogrammetric processing;- creation and updating of the terrain;- automated decryption and vectorization of data; - creation and updating of the terrain; - creation and updating of DTC and CPG baselines; - generalization of DTC and CPG to obtain derivatives (smaller) scales; - quality control and data correction; - placing DTC and CPG in a temporary archive; - updating the spatial database. Practical work was carried out at the object Intersection of Dagbitskaya and Registan streets. Based on the performed work results, a topographic plan of the slope area was drawn up, indicating where the seasonal rainwater accumulation begins.

Vehicle identification using modified region based convolution network for intelligent transportation system

Intelligent transportation systems (ITS) are the integration of information and communications technologies with applications which are significant in traffic control and management. The increased number of on road vehicles in urban areas urges the need of development of automated methods for traffic management. Vehicle identification, classification and analysis enable the intelligent transportation systems to make decisions. In this paper, an automated method for video analysis for vehicle identification using a modified Region based Convolution Neural Network (RCNN) has been proposed. The traffic videos collected by CCTV cameras installed on the roads are analyzed for vehicle identification in a given frame. The pretrained google net is used to extract features. These features are used by the Region based Convolution Neural Network for vehicle identification. The vehicles are identified using probability score computed using intersection of objects (IoU). The identified vehicles are classified into ten different vehicle classes. The proposed network concatenates features from previous layers to reduce loss and consequently improve the vehicle identification accuracy. The vehicle identification method is further extended for vehicle counting and behavioral analysis. The vehicle counting information can be used for congestion control in smart cities. The behavioral analysis includes computation of speed of vehicles. The speed information is useful for traffic law enforcement in smart cities. The proposed method is applied on MIO-TCD vehicle dataset and EBVT video dataset. The results are calculated using three different metrics namely average accuracy, mean precision and mean recall. Obtained results are also compared with other state of the art methods. The results show significant improvement and thus the method can be effectively used for video analysis.

A Refined Lines/Regions and Lines/Lines Topological Relations Model Based on Whole-Whole Objects Intersection Components

Refined topological relations play an important role in spatial database quality control. Currently, there is no unified and reasonable method to represent refined line/region and line/line topological relations in two-dimensional (2D) space. In addition, the existing independent line/region and line/line models have some drawbacks such as incomplete type discrimination and too many topological invariants. In this paper, a refined line/region and line/line topological relations are represented uniformly by the sequence, dimension, and topological type of the intersection components. To make the relevant definitions conform to the traditional cognitions in 2D Euclidean space, the (simple) spatial object is defined based on manifold topology, and the spatial intersection components are defined based on the whole-whole object intersection set. Then the topological invariant of node degree is introduced, and the adjacent point kinds (e.g., “Null”, “On”, “In”, and “Out”) are defined to distinguish the intersection component types. Excluding impossible and symmetrical types, 29 types of intersection-lines (including 21 between lines/regions and 8 between lines/lines), and 6 types of intersection-points (including 2 between lines/regions and 4 between lines/lines) are classified. On this basis, a node degree-based whole-whole object intersection sets (N-WWIS) model for refined line/region and line/line topological relations is presented, and it can be combined with the Euler number-based whole object intersection and difference (E-WID) model (coarse level) to form a hierarchical representation method of topological relations. Furthermore, a prototype system based on the N-WWIS model for automatic topological integrity checking is developed and some evaluation experiments are conducted with OpenStreetMap (OSM) data is presented based on the classification of intersection components. The experimental results show that the N-WWIS model will enable the geographic information systems (GIS) community to develop automated topological conflict checking and dealing tools for spatial data updates and quality control.

43] The digital museum as a place for the intersection of objects: the design and implementation of Exhibition RoomX

43] The digital museum as a place for the intersection of objects: the design and implementation of Exhibition RoomX

Becoming a construction worker in the connected classroom: Opposing school work with smartphones as happy objects

This article aims to fill a very specific and acute gap; in addition to the few studies on youth and digitalisation, smartphones and other mobile tools, it is very clear that the field of research concerning these issues in regards to vocational education and training is close to non-existent. By examining male Building and Construction programme students’ collective use of smartphones in interaction during classes, this study contributes to increased knowledge about some of the challenges and possibilities that arise with the digitalisation of vocational education and training. The study uses new and innovative methods regarding how students’ digital activities in the classroom could be captured and studied, and approaches video recorded data through the lens of Sara Ahmed’s ideas of happy objects (2010), and the concept of community of practice (Wenger, 1998). The analyses show how the identity constructing processes that take shape when the students orient towards the smartphone as a happy object intersect with the students’ future vocational identity as building and constructing workers, as well as explicating an anti-school culture.

Optimized layered method for real-time interactive holographic display based on ray-tracing technique

The extreme computational complexity in generating a hologram makes the interactive holographic display very difficult. An optimized layered rendering method for real-time interactive holographic display based on ray-tracing technique is presented in order to overcome this challenge and realize real-time interaction of three-dimensional scenes. Ray tracing is able to render more complex and photorealistic images for different illuminations and circumstances. Rays are launched in parallel and traced to obtain ray–object intersections, and then the complex wave field of the effective pixels in different layers is determined. The occlusions between different layers are also considered to effectively reduce the overall computation. A computer-generated hologram is fused with the multilayer-hologram-diffraction results using the layered fast Fourier transform algorithm. The experimental results demonstrate the effectiveness of the proposed method. The reconstructed holographic image with real depth cues is demonstrated by the experiments, and the optical reconstruction images can be interacted in real-time.

How Does Faith Feel?

Abstract Research on emotion in anthropology has been supplanted by an ethnographic turn toward ‘subjectivity’, ‘embodiment’, ‘personhood’, and ‘experience’. In this article, I explore how these interrelated modes of analysis can help ethnographers to better understand the cultural processes that constitute how people feel. I show that among my Christian Dusun interlocutors in Ranau, Malaysian Borneo, the interactive engagement between subjects and their environment determined the vectors of emotional possibility in terms of belief. The intersection of religious objects (God, the Holy Spirit, Satan) and mutual obligations in the community produce what I refer to as the ‘faith network’. I trace these collective attachments to consider how ‘believing in’ regulates feeling in relation to situations of crisis, impasse, and tragedy. The combined efforts of my interlocutors, I suggest, created an active commitment that pulsated through the faith network, which sustained an intensive and defining mode of their relational experience.

Development of interventions for an intelligent and individualized mobile health care system to promote healthy diet and physical activity: using an intervention mapping framework

BackgroundThe mortality of coronary heart disease can be largely reduced by modifying unhealthy lifestyles. However, the long-term effectiveness of interventions for modifying unhealthy diet and physical inactivity of patients with coronary heart disease remain unsatisfactory worldwide. This study aims to systematically design a set of theory-based and evidence-based, individualized, and intelligent interventions for promoting the adoption and maintenance of a healthy diet and physical activity level in patients with coronary heart disease.MethodsThe interventions will be delivered by a mobile health care system called Individualized, Intelligent and Integrated Cardiovascular Application for Risk Elimination. Three steps of the intervention mapping framework were used to systematically develop the interventions. Step 1: needs assessment, which was carried out by a literature review, in-depth interviews and focus group discussions. Step 2: development of objective matrix for diet and physical activity changes, based on the intersection of objectives and determinants from the Contemplation-Action-Maintenance behavior change model. Step 3: formulation of evidence-based methods and strategies, and practical applications, through a systematic review of existing literature, research team discussions, and consultation with multidisciplinary expert panels.ResultsThree needs relevant to content of the intervention, one need relevant to presentation modes of the intervention, and four needs relevant to functional features of the application were identified. The objective matrix includes three performance objectives, and 24 proximal performance objectives. The evidence-based and theory-based interventions include 31 strategies, 61 evidence-based methods, and 393 practical applications.ConclusionsThis article describes the development of theory-based and evidence-based interventions of the mobile health care system for promoting the adoption and maintenance of a healthy diet and physical activity level in a structured format. The results will provide a theoretical and methodological basis to explore the application of intervention mapping in developing effective behavioral mobile health interventions for patients with coronary heart disease.Trial registrationChinese Clinical Trial Registry: ChiCTR-INR-16010242. Registered 24 December 2016. http://www.chictr.org.cn/index.aspx