Features Of Relations Research Articles

Power and Status Inconsistency and Conflict in Teams

Power and status, two forms of social hierarchy, are fundamental feature of social relations, and they have two functions that are especially important for teams that lack formal hierarchy: 1) to establish order and coordination, and 2) to motivate employees to strive for promotion. Hierarchy provides clear social governance in groups by allowing leaders to assign tasks or allocate resources, and it provides incentives for individuals to climb the organizational ladder to obtain greater compensation and comfort. Individuals with high power and/or high status are more likely to perform leadership behaviors in teams. However, they may also cause conflict by activating threat and distrust when other team members perceive that aspirants’ leadership behaviors are misaligned with their power and/or status. This misalignment can be complex because social hierarchy has multiple forms such as status and power and multiple levels such as team and organizational levels. The current study investigates (1) how the inconsistency between power and status of a team member affect conflict with other team members and (2) how team member’s needs for power and needs for status moderate the power-status inconsistency and conflict relationship. Data was collected from a sample of MBA student teams at a large mid-western university in the United States. In the results the study found no evidence of the power-status inconsistency and conflict relationship, yet found the moderating effect of needs for power on that relationship.

Management of Inter-Religious Conflicts in Kwara State: Prospects and Challenges

Nigeria has experienced various inter-religious conflicts that have obvious effects on peace. In Kwara State, for example, inter-religious conflict has surfaced as a major feature of inter-group relations. This paper examines the management of inter-religious conflict in Kwara State. Nigeria with the use of descriptive analysis. Conflict theory was employed as the basis of the study. The Kwara State Government applied various strategies in the management of inter-religious conflict, among them attempts to provide good governance and equitable development, litigation, use of police actions to prevent or quell emergent violence, establishment of inter-faith dialogue committees and partnership with traditional and religious institutions such as the Ilorin Emirate Council. Other government response strategies include court processes and the deployment of security forces. These have attracted considerable attention. In view of the situation on ground, the study advocates for some measures towards effective management of inter-religious conflicts in the state. These include the establishment of a committee comprising all representatives of social groups within the state. The committee should provide a forum for discussing and resolving communal issues instead of resorting to violence. Besides, it is further recommended research be carried out for an in-depth understanding of the causes and solutions to inter-religious conflicts in the state, where experts in conflict resolution would be used to curtail possible outbreak of conflicts, while increased attention is devoted to the prevention of social conflicts, the peaceful resolution of existing conflicts should be intensified through constant dialogue.

Transformation of the currency regulation regime and foreign economic relations of Russia in the context of expanding anti-Russian sanctions

The article discusses the features of currency regulation and foreign economic relations of the Russian Federation (hereinafter referred to as RF, Russia) in the context of the introduction and growth of anti-Russian sanctions by the United States of America, European Union countries and other unfriendly countries, issues related to ensuring the sustainability of the Russian economy and its ability to withstand negative internal and external influences, issues of financial security, which is an important component of the country’s economic security, issues of the current state of the state’s banking system and the Russian economy as a whole. The article also discusses the possible expansion of international economic relations between Russia, China, North Korea and other countries. The purpose of the article is to identify directions for the transformation of the currency regulation regime and foreign economic relations of the RF in the context of the expansion of anti-Russian sanctions. The subject of the study is the currency and foreign economic relations of the RF with friendly and unfriendly countries in the current conditions of growing anti-Russian sanctions and the conduct of a special military operation in Ukraine. In conclusion, the article highlights key changes associated with changes in Russia’s foreign exchange and foreign economic activeities, promising opportunities for further expansion of international economic relations with China, the Democratic People’s Republic of Korea and other friendly countries.

Document-level multiple relations extraction method via evidence guidance and relation correlation

Document-level Relation Extraction (DocRE) aims to extract semantic relations between entity pairs, spanning multiple sentences, paragraphs or even the entire document. These relations can often be predicted by partial sentences within the document, the evidence sentence. However, the relation derived only from sentence information is incomplete, because it ignores the case of multiple relations between entity pairs. Therefore, how to select effective evidence sentences and how to predict multiple relations more accurately have become challenges for the existing DocRE models. In response to these challenges, we introduce Reinforcement Learning (RL) to select more effective evidence sentences, while using heuristic rules to narrow down the search space of RL. Secondly, we utilize GAT to acquire the features of co-occurrence relations, which can greatly improve multiple relations prediction performance. Moreover, the combination of the features of co-occurrence relations and the evidence sentence information enables our method to achieve both high effectiveness and precision. The experimental results show that, compared with other advanced methods, our method achieves an F1 score of 66.56 and the EviF1 score of 56.69, which attains the state-of-the-art performance on public datasets.

Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indicate that machine learning can be applied to momentary data to improve classification of SI. This study examined whether the classification accuracy of these models varies as a function of type of training data or characteristics of ideation. A total of 257 psychiatric inpatients completed a 3-week battery of ecological momentary assessment and measures of suicide risk factors. The accuracy of machine learning models in classifying the presence, duration, or intensity of ideation was compared across models trained on baseline and/or momentary suicide risk data. Relative feature importance metrics were examined to identify the risk factors that were most important for outcome classification. Models including both baseline and momentary features outperformed models with only one feature type, providing important information in both correctly classifying and differentiating individual characteristics of SI. Models classifying SI presence, duration, and intensity performed similarly. Results of this study may not generalize beyond a high-risk, psychiatric inpatient sample, and additional work is needed to examine temporal ordering of the relationships identified. Our results support using machine learning approaches for accurate identification of SI characteristics and underscore the importance of understanding the factors that differentiate and drive different characteristics of SI. Expansion of this work can support use of these models to guide intervention strategies.

Attention-disentangled re-ID network for unsupervised domain adaptive person re-identification

Unsupervised domain adaptation (UDA) for person re-identification (re-ID) aims to bridge the domain gap by transferring knowledge from the labeled source domain to the unlabeled target domain. Recently, pseudo-label-based approaches have become the dominant solution for addressing this issue. However,most pseudo-label-based methods neglect class boundary samples to reduce the influence of false pseudo labels, sacrificing intra-class semantic diversity. Although hard sample memory bank-based approaches can discover relationships between samples and describe intra-class diversity, they are prone to increasing the risks of generating incorrect pseudo labels. Balancing intra-class variety and pseudo-label accuracy in cross-domain person re-ID poses a challenge. In this study, we propose an attention-disentangled re-ID network (ADDNet) to enhance the discriminative ability of re-ID related feature representations, addressing the contradiction between intra-class diversity and pseudo-label accuracy. Unlike most disentanglement learning-based re-ID approaches that focus on separating explanatory factors, we design a spatial attention-disentangled mechanism to separate re-ID related and unrelated weights, enhancing the discriminative ability of cross-domain feature representation. Additionally, a multiple hard sample memory learning strategy is designed to express intra-class diversity of target samples using re-ID related features extracted from ADDNet. Extensive experiments show that ADDNet achieves 46.7% mAP on the Market-to-MSMT cross-domain re-ID task, outperforming state-of-the-art methods by 6.5 points.

A 2D echocardiographic method for accurate right ventricular quantification using deep learning

Abstract Background/Introduction Transthoracic two-dimensional echocardiography (2DE) remains the backbone for non-invasive assessment of the right ventricle (RV) size and function. However, calculation of RV end-diastolic (RVEDV), end-systolic volume (RVESV) and ejection fraction (RVEF) cannot be performed using 2DE due to the complex RV shape (1), whereas currently used, respective 2DE indices lack diagnostic accuracy. Deep learning (DL) may be able to assist in developing new 2DE methods for RV quantification. Purpose To develop a novel DL-based 2DE method for quantitative evaluation of RV size and function without geometric assumptions. Methods 50 adult patients underwent 2DE and CMR within 30 days as part of routine cardiac care between 6/2020 – 9/2021 at a medical center. We excluded patients with prior cardiac surgery of the RV, tricuspid or pulmonic valve repair or replacement, complex congenital heart disease, more than small pericardial effusion, cardiac tamponade, atrial fibrillation at the time of either study, severe RV dysfunction or inadequate imaging quality. End-diastolic and end-systolic areas derived from planimetry of 8 2DE RV views, along with patient age and gender, were to used to train and test a DL algorithm, namely the Feature-Tokenizer and Transformer (Figure 1) in order to predict the corresponding, CMR-derived, RVEDV or RVESV, and subsequently calculate RVEF. We used 5-fold cross-validation for training and evaluation. We assessed the relative importance of each 2DE view using gain metric-powered explainability analysis and subsequently performed a sensitivity analysis to identify optimal combinations of views. Variability analysis was performed for a random subcohort of 10 patients. Results Median age was 51 (interquartile range 32-62) and 42% were women. Mean RVEDV, RVESV and RVEF) by CMR were 163±70ml, 82±42ml and 51±8% respectively. Six patients (12%) had RV dilatation and 9 (18%) had RV dysfunction when using published CMR criteria. Explainability analysis showed that the 3 most important views were parasternal long axis (PLAX) (relative feature importance [RFI] = 0.197), 4-chamber (RFI=0.145) and parasternal short axis at the base (PSAX-base) (RFI=0.112). The 3-view combination of PLAX, 4-chamber and PSAX-base achieved high accuracy when compared to CMR (R2=0.964, absolute percentage error [APE]=8.09±12.07% for RV volumes and APE=9.86±10.68% for RVEF). Good (intraclass correlation coefficient [ICC]>0.75) to excellent (ICC>0.90) intra- and interobserver variability was found for these 3 views. Conclusions We propose an innovative, clinically applicable DL-based 2DE method for quantification of RV size and function that requires manual RV planimetry of 3 views, namely 4-chamber, PLAX and PSAX-base (Figure 2). This method may revolutionize 2DE RV assessment. Further studies with largest number of patients and wider range of RV size and function are needed to validate this method.Deep learning architectureGraphical abstract

Cardio-pulmonary exercise testing in adult congenital heart patients: a new data-focused approach to predict mortality

Abstract Background/Introduction The cardio-pulmonary exercise test (CPET) has been a pivotal tool for functional and prognostic evaluation in adults with congenital heart disease (ACHD). During a routine CPET test, a myriad of variables are collected reflecting the cardiovascular, pulmonary, and skeletal muscle systems. However, clinicians typically use only a few easily and accessible variables to establish prognosis, which can come at an accuracy cost. Machine learning algorithms have undergone significant development in recent years and have found applications in the medical field. One of their main advantages is the ability to handle a large number of variables and extract information from their various combinations, while excluding redundant data, thus offering a higher level of prediction accuracy. Purpose Our objective was to develop a machine learning model to predict death during follow-up in a large population of ACHD patients who have undergone routine CPET in a large-volume single specialist centre. Methods All available CPET studies for adult ACHD patients (age > 15 years) performed from December 1999 to December 2021 at our centre were included, and all standard available variables were extracted. The primary outcome was all-cause mortality since the CPET, collected up to November 2023. Data exploration, data cleaning, and feature engineering steps were conducted using Python (v3.8). Continuous variables were standardised. The supervised machine learning algorithm XGBoost was used for classification (all-cause mortality), with the best hyperparameters selected through cross-validation. For the final model, a feature importance analysis based on permutation techniques was used to explore the variables with more impact on model accuracy. Results A total of 6361 studies were included, with median age of 31 years (IQR 23-43), 56% male. During follow-up, a total of 491 deaths were recorded. Of available demographic, clinical and exercise parameters (n=129 variables), 21 were deemed sufficient in terms of completeness, absence of significant correlation and clinically relevant for the subsequent analysis. These included demographic variables (age and sex) and CPET variables (peak VO2, FEV1, peak heart rate, exercise time, etc.) and were used to train the model. The accuracy of the model in predicting death after CPET in the test set had an accuracy of 93.5% and an area under the ROC curve (AUC) of 85.0% (Figure1). The parameters with the highest relative feature importance were FEV1, body mass index and percent predicted peak VO2 (Figure 2). Conclusions Machine learning models can be effectively employed in specialised cardiac populations, such as ACHD, potentially providing a high level of prediction accuracy by efficiently harnessing the potential of available data. However, its applicability to mortality prediction for ACHD patients requires further external validation.ROC curve AUC for the model predictionsFeature importance using permutation

Russian wheat export to African countries at the present stage: key development features, challenges and opportunities

The Russian Federation traditionally remains one of the most important producers and suppliers of wheat on the world market of agricultural raw materials and food. The countries of the African continent, on the contrary, form the largest group of importers and consumers of this product. The purpose of the article is to identify the main trends in the dynamics of Russian wheat exports to African countries at the present stage, as well as to identify the key features of this aspect of trade relations between the parties. The article examines the dynamics and features of export-import relations between the Russian Federation and African countries in the field of international wheat trade through the prism of food security theory. The paper presents an analysis of numerical indicators of wheat production in Russia and its exports to African countries in comparison with indicators of total supplies to the world market, identifies specific features of Russian exports focused on supplies to the African region, and concludes that Russia needs to expand its participation in ensuring food security in the Global South. The source base is statistical materials containing numerical indicators of wheat production and trade by countries and regions of the world, published by international organizations (FAO, International Trade Center, UNCTAD/WTO), national executive authorities, their subordinate institutions and the Bureau of Statistics (Rosstat, USDA); as well as analytical and information and news publications on the topic.

Characterization and property prediction of fibre structures within discontinuous-fibre reinforced polymer matrix composites using 3D fibre cells assisted by contrastive learning

Fibre-cell-based fibre structure characterization approach was proposed recently to characterize the fibre distribution within discontinuous-fibre reinforced polymer matrix composites (DFR PMCs) over a 2D domain. This approach determines the distribution state of each fibre based on the relative size and topological features of its fibre cell. In this study, the fibre-cell-based approach is extended for 3D fibre domains. A convolutional neural network (CNN) encoder is trained through contrastive learning to quantitatively represent topological features of 3D fibre cells. Subsequently, the feature–property correlations are established using an artificial neural network (ANN). For practical application, the ANN is integrated with an image analysis software to provide in situ predictions of local elastic modulus of a DFR PMC based on its fibre structures observed from micro-CT images. The predictions are also compared with the experimental measurements acquired through microindentation testing, and it shows a good agreement.

Concepts and features of state-service relations in law enforcement agencies

Concepts and features of state-service relations in law enforcement agencies

IQTISODIY - INNOVATSION TAFAKKUR VA ULARNING DINAMIK XUSUSIYATLARI

In this article, the dynamic features of socio-economic relations encourage business entities and entrepreneurs to develop new products, constantly expand the range of services, fully satisfy the needs of the population, and economic and innovative development is a product of relying on human thinking and potential, and its effective use. will be considered.

AMTrack:Transformer tracking via action information and mix-frequency features

Nowadays, Transformer-based visual tracking algorithms have been developing quickly because of the self-attention mechanism of Transformer, which has the capability to model global information. Although the self-attention mechanism in Transformer can effectively capture long-range dependencies in feature space, they only use flattened two-dimensional features and are unable to capture long-range temporal dependencies. Furthermore, since the self-attention in Transformer functions as a low-pass filter, it picks up on low-frequency features of the target while ignoring high-frequency features. This research suggests a Transformer tracker based on action information and mix-frequency features (AMTrack) to address these problems. Specifically, to address the lack of temporal remote dependencies, we introduce the target action aware module and the target action offset module. The target action aware module sets up several pathways to extract spatio-temporal, channel, and motion feature independently. In contrast, the target action offset module computes the target’s offset information by computing relative feature maps. Furthermore, in order to address the imbalance between high and low frequency features, we propose a mix-frequency attention and multi-frequency self-attention convolutional block. The mix-frequency attention uses high-frequency features within partitioned local windows as input for the high-frequency branch and average-pooled low-frequency features as the input for the low-frequency branch, calculating attention scores in both branches respectively. The multi-frequency self-attention convolutional block uses self-attention to capture low-frequency features and convolution to capture high-frequency features. Extensive experiments are carried out on eight challenging tracking datasets (e.g., OTB100 (Object Tracking Benchmark 100), NFS (Need For Speed), UAV123 (Unmanned Aerial Vehicles 123), TC128 (Temple Color 128), VOT2018 (Visual Object Tracking 2018), LaSOT (Large-scale Single Object Tracking), TrackingNet (Tracking Network), GOT-10k (Generic Object Tracking-10k)), and the experimental results show that our tracker achieves excellent tracking performance when compared with several state-of-the-art tracking algorithms. The experimental results show that on LaSOT, the success rates AUC (Area Under Curve), PNorm, and P reach 65.8%, 69.2%, and 68.0%, respectively, where the AUC value is 2.1% higher than the baseline algorithm TrDiMP (Transformer Discriminative Model Prediction). On other datasets, our tracker also achieves excellent tracking performance.

Peer Review and Natural-Like Social Relations of Production in Academia

ABSTRACT There is a paucity of studies addressing the nature of the social relations of production prevailing in academia prior to the commodification of academic research. By filling that gap, this paper enables us to better understand the historical presuppositions from which the process of knowledge commodification in academia has evolved. Our theoretically informed analysis will focus on peer review, given that it is one of the few academic practices where traces of that historical past can still be found. On the basis of Marx’s exposition of the main features of pre-capitalist social relations of production in the Grundrisse, it will be concluded that peer review reveals that social relations of production in academia were of a pre-capitalist, or natural-like, nature: peer review is labour of a direct nature; is the practice through which the academic community mediates its own reproduction, determining how – and, sometimes, if – its members enter into contact with the objective conditions of production; finally, in this latter capacity, peer review presupposes that the academic community relates to the conditions of knowledge production as though they were their collective property. These findings are employed to account for the underlying logics of the ongoing ‘functional transformation’ of peer review.

Routine ICU Surveillance after Brain Tumor Surgery: Patient Selection Using Machine Learning.

Background/Objectives: Routine postoperative ICU admission following brain tumor surgery may not benefit selected patients. The objective of this study was to develop a risk prediction instrument for early (within 24 h) postoperative adverse events using machine learning techniques. Methods: Retrospective cohort of 1000 consecutive adult patients undergoing elective brain tumor resection. Nine events/interventions (CPR, reintubation, return to OR, mechanical ventilation, vasopressors, impaired consciousness, intracranial hypertension, swallowing disorders, and death) were chosen as target variables. Potential prognostic features (n = 27) from five categories were chosen and a gradient boosting algorithm (XGBoost) was trained and cross-validated in a 5 × 5 fashion. Prognostic performance, potential clinical impact, and relative feature importance were analyzed. Results: Adverse events requiring ICU intervention occurred in 9.2% of cases. Other events not requiring ICU treatment were more frequent (35% of cases). The boosted decision trees yielded a cross-validated ROC-AUC of 0.81 ± 0.02 (mean ± CI95) when using pre- and post-op data. Using only pre-op data (scheduling decisions), ROC-AUC was 0.76 ± 0.02. PR-AUC was 0.38 ± 0.04 and 0.27 ± 0.03 for pre- and post-op data, respectively, compared to a baseline value (random classifier) of 0.092. Targeting a NPV of at least 95% would require ICU admission in just 15% (pre- and post-op data) or 30% (only pre-op data) of cases when using the prediction algorithm. Conclusions: Adoption of a risk prediction instrument based on boosted trees can support decision-makers to optimize ICU resource utilization while maintaining adequate patient safety. This may lead to a relevant reduction in ICU admissions for surveillance purposes.

Relation-Aware Heterogeneous Graph Network for Learning Intermodal Semantics in Textbook Question Answering.

Textbook question answering (TQA) task aims to infer answers for given questions from a multimodal context, including text and diagrams. The existing studies have aggregated intramodal semantics extracted from a single modality but have yet to capture the intermodal semantics between different modalities. A major challenge in learning intermodal semantics is maintaining lossless intramodal semantics while bridging the gap of semantics caused by heterogeneity. In this article, we propose an intermodal relation-aware heterogeneous graph network (IMR-HGN) to extract the intermodal semantics for TQA, which aggregates different modalities while learning features rather than representing them independently. First, we design a multidomain consistent representation (MDCR) to eliminate semantic gaps by capturing intermodal features while maintaining lossless intramodal semantics in multidomains. Furthermore, we present neighbor-based relation inpainting (NRI) to reduce semantic ambiguity via repairing fuzzy relations with correlations of relations. Finally, we propose hierarchical multisemantics aggregation (HMSA) to guarantee the completeness of semantics by aggregating features of nodes and relations with a reconstruction network (RN). Experimental results show that IMR-HGN could extract the intermodal semantics of answers, achieving a 2.16% improvement on the validation set of the TQA dataset and a 3.04% increase on the test set of the AI2D dataset.

Partnership Relations Between the National Police and the Community in the De-Occupied Territories of Ukraine

The purpose of the article is to determine the essence and features of partnership relations between the National Police and the community on the basis of current Ukrainian and foreign legislation, different opinions of scientists, and empirical data, as well as identify the main problems and propose effective methods of solving them in the de-occupied territories of Ukraine. The methodological basis of the study is comparative legal and systemic analysis, formal legal method, interpretive method, as well as methods of analysis and synthesis. The partnership of the National Police with the population was characterized and its features were revealed. In the context of the research topic, individual points of view of scientists regarding building an effective partnership between the police and the community were considered. Such legal categories as “occupation”, “de-occupation” and “reintegration” of territories were characterized and their features were distinguished. The problems of organization and functioning of partnership relations between the National Police and the community were considered. A conclusion was made about the importance of cooperation with active residents of the community in the de-occupied territories with the National Police in order to more effectively prevent new crimes. Taking into account the current legislation and positive international experience in this field of legal relations, optimal directions and forms of interaction between the National Police of Ukraine and public formations in the de-occupied territories were proposed.

A flexible sensor based on ionogel/knitted coil prepared by a simple method for accurate monitoring of human physiological signals

Flexible sensors have received increasing attention due to their various applications in health monitoring, artificial intelligence, and other fields. The simple fabrication of flexible sensing devices with high performances has been a research hotpot. In this work, an ionogel composite sensor combined with elastic knitted coil (EIC) is proposed as a wearable device to monitor human movement and related feature information. This sensor can be fabricated in a simple and easy protocol, which can work as a clothing accessary to track the wellness status parameters in daily life. After washing, the redundant gel originally embedded between the fibers was removed, improving the sensitivity of the sensor, which can provide accurate information about human joint movements and other physiological activities. The EIC sensor is in high sensitivity with superior working durability, which can provide continuous piezoresistive signals in detection mode. Other than the common joint bending movements (wrist, knee, elbow and finger) of human body, this sensor can also accurately output monitoring data on periocular, oral, and pharyngeal movements, providing information about human health status. Besides, a Morse codes transmission system can be constructed based on the piezoresistive outputs of this EIC sensor under different tapping modes, providing a new working platform in other related applications.

Relative Feature Classification Method for Diabetic Retinopathy Detection from Scanned Eye Images

Relative Feature Classification Method for Diabetic Retinopathy Detection from Scanned Eye Images

A novel hybrid features grey incidence model and its application in identifying key factors influencing air pollution in Jiangsu Province

Accurately assessing the key factors influencing air pollution is crucial for effective air pollution control. To address this need, we propose a novel Hybrid Features Grey Incidence Model (HFGIM), which integrates geometric feature differences from both proximity and similarity perspectives. Firstly, we extract geometric feature difference vectors of proximity and similarity from time series data and measure the overall feature difference degree by calculating vector norms. Secondly, we calculate the relative feature differences and information contribution rates of proximity and similarity to derive the hybrid feature differences coefficient between sequences, thereby obtaining the hybrid features incidence degree. After detailing the model’s properties and modelling steps, we introduce the Cross-sectional Data Hybrid Features Grey Incidence Model (C-HFGIM) and the Panel Data Hybrid Features Grey Incidence Model (P-HFGIM) for handling cross-sectional and panel data, respectively. Applying HFGIM, we identified the key pollutants and primary pollution source indicators of air pollution in Jiangsu Province. We also compared HFGIM with other classical grey incidence models to verify the proposed model’s effectiveness. Based on the analysis results, we propose policy recommendations for air pollution control in Jiangsu Province.