Related Information Research Articles

Simulated annealing-particle swarm optimization initial orbit determination method for too-short-arcs

The initial orbit determination (IOD) of a non-cooperative satellite through space-based optical angle-only measurement data is a fundamental task. However, due to the lack of relative distance information of the targets, traditional methods have problems with poor accuracy and robustness in orbit determination results. The application of particle swarm optimization algorithm for IOD under optical too-short-arcs (TSA) measurement data has achieved good results. However, there is still a problem of convergence to local extremum. This article proposes a simulated annealing-particle swarm optimization (SA-PSO) hierarchical algorithm, which combines the efficient searchability of particle swarm optimization with the fine searchability of simulated annealing algorithm to achieve robust and precise optimization of non-cooperative targets orbit elements. The experimental results show the SA-PSO algorithm significantly improves the accuracy of orbit elements prediction compared to the particle swarm optimization algorithm and proves its effectiveness.

Integrated Electronic Health Record of Multidisciplinary Professionals Throughout the Cancer Care Pathway: A Pilot Study Exploring Patient-Centered Information in Breast Cancer Patients.

The aim of this pilot study was to first aggregate and then integrate the medical records of various healthcare professionals involved with breast cancer patients to reveal if and how patient-centered information is documented in multidisciplinary cancer care. We aggregated 20 types of medical records from various healthcare professionals such as physicians, nurses and allied healthcare professionals (AHPs) throughout three breast cancer patients' care pathways in a department of breast surgery at a university hospital. Purposeful sampling was used, and three cases were examined. The number of integrated type of records was 14, 14, 17 in case 1, 2 and 3, respectively. We manually annotated and analyzed them exploratively using a thematic analysis. The tags were produced using both a deductive template approach and a data-driven inductive approach. All records were then given tags. We defined patient-centered information related tags and biomedical information related tags and then analyzed for if and how patient-centered information was documented. The number of patient-centered information related tags accounted for 30%, 30% and 20% of the total in case 1, 2 and 3, respectively. In all cases, patient-centered information was distributed across various medical records. The Progress Note written by doctors provided much of the patient-centered information, while other records contained information not described elsewhere in the Progress Notes. The records of nurses and AHPs included more patient-centered information than the doctors' notes. Each piece of patient-centered information was documented in fragments providing from each of the healthcare professionals' viewpoints. The documented information throughout the breast cancer care pathway in the cases examined was dominated by biomedical information. However, our findings suggest that integrating fragmented patient-centered information from various healthcare professionals' medical records produces holistic patient-centered information from multiple perspectives and thus may facilitate an enhanced multidisciplinary patient-centered care.

How to Select an Objective Function Using Information Theory

AbstractIn machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information‐theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well‐suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of “maximizing information and general utility” extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data.

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios.

Planar Reconstruction of Indoor Scenes from Sparse Views and Relative Camera Poses

Planar reconstruction detects planar segments and deduces their 3D planar parameters (normals and offsets) from the input image; this has significant potential in the fields of digital preservation of cultural heritage, architectural design, robot navigation, intelligent transportation, and security monitoring. Existing methods mainly employ multiple-view images with limited overlap for reconstruction but lack the utilization of the relative position and rotation information between the images. To fill this gap, this paper uses two views and their relative camera pose to reconstruct indoor scene planar surfaces. Firstly, we detect plane segments with their 3D planar parameters and appearance embedding features using PlaneRCNN. Then, we transform the plane segments into a global coordinate frame using the relative camera transformation and find matched planes using the assignment algorithm. Finally, matched planes are merged by tackling a nonlinear optimization problem with a trust-region reflective minimizer. An experiment on the Matterport3D dataset demonstrates that the proposed method achieves 40.67% average precision of plane reconstruction, which is an improvement of roughly 3% over Sparse Planes, and it improves the IPAA-80 metric by 10% to 65.7%. This study can provide methodological support for 3D sensing and scene reconstruction in sparse view contexts.

HISTORICAL WRITE-UP OF VRANA WITH SPECIAL REFERENCE TO SAMHITAS AND PURANAS: A REVIEW

The history of medicine includes studies exploring and understanding medical practices, both in past and present, throughout human societies. A period which lacked written sources regarding medicine related information is instead drawn from archaeological sources. This field tracks the evolution of human societies' approach to health, illness, and injury ranging from prehistory to the modern day, the events that shape these approaches, and their impact on populations. Public health measures were developed especially in the 19th century as the rapid growth of cities required systematic sanitary measures. Human societies living in those eras were susceptible to various diseases, wounds and that was the time when they sought help from the surroundings in form of natural resources to heal themselves. When we consider the great wars of ancient times like Kurukshetra in dwapara yuga and war between Rama and Ravana in treta yuga, people got injured and developed wounds from the weapons chariots etc and they sought treatment from sages who were like healers, used the natural sources in the form of treatment. The science of healing has always been a matter of serious concern to ancient surgeons. This fact can be very well understood when going through Sushruta Samhita where a good number of chapters dealing with vrana and its management is found. The mankind was susceptible to injury during the ancient times too, by this we can understand the knowledge of vrana and its treatment principles even in those eras. Hence this study will review the aspect of vrana based on different samhita’s and puranas.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects

This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure–Volume–Temperature (PVT) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. We catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML-building process, and the analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.

Inflammatory bowel disease alters in vivo distribution of orally administrated nanoparticles: Revealing via SERS tag labeling technique

Nanoparticles (NPs) could be uptake orally and exposed to digestive tract through various sources such as particulate pollutant, nanomedicine and food additive. Inflammatory bowel disease (IBD), as a global disease, induced disruption of the intestinal mucosal barrier and thus altered in vivo distribution of NPs as a possible consequence. However, related information was relatively scarce. Herein, in vivo distribution of typical silica (SiO2) and titania (TiO2) NPs was investigated in healthy and IBD models at cell and animal levels via a surface-enhanced Raman scattering (SERS) tag labeling technique. The labeled NPs were composed of gold SERS tag core and SiO2 (or TiO2) shell, demonstrating sensitive and characteristic SERS signals ideal to trace the NPs in vivo. Cell SERS mapping revealed that protein corona from IBD intestinal fluid decreased uptake of NPs by lipopolysaccharide-induced RAW264.7 cells compared with normal intestinal fluid protein corona. SERS signal detection combined with inductively coupled plasma mass spectrometry (ICP-MS) analysis of mouse tissues (heart, liver, spleen, lung and kidney) indicated that both NPs tended to accumulate in lung specifically after oral administration for IBD mouse (6 out of 20 mice for SiO2 and 4 out of 16 mice for TiO2 were detected in lung). Comparatively, no NP signals were detected in all tissues from healthy mice. These findings suggested that there might be a greater risk associated with the oral uptake of NPs in IBD patients due to altered in vivo distribution of NPs.

Collaborative approaches to greenhouse gas inventory in higher education: Insights from the Universities Denmark Group

The challenges faced by universities and private industry in estimating their emissions for decarbonisation are similar, but the task of linking emissions to university operations, namely procurement, is complex and time-consuming due to the wide range and types of purchases. Here, participatory action research is used to address these challenges, with a group of Danish universities to investigate the functions and improve the completeness of university GHG inventories. The research enabled knowledge sharing and collaboration, leading to a better understanding of the complexities and possibilities of GHG inventories. The main conclusions drawn from discussions are that the GHG inventory should serve multiple functions; an inward-facing decision support material, and an externally-facing communication tool. EXIOBASE, an environmentally extended input-output model, was identified as a useful tool for future inventories, particularly in procurement, due to its comprehensive spend-based assessment of purchases also relevant to universities. With more universities adopting spend-based practices, the presented conclusions shed light on potential risks of this method that have not yet been discussed in this context. A consensus on methodological trade-offs, relevant activities, and data considerations for a GHG inventory are reached and reflected on. As suppliers can increasingly deliver product specific climate related information, a data ontology is needed to appropriately incorporate supplier-specific data into consistent inventories without conflicting with methodological principles and upholding proprietary requirements of suppliers. Addressing challenges identified through this collaborative investigation will expand on the dialogue in the literature and help shape how universities conduct and use GHG inventories. Keywords: Sustainable university; Action Research; Consequential Attributional; GHG Protocol.

Cognitive complexity explains processing asymmetry in judgments of similarity versus difference

Human judgments of similarity and difference are sometimes asymmetrical, with the former being more sensitive than the latter to relational overlap, but the theoretical basis for this asymmetry remains unclear. We test an explanation based on the type of information used to make these judgments (relations versus features) and the comparison process itself (similarity versus difference). We propose that asymmetries arise from two aspects of cognitive complexity that impact judgments of similarity and difference: processing relations between entities is more cognitively demanding than processing features of individual entities, and comparisons assessing difference are more cognitively complex than those assessing similarity. In Experiment 1 we tested this hypothesis for both verbal comparisons between word pairs, and visual comparisons between sets of geometric shapes. Participants were asked to select one of two options that was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard; on ambiguous trials, one option was more featurally similar to the standard, whereas the other was more relationally similar. Given the higher cognitive complexity of processing relations and of assessing difference, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was replicated using more complex story stimuli (Experiment 2). We showed that this pattern can be captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments.

The degree of importance for partially labeled incomplete real-valued data with application to semi-supervised attribute reduction

Incomplete real-valued data often misses some labels due to the high cost of labeling data. This paper investigates for partially labeled incomplete real-valued data and considers its application in semi-supervised attribute reduction. There are two decision information systems (DISs) in a partially labeled incomplete real-valued data DIS (p-IRVDIS): a labeled incomplete real-valued data DIS (l-IRVDIS) and a unlabeled incomplete real-valued data DIS (u-IRVDIS). The degree of importance on an attribute subset in a p-IRVDIS are defined using an indistinguishable relation and conditional information entropy. It is the weighted sum of l-IRVDIS and u-IRVDIS using the missing rate of label to measure p-IRVDIS uncertainty. Based on the degree of importance, an adaptive semi-supervised attribute reduction algorithm in a p-IRVDIS is proposed. This algorithm can automatically adapt to various missing rates of label. The experimental results on 8 datasets reveal that the proposed algorithm performs statistically better than some state-of-the-art algorithms.

A novel attribute reduction approach using coverage-credibility-based rough decision entropy for interval-valued data

Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms.

SENTIMENT ANALYSIS OF WEIBO COMMENTS BASED ON CONVOLUTIONAL NEURAL NETWORK

Sentiment analysis of social media comments plays a crucial role in understanding public opinion and sentiment trends. This study focuses on sentiment analysis of Weibo comments, a popular microblogging platform in China, using a (CNN) approach. We propose a novel methodology that leverages the inherent structural dependencies among comments and users in the Weibo network to capture nuanced sentiment patterns. By representing Weibo comments as a graph, with users and comments as nodes and their interactions as edges, we exploit the relational information to enhance sentiment classification accuracy. Furthermore, we employ attention mechanisms to prioritize influential users and comments in the sentiment analysis process. Through extensive experiments on real Weibo datasets, our proposed CNN-based sentiment analysis framework demonstrates superior performance compared to traditional methods, achieving high accuracy in sentiment classification tasks. This research contributes to advancing sentiment analysis techniques in social media platforms and provides valuable insights for understanding public sentiment dynamics in online communities like Weibo.

Revenue Recognition (Topic 606) at Salesforce.com, Inc.

This case uses an array of carefully selected and excerpted revenue recognition related information contained in Salesforce.com's January 31, 2019, 10-K. Maria, the fictional protagonist, is seeking to understand those disclosures as part of her preparation for an upcoming job interview with the company. As such, she is relying on those disclosures to provide insights as to the company's main product/service lines, the events that signal when and how much revenue the company has earned (i.e., the essence of its business model), along with the related official generally accepted accounting principles (GAAP) criteria pertinent to the valuing and timing of recorded revenues.

Freedom of Expression/Right of Information in Relation to the Protection of Personal Data in the Framework of Globalism in Albania

Freedom of expression and the right to information are fundamental rights of citizens everywhere in the world, including our country Albania, where these rights are sanctioned both in the Constitution and in other international acts, such as the European Convention of Human Rights (ECHR) and the Universal Declaration of Human Rights (UDHR) which are an inseparable part of our legal system. Received: 25 December 2023 / Accepted: 25 February 2024 / Published: 23 April 2024

Behavioral Analysis of Suspects

Behavioral analysis is a method used in conducting a targeted interview with suspects, where the ultimate goal is to obtain reliable information. The main task of the behavioral analysis is to distinguish the sincere from the insincere subject on the basis of their behavior. It can also be used by experts as a preliminary interview, during which the verbal, non-verbal and para-verbal behavior of the subject is analyzed. It is also very useful in screening procedures, in which there is lack of sufficient information in relation to the specific case at hand, and at the same time there are many suspects. This article presents the essence of behavioral analysis, basic guidelines for the procedure of conducting it, and a comparative analysis of the reactions of sincere and insincere suspects.

Nanoscale detection of carbon dots-induced changes in actin skeleton of neural cells

Understanding the cytotoxicity of fluorescent carbon dots (CDs) is crucial for their applications, and various biochemical assays have been used to study the effects of CDs on cells. Knowledge on the effects of CDs from a biophysical perspective is integral to the recognition of their cytotoxicity, however the related information is very limited. Here, we report that atomic force microscopy (AFM) can be used as an effective tool for studying the effects of CDs on cells from the biophysical perspective. We achieve this by integrating AFM-based nanomechanics with AFM-based imaging. We demonstrate the performance of this method by measuring the influence of CDs on living human neuroblastoma (SH-SY5Y) cells at the single-cell level. We find that high-dose CDs can mechanically induce elevated normalized hysteresis (energy dissipation during the cell deformation) and structurally impair actin skeleton. The nanomechanical change highly correlates with the alteration of actin filaments, indicating that CDs-induced changes in SH-SY5Y cells are revealed in-depth from the AFM-based biophysical aspect. We validate the reliability of the biophysical observations using conventional biological methods including cell viability test, fluorescent microscopy, and western blot assay. Our work contributes new and significant information on the cytotoxicity of CDs from the biophysical perspective.

OSSEOINTEGRATION IN LIMB REPLACEMENT SURGERY: AN ANALYSIS OF THE QUALITY AND READABILITY OF AVAILABLE INFORMATION ON THE INTERNET

IntroductionThe use of osseointegrated limb replacement prostheses for amputees are rapidly growing in popularity due to the many benefits over conventional sockets. Many institutions worldwide are beginning to provide this surgical option to their patients and are advertising the service online. The aim of this study was to assess the quality and reading level of related information available online.Materials & MethodsA search was performed using the 3 most popular English language search engines reviewing the top 40 websites from each, using the term osseointegration on. Duplicates, videos, medical journal articles and dentistry websites were excluded. All included websites were assessed for reading using the FKGL, G-Fog and FRE. Quality was assessed using the JAMA benchmark criteria, HONC erificate and a novel 16-point scoring system developed specifically for limb replacement osseointegration.ResultsThe search resulted in 9,985,000 websites, 120 were assessed. 23 websites were included after removal of duplicates, medical journals and dentistry websites. None with reading grades of 6 or lower. Mean FKGL grade was 11.3/18. Mean FRE was 38.83/100. Mean G-FOG was 12.57/20. No websites had HONC. Mean JAMA score was 1.76/4. Mean HSSOIS was 5.70/16. Most provided information on indications(16/23) and explanation of osseointegration 17/23) but few discussed costs(3/23) or restrictions(9/23).ConclusionsThere is already a large amount of information available online regarding osseointegrated prostheses. However, the most accessible information may exceed most patients’ reading levels, limiting is usefulness. Further, the information available is generally of low quality, The quality of information and the readability of online content should be improved to ensure more effective patient education.

Celiac disease - a pluripathological model in pediatric practice.

Being defined as an autoimmune, chronic pathology, frequently encountered in any age group, but especially in pediatrics, celiac disease (also called gluten enteropathy), is gaining more and more ground in terms of diagnosis, but also interest in research. The data from the literature of the last decades attest the chameleonic way of its presentation, there may be both classic onset symptoms and atypical symptoms. Given the impact played by celiac disease, especially in the optimal growth and development of children, the current narrative review aims to highlight the atypical presentation methods, intended to guide the clinician towards the inclusion of the pathology in the differential diagnosis scheme. To these we add the summary presentation of the general data and therapeutic lines regarding the underlying condition and the existing comorbidities. In order to place the related information up to date, we performed a literature review of the recent articles published in international databases. We bring forward the current theories and approaches regarding both classic celiac disease and its atypical manifestations. Among these we note mainly constitutional, skin or mucous, bone, neuro-psychic, renal, reproductive injuries, but also disorders of biological constants and association with multiple autoimmunities. Knowing and correlating them with celiac disease is the key to optimal management of patients, thus reducing the subsequent burden of the disease.